Resource Assessment Research Articles

Review of soil form and wetness indicators for wetland delineation in South Africa

Wetland delineation in South Africa incorporates soil form and soil wetness indicators, requiring formal soil classification and description of soil redox morphology. The current wetland definition used administratively in South Africa focuses on saturated (hydric) soil signatures within plant root zones. Saturated soil horizons deeper than plant root zones fall outside the 50 cm criterion in the local approach as well as the accepted zone in USA literature. The field of hydropedology accommodates the classification of the various hydrologically active horizons and provides a tool for the handling of horizons with ephemeral wetness. This approach has been variably accepted by mandated authorities in South Africa. The South African soil classification system has evolved through three editions over the past 50 years while retaining the same redox morphology understanding. However, despite the concepts and context of redox morphology having been thoroughly technically adopted by soil scientists, this is not the case within the wetland research and management environment. This especially because the classification system is structured differently from other international systems, and the South African landscape is geologically ancient with mature soils, introducing challenges to resource assessment specialists who rely on international norms and approaches for wetland assessment. This paper reviews the various components of soil classification and redox morphology based on Fe and Mn minerals within the context of the South African soil classification system, the field of hydropedology and wetland delineation indicators. We provide a qualitative correlation between the various diagnostic horizons and materials in the system and their related redox morphology contexts that are relevant to wetland assessment, delineation, and protection in South Africa. This paper therefore aims to serve as a reference point for the description and correlation of various soil hydrological parameters used in formal assessments.

SYNTHESIS OF MATHEMATICAL MODELS FOR MONITORING THE TECHNICAL CONDITION OF VEHICLES DURING THEIR OPERATION

One of the problems of mathematical modeling of monitoring the technical condition of vehicles during their operation is the lack of practical recommendations regarding the use of various types of modeling for specific diagnostic tests. A distinctive feature of monitoring the technical condition of the elements of vehicles during their intensive operation is the uncertainty of the nature of the loads, which makes it impossible to use traditional diagnostic methods and encourages to move to their complex use. In these conditions, the informational support of conclusions about the suitability of products for further operation and the assessment of the residual resource of the equipment becomes especially relevant. The need to create intelligent support for monitoring the technical condition of vehicles during operation is due to the fact that the uncertainty of the nature of the magnitude of loads during the operation of complex structures complicates the use of existing information. The synthesis of mathematical models of the technical state of metal structures, which are based on the general principle of transforming the degradation of the structure into a fixed signal of different nature of origin, has been performed. The presented structuring is considered as informational support for intelligent monitoring of changes in the properties of transport objects, each of which has a specific purpose. The novelty of the structuring of the main models is a practical focus on solving the issues of their functional purpose in relation to test operations in the monitoring and diagnosis of vehicles and the identification of developing defects. On the basis of the synthesis of mathematical models, analysis of their shortcomings, advantages and possibilities of use for monitoring transport devices during their operation, their main properties from the position of their time series, field of knowledge, methods of implementation and rationality of application were established. Distinctive features have been established and the classification of mathematical models has been performed, which makes it possible to significantly simplify the selection of appropriate models, to ensure the correct use of the mathematical apparatus and analytical ideas about the possibilities of assessing the remaining resource and the quality of monitoring in general.

Quantitative sampling and thermal extraction of the lunar regolith for lunar volatile exploration: Method and validation

Water and other volatiles are present in the permanently shadowed region of the moon. Quantitative analysis of the content is of great significance for lunar resource assessment and geological evolution. However, no detector has yet carried out in situ analysis on the moon, and quantitative sampling and low-power thermal extraction of lunar regolith are also difficult. Thus, a systematic method is proposed to realize quantitative sampling & low power thermal extraction of lunar regolith that detects the abundances of volatiles more accurately. Considering the requirements of the amount and particle size of the regolith, a sampler with a specific structure and motions is designed to quantitatively collect some lunar regolith. After that, induction heating is applied to heat the sampler and indirectly increase the temperature of the internal lunar regolith for volatile thermal extraction. The results demonstrate that this sampling method can achieve high-quality quantitative sampling. Moreover, the sampler and sampled regolith can be heated to the desired temperature with low power consumption. This work offers a feasible solution for the lunar volatile exploration of Chang’E-7.

The potential role of airborne and floating wind in the North Sea region

Abstract Novel wind technologies, in particular airborne wind energy (AWE) and floating offshore wind turbines, have the potential to unlock untapped wind resources and contribute to power system stability in unique ways. So far, the techno-economic potential of both technologies has only been investigated at a small scale, whereas the most significant benefits will likely play out on a system scale. Given the urgency of the energy transition, the possible contribution of these novel technologies should be addressed. Therefore, we investigate the main system-level trade-offs in integrating AWE systems and floating wind turbines into a highly renewable future energy system. To do so, we develop a modelling workflow that integrates wind resource assessment and future cost and performance estimations into a large-scale energy system model, which finds cost-optimal system designs that are operationally feasible with hourly temporal resolution across ten countries in the North Sea region. Acknowledging the uncertainty on AWE systems’ future costs and performance and floating wind turbines, we examine a broad range of cost and technology development scenarios and identify which insights are consistent across different possible futures. We find that onshore AWE outperforms conventional onshore wind regarding system-wide benefits due to higher wind resource availability and distinctive hourly generation profiles, which are sometimes complementary to conventional onshore turbines. The achievable power density per ground surface area is the main limiting factor in large-scale onshore AWE deployment. Offshore AWE, in contrast, provides system benefits similar to those of offshore wind alternatives. Therefore, deployment is primarily driven by cost competitiveness. Floating wind turbines achieve higher performance than conventional wind turbines, so they can cost more and remain competitive. AWE, in particular, might be able to play a significant role in a climate-neutral European energy supply and thus warrants further study.

A Comprehensive Resource Assessment for Wind Power Generation on the Rural Island of Sibuyan, Philippines

Amid rising energy demands in rural areas, thorough resource assessments for initiatives such as wind power are crucial. This study involves a land resource assessment for wind power generation on the rustic Sibuyan Island in the Philippines, which is currently experiencing an electricity shortage. A comprehensive overview of the island’s suitability for wind energy projects is performed via evaluation and analysis using geospatial data and multi-criteria decision making (MCDM). The research results indicate that 50.44% (220.68 km2) of the island’s land area is categorized as ‘poorly suitable’ since it considers protected areas where developments are not allowed. Only 0.08% (0.35 km2) of the island can be classified as ‘marginally suitable’, while 9.15% (40.73 km2), 36.64% (176.39 km2), and 0.69% (3.05 km2) are labeled as ‘moderately suitable’, ‘suitable’, and ‘highly suitable’, respectively. This confirms the potential for wind energy exploration on the island. Delineating the suitability levels provides a foundational framework for stakeholders that enables them to identify optimal sites for wind power, sustain the island’s resources, and contribute to the renewable energy landscape of this rural location. Overall, this study, underpinned by data analysis, offers invaluable insights for decision making in wind power development, with the presented framework adaptable to other areas of interest.

The 2023 National Offshore Wind data set (NOW-23)

Abstract. This article introduces the 2023 National Offshore Wind data set (NOW-23), which offers the latest wind resource information for offshore regions in the United States. NOW-23 supersedes, for its offshore component, the Wind Integration National Dataset (WIND) Toolkit, which was published a decade ago and is currently a primary resource for wind resource assessments and grid integration studies in the contiguous United States. By incorporating advancements in the Weather Research and Forecasting (WRF) model, NOW-23 delivers an updated and cutting-edge product to stakeholders. In this article, we present the new data set which underwent regional tuning and performance validation against available observations and has data available from 2000 through, depending on the region, 2019–2022. We also provide a summary of the uncertainty quantification in NOW-23, along with NOW-WAKES, a 1-year post-construction data set that quantifies expected offshore wake effects in the US Mid-Atlantic lease areas. Stakeholders can access the NOW-23 data set at https://doi.org/10.25984/1821404 (Bodini et al., 2020).

Lactate contents in the blood of highly qualified football players during the pre-competition period

Relevance. In the realm of elite sports, the necessity for a straightforward, lucid, and dependable assessment of adaptive resources and mechanisms cannot be overstated. Given the prevalence of near maximum loads in athletic endeavors, there exists a propensity for athletes to teeter on the brink of overtraining. The body, being a single whole, responds to any impact, including physical activity, with a set of changes in its indicators. Fundamental research in the field of sports biochemistry carried out in recent decades, and, in particular, on changes in carbohydrate, fat, and protein metabolism under the influence of various sports activities, has made it possible to widely use the parameters of energy metabolism in sports practice. Lactate analysis is a reliable tool for monitoring the training process since changes in lactate levels reflect the intensity of physical activity and the body's adaptation to stress.The purpose of the study was to study the lactate content in the blood of highly qualified football players in the pre-competitive period. The work consists of optimizing the training process to achieve high results in competitions.Methods. The content of lactate in the blood of highly qualified football players in the pre-competition period is being studied, which indicates the importance of this parameter in assessing their physical fitness before competitions. Lactate analysis is an important part of monitoring the training process and allows you to optimize it to achieve the highest results in competitions. To conduct blood lactate testing, a portable lactate analyzer Lactate Plus (Sports) was used.Conclusions. Optimization of the training process provides coaches and medical personnel with information about the reaction of football player's bodies to training loads in the pre-competition period and helps adapt training programs to the individual needs of each athlete.

DEVELOPMENT OF A WEBGIS FOR SOLAR PV RESOURCE AND INSTALLATION ASSESSMENT USING GEOSPATIAL TECHNOLOGIES

Abstract. The SINAG Web Portal, a web-based GIS platform that hosts spatial data and a forecasting model for solar PV installation assessment, serves as a repository for the products and outputs of the SolarPot and OutSolar components of a research project called Solar PV Resource and Installation Assessment Using Geospatial Technologies (SINAG). The website was developed with frontend and backend services which involved the use of a content management system, database systems, cloud virtual machines, Linux operating systems, programming languages, specifically Python and JavaScript, and a style scripting framework. All outputs generated by the two components of Project SINAG are stored on the dedicated cloud server of the website. It includes processed spatial data, a graphical summary of the created models, the ability to explore historical data, data requests and downloads, and the use of mapping tools. It is anticipated that the web portal will play a significant role in the assessment and decision-making process for selecting candidate sites for installing solar PV systems across the Philippine archipelago.

Peningkatkan Kinerja Industri Kerajinan Batik Melalui Penguatan Sumber Daya Manusia Hijau (Studi Literatur)

Community empowerment is one of the impacts produced by the tourism sector. On the next trip, the development of tourism also stimulated the emergence of tourism-supporting industries such as transportation, culinary, souvenirs, and crafts. In an effort to create sustainable tourism, the presence of the batik craft industry as a tourism supporter is very much needed. Talking about green environmental issues, the batik industry also has the potential to threaten environmental sustainability. Referring to this, management needs to pay attention to and prepare various things that support business sustainability. Green human resources are interpreted as an effort to maximize their potential. This step is an organizational strategy for responding to green challenges and performance. This study will look at the extent to which green human resource management practices support the management of the management business. A qualitative research design through a literature study was used with reference to existing provisions. The results of the study explain that green human resource practices are able to support improving the quality of human resources. Some efforts that can be made include searching for human resources through screening, providing training and optimizing human resources, conducting human resource assessments, and providing appropriate rewards for the performance provided.

Why are non-wood forest products still the poor relative in Global Forest Resources Assessments?

To conserve and use forests sustainably, it is helpful to have accurate and regular assessments of their health and status. A key tool in this regard is the regular global overview provided by the Food and Agriculture Programme (FAO) in their Global Forest Resources Assessments (GFRA), now issued every five years. As of 2000, the GFRA required member countries to report statistics related to non-wood forest products (NWFPs). However, the NWFP statistics in the country appendices to the GFRA bear little resemblance to the situation on the ground, as shown by our assessment of entries for India, Nigeria, and South Africa against verified in-country studies. Our analysis shows that GFRAs often inaccurately report NWFP quantities, lack consistency between reports, omit data on amounts or values even when in-country studies exist, list the top ten NWFPs inaccurately, and fail to cite sources. Taken together, these shortcomings mean that the NWFP country statistics in the GFRA cannot be used to make comparisons between countries, regions or globally, track trends, or make policy or management decisions. The underlying reasons for these shortcomings are considered. Lastly, we suggest nine steps that need to be implemented to make the NWFP section of the GFRA reports a reliable and valued source of data and global analysis, that can be used by policy- and decision-makers and researchers globally.

Analysis of wind characteristics and wind energy resource assessment for Tonga using eleven methods of estimating Weibull parameters

Analysisof wind characteristics and assessment of wind energy resource is carried out at a location in Tonga with the help of twelve months of measurements carried out at 34 m and 20 m heights above ground level. The daily, monthly and annual averages are computed. The wind shear analysis and its diurnal variation were studied and compared with the temperature variation. The turbulence levels at the two heights were estimated for the entire measurement period as well as for some typical days. For estimating the Weibull parameters, eleven methods were employed along with goodness of fit and error estimates to find the best method. The overall averaged wind speed for the entire period of study is estimated to be 4.41 m/s at 34 m above ground level. The predominant wind direction was south-east for Tonga. ‘Moments’ is seen to be the best method to determine accurate Weibull parameters. The average net annual energy production from one Vergnet 275 kW wind turbine is 198.57 MWh. A payback period of 8.95 years by installing five turbines near the measurement location was estimated, which is very encouraging in terms of investment. Installing wind turbines will lower the heavy reliance on the imported fossil fuels in the country and also help in achieving Sustainable Development Goal 7.

Development of a convolutional neural network to accurately detect land use and land cover

The detection and modeling of Land Use and Land Cover (LULC) play pivotal roles in natural resource management, environmental modeling and assessment, and ecological connectivity management. However, addressing LULCC detection and modeling constitutes a complex data-driven process. In the present study, a Convolutional Neural Network (CNN) is employed due to its great potential in image classification. The development of these tools applies the deep learning method. A methodology has been developed that classifies the set of land uses in a natural area of special protection. This study area covers the Sierra del Cando (Galicia, northwest Spain), considered by the European Union as a Site of Community Interest and integrated in the Natura 2000 Network. The results of the CNN model developed show an accuracy of 91 % on training dataset and 88 % on test dataset. In addition, the model was tested on images of the study area, both from Sentinel-2 and PNOA. Despite some confusion especially in the residential class due to the characteristics in this area, CNNs prove to be a powerful classification tool.•Classifications based on a CNN model•LULC are classified into 10 different classes•Training and test accuracy are 91 % and 88 %, respectively

Surface Solar Radiation Resource Evaluation of Xizang Region Based on Station Observation and High-Resolution Satellite Dataset

Xizang boasts a vast and geographically complex landscape with an average elevation surpassing 4000 m. Understanding the spatiotemporal distribution of surface solar radiation is indispensable for simulating surface processes, studying climate change, and designing photovoltaic power generation and solar heating systems. A multi-dimensional, long-term, spatial, and temporal investigation of solar radiation in Xizang was conducted using three unique datasets, including the difference in surface solar radiation (SSR) between high-resolution satellite and ground station data, the annual and monthly distribution of SSR, and the interannual–monthly–daily variation and the coefficient of hourly variability. Combined with high-resolution elevation data, a strong linear correlation was shown between the radiation and the elevation below 4000 m. Furthermore, analysis reveals greater differences in data between east and west compared to the center, as well as between summer and winter seasons. SSR levels vary in steps, reaching the highest from Ngari to Shigatse and the lowest in a U-shaped area formed by southeastern Shannan and southern Nyingchi. In June, high monthly SSR coverage was the highest of the year. Since 1960, the annual mean SSR has generally exhibited a declining trend, displaying distinctive trends across various seasons and datasets. Owing to intricate meteorological factors, some regions exhibited double peaks in monthly SSR. Finally, we have introduced a solar resource assessment standard, along with a multidimensional evaluation of the resources, and categorized all townships. We offer a thorough analysis of Xizang’s solar radiation to provide a comprehensive understanding, which will help to prioritize recommendations for PV construction in Xizang.

Quantifying the production of plant pollen at the farm scale.

Plant pollen is rich in protein, sterols and lipids, providing crucial nutrition for many pollinators. However, we know very little about the quantity, quality and timing of pollen availability in real landscapes, limiting our ability to improve food supply for pollinators. We quantify the floral longevity and pollen production of a whole plant community for the first time, enabling us to calculate daily pollen availability. We combine these data with floral abundance and nectar measures from UK farmland to quantify pollen and nectar production at the landscape scale throughout the year. Pollen and nectar production were significantly correlated at the floral unit, and landscape level. The species providing the highest quantity of pollen on farmland were Salix spp. (38%), Filipendula ulmaria (14%), Rubus fruticosus (10%) and Taraxacum officinale (9%). Hedgerows were the most pollen-rich habitats, but permanent pasture provided the majority of pollen at the landscape scale, because of its large area. Pollen and nectar were closely associated in their phenology, with both peaking in late April, before declining steeply in June and remaining low throughout the year. Our data provide a starting point for including pollen in floral resource assessments and ensuring the nutritional requirements of pollinators are met in farmland landscapes.

Groundwater Resource Assessment by Applying Long-Term Trend Analysis of Spring Discharge, Water Level, and Hydroclimatic Parameters

Groundwater Resource Assessment by Applying Long-Term Trend Analysis of Spring Discharge, Water Level, and Hydroclimatic Parameters

Population Genetic Assessment Model Reveals Conservation Priorities for Gymnocypris Species Resources on the Qinghai-Tibetan Plateau.

The Qinghai-Tibetan Plateau (QTP) has nurtured a rich diversity of species because of its unique geographical and environmental conditions. Gymnocypris species (subfamily Schizopygopsinae) are primitive fishes that live in the special environment of the plateau, and their evolution and distribution are inseparable from the historical changes of the QTP. Recently, the resources of Gymnocypris species have been decreasing due to habit deterioration and the intensification of human activities. Therefore, the scientific conservation of the genetic resources of Gymnocypris species is urgently required. In this study, we established two models for the priority conservation assessment of germplasm resources of Gymnocypris species on the basis of the genetic diversity and phylogenetic relationships of 674 individuals from eight Gymnocypris species populations. The results show that the Gymnocypris potanini (GPO), Gymnocypris eckloni (GE), and Gymnocypris przewalskii (GPR) populations are the most genetically diverse in terms of combined genetic diversity values and should be prioritized for conservation. In terms of genetic contribution, the GPO, GE, and GPR populations have a positive impact on maintaining the distinctiveness and diversity of the entire Gymnocypris species population and should be prioritized for conservation. However, in terms of different evolutionary clades, the Gymnocypris namensis, Gymnocypris waddellii, Gymnocypris dobula, and GE populations in clade A should be given priority for protection, the GE population in clade B should be given priority, and the GPR population in clade C should be given priority. In conclusion, the two models and assessment of conservation priorities will provide a scientific basis for the conservation of Gymnocypris species.

Optimization and Evaluation of the Weather Research and Forecasting (WRF) Model for Wind Energy Resource Assessment and Mapping in Iran

This study aims to optimize the Weather Research and Forecasting (WRF) model regarding the choice of the best planetary boundary layer (PBL) physical scheme and to evaluate the model’s performance for wind energy assessment and mapping over the Iranian territory. In this initiative, five PBL and surface layer parameterization schemes were tested, and their performance was evaluated via comparison with observational wind data. The study used two-way nesting domains with spatial resolutions of 15 km and 5 km to represent atmospheric circulation patterns affecting the study area. Additionally, a seventeen-year simulation (2004–2020) was conducted, producing wind datasets for the entire Iranian territory. The accuracy of the WRF model was assessed by comparing its results with observations from multiple sites and with the high-resolution Global Wind Atlas. Statistical parameters and wind power density were calculated from the simulated data and compared with observations to evaluate wind energy potential at specific sites. The model’s performance was sensitive to the horizontal resolution of the terrain data, with weaker simulations for wind speeds below 3 m/s and above 10 m/s. The results confirm that the WRF model provides reliable wind speed data for realistic wind energy assessment studies in Iran. The model-generated wind resource map identifies areas with high wind (wind speed > 5.6 m/s) potential that are currently without wind farms or Aeolic parks for exploitation of the wind energy potential. The Sistan Basin in eastern Iran was identified as the area with the highest wind power density, while areas west of the Zagros Mountains and in southwest Iran showed high aeolian potential during summer. A novelty of this research is the application of the WRF model in an area characterized by high topographical complexities and specific geographical features. The results provide practical solutions and valuable insights for industry stakeholders, facilitating informed decision making, reducing uncertainties, and promoting the effective utilization of wind energy resources in the region.

Tracing the land use specific impacts on groundwater quality: a chemometric, information entropy WQI and health risk assessment study.

Understanding the nexus of land use and water quality can potentially underline the influences within the groundwater management. The study envisages land use-specific qualitative assessment of the groundwater resources in Ghaziabad district, in western Uttar Pradesh, India. For encountering the relative impacts of land use on the groundwater quality, chemometric analysis has been employed to apportion the pollution sources. The integration of quality parameters, in the information entropy index modeling, has segregated the quality classes and visualized the seasonal suitability trends as per potability standards along with non-carcinogenic health hazard risk assessment (HHRA). The qualitative assessment of the groundwater resources, along with spatial distribution, has deciphered a polluting impact, specifically in western and south western parts of district, and observed the linkages with direct and indirect discharges/seepages from densely populated residential and industrial land use types localized in urbanized areas. Statistically significant annual and seasonal variations have been found exclusively for EC, Mg2+, F-, Cd, Cr(total), Ni, and Pb which inferred variable concentrations, whereas land use types showed a non-significant variation within groundwater quality. Chemometric-based source apportioning and hierarchical cluster analysis (HCA) have derived salinization and enrichment of dissolved salts, arising from mixed sources and contributes to metal pollution, i.e., mainly from anthropogenic sources. Information EWQI derived poor to extremely poor category represented degraded potability specifically for fewer sites located within western and southern parts on the Yamuna-Hindon flood plains for limited sites of residential, industrial, and agricultural in an urbanized region. However, majority of the samples fall under excellent to good groundwater quality, recommendable in the north and north-eastern (peri-urban) regions. Non-carcinogenic HHRA has shown that majority of the samples categorized under unsafe value for hazard index (HI > 1), for females and children and thus, presumed probable health hazard risk from metal groundwater pollution in south-western part, eastern, and northern regions.

Dynamic geothermal resource assessment: Integrating reservoir simulation and Gaussian Kernel Density Estimation under geological uncertainties

This paper presents a dynamic geothermal resource assessment method that integrates reservoir simulation and Gaussian kernel density estimation (KDE). This approach addresses geological uncertainties by employing reservoir simulation techniques to model the fluid and heat flow under the condition of permeability heterogeneity. Incorporating probabilistic resource assessment through Gaussian KDE, the study quantifies uncertainties, estimating the probability density function (PDF) of ensemble results under conditions like thermal breakthrough thresholds, fixed reservoir lifespans, and target energy production. The demonstrations of assessment start with a simple homogeneous model. The results show that larger doublet well distances result in extended lifespan, higher final production well temperatures, and increased energy production. Brugge reservoir emphasizes the impact of heterogeneity and uncertainty on production outcomes, especially at smaller doublet well distances. Assessment of fluvial Egg model reveals that drilling in fluvial channels causes rapid thermal breakthrough. This result indicates that, to optimize reservoir performance, it is recommended to refrain from drilling doublet wells within high-permeability fluvial channels. Furthermore, it is worthy of mention that the Gaussian kernel is not always favored for KDE, particularly in scenarios involving non-Gaussian distribution ensembles. The proposed method, which integrates reservoir simulation and Gaussian KDE, enhances understanding of geological uncertainties and the intricate nature of geothermal reservoirs, facilitating more reliable and accurate resource assessments.

Generative Artificial Intelligence in Business: Towards a Strategic Human Resource Management Framework

AbstractAs businesses and society navigate the potentials of generative artificial intelligence (GAI), the integration of these technologies introduces unique challenges and opportunities for human resources, requiring a re‐evaluation of human resource management (HRM) frameworks. The existing frameworks may often fall short of capturing the novel attributes, complexities and impacts of GAI on workforce dynamics and organizational operations. This paper proposes a strategic HRM framework, underpinned by the theory of institutional entrepreneurship for sustainable organizations, for integrating GAI within HRM practices to boost operational efficiency, foster innovation and secure a competitive advantage through responsible practices and workforce development. Central to this framework is the alignment with existing business objectives, seizing opportunities, strategic resource assessment and orchestration, re‐institutionalization, realignment and embracing a culture of continuous learning and adaptation. This approach provides a detailed roadmap for organizations to navigate successfully the complexities of a GAI‐enhanced business environment. Additionally, this paper significantly contributes to the theoretical discourse by bridging the gap between HRM and GAI adoption, the proposed framework accounting for GAI–human capital symbiosis, setting the stage for future research to empirically test its applicability, explore its implications on HRM practices and understand its broader economic and societal consequences through diverse multi‐disciplinary and multi‐level research methodologies.