Minimum Temperature Values Research Articles

Distribution of air quality data across stratified areas of Port Harcourt metropolis

Distribution of Air Quality Data across seven stratified areas of Port Harcourt Metropolis was carried out. The study utilized a total of 18 parameters from four different air quality datasets obtained from 24 sampling points during field data collection. The sampling points were structured and stratified into residential, industrial, commercial, recreational, transport, dumpsite and abattoir areas. Air quality data were acquired using a multi-digital potable tool, Aerosol Gas Monitor and TESTO Gas Analyser for meteorological data, particulate matters (PM), gaseous emissions and heavy metals. Results showed that the concentrations of air quality data were not evenly distributed across the seven stratified areas, an indication of the influence of location-based activities on the distribution of air quality parameters. The minimum and maximum values of wind speed, temperature and relative humidity were obtained within Residential and Transport Areas, Abattoir and Dumpsite Areas, and, Industrial and Recreational Areas with numerical values of 1.1ms-1 and 3.7ms-1, 27.20C and 350C, and, 67.8% and 923% for wind speed, temperature and relative humidity respectively. The values of TSP, PM2.5 and PM10 ranged from 53 to 255 μg/m3, 13 to 90 μg/m3 and 40 to 169μg/m3 for TSP, PM2.5 and PM10 respectively with the lowest and highest values obtained within Residential and Dumpsite Areas respectively. The concentrations of NO2 and SO2 exceeded the standards of WHO, NAAQS and FMEnv in Industrial, Dumpsite and Abattoir Areas. Lead ( Pb) showed very high values within Dumpsite Area (0.954mg/kg), Industrial Area (0.372mg/kg) and Transport Area (0.077mg/kg).

The Spatiotemporal Dynamics of Temperature Variability Across Mts. Qinling: A Comparative Study from 1971 to 2022

Analyzing the spatiotemporal patterns of atmospheric temperature in sensitive areas is critically important for understanding the broader implications of global climate change, which remains a prominent topic in geosciences. It also plays a crucial role in advancing sustainable development. This study utilized daily minimum, maximum, and mean temperature data from twelve meteorological stations across the South and North Mts. Qinling (Qinling Mountains). Employing trend analysis, the Mann–Kendall mutation test, and Morlet wavelet analysis, we explored the predominant temperature trends and characteristics from 1971 to 2022. Our findings revealed consistent inter-annual warming trends in both regions, with more rapid temperature increases in the North compared to the South. Notably, significant shifts occurred in 2003 for both mean and minimum temperatures in the North, while the maximum and minimum temperature values were recorded in the 2010s and 1980s, respectively. Both regions exhibited a primary temperature fluctuation cycle of 28 years. Seasonally, the strongest warming effects appeared in spring, with the weakest in autumn, and moderate effects in winter and summer, indicating that spring contributes most significantly to regional warming. Monthly analysis showed positive temperature trends across all months, with higher rates in the North. The weakening temperature boundary effect of the Mts. Qinling suggested a weakening North–South division, particularly highlighted by the northward shift of the 1 °C isotherm curve for the coldest month, moving away from the previously observed 0 °C isotherm. This northward shift highlights the differential warming rates between the northern and southern regions. Overall, the analysis confirms a robust warming trend, with notable fluctuations in January’s temperatures since 1998, suggesting the Mts. Qinling’s emerging role as a climatic divider in the Chinese Mainland. This introduces new challenges for regional ecosystems, agricultural production, and water resource management, highlighting the pressing need to advance regional sustainable development in the face of climate change.

Predicting dryland winter wheat yield in cold regions of Iran

AbstractWheat is a crucial staple worldwide, serving both human and animal needs. In Iran, where climate conditions vary widely, wheat farming faces significant challenges, especially in areas facing freezing winters and unfavorable temperatures during reproductive stages. Unfortunately, existing models often fail to account these extreme and specific climate conditions, leading to inaccurate predictions, notably in cold areas. To address this issue, wheat dryland farming (WDF) model was evaluated in predicting dryland winter wheat yields in five distinct areas including Shahrekord, Borujen, Koohrang, Farsan, Lordegan, and Ardal in Chahar‐Mahal and Bakhtiari province, Iran. The results showed that changes in precipitation and temperature significantly impacted dryland wheat production. While higher precipitation generally associates with higher yields, this relationship is not always straightforward due to factors like unfavorable precipitation patterns and types (i.e., rainfall or snow). Likewise, unfavorable temperatures, particularly during crucial growth stages and winter freezes, pose significant challenges to wheat growth and yield modeling. The WDF model's performance was evaluated across various temperature conditions in the study area, and it was more accurate in regions with certain minimum and maximum temperature values above thresholds. However, the model performance was poor in colder areas, where freezing temperatures were occurred in winter duration (Shahrekord, Borujen, Koohrang, and Farsan). In order to improve the model's accuracy, a correction factor based on the minimum and maximum air temperatures was incorporated in the model. The findings emphasized the importance of considering both precipitation and temperature dynamics when modeling winter wheat yields, especially in regions with diverse climates. By refining models like WDF, agricultural planners can better forecast the yield fluctuations and address the impacts of climate variability on food security in Iran and similar regions worldwide.

Level of Contamination and Alteration of Cuttlefish Fished and Processed in Companies in Côte d’Ivoire

This study was carried out from 2019 to 2021. Its aim was to assess the quality of cuttlefish intended for local and international markets in a company processing fishery products in Côte d'Ivoire. The biochemical analyses (Total Volatile Basic Nitrogen (TVBN)) were based on the distillation method of an extract deproteinised by perchloric acid (HClO<sub>4</sub>). Microbiological analyses (total aerobic mesophilic flora (TAMF), staphylococci (SPP), anaerobic sulphite-reducing bacteria (RAS), faecal coliforms (FC) and salmonella) involved isolation and identification techniques. The various analyses carried out by the National Laboratory for Support to Agricultural Development (LANADA) made it possible to assess the median contamination values and satisfaction percentages (TAMF, FC, SPP), which are respectively 2.2.10<sup>4</sup> CFU/g with 94%; less than 10 CFU/g with 94%; less than 100 CFU/g with 100%. RSA and salmonella were absent in all the products analysed. The temperature of the cuttlefish was high just after processing. The maximum temperature value (26.4°C) was obtained after processing, while the minimum temperature value (1.6°C) was recorded during storage in a positive cold room. However, it is essential to improve manufacturing practices and preservation methods in order to avoid poisoning the population. These data should also encourage fishermen to practise good preservation methods.

Lizard Body Temperature Acquisition and Lizard Recognition Using Artificial Intelligence.

The acquisition of the body temperature of animals kept in captivity in biology laboratories is crucial for several studies in the field of animal biology. Traditionally, the acquisition process was carried out manually, which does not guarantee much accuracy or consistency in the acquired data and was painful for the animal. The process was then switched to a semi-manual process using a thermal camera, but it still involved manually clicking on each part of the animal's body every 20 s of the video to obtain temperature values, making it a time-consuming, non-automatic, and difficult process. This project aims to automate this acquisition process through the automatic recognition of parts of a lizard's body, reading the temperature in these parts based on a video taken with two cameras simultaneously: an RGB camera and a thermal camera. The first camera detects the location of the lizard's various body parts using artificial intelligence techniques, and the second camera allows reading of the respective temperature of each part. Due to the lack of lizard datasets, either in the biology laboratory or online, a dataset had to be created from scratch, containing the identification of the lizard and six of its body parts. YOLOv5 was used to detect the lizard and its body parts in RGB images, achieving a precision of 90.00% and a recall of 98.80%. After initial calibration, the RGB and thermal camera images are properly localised, making it possible to know the lizard's position, even when the lizard is at the same temperature as its surrounding environment, through a coordinate conversion from the RGB image to the thermal image. The thermal image has a colour temperature scale with the respective maximum and minimum temperature values, which is used to read each pixel of the thermal image, thus allowing the correct temperature to be read in each part of the lizard.

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.

La Nia and climate degree of contact

Studies have shown that the 2022 anomalous climate change is different from previous La Nia phenomena. Using the data of the maximum temperature, minimum temperature and temperature difference from 2020 to March 2021 in Hong Kong, the grey model was used to compare the maximum and minimum temperature values in 2022 with the actual values and found that the values were close. The La Nia phenomenon occurred in March 2022, concluding that La Nia is not a major contributor to climate change in Hong Kong. Climate change in Hong Kong is also linked to typhoons, increased carbon dioxide levels in the air, ocean temperatures and other factors. The study concluded that the main cause of the extreme weather in Hong Kong in 2022 was not the occurrence of La Nia, but a combination of factors such as cumulative carbon dioxide emissions, human activities, the altitude of the Tibetan Plateau, and typhoons.

Evaluation of CRU-JRA gridded meteorological dataset for modeling of wheat production systems in Iran.

Meteorological variables are essential inputs for agricultural simulation models and the lack of measured data is a big challenge for the application of these models in many agricultural zones. Studies indicated that gridded meteorological datasets can be proper replacements for measured data. This paper aimed to examine a new gridded meteorological dataset namely CRU-JRA for crop modeling intents. The CRU-JRA is a 6-hourly dataset with a spatial resolution of 0.5° × 0.5° that was primarily constructed for modeling purposes. The CERES-Wheat model in the Decision Support System for Agrotechnology Transfer (DSSAT) was used for the simulation of irrigated and rainfed wheat production systems in Iran. Results showed that the CRU-JRA maximum and minimum temperature values had a relatively fine accuracy with a normalized root mean square error (NRMSE) of 14% for the simulated grain yield. The performance of the CRU-JRA solar radiation values for the simulation of grain yield was similar with a NRMSE of 14.4%. The weakest performance was found for the CRU-JRA precipitation values with a NRMSE of 18.9%. Overall, the CRU-JRA dataset performed comparatively acceptable and similar to existing gridded meteorological datasets for crop modeling purposes in the study area, however further calibrations can improve the accuracy of the next versions of this dataset. More research is necessary for the investigation of the CRU-JRA dataset for agricultural modeling purposes across diverse climates.

Assessment of the effect of heat storage on the production of clean geothermal energy using the medium and deep U-type borehole heat exchanger system

Heat storage is a critical measure to realize the utilization of waste heat and to enhance the heating capacity of the medium-deep U-type borehole heat exchanger (MDUBHE) system. However, the effects of heat storage on the operation of the MDUBHE system remain unclear. The effects of heat storage measures on the building and the environment have not been assessed. In this paper, the effects of heat storage measures on system performance are revealed and assessed using the numerical heat transfer model. The results show that heat storage measures are particularly necessary in severe cold regions of China. The minimum value of the storage inlet temperature in this study should be more than 42.25 °C, and the flow rate is recommended at no more than 15 kg/s. Although increasing the inlet temperature of heat storage enhances the MDUBHE system's heating performance, the storage system's energy and exergy efficiencies are attenuated. Heat is mainly stored in the underground region within 6 m from the MDUBHE and at a depth of up to 2000 m, and the shape of the thermal plume is an inverted funnel. After adopting heat storage measures, the heating area supplied to buildings by the MDUBHE system increases, and reduces environmental pollution. Compared with the no heat storage measure, the heating area of the MDUBHE system is increased by 6770 m2 when the storage inlet temperature is 80 °C, and the corresponding solar collector area needs to be 756.1 m2. When Tins increases from 45 °C to 80 °C, the reduction of CO2 emissions increases by 12.43 tons, 21.05 tons, 38.26 tons, 55.41 tons, and 72.50 tons, respectively. This study could promote the application of the MDUBHE system and the heat storage system in severe cold regions.

Land use in catchments of small streams and a hydrological urban heat island – a case study in Kielce city (Poland)

ABSTRACT This article presents the spatial and temporal value and dynamics of water temperature in small streams flowing through a city of 200,000 inhabitants. The analysis is based on stationary measurements of a water temperature in eight (8) catchment outlets of various uses (including forest, suburban, urbanised). The water temperature was measured daily for 4 years (06 UTC; electronic thermometers; accuracy: 0.1°C). The study has shown that the water temperature in streams is clearly modi fied by the nature of land use prevailing in the catchment, represented by the imperviousness index. Urbanised areas caused a significant increase in minimum, mean and maximum temperature values on a daily, monthly and annual cycle. The determined relationship indicates that every 1% increase in impervious catchment area causes a water temperature increase of 0.1°C. The presented and documented facts justify the use of the term ‘hydrological urban heat island’, which began to appear in the literature.

Пространственно-временная динамика теплового загрязнения среды г. Ессентуки

The object of the study is the urban landscapes of the city of Essentuki. The aim of the work is to study spatial-temporal dynamics of thermal pollution of the environment in Essentuki and identify the factors influencing this process. The key task of the study is to identify the dependence of changes in the temperature of the earth’s surface on the degree of residential development and landscape. Methods of remote sensing and geographic information systems have been applied. Satellite images of the Landsat 8 and Sentinel-2 satellites were used for the analysis. The work involves channels – infrared near and thermal (Landsat 8 satellite), as well as the visible range. The average, maximum and minimum values of surface temperature for microdistricts are calculated. For a comparative analysis, the coefficients of landscaping, urban planning load and free territory were applied. Data processing has been verified in the QGIS environment. The average, maximum and minimum values of surface temperature for microdistricts are calculated. The coefficients of landscaping, urban planning load and free territory are calculated. Based on the results of the work, the spatial features of the distribution of thermal anomalies associated with the active development of microdistricts of the city were established. Cartographic material on the temperature of the earth’s surface for 2014, 2018 and 2022 years has been created and the areas of built-up areas and green infrastructure have been calculated. Such studies provide a comprehensive assessment of such a phenomenon as “heat islands” and make a significant contribution to the study of the city’s climate and the impact of urbanization processes on the environment, allowing to control the microclimate of urban areas, identify the direction and dynamics of changes existing in the urban environment. When planning a city, it is necessary to take into account the results of the study in order to compensate for the impact of the urban heat island. The primary task is to transfer knowledge about the urban climate into the daily practice of urban planning and design.

К РЕШЕНИЮ ЗАДАЧИ КОЛИЧЕСТВЕННОГО РЕГУЛИРОВАНИЯ В СИСТЕМАХ ТЕПЛОСНАБЖЕНИЯ

The quantitative regulation of the heat supply process has a number of significant advantages and is widely used abroad. This method of regulation is also used in the Russian Federation. At the same time, the algorithmization of quantitative regulation is currently still quite problematic and requires further development. For example, the literature data on the algorithm proposed by E.Ya. Sokolov are characterized by the “omission” of the procedures of inference and justification, which is particularly undesirable for educational applications. In addition, the algorithm does not work across the entire range of thermal loads (outside temperatures). Purpose of the study. To develop an algorithm for quantitative regulation in terms of its subsequent use as part of the algorithmic support of an automated control system (ACS) for heat supply. Materials and methods. Literature data on the issue under consideration was analyzed. Using models of heating systems and heat losses of buildings, we compiled a balance equation that makes it possible to find the necessary coolant flow for heating. Results. A detailed substantiation and derivation of the algorithm for quantitative regulation of heat supply according to the heating load is proposed, and its characteristics were analyzed. The conditions for the physical feasibility of heating are taken into account. The algorithm as a whole is represented by a two-interval formula; in the second range of outdoor temperatures, the heat carrier flow rate is calculated based on the condition that the return water temperature should be noticeably higher than the indoor air temperature of the heated premises. However, when regulating the heat carrier flow in the second interval, an overestimated power of the heating system will be observed, i. e. some overshoot is possible. It is shown that with an increase in the minimum allowable value of the return water temperature, the range of outdoor temperatures in which regulation is possible in the first interval (ideal regulation without overflow) noticeably narrows, and when the water temperature in the supply line decreases, conversely, it expands. Conclusion. The results of the work can be used to develop algorithmic support for automated control systems for heat supply.

Assessment and efficiency of CMIP6 models in simulation and prediction of climatic parameters of precipitation and temperature in the Samalghan basin, Iran

In the present study, four global climate models MRI-ESM2-0, IPSL-CM6A-LR, CanESM5, and GFDL-ESM4 from the set of CMIP6 models are assessed to select the best model and determine the effects of climate change on temperature and precipitation parameters under three shared socioeconomic pathway scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5) for the base period (1988–2017) and a future period (2020–2049) in the Samalghan basin. Statistical measures such as mean absolute error, root mean square error, mean bias error are applied to test the models, and the correlation coefficient is used to compare the results of the historical period of the models with the observational data of the selected stations. Taking the obtained results into account, the global climatic model IPSL-CM6A-LR is chosen to study the trend of temperature and precipitation changes in the future period under scenarios. The results of this study indicate an increasing trend of the average annual precipitation in the desied period compared to the base period for the SSP1-2.6 and SSP2-4.5 scenarios at all stations. Also, it increases in the SSP5-8.5 scenario for all stations except Besh Ghardash, Hesegah and Darkesh stations. The predictions of temperature show an increase in the minimum and maximum temperature values under all scenarios compared to the base period.

Mesoscale activity in the northeastern pacific water mass convergence region and its influence on fish larvae distribution by development stages (October 2022)

Based on satellite, hydrographic, and zooplankton data, the mesoscale activity, and its effect on the three-dimensional distribution of fish larvae in the northeastern Pacific water mass convergence region off Mexico during October 2022 were analyzed. Three eddies were observed during the cruise, but two were measured. An anticyclonic eddy (not crossed by the cruise) was observed by satellite data in the mouth of the Gulf of California. It formed off Mazatlán in Transitional Water and moved southeast of Cabo San Lucas. It was the smallest (10,207.4 km) and least energetic (140.7 cm2s−2) of the eddies. A parallel sampling transect to this eddy showed predominance of fish larvae in preflexion stage in the surface mixed layer with dominance of Benthosema panamense and Syacium ovale species (epi-mesopelagic and soft-bottom demersal species). A second anticyclonic eddy had formed about a month earlier in the Tropical Surface Water when this was measured. Its trajectory showed that it came from the mainland coast off Cabo Corrientes. It was the most energetic (216.6 cm2s−2) and presented the maximum anomalies of temperature (7.4 °C) and Dissolved Oxygen (>50 μmolkg−1), due to the downwelling that it produced (up to 95 m depth). It was also associated with the highest concentration of fish larvae (231.3 larvae100m−3) from ∼200 m depth to surface, observing dominance of larvae in flexion stage of B. panamense and Bregamaceros bathymaster (coastal neritic species). This eddy could also retain and transport species of soft-bottom demersal habits (Dormitator latifrons). One cyclonic eddy, located off Cabo San Lucas had California Current Water. It was the largest (26,032 km2) and oldest forming about nine months earlier off mainland coast in front of Pabellones Cove. Due to the upwelling of deeper water (up to 530 m depth), the minimum anomaly values of temperature (−5.8 °C), salinity (<−0.5 gkg−1) and Dissolved Oxygen (<−50 μmolkg−1) were present. Low abundance of species such as Vinciguerria lucetia and Diogenichthys laternatus (both epi-mesopelagic) in the postflexion stage were presented there. Results showed that mesoscale eddies affected the three-dimensional fish larvae distribution in different ways. This could broaden the vision of the mesoscale evolution of physical-biological interactions in the oceans.

Relative importance of VECTRI model parameters in the malaria disease transmission and prevalence.

In this study, a sensitivity analysis on a VECTRI dynamical model of malaria transmission is investigated to determine the relative importance of model parameters to disease transmission and prevalence. Apart from being most climatic prone, Odisha is a highly endemic state for malaria in India. The lack in sufficient modeling studies severely impacts the malarial process studies which further hinder the possibility of malaria early warning systems and preventive measures to be undertaken beforehand. Therefore, modeling studies and investigating the relationship between malaria transmission process studies and associated climatic factors are the need of the hour. Environmental conditions have pronounced effects on the malaria transmission dynamics and abundance of the poikilothermic vectors, but the exact relationship of sensitivity for these parameters is not well established. Sensitivity analysis is a useful tool for ascertaining model responses to different input variables. Therefore, in order to perform the requisite study, a dynamical model, VECTRI, is utilized. The study period ranges from 2000 to 2013, where several sensitivity tests are performed using different model parameters such as infiltration and evaporation rate loss of ponds, degree-days for parasite development, threshold temperature for parasite development, threshold temperature for egg development in the vector, and maximum and minimum temperature for larvae survival. The experiments suggest that the lower value of minimum temperature for larvae survival (rlarv_tmin), i.e., 16°C, provides higher vector density and entomological inoculation rate (EIR) values. EIR reaches its maximum, when the threshold temperature for parasite development (rtsporo) is 22°C and degree-days for parasite development (dsporo) is 8 degree-days. No change is observed in the vector density; even when rtsporo is 30°C, values of EIR are close to 0. A successive increment of infiltration and evaporation rate loss of ponds (rwaterfrac evap126) values from 130 to 200 mm/day result in approximately 5% consistent decline in vector density and EIR. The study concludes that the most sensitive parameters are dsporo, rlarv_tmin, and rwaterfrac evap126. The VECTRI model is rather insensitive to maximum temperature for larvae survival (rlarv_tmin) for vector density and EIR variables. Further certain modifications and improvements are required in VECTRI to predict out variables like vector density and EIR more accurately in highly endemic region.

Deep reinforcement learning based toolpath generation for thermal uniformity in laser powder bed fusion process

Laser powder bed fusion (LPBF) is a widely used metal additive manufacturing technology. However, the accumulation of internal residual stress during printing can cause significant distortion and potential failure. Although various scan patterns have been studied to reduce possible accumulated stress, such as zigzag scanning vectors with changing directions or a chessboard-based scan pattern with divided small islands, most conventional scan patterns cannot significantly reduce residual stress. The proposed adaptive toolpath generation (ATG) algorithms, aiming to minimize the thermal gradients, may result in extremely accumulated temperature fields in some cases. To address these issues, we developed a deep reinforcement learning (DRL)-based toolpath generation framework, with the goal of achieving uniformly distributed heat and avoiding extremely thermal accumulation regions during the LPBF process. We first developed an overall pipeline for the DRL-based toolpath generation framework, which includes uniformly sampling, agent moving and environment observation, action selection, moving constraints, rewards calculation, and the training process. To accelerate the training process, we simplified the data-intensive numerical model by considering the turning angles on the toolpath. We designed the action spaces with three options, including the minimum temperature value, the smoothest path, and the second smoothest path. The reward function was designed to minimize energy density to ensure the temperature field remains relatively stable. To verify the effectiveness of the proposed DRL-based toolpath generation framework, we performed numerical simulations of polygon shape printing domains. In addition, four groups of thin plate samples with different scan patterns were compared using the LPBF process. The results indicated that using our proposed DRL algorithms for toolpath generation reduced the maximum distortion of the sample by approximate 47% compared to Zigzag patterns, 29% compared to Chessboard patterns, and 17% compared to ATG patterns. Our work demonstrated the effectiveness of implementing machine learning algorithms in the toolpath generation of the LPBF process.

Study on physiological response of drought tolerant groundnut genotypes associated with different stress levels

Identification of drought tolerant genotypes with superior drought tolerant physiological traits is essential for the success of drought tolerance breeding program. This study was conducted to observe physiological response of drought tolerant groundnut genotypes associated with different stress levels. During 2021-2022 post-monsoon season, eight groundnut genotypes were evaluated under non-stress (field capacity), moderate stress (50 % available water) and severe stress (25 % available water) conditions. The experiment was undertaken using split plot design. Increasing drought stress levels decreased relative water content and increased canopy temperature and proline content. Total chlorophyll content increased under moderate stress condition and decreased under severe stress condition in some genotypes. The genotype ICGV-07235 and the drought tolerant check variety, Sinpadetha-12 showed consistent RWC values under different stress levels. The genotypes ICGV-07235, ICGV-07406 and Sinpadetha-12 possessed minimum canopy temperature values among the tested genotypes. Total chlorophyll content of the genotypes ICGV-07286 and ICGV-07235 were higher than that of the other tested genotypes. The genotypes ICGV-07390, YZG-07084 and ICGV-07286 had the highest proline content under stress conditions. Based on the results, the genotypes YZG-07084, ICGV-07286, ICGV-07235, ICGV-07390 and ICGV-07406 possessed desired physiological traits and these genotypes could be effectively utilized for developing drought tolerant groundnut genotypes.

Synthesis and thermal investigation of poly(propylene terephthalate-co-2,5-furan dicarboxylate) copolyesters

Herein, we report the synthesis of new poly(propylene terephthalate-co-2,5-furan dicarboxylate) (PPTF) copolyesters with high molecular weight. Copolymers having various PT/PF molar fractions were synthesized via a two-stage melt polycondensation reaction from the dimethyl esters of terephthalic acid (TPA) or 2,5-furandicarboxylic acid (FDCA) with 1,3-propanediol. The combination of the crystallization ability of PPT with the biobased nature of PPF could make the PPTF copolyesters very appealing. Based on nuclear magnetic resonance spectroscopy, the structure and the actual molar composition of the copolyesters were identified and all the copolymers were found to be random. The investigation of the cocrystallization behavior with XRD showed that isodimorphism predominates, and DSC confirmed the random nature of the copolymers by the presence of a minimum value of the melting temperature as a function of copolymers’ composition. Moreover, isothermal melt crystallization studies were conducted. The melting point depression was investigated through the implementation of a plethora of thermodynamic models, and it was found that the incorporation of the PF units into the PPT crystal was preferred. In all cases, the effect of the furan ring was noticeable, as the copolyesters with higher PT content demonstrated higher crystallization rates. Finally, through polarized light microscopy the spherulitic morphology of the polymers was examined.

Investigation of dust ignition sensitivity and co-pyrolysis properties of biomass and plastic waste during resource utilization

This study investigated the sensitivity of dust to ignition and the co-pyrolysis properties of biomass-plastic composites (BPCs) during resource utilization process. A model of co-pyrolysis process for four BPCs was developed using a BP neural network algorithm. The addition of polyethylene increased the biomass powder's tendency to pyrolyze. In the main pyrolysis stage, the lignin and polyethylene fractions significantly impact the synergistic effect more than the other fractions. Corn stalk/polyethylene and rice husk/polyethylene are synergistic during the pyrolysis process making these BPC products relatively promising for utilization. However, more attention should be paid to the dust explosion hazard posed by the BPCs. PE increases the ignition sensitivity of the biomass powder. Compared to other BPCs, minimum ignition temperature (420 °C) and minimum ignition energy (21 mJ) values for rice husk/polyethylene composites are the lowest. The volatile fraction of biomass content is one of the most influential factors affecting the ignition sensitivity of BPCs.

Analyzing Extreme Temperature Patterns in Subtropical Highlands Climates: Implications for Disaster Risk Reduction Strategies

This study utilized hot and cold indices to evaluate the changes in extreme temperature events that occurred in subtropical highland climates from 1991 to 2020. The modified Mann–Kendall (MMK) test and the Theil–Sen (TS) slope estimator were used to analyze the linear trends in the time series of the extreme temperature indices. The northern highlands of Pakistan (NHP) were considered as a case study region. The results showed that the annual maximum temperature had a slightly increasing tendency (at the rate of 0.14 °C/decade), while the annual minimum temperature had a slightly decreasing tendency (at the rate of −0.02 °C/decade). However, these trends were not significant at the 5% significance level. The decadal averages of the hot indices were the highest in the second decade (2000s), while they were the lowest in the subsequent decade (2010s). In comparison, all the cold indices except the annual minimum value of the maximum temperature (TXn) showed a persistent decline in their decadal averages throughout the 2000s and 2010s. Overall, the frequency of hot days significantly increased in the NHP during the study period. This study found that the hot days and coldest days increased over the past three decades in the NHP. However, there was a decreasing trend in the cold spell duration, cold nights, and the coldest nights over the past three decades, as demonstrated by the trends of the cold spell duration index (CSDI), the temperature of cold nights (TN10p), and the annual minimum value of the minimum temperature (TNn) indices. These changes may impact the environment, human health, and agricultural operations. The findings provide useful insights into the shifting patterns of extreme temperature events in northern Pakistan and have crucial implications for the climate-change-adaptation and resilience-building initiatives being undertaken in the region. It is suggested that the continuous monitoring of extreme temperature events is necessary to comprehend their effects on the region and devise strategies for sustainable development.