Climatic Zones Research Articles

Georgian Medicinal Plants as Rich Natural Sources of Antioxidant Derivatives: A Review on the Current Knowledge and Future Perspectives.

The study of antioxidants is of pivotal importance in biomedicine as these molecules could be involved in biological pathways associated with disease. The identification of new antioxidants together with the acquisition of a deeper knowledge on their biology, could lead to the use of these compounds as drugs for innovative treatments. Plants are an important reservoir of phytodrugs that in many cases can be isolated with good extraction yields directly from the vegetal source and are often endowed with a low toxicity profile. Georgia, a country situated on the Black Sea coast in the Caucasus region at the intersection of Western Asia and Eastern Europe, is renowned for its unique woodland habitats and immense biological diversity due to the great variety of climate zones and landscapes. Many wild plants in the area are used as remedies for a number of illnesses in the local traditional medicine. However, the scientific knowledge of these sources of natural drugs and of their molecular components is still far from exhaustive. Therefore, with the present work we reviewed the scientific literature on some of the main Georgian medicinal plants and found that several species are a valuable source of hydrophilic and hydrophobic antioxidants, endowed in some cases with a high ROS-scavenging ability. The analysis of the literature also demonstrated that most of the medicinal extracts and compounds isolated from these plants are beneficial in suppressing multiple diseases in vitro. This review will provide information for scientists looking to develop secure plant-based pharmaceuticals as well as a rationale for using Georgian medicinal plants for the treatment of a range of diseases.

Potential of Gridded Precipitation Estimate Products for Hydrological Modelling: The Case of Small Watersheds in Côte d'Ivoire

Rainfall information measured on the ground is not sufficiently representative the watershed scale in Côte d'Ivoire due to a low density of the rain gauge network. Many watersheds are ungauged or present a high rate of missing data on flow record since 1990s. The use of gridded precipitation estimate products is an alternative to remedy this lack of information. Therefore, the aim of this study was to evaluate the performance of CHIRPS, GPCC, MSWEP, PERSIANN-CDR, PERSIANN-CCS-CDR, TAMSAT, WFDE5-CRU, and WFDE5-CRU-GPCC precipitation estimates for hydrological modelling in Côte d'Ivoire over the period from 1984 to 2004. A continuous-based flow simulation was made for 14 small watersheds using GR4J lumped model. The methodology was based on a global analysis to identify the products that gave the best scores in both calibration and validation over the selected watersheds. KGE2 was the major metric considered along with NSE, VE, PBIAS, and ME. The performance of these gridded precipitation estimate products was then evaluated according to the different climatic zones of Côte d'Ivoire. The results showed that WFDE5-CRU-GPCC performed well in all climatic zones with KGE2 > 0.6 for at least the half watersheds for each climatic zone. CHIRPS and PERSIANN-CDR exhibited the best performance in the transitional tropical and mountainous zones (KGE2mean > 0,7), whereas WFDE5-CRU was good, mainly in the mountainous zone with KGE2 > 0,7 for 2 watersheds out of 3.

A Comprehensive Characterization of Environmental Severity Across 25 Sites in Nine Climactic Zones. Part 1: Witness Coupon Evaluation

ABSTRACT Understanding the risk of corrosion due to local environmental conditions is critical to aviation assets spread across the world. Oftentimes, maintenance intervals are set to the most conservative values assuming all sites are equal, resulting in significant and potentially excessive labor and material expenditures. While research efforts have been underway for the past 50+ years to assess environmental severity using witness coupons, recent advances in technologies have also provided users time-resolved monitoring equipment to monitor environmental conditions at the test site with minimal additional effort. To capitalize on these technologies and facilitate maintenance optimization, 25 Naval Air Stations across the world were analyzed to assess the severity of environmentally driven corrosion. This testing utilized the latest recommendations developed within the AMPP SC-07 committee and included nine different data sets for developing a comprehensive understanding of site conditions. In the end, testing successfully monitored the risk of environmental corrosion at sites across multiple climatic regions and provided valuable feedback for environmental severity monitoring techniques. This first of a series of reports will focus on data evaluating witness coupon damage.

Do Climate Shocks Disadvantage Household Investment in Human Capital in Benin? An Approach Based on the Endogenous Treatment Regression Model

The increasing number of drought and flood shocks in Benin is causing considerable economic losses and social disruption. This article looks specifically at the effects of these shocks on household human capital expenditure in three climatic zones of Benin to highlight the effects specific to each zone. The data used come from the Harmonized Survey of Household Living Conditions. A linear regression model of the endogenous treatment was used for the analyses. The findings indicate that climate shocks reduce household capital expenditure and that this impact depends on the household’s climatic zone of residence. Investment in resilient infrastructure, such as water drainage and anti-flood systems, is suggested. The implementation of social and health assistance measures and school subsidies to cushion the impact of climatic shocks on the human capital expenditure of disaster-stricken households. The development and maintenance of an early warning system to anticipate the occurrence of shocks, floods, and droughts would also contribute to mitigation actions.

Evaluation of the intensity of urban heat islands during heat waves using local climate zones in the semi-arid, continental climate of Tehran

Increasing land surface temperature (LST) and urban heat island intensity (UHII) have been reported to be closely associated with a large number of environmental issues. Understanding LST and UHII rise in cities during heatwaves is crucial for implementing mitigation measures. This paper aims to explain these variations in response to LST and UHII during a heat wave in the metropolis of Tehran in Iran. 13 heat waves were selected and evaluated during the years 2000 to 2020. The average LST was 3.34 °C higher at the same time as the heat wave event compared to the period without the wave. Despite the increase in LST during the heat wave occurrence, UHII experienced lower values compared to the time without the wave. The outputs in this regard show that the average UHII value for the period without a heat wave is 5.11 °C, while during the heat wave, its rate is 3.62 °C. The maximum intensity of the heat island for both periods before and at the same time as the heat wave is related to man-made compact midrise buildings and heavy industry. Extra-local factors during heat waves can lead to diminish land use diversity and vegetation density's effectiveness in reducing UHII.

Genome size is positively correlated with extinction risk in herbaceous angiosperms.

Angiosperms with large genomes experience nuclear-, cellular-, and organism-level constraints that may limit their phenotypic plasticity and ecological niche, which could increase their risk of extinction. Therefore, we test the hypotheses that large-genomed species are more likely to be threatened with extinction than those with small genomes, and that the effect of genome size varies across three selected covariates: life form, endemism, and climatic zone. We collated genome size and extinction risk information for a representative sample of angiosperms comprising 3250 species, which we analyzed alongside life form, endemism, and climatic zone variables using a phylogenetic framework. Genome size is positively correlated with extinction risk, a pattern driven by a signal in herbaceous but not woody species, regardless of climate and endemism. The influence of genome size is stronger in endemic herbaceous species, but is relatively homogenous across different climates. Beyond its indirect link via endemism and climate, genome size is associated with extinction risk directly and significantly. Genome size may serve as a proxy for difficult-to-measure parameters associated with resilience and vulnerability in herbaceous angiosperms. Therefore, it merits further exploration as a useful biological attribute for understanding intrinsic extinction risk and augmenting plant conservation efforts.

Gaussian-based plug load profile prediction in non-residential buildings archetype

This paper presents an innovative approach to addressing the prevalent challenge of simulation uncertainty in urban building energy modeling (UBEM), focusing on accurately determining occupant-related input parameters. Traditional UBEM methods typically rely on standard schedules to create archetype models, which often fail to reflect the variability observed in real-world scenarios. To overcome this limitation, this research introduces a novel framework for generating electricity use profiles in institutional building archetypes across various climate zones. This framework integrates k-means clustering with Gaussian processes, effectively incorporating uncertainties into the prediction models. The evaluation of this stochastic model suggests that the methodology can give acceptable predictions on the electricity consumption of institutional buildings. The model demonstrates robust predictive capabilities, achieving a CVRMSE as low as 11% on weekdays and 8.7% on weekends, reflecting its strong predictive performance. However, its performance varies among different clusters and time periods, with specific clusters displaying more significant predictive inaccuracies at particular times. These results emphasize the importance of fine-tuning models and offer opportunities for improvement in predicting urban building energy consumption. This can be achieved by incorporating sensor-derived data to develop more detailed building profiles that include variable electricity usage patterns. This methodology has been integrated into a UBEM tool, enabling the generation of more realistic electricity load profiles.

X-band Radar and Surface-based Observations of Cold Season Precipitation in Western Colorado’s Complex Terrain

Abstract Hydrologic processes associated with intermountain cold-season precipitation in the Upper Colorado River Basin have important impacts on avalanche forecasting and water resource management. However, traditional weather radar networks struggle with observations in this complex terrain. Data collected during the Study of Precipitation, the Lower Atmosphere, and the Surface for Hydrometeorology (SPLASH) and its sister campaign, Surface Atmosphere Integrated Field Laboratory (SAIL) in the East River Watershed of Western Colorado is used to examine a multi-storm period from 23 December 2021 to 1 January 2022 that contributed 35% of the total winter precipitation in this watershed. Dual-polarization X-band radar and disdrometer measurements show ~30 mm differences in precipitation amount at two sites in proximity over four distinct storm events within the period. Wind patterns, synoptic forcings, microphysical characteristics of precipitation, and surface meteorology are analyzed to explain the observed spatial variability of cold season precipitation in complex mountainous terrain. Analysis shows that differences over time within this event are mainly accounted for by synoptic forcings, such as frontal passages; differences between sites are accounted for by the impact of variations in local wind patterns on precipitation microphysics. Patterns of surface precipitation intensity are compared and found to be correlated with X-band radar signatures; a relationship between a strong dendritic growth stage and intense low-density surface precipitation is reinforced by this study. This relationship demonstrates the importance of particle growth mechanisms on surface snowfall patterns in high-altitude complex terrain, underscoring the importance of realistic microphysical parameterizations.

Monitoring the Spatial Distribution of Cover Crops and Tillage Practices Using Machine Learning and Environmental Drivers across Eastern South Dakota.

The adoption of conservation agriculture methods, such as conservation tillage and cover cropping, is a viable alternative to conventional farming practices for improving soil health and reducing soil carbon losses. Despite their significance in mitigating climate change, there are very few studies that have assessed the overall spatial distribution of cover crops and tillage practices based on the farm's pedoclimatic and topographic characteristics. Hence, the primary objective of this study was to use multiple satellite-derived indices and environmental drivers to infer the level of tillage intensity and identify the presence of cover crops in eastern South Dakota (SD). We used a machine learning classifier trained with in situ field samples and environmental drivers acquired from different remote sensing datasets for 2022 and 2023 to map the conservation agriculture practices. Our classification accuracies (>80%) indicate that the employed satellite spectral indices and environmental variables could successfully detect the presence of cover crops and the tillage intensity in the study region. Our analysis revealed that 4% of the corn (Zea mays) and soybean (Glycine max) fields in eastern SD had a cover crop during either the fall of 2022 or the spring of 2023. We also found that environmental factors, specifically seasonal precipitation, growing degree days, and surface texture, significantly impacted the use of conservation practices. The methods developed through this research may provide a viable means for tracking and documenting farmers' agricultural management techniques. Our study contributes to developing a measurement, reporting, and verification (MRV) solution that could help used to monitor various climate-smart agricultural practices.

The magnitude and frequency of detected precipitation determine the accuracy performance of precipitation data sets in the high mountains of Asia

The grid-based precipitation dataset is an important source for studying precipitation change in the high mountains of Asia due to where precipitation stations are sparse. It is essential to evaluate the accuracy of grid-based precipitation datasets in the high mountains of Asia before selecting an appropriate grid-based dataset. Therefore, this study comprehensively evaluated the precipitation errors of four commonly utilized precipitation datasets (multi-source weighted-ensemble precipitation (MSWEP), global precipitation climatology centre (GPCC), global precipitation measurement (GPM), and soil moisture to rain-advanced scatterometer (SM2RAIN-ASCAT)) in the high mountains of Asia from temporal and spatial perspectives. It then decomposed the precipitation errors to reveal their sources. The results showed that MSWEP, GPCC, GPM, and SM2RAIN-ASCAT overestimated precipitation amount and probability compared with station observations. Meanwhile, all precipitation data sets except MSWEP data underestimated precipitation in the dry season. In terms of the average values of the error metrics, GPCC performed the best. There was an evident annual periodicity in the error assessment metrics for the four precipitation data sets. Multiple linear regression analysis revealed that four precipitation-related factors (false alarm precipitation, missed amount of precipitation, precipitation detected presented, and precipitation detected event) explained the root mean square error values for four precipitation data sets, with precipitation detected presented having the largest weight. The root mean square error of each product exhibits periodic fluctuations with changes in precipitation quantity, attributed to the occurrences of precipitation detected presented and precipitation detected events. These findings provide useful reference information for correcting biases in precipitation data sets for high mountains of Asia.

Pengelompokkan Daerah Rawan Demam Berdarah (DBD) di Jawa Timur Menggunakan Metode K-Means

Tropical diseases are common in areas with tropical and subtropical climates. As a country with a tropical climate, Indonesia is vulnerable to various tropical diseases. A large number of tropical diseases can occur in the temperate climate zone, differing only in the frequency with which they are affected. Dengue hemorrhagic fever is a tropical disease in Indonesia. DHF occurs as a result of infection with the dengue virus which is transmitted through the bite of the female Aedes aegypti mosquito. The high prevalence of DHF in East Java requires a data collection process to identify areas that are frequently infected with DHF. Therefore, we need a clustering system that can help classify areas that often experience DHF cases. This study aims to find out which districts/cities have the predominant cases of dengue fever. The clustering method used is K-Means clustering. Based on the research conducted, 2 clusters were obtained with a silhouette coefficient value of 0.76. Cluster 1 covers 36 districts/cities and is an area with a low level of vulnerability to dengue fever, while cluster 2 covers 2 districts/cities and is an area with a high level of vulnerability to dengue fever.

Molecular mechanisms of temperature tolerance plasticity in an arthropod.

How species thrive in a wide range of environments is a major focus of evolutionary biology. For many species, limited genetic diversity or gene flow among habitats means that phenotypic plasticity must play an important role in their capacity to tolerate environmental heterogeneity and to colonize new habitats. However, we have a limited understanding of the molecular components that govern plasticity in ecologically relevant phenotypes. We examined this hypothesis in a spider species (Stegodyphus dumicola) with extremely low species-wide genetic diversity that nevertheless occupies a broad range of thermal environments. We determined phenotypic responses to temperature stress in individuals from four climatic zones using common garden acclimation experiments to disentangle phenotypic plasticity from genetic adaptations. Simultaneously, we created data sets on multiple molecular modalities: the genome, the transcriptome, the methylome, the metabolome and the bacterial microbiome to determine associations with phenotypic responses. Analyses of phenotypic and molecular associations reveal that acclimation responses in the transcriptome and metabolome correlate with patterns of phenotypic plasticity in temperature tolerance. Surprisingly, genes whose expression seemed to be involved in plasticity in temperature tolerance were generally highly methylated contradicting the idea that DNA methylation stabilizes gene expression. This suggests that the function of DNA methylation in invertebrates varies not only among species but also among genes. The bacterial microbiome was stable across the acclimation period; combined with our previous demonstrations that the microbiome is temporally stable in wild populations, this is convincing evidence that the microbiome does not facilitate plasticity in temperature tolerance. Our results suggest that population-specific variation in temperature tolerance among acclimation temperatures appears to result from the evolution of plasticity in mainly gene expression.

Genetic differentiation across a steep and narrow environmental gradient: Quantitative genetic and genomic insights into Lake Superior populations of Quercus rubra.

Adaptive differentiation of traits and underlying loci can occur at a small geographical scale if natural selection is stronger than countervailing gene flow and drift. We investigated this hypothesis using coupled quantitative genetic and genomic approaches for a wind-pollinated tree species, Quercus rubra, along the steep, narrow gradient of the Lake Superior coast that encompasses four USDA Hardiness Zones within 100 km. For the quantitative genetic component of this study, we examined phenotypic differentiation among eight populations in a common garden, measuring seed mass, germination, height, stem diameter, leaf number, specific leaf area and survival. For the genomic component, we quantified genetic differentiation for 26 populations from the same region using RAD-seq. Because hybridisation with Quercus ellipsoidalis occurs in other parts of the species' range, we included two populations of this congener for comparison. In the common garden study, we found a strong signal of population differentiation that was significantly associated with at least one climate factor for nine of 10 measured traits. In contrast, we found no evidence of genomic differentiation among populations based on FST or any other measures. However, both distance-based and genotype-environment association analyses identified loci showing the signature of selection, with one locus in common across five analyses. This locus was associated with the minimum temperature of the coldest month, a factor that defines the climate zones and was also significant in the common garden analyses. In addition, we documented introgression from Q. ellipsoidalis into Q. rubra, with rates of introgression correlated with the climate gradient. In sum, this study reveals signatures of selection at the quantitative trait and genomic level consistent with climate adaptation, a pattern that is more often documented at a much broader geographical scale, especially in long-lived wind-pollinated species.

Microclimatic Zonation and Climatic Variability of Sikkim Himalaya

The Köppen classification of climate integrates precipitation and temperature information with natural vegetation patterns to create a precise representation of any particular region's climate. This integration depends on the empirical relationship of climate and vegetation, which indicates that distinct locations in the same class have similar vegetation attributes. Köppen climatic classification factors are explained and Sikkim's climate characteristics are regionalized based on it. The method for making representations of air temperatures and precipitation has been described, and an illustration of Sikkim's climatic zones with variability is generated as a result of these changes. The geographic pattern of climatic types and subtypes in Sikkim has been briefly addressed using an available high-resolution gridded dataset (ERA5-Land). This is described that the constraints of microclimatic zonation emanate from the empirical prerequisite of climate classifications, as well as the nature of data selection and the methodologies employed for climate variability analysis and classification. Based on the Köppen classification for the long term (1980–2021), the Sikkim Himalaya contains three primary climatic classes, particularly ETc (cold, tundra, and cool summer), Cfc (moderately warm, humid, and cool summer), and Cfb (moderately warm, humid, and warm summer). Climate variability on the basis of temperature and precipitation change with respect to 1980–2021 over the entire Sikkim Himalaya concludes that the climatic pattern of the Sikkim has been changed from cold-dry to warm-wet. The alteration in the corresponding climatic pattern is further supported by changes in LULC and NDVI. The results suggest that the precipitation change in the past two decades (1980–2000) is negative, while a significant positive change has been noticed in the recent two decades (2001–2021). Subsequently, the number of extremely wet days decreases in the entire ETc and Cfc climate zones. Regardless, the southern part of the Cfb climatic zone has experienced an increase in extremely wet days. This study's findings will significantly contribute to the development of future policies and initiatives by providing insights crucial for achieving sustainable development, environmental protection, and climate change adaptation.

Impact of urban form on building energy consumption in different climate zones of China

Despite numerous studies on the relationship between urban form and building energy demand, understanding the impact mechanisms of urban form is still insufficient. Here, we identified the direct and indirect influences of urban form on building energy demand. The direct influence includes the blocking of solar radiation. The indirect influence is exerted through the urban heat island (UHI) effect as different urban forms produce different UHI intensities. The UHI effect was calculated using the urban weather generator (UWG) tool. Then, the analysis of the direct and indirect influences on the building energy demand was carried out using building energy modeling (BEM) based on the output of UWG. Five cities in different building climate zones in China were investigated, and the corresponding direct and indirect influences were quantified based on the proposed UWG + BEM framework. Results show that warmer temperatures lead to an average increase in space cooling of 16 %–46 % while reduced space heating of 8 %–17 %. The shading of surrounding buildings allows an increase of 7 %–36 % in space heating and a decrease of 14 %–48 % in space cooling compared to that of an isolated building. In addition, direct influence due to shading is prominent in Shuangyashan, Xuzhou, and Fuzhou, accounting for 50 %, 80 %, and 57 % of variations in building energy consumption.

Year-round performance evaluation of photovoltaic-thermal collector with nano-modified phase-change material for building application in an arid desert climate zone

This study provides a comprehensive analysis of the energetic and thermoelectric characteristics of a solar photovoltaic-thermal (PVT) collector equipped with a nano-modified phase-changing material (NPCM) for PV cooling. The operational effectiveness of the collector is evaluated using various parameters such as PV temperature, electrical and thermal powers, melting time, PCM liquid fraction, and PV cooling duration. These factors are meticulously examined under different operating conditions of solar irradiance, ambient air temperature, and the thickness of the NPCM layer. Hail, Saudi Arabia, a city known for its arid desert climate, is chosen as the geographic setting for the study. A numerical methodology based on finite volume discretization is used to solve the governing mathematical models. The year-round performance of the combined PVT and NPCM unit is systematically assessed in terms of monthly averages across the four seasons, considering varying NPCM layer thickness over different time frames. The results indicate that an NPCM with a nanoparticle volumetric concentration of 5% and an appropriately chosen NPCM layer thickness provides effective PV cooling, with cooling durations ranging from 54 to 305 min throughout the year. In terms of electrical performance, months with high solar irradiance yielded a greater power output, peaking at approximately 52.5 Watts, while the winter months exhibited the lowest output. The thermal performance followed a similar pattern, demonstrating the significant influence of solar irradiance on thermal efficiency, with peak outputs reaching up to 225 Watts during high solar intensity months.

Study on the contributions of 2D and 3D urban morphologies to the thermal environment under local climate zones

With the changes in global climate and rapid urbanization, identifying the contribution of urban morphology to the thermal environment under different scenarios could be helpful in proposing differentiated optimization responses of different scenarios for improving the thermal environment by means of natural cooling. This study calculated 2D and 3D metrics and retrieved surface temperature based on building vector data, land use, electronic map, the remote sensing images. Then, the study of spatial scale sensitivity was developed to build a nested multi-scale Local Climate Zone (LCZ) (NMSLCZ) map. Finally, the eXtreme Gradient Boost (XGBoost) regression model was performed to explore the relative contributions of 2D and 3D morphologies of different LCZ scenarios to surface temperatures. The results of scale sensitivity analysis showed the size of the optimal spatial scale decreased with the increase in building height. The 3D morphologies show more significant effects on LST variations than 2D morphologies. The surface temperature mainly by built type is significantly higher than that mainly by land cover type. Scenarios representing dense trees, shrubs, low vegetation, and water have significant cooling effects. In the scenarios of built types, the surface temperature of compact and large low-rise buildings is higher than that of the other building forms. Our findings provide a new perspective for LCZ mapping of scale optimization of the thermal environment. Meanwhile, the contributions of 2D and 3D morphologies on LSTs could help urban planners and managers understand the impacts of climate change and propose relevant strategies toward sustainable cities.

Clusters of Regional Precipitation Seasonality Change in the Community Earth System Model version 2

Clusters of Regional Precipitation Seasonality Change in the Community Earth System Model version 2

Racial/ethnic disparities in the distribution of heatwave frequency and expected economic losses in the US

Previous research on social disparities in heat exposure has not examined heatwave frequency or economic damage at the local or neighborhood level. Additionally, most US studies have focused on specific cities or regions, and few national-scale studies encompassing both urban and rural areas have been conducted. These gaps are addressed here by analyzing racial/ethnic disparities in the distribution of annual heatwave frequency and expected economic losses from heatwave occurrence in the contiguous US. Census tract-level data on annualized heatwave frequency and expected loss from the FEMA’s National Risk Index are linked to relevant variables from the American Community Survey. Results indicate that all racial/ethnic minority groups except non-Hispanic Black are significantly overrepresented in neighborhoods with greater annual heatwave frequency (top 10% nationally), and all minority groups are overrepresented in neighborhoods with greater total expected annual loss from heatwaves, compared to non-Hispanic Whites. Multivariable models that control for spatial clustering, climate zone, and relevant socio-demographic factors reveal similar racial/ethnic disparities, and suggest significantly greater heatwave frequency and economic losses in neighborhoods with higher percentages of Hispanics and American Indians. These findings represent an important starting point for more detailed investigations on the adverse impacts of heatwaves for US minority populations and formulating appropriate policy interventions.

Green urban transition: interdisciplinary insights on green façades design in hot climates as one of crucial strategies for low-carbon development

AbstractThe purpose of this study is to present interdisciplinary insights on vertical green systems (VGS) design in hot climate zones. The approach to the VGS design proposed in this article combines architectural design, structural systems, green building and township development with sustainable urban farming, circularity, waste and water management. On the basis of in-depth study on green façade performance in hot climates, the authors identified methods derived from various fields of science which, when correctly combined, enable effective protection of plants against excessive solar radiation. The main finding of the research is the identification of the most effective combination of methods supporting healthy growth of the VGS. This set of solutions includes orientation of the façade to cardinal directions correlated with appropriate plants selection, shading strategies, such as passive and kinetic shields and organic fertilizers supporting plants’ resilience. The authors presented key challenges and opportunities of VGS application and discussed their role in the decarbonization process of the building sector. The multidisciplinary analysis of the state of knowledge highlighted areas that require further investigation, such as the practical implementation of proposed strategies and their effectiveness in real-world scenarios. Graphical abstract