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The metal additive manufacturing (MAM) process has most employed methods to build complex geometry and lightweight 3-dimensional (3-D) parts directly from a computerized solid model. Distortion of the printed part is a highly significant concern within the MAM process. This issue is because of the heating and cooling effect of printing process that could accumulate residual stress (RS) during part building up. The aim of the literature work is to present various methodologies for measuring RS in MAM components and to furnish a brief summary of recent developments in the domain. These details aid scholars in the discernment of suitable techniques, namely destructive, semi-destructive, or non-destructive, contingent on their particular applications and the accessibility of these methods. Moreover, it facilitates the explication of their formation mechanisms, effectiveness of process parameters, prediction, and control techniques. The effect of RS on the mechanical characteristics of printed parts is analyzed and presented. Additionally, common defects incorporated into RS are discussed. Moreover, this review article discusses about the future challenges and opportunities in the RS analysis of MAM parts.Highlights: ●Different technique of residual stress measurement in additive manufacturing process. ●Influence of process parameter on residual stress. Scanning speed, layer height, laser power, laser energy density etc. ●Impact of residual stress on mechanical properties. ●Common defect due to residual stress. Delamination, parts cracking, separation from support, grain boundary cracking etc. ●Impact of in-process and post-process techniques in residual stress modification. Shot peening, heat treatment, hot isotatic pressing, laser shock peening etc. [ABSTRACT FROM AUTHOR]

ONE OF EGYPT'S top targets for future agricultural development is the Northern El Bahrya Oases region. The current study aims at digitally mapping the soils of the area under consideration and producing land capability and suitability digital maps. The considered region lies between longitudes 29° 9' 28.433'', 29° 22' 32.785'' East and latitudes 28° 57' 38.309'', 28° 58' 41.324" N. The region is covering roughly 46228 Fed. The Digital Elevation Model (DEM) is utilized along with the thematic maps of soil properties to produce digital land capability and suitability maps. The spatial analysis tool in the Arc-GIS 10.8 software generated thematic layers. Physiographic mapping units were created and showed that the studied area has one landform which is plateau. The land capability was evaluated by the method of Storie index where the studied area was classified into three capability grades, namely; grade 2, 3 and 4. The soils of grade 2 (66.41 % of the whole region) have moderate limitations for agricultural crops and texture is the main limiting factor. The area of grade 3 (25.59%), has soils with texture and salinity limiting factors, while the soils of grade 4 (7.99%), have texture, depth and salinity main limiting factors. Land suitability was done according to Sys and others for some crops and transferred to GIS to produce digital maps. The fertility maps of soil nutrients for N, P, K, Fe, Mn, Zn, and Cu are included. The study established a detailed database to serve decision-makers and help in sustainable development. [ABSTRACT FROM AUTHOR]

This paper introduces the conceptual framework of the multi-criteria decision-making (MCDM) rank model, which embodies the integration and harmonization of the aggregation method, the weighing method, the decision matrix normalization technique, and the selection of distance metrics. This definition serves to broaden the spectrum of acceptable MCDM methodologies for problem-solving and specifiing the associated tools. A Multi-Method Model (3M) approach is employed for multi-criteria selection to enhance the reliability of the results. The methodology is outlined for adjusting the rankings of alternatives to account for the distinguishability of ratings in a particular MCDM model using the Relative Performance Indicator (RPI) of alternatives. Through RPI, four methods are established for aggregating individual characteristics of alternatives that yield identical results: Weighted Sum Model (WSM), Multi-Attributive Border Approximation area Comparison (MABAC), Technique for Order Preference by Similarity to Ideal Solutions (TOPSIS (L1)), and Ratio System approach (RS), eliminating the need to duplicate these methods in the 3M approach. A comprehensive comparison of numerous multi-criteria methods is conducted based on two lists: ranking and rating. Additionally, a method for step-by-step linear transformation of alternative ratings obtained from various MCDM models is defined, facilitating comparison and aggregation of ratings. [ABSTRACT FROM AUTHOR]

Copyright of Turkish Journal of Remote Sensing & GIS is the property of Halil Akinci and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Remote Sensing for Natural Resources is the property of Remote Sensing for Natural Resources Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Based on RS and GIS data, the knowledge about localities (2nd cent. CE–early 20th cent. CE) connected with pioneer sea hunters of the Kolyuchin, Wrangel, and Herald islands (KI, WI, and HI) has been revised, including the data obtained by the author in 1978–1988 during zoological expeditions. The study was initiated by the intention to save rare evidence from being accidently destroyed by th heavy bombardments of Kyiv by Russia. Thus, the description of previously unknown Stone Age artifacts found on KI was saved; the coordinates of some previous locations were reconstructed and the geo-positions of the new ones were added. According to all the data it was proved that the former Soviet polar station (KI) was built on a mosaic of ancient camps, which definitely affected the integrity of the cultural layer. The first study of these camps carried out in the 1960s was not exhaustive. For WI: a) we used the latest e-maps, RS data and the new comparisons to indicate the most likely possible vector of a new search for traces of the Paleolithic site that were described in the 1930s (by the geologist L. Gromov), but which had been lost; b) we reconstructed the coordinates of dozens of ancient hunting shelters, which seem to have changed faster than others due to the destructive influence of the sea; c) we added a description of traces corresponding to sites of seabird hunters (on the west) and mammal hunters (on the north), the latter retaining a whale rib among the remains. Little-known literature data about rare finds of remains of the sailing fleet time on the coast of WI, which were not paid attention to before, were added. The author shares some rare survey references about the finding of an ancient weapon of the 17th (?) century on HI, and a coin of the 19th century on WI. The article is illustrated with little-known pictures of clusters of marine mammal skulls on KI, as well as of some rare products related to the former archaic maritime industry of WI. The Soviet views on toponymy, and the practice of excessive enthusiasm for descriptions of discoveries of polar islands ‘beyond the horizon’ of the 19th century are criticised. The article would be an auxiliary source for lecturers and students of the National Univerity of Life and Envorpnmental Sciences of Ukraine and of the Taras Shevchenko National University of Kyiv, who teach or study landscape and applied ecology, conservation biology, as well as be the author’s contribution into the new European initiatives, such as ‘Arctic in Eastern Europe. Knowledge, Perception and Communi­cation in the 18th–19th Centuries’ (Torun, Poland).

Abstract Groundwater aquifers constitute the primary water supply for populations in arid regions, exemplified by the Goharkooh Plain in Iran's driest drainage basin, where conditions of high evapotranspiration and low precipitation prevail. With the escalating demand for water resources, driven mainly by agricultural expansion, the strategic management of groundwater assets has become increasingly critical. This study focuses on delineating groundwater potential zones (GWPZs) through an integrated approach combining multi-criteria decision analysis and geospatial tools. Based on an extensive literature review, nine thematic layers were selected and developed: lithology, geology, drainage density, slope gradient, elevation, vegetation cover, lineament density, land use, and precipitation. These criteria were initially weighted using the analytical hierarchical process (AHP) and subsequently integrated via weighted overlay analysis. In this research, the strategic selection of thematic layers for assessing groundwater potential in arid regions has been identified as an innovative approach that could significantly advance studies in similar settings. The analysis revealed that approximately 60% of the study area, primarily in the southwestern parts, exhibited moderate to very high groundwater potential. This potential is primarily attributed to the presence of alluvial deposits, low drainage density, and favorable slope and elevation conditions. Applying the receiver operating characteristic (ROC) curve yields an area under the curve (AUC) of 81.5%, indicating a relatively high level of predictive accuracy. These findings demonstrate the efficacy of this integrated approach, suggesting its broader applicability in regions with analogous groundwater challenges and management needs.

Abstract The built environment is associated with higher air and surface temperatures in urban areas compared to rural counterparts. This study focuses on the Sheikhupura district in Punjab, Pakistan, where rapid urbanization and deforestation have significantly impacted land surface temperature (LST) from 2000 to 2020. Utilizing Geographic Information Systems (GIS) and Remote Sensing (RS) techniques, four Landsat images from the United States Geological Survey (USGS) were analyzed to assess spatiotemporal variations in LULC and LST. The single infrared channel approach was employed to calculate LST, and LULC maps were created using supervised classification for the years 2000, 2010, and 2020. The results indicate a substantial increase in built-up areas from 66.9708 square kilometers in 2000 to 259.8147 square kilometers in 2020, accompanied by a significant reduction in vegetation and agricultural land. Correspondingly, the maximum LST rose from 28.9354 °C in 2000 to 31.5173 °C in 2020, highlighting the urban heat island effect. Zonal statistics revealed a decrease in mean high temperatures from 24.45 to 20.3 °C over the same period, suggesting variability in temperature distribution due to changes in land cover types. These findings underscore the need for sustainable urban planning to mitigate the adverse effects of urbanization on local climates. Future work should focus on developing strategies to increase green spaces and reforestation efforts to counteract rising temperatures. Additionally, further research is recommended to explore the long-term impacts of these changes on local biodiversity and ecosystem services, providing a holistic approach to urban sustainability.

Groundwater aquifers constitute the primary water supply for populations in arid regions, exemplified by the Goharkooh Plain in Iran's driest drainage basin, where conditions of high evapotranspiration and low precipitation prevail. With the escalating demand for water resources, driven mainly by agricultural expansion, the strategic management of groundwater assets has become increasingly critical. This study focuses on delineating groundwater potential zones (GWPZs) through an integrated approach combining multi-criteria decision analysis and geospatial tools. Based on an extensive literature review, nine thematic layers were selected and developed: lithology, geology, drainage density, slope gradient, elevation, vegetation cover, lineament density, land use, and precipitation. These criteria were initially weighted using the analytical hierarchical process (AHP) and subsequently integrated via weighted overlay analysis. In this research, the strategic selection of thematic layers for assessing groundwater potential in arid regions has been identified as an innovative approach that could significantly advance studies in similar settings. The analysis revealed that approximately 60% of the study area, primarily in the southwestern parts, exhibited moderate to very high groundwater potential. This potential is primarily attributed to the presence of alluvial deposits, low drainage density, and favorable slope and elevation conditions. Applying the receiver operating characteristic (ROC) curve yields an area under the curve (AUC) of 81.5%, indicating a relatively high level of predictive accuracy. These findings demonstrate the efficacy of this integrated approach, suggesting its broader applicability in regions with analogous groundwater challenges and management needs. [ABSTRACT FROM AUTHOR]

Taking Jixi City, a typical mining city as an example, this paper quantitatively analyzed the changing characteristics of land use patterns in mining cities by using the methods of terrain location index, distribution index and land use change map under the support of RS and GIS. The results showed that affected by mining activities and urban expansion, land use types had strong differences in different terrain gradients;arable land and construction land had a high frequency of change in low-lying areas, and gradually expanded to high-lying areas;alternate transformation of arable land, forest land and grassland occurs frequently in topographic areas. [ABSTRACT FROM AUTHOR]

The present study focuses on monitoring the spatial distribution of the normalized difference built-up index (NDBI) for Babylon governorate in two different years 2012 and 2022. The paper based on the LANDSAT 7, 8 data, in particular by using two bands (short-wave infrared, and near-infrared) and the Power NASA website. The spatial distribution of the NDBI was presented in two maps. Descriptive statistics of the NDBI were determined for each map (year) to describe and understand the features of the NDBI in the study area. The climate changes such as the air temperature and the land surface temperatures were studied with the change in the distribution of the NDBI in the governorate of Babylon for the period of study. The increase in NDB caused an increase in the temperature of the land surface temperature as well as in the air temperature at an altitude of 2 meters above the Earth's surface by an amount 1.5 C°. [ABSTRACT FROM AUTHOR]

THIS RESEARCH aims to create and analyze thematic soil maps for promising areas for agricultural expansion along the northwest coast of Egypt. The study area is 3151.671 km² and consists of a plateau and elevated areas merging into coastal lowlands. Remote sensing data were used with a digital elevation model to map the physiographic units and characterize land surface features. Five different physiographic entities are recognized; each mostly associated with the soil sub-great groups Typic Haplocalcids and Typic Torripsamments. Data collected through land survey and laboratory analysis were used to create thematic soil layers that were analyzed to determine land factors that limit agricultural development. The results showed that the land surface factors hinder agricultural development due to the steep slope and undulation of the land surface. Analysis of thematic soil maps showed that agricultural development in the region is mainly constrained by coarse soil texture, shallow soil depth, high CaCO3 content, and low CEC. To achieve agricultural sustainability in the region, improved drainage, organic fertilization, and crop selection are required. [ABSTRACT FROM AUTHOR]

The spectrophotometer method is costly, time-consuming, laborious, and destructive to the plant. Samples will be lost during the transportation process, and the method can only obtain sample point data. This poses a challenge to the estimation of chlorophyll content at the regional level. In this study, in order to improve the estimation accuracy, a new method of collaborative inversion of chlorophyll using Landsat 8 and Global Ecosystem Dynamics Investigation (GEDI) is proposed. Specifically, the chlorophyll content data set is combined with the preprocessed two remote-sensing (RS) factors to construct three regression models using a support vector machine (SVM), BP neural network (BP) and random forest (RF), and the better model is selected for inversion. In addition, the ordinary Kriging (OK) method is used to interpolate the GEDI point attribute data into the surface attribute data for modeling. The results showed the following: (1) The chlorophyll model of a single plant was y = 0.1373x1.7654. (2) The optimal semi-variance function models of pai, pgap_theta and pgap_theta_a3 are exponential models. (3) The top three correlations between the two RS data and the chlorophyll content were B2_3_SM, B2_3_HO, B2_5_EN and pai, pgap_theta, pgap_theta_a3. (4) The combination of the Landsat 8 imagery and GEDI resulted in the highest modeling accuracy, and RF had the best performance, with R2, RMSE and P values of 0.94, 0.18 g/m2 and 83.32%, respectively. This study shows that it is reliable to use Landsat 8 images and GEDI to retrieve the chlorophyll content of Dendrocalamus giganteus (D. giganteus), revealing the potential of multi-source RS data in the inversion of forest ecological parameters. [ABSTRACT FROM AUTHOR]

High- and very high-resolution (HR, VHR) remote sensing (RS) images can provide comprehensive and intricate spatial information for land cover classification, which is particularly crucial when analyzing complex built-up environments. However, the application of HR and VHR images to large-scale and detailed land cover mapping is always constrained by the intricacy of land cover classification models, the exorbitant cost of collecting training samples, and geographical changes or acquisition conditions. To overcome this limitation, we propose an unsupervised domain adaptation (UDA) with contrastive learning-based discriminative feature augmentation (CLDFA) for RS image classification. In detail, our method first utilizes contrastive learning (CL) through a memory bank in order to memorize sample features and improve model performance, where the approach employs an end-to-end Siamese network and incorporates dynamic pseudo-label assignment and class-balancing strategies for adaptive domain joint learning. By transferring classification models trained on a source domain (SD) to an unlabeled target domain (TD), our proposed UDA method enables large-scale land cover mapping. We conducted experiments using a massive five billion-pixels dataset as the SD and tested the HR and VHR RS images of five typical Chinese cities as the TD and applied the method on the completely unlabeled world view 3 (WV3) image of Urumqi city. The experimental results demonstrate that our method excels in large-scale HR and VHR RS image classification tasks, highlighting the advantages of semantic segmentation based on end-to-end deep convolutional neural networks (DCNNs). [ABSTRACT FROM AUTHOR]

[Objective]The aims of this study are to investigate the spatiotemporal characteristics of urban construction land expansion and its occupation of arable land in Sichuan Province, and to provides a theoretical basis for protecting the sustainable use of arable land resources in Sichuan Province and ensuring regional food security. [Methods]Sichuan Province was taken as the research object. The spatiotemporal variation characteristics of urban construction land expansion on arable land occupation process from 2000 to 2020 were analyzed based on remote sensing data and GIS technology. [Results]Over the past 20 years, urban construction land in Sichuan Province had continued to expand, increasing by 3 260. 56 km2, with a growth rate of 110. 34％. Among them, 2 900. 34 km² of newly added construction land had been transferred to arable land, which was the main source of urban construction land expansion in Sichuan Province. The continuous expansion of construction land continued to occupy the arable land resources around the city, which might threaten the regional food production security. The expansion of construction land in Sichuan Province was characterized by centralized distribution in space. The spatial difference of construction land expansion among cities and prefectures was significant. The distribution of expanded construction land was a high-value area which centered in Chengdu, and then gradually spreaded to the eastern cities in a plane shape, while distribution of the expanded construction land in the western cities was characterized as sparse spots. The expansion of construction land in Sichuan Province was highly dependent on occupying arable land. In recent years, although measures had been actively implemented to optimize the grain production structure, to continue to implement land consolidation, agricultural structure adjustment, and to continuously increase support for grain production in Sichuan Province. [Conclusion]Overall, the decrease in arable land area in Sichuan Province has little impact on the formation of grain production, but if the trend of expanding construction land and occupying more arable land is not effectively curbed, a potential threat will be still posed to future food production security. [ABSTRACT FROM AUTHOR]

Context and background Groundwater is the most vital and promising natural resource for ecosystems and societies particularly in semi-arid areas. Due to urbanization and various anthropogenic activities the demand of water is hugely increasing. Proper evaluation and demarcation of groundwater potential zones can facilitate proper physical exploration of ground water and hence simplify identification of proper locations for borehole drilling. Sustainable use of groundwater is essential in Tanzania so as to increase long-term agricultural and industrial sustainability as well as to maintain the pace of socio-economic development for poverty reduction and eradication. Goal and Objectives: The objective of this research is to delineate prospective zones for groundwater exploration in Wami-Ruvu Basin Water Board (BWB), Tanzania.. Methodology: A Remote Sensing (RS) and Geographic Information System (GIS) based on Analytical Hierarchical Process (AHP) has been utilized. Results: The final groundwater potential map was prepared by assigning appropriate weightage and integration of thematic layers using weighted overlay analysis. The groundwater potential areas have been categorized into five categories very low (0.3%), low (32.0%), moderate (62.8%), high (4.8%) and very high (0.1%). Validation of the identified potential zones indicates a Pearson correlation of 0.6 between yields and GWPZ. This study clearly highlights the efficacy of RS and GIS-based multi-criteria decision technique as useful modern approach for proper groundwater resources evaluation; providing quick prospective guides for groundwater exploration and exploitation. We recommend RS and GIS based identification of Ground potential zones for all Basin Water Boards in Tanzania to support physical groundwater exploration. Future work could test the use of machine learning algorithm particularly random forest and deep learning and incorporation of other parameters for delineating groundwater potential zones.

Groundwater (GW) emerges as a sustainable resource to meet water demand, yet current GW reservoirs fall short of projected population needs. Artificial GW recharge, bolstered by Geographical Information System (GIS) and Remote Sensing (RS) technologies, stands out as a pivotal solution. Examining Aurangabad district in Maharashtra, India the study uncovers distinct challenges: Aurangabad faces escalating water demand due to population growth, leading to GW over exploitation. Leveraging hydrogeological parameters, rainfall analysis, and GW availability, the study proposes tailored GW recharge techniques. Utilizing MODFLOW simulation, the study explores diverse recharge rates and existing GW availability, showcasing promising results. Integration of stream augmentation, bladder pillow tank methods, and dug well recharge is examined via GIS and RS techniques. Simulation outcomes project significant GW level increases (11.55–23.70 m) with varying recharge rates, effectively meeting region-specific water demand projections. The findings underscore the imperative of sustainable water management practices and technological advancements in addressing water scarcity crises. HIGHLIGHTS Groundwater recharge using GIS and RS.; Parameters to select proper method of GW recharging using like hydrogeological data, and rainfall data.; Stream augmentation and bladder pillow method.; Three cases with different percentages of available GW.; Solving GW depletion problem.;

Soil erosion significantly impacts dam functionality by leading to reservoir siltation, reducing capacity, and heightening flood risks. This study aims to map soil erosion within a Geographic Information Systems (GIS) framework to estimate the siltation of the K'sob dam and compare these estimates with bathymetric observations. Focused on one of the Hodna basin’s sub-basins, the K'sob watershed (1477 km2), the assessment utilizes the Revised Universal Soil Loss Equation (RUSLE) integrated with GIS and remote sensing data to predict the spatial distribution of soil erosion. Remote sensing data were pivotal in updating land cover parameters critical for RUSLE, enhancing the precision of our erosion predictions. Our results indicate an average annual soil erosion rate of 7.83 t/ha, with variations ranging from 0 to 224 t/ha/year. With a typical relative error of about 13% in predictions, these figures confirm the robustness of our methodology. These insights are crucial for crafting mitigation strategies in areas facing high to extreme soil loss and will assist governmental agencies in prioritizing actions and formulating effective soil erosion management policies. Future studies should explore the integration of real-time data and advanced modeling techniques to further refine these predictions and expand their applicability in similar environmental assessments.

Copyright of lnternational Journal of Geography & Geography Education is the property of Marmara University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In recent years, the rapid development of the photovoltaic (PV) industry has resulted in a saturation of research on onshore PV power plant construction. However, current studies on the impact of marine PVs on the marine environment remain limited and scarce. In order to facilitate the implementation of carbon reduction goals and promote the sustainable development of the offshore PV industry, this study analyzes the environmental impact of PV sea-use resources based on spatial information technology in the western part of Gaotang Island. The findings show that the MIKE21FM model provides relatively accurate simulations of tidal flow and tide level in the marine PV area. Flow velocity in the marine PV area exhibits a decreasing trend, with an average decrease ranging from 0.03 to 0.07 m/s. This decrease minimally affects surrounding navigational channels and large-scale flow fields. The resulting siltation is also deemed less significant, with an annual deposition from 0.03 to 0.06 m/a. Moreover, offshore PV construction resulted in a total intertidal biological loss of 123.45 t. The suspension of sediment during cofferdam construction and removal has a potential effect on zooplankton and fishery resources. Overall, it is proposed that careful planning, prudent site selection, and the execution of countermeasures during marine PV construction will combine to minimize the impact on the marine environment. [ABSTRACT FROM AUTHOR]

Faster-than-Nyquist (FTN) signaling has attracted increasing interest in the past two decades. However, when the fifth-generation (5G) communication low-density parity check (LDPC) code is applied to FTN signaling with low Bahl–Cock–Jelinek–Raviv (BCJR) states of detection and few turbo equalization iterations, an error floor near 10 − 5 is found, which does not exist in the original LDPC used for orthogonal signaling. This can be eliminated through many detection and decoding iterations, but this is unacceptable considering the increase in latency and storage. To solve this problem, we propose an LDPC and Reed–Solomon (RS) concatenation code, shortening, and perturbation scheme to lower the error floor. We propose a parallel encoder architecture for RS component code and a concise algorithm to calculate its constant multiplier coefficients, leveraging a traditional serial encoder, which can also be used for other parallelisms, rates, and lengths. The simulation results show that the proposed concatenation and shortening scheme can lower the error floor to about 10 − 7 . The proposed scheme has an error correction capability for coded FTN signaling and successfully lowers the error floor with the limitation of few turbo iterations and few BCJR states. [ABSTRACT FROM AUTHOR]

Climate change has resulted in an increase in extreme rainstorm events, posing the challenges of urban waterlogging and runoff pollution. Low Impact Development (LID) is widely used to address the issues above, but its effectiveness is unknown in mountainous areas. Due to a flash flood and high flood peak, storage pumping stations are also needed to drain. Thus, a framework composed of storage pumping stations and Low Impact Developments (LIDs) was proposed based on the topography and the regional upstream and downstream relationships. The water quantity in this framework is applied to YI County in Hebei Province, China. The results showed that individual LIDs effectively reduced runoff volume, with the implementation area being more crucial than the location. Combining storage pumping stations with LIDs significantly reduces peak outflow and delays it by 5 to 51 min. The combined downstream implementation of storage pumping stations and LIDs yielded the most effective results. These findings offer important insights and management strategies for controlling waterlogging in mountainous cities of developing countries. [ABSTRACT FROM AUTHOR]

Green bananas contain a substantial amount of resistant starch (RS), dietary fiber (DF), and phytochemicals, which exhibit potent antioxidant capabilities, primarily attributable to the abundance of polyphenols. The objective of this study was to assess the variations in the contents and bioaccessibility of RS, DF, and phenolic compounds in three types of Australian green bananas (Cavendish "Musa acuminata", Ladyfinger "Musa paradisiaca L.", and Ducasse "Musa balbisiana"), along with their antioxidant capacities, and the production of short-chain fatty acids (SCFAs) following in vitro simulated gastrointestinal digestion and colonic fermentation. The studied cultivars exhibited significant levels of RS, with Ladyfinger showing the greatest (49%). However, Ducasse bananas had the greatest DF concentration (38.73%). Greater TPC levels for Ladyfinger (2.32 mg GAE/g), as well as TFC and TTC (0.06 mg QE/g and 3.2 mg CE/g, respectively) in Cavendish, together with strong antioxidant capacities (DPPH, 0.89 mg TE/g in Cavendish), have been detected after both intestinal phase and colonic fermentation at 12 and 24 h. The bioaccessibility of most phenolic compounds from bananas was high after gastric and small intestinal digestion. Nevertheless, a significant proportion of kaempferol (31% in Cavendish) remained detectable in the residue after colonic fermentation. The greatest production of SCFAs in all banana cultivars was observed after 24 h of fermentation, except valeric acid, which exhibited the greatest output after 12 h of fermentation. In conclusion, the consumption of whole green bananas may have an advantageous effect on bowel health and offer antioxidant characteristics. [ABSTRACT FROM AUTHOR]

Background and Objectives: Forest ecosystem degradation is a global concern, prompting natural resource managers to prioritize its prevention and mitigation. Incompatible land uses pose a significant threat, particularly to biodiversity. Techniques like remote sensing and geographic information systems (GIS) with spatial analysis capabilities are valuable tools for detecting and evaluating these threats and their impact on forest ecosystems. This study aimed to evaluate forest degradation in the Dadabad region using a sub-unit destruction model and identify the most impactful human activities. Materials and Methods: We employed a land-use/forest cover map for the Dadabad region, divided into 13 sub-areas, to quantify land features. Landsat satellite imagery (1988-2018) was pre-processed and classified for land-use change analysis. Landscape destruction and ecological vulnerability were calculated using the landscape destruction model, considering activity intensity and total destruction. Subsequently, land surface analysis and zoning were performed based on derived destruction coefficients. The Analytic Network Process (ANP) method, a multi-criteria decision-making approach, was employed to prioritize human activities contributing to destruction in the region. ANP allows for factor dependence and utilizes network analysis for non-linear structuring. Experts' opinions were incorporated to identify critical factors influencing forest cover degradation. Calculations were performed using a multi-criteria decisionmaking model. Results: Sub-areas 3 and 5 exhibited the highest levels of land surface destruction, while sub-areas 4, 8, 9, 12, and 13 displayed the least fragmentation. Notably, 40.58% of the total Dadabad region area was classified as minimally degraded and suitable for development with ecological considerations. The low degradation in these areas can be attributed to their mountainous terrain, limiting human activity. Additionally, 25.33% of the area requires restoration, and 8.37% requires protection measures. Conclusion: Among the identified factors, conversion of national lands to agriculture, socio-economic issues, and extensive construction activities (road construction) were the most significant contributors to destruction, with final weights of 0.2, 0.182, and 0.18, respectively. This study's findings can significantly inform the development of efficient forest protection and management programs in Lorestan province by prioritizing the most critical factors driving forest degradation. [ABSTRACT FROM AUTHOR]

Soil respiration (Rs) is a widely monitored parameter in global forest management that results in activities that contribute to ecosystem functions. Rs can vary depending on different disturbance levels and ecosystem types as a result of changes in forest management practices. Understanding the mechanisms through which different forest management practices affect Rs can provide a general reference for ecological management and restoration practices. However, the global drivers of Rs across different forest management practices have not been sufficiently studied in the literature. In this study, we investigated the changing trends in Rs based on the relationships evident between biomass and Rs across different forest management practices. We used simple linear models to explore the relationships between biomass (aboveground and belowground biomasses) and Rs at a global scale based on different types of forest management practices and biomes. We observed significant differences in the mean values of Rs among various forest management practices. Furthermore, significant positive relationships between forest biomass and Rs were evident globally. Soil temperature had a significant effect on Rs, but the influences of soil temperature and moisture on Rs changed with the variations in forest management practices. Biome type can regulate the relationships between forest biomass and Rs across different forest management practices. We observed that the relationships between forest biomass and Rs were the strongest for naturally regenerating forests, both with and without signs of management, in tropical and subtropical coniferous and temperate broadleaf and mixed forests. Forest plantations and agroforestry can favor the establishment of similar positive relationships in temperate forest biomes (i.e., temperate conifer forests and boreal forests/taiga). Our results show that aboveground and belowground biomasses can be applied as effective ecological indicators for monitoring Rs levels, depending on different forest management practices and biomes. In this study, we provide evidence for monitoring Rs levels under different forest management practices globally. [ABSTRACT FROM AUTHOR]

Flood and drought are natural phenomena whose probability of occurrence increases with changing weather conditions. Climate change has affected the hydrologic cycle's parameters and led to changes in temperature and precipitation pattern, which may increase the probability of occurrence of floods and droughts. Arid and semi‐arid areas are subject to extreme weather conditions and water resources scarcity. This study aims to assess drought and flood in Syria using remote sensing data. Monthly precipitation data have been collected from 10 land stations in the western part of Syria for the period (1991–2009) and satellite images for monthly precipitation from 1983 to 2020 were collected. The land stations data were used to correct the satellite images and correction equation was developed for each station. Standardized Precipitation Index (SPI) is used for analysis of drought and flood. Also, the main parameters for drought and flood, severity, intensity, duration, and the average return time were estimated. The results showed extreme flood events were recorded in 1988, 2002, 2012, and 2019 and extreme drought events were recorded in 1990, 2014, and 2016. The highest severity of drought and flood were recorded in 2014 and 1988 respectively. However, the highest intensity of droughts and floods was recorded in 2016 and 1988, respectively. If land station data are unavailable, as in the case of Syria owing to the protracted war, satellite images may be a useful source of data for drought and flood analysis after correction. This methodology can help in drought/flood analysis and estimate severity, intensity, duration, and the average return time of flood and drought in other locations of the world. The results proved that SPI is a useful tool to predict the time of occurrence of both flood and drought that could help decision‐makers for putting efficient plans for flood and drought risk management that could mitigate the impacts of such risks. [ABSTRACT FROM AUTHOR]

Abstract This research aimed at evaluation of a given land resource suitable for selected rain fed crops production (malt barley, wheat and teff) in Ambesh watershed. It also quantified suitable land and classified into the land mapping units (LMUs), and presents a land suitability map. Land suitability assessment (LSA) made using climatic condition, topography, soil physical and chemical properties as a major factor integrated with a multi criteria decision making (MCDM) analysis in Ambesh watershed. Fourteen composite soil samples were taken by categorizing the watershed into different land mapping units (LMUs) and analyzed in a soil laboratory. Climatic data, rainfall was obtained from two meteorological stations nearby to the study area. Temperature data derived from Landsat 8 satellite thermal bands data. Data obtained from the soil laboratory and others were finally analyzed using ArcGIS environment and priority estimation tool (PriEsT) software’s. Weighted Sum Overlay was implemented to investigate the final LSA map of the watershed. Results revealed that LMUs, VRe–LPq and LPK.Pq–FLc LMUs has higher overall suitability for all the selected rain fed crops. However, LMUs (VRe–NTu and NTu–VRe) has lower overall suitability values particularly for S1 suitability class (0.05% and 10.6%, respectively). The least suitable LMU is VRe–NTu with 0.05% S1 suitability class and above 99% of the land laid under the suitability classes of moderately suitable, marginally suitable and not suitable for the selected land utilization types. Moreover, about 219.06 ha (17.76%), 217.6 ha (17.64%), 168.9 ha (13.7%), of land are highly suitable for malt barley, teff and wheat crop production, respectively. In conclusion, during MCDM, classifying the land into closer homogeneities (LMU) an important application of LSA integrated with remote sensing and GIS for a better decision making. Meanwhile, majority (above two third’s) of the land in the watershed is under moderate and marginally suitable, it needs intensive land management activities to increase the land qualities and obtain high yields. LSA recommended before land utilization decision has to be made. It is also important to classifying the land into LMUs to make it more homogeneous for sample taking and reducing the prestigious soil laboratory analysis costs.

Coastal dunes record the evolutionary process of coastal dynamic geomorphological systems, and provide valuable information on climate change, sea level changes, environmental evolution. These dunes are essential for global change research. In view of the dynamic changes in coastal dunes caused by extreme weather, coastal erosion and human activities, we propose a comprehensive evaluation method for coastal dune evolution, which monitors Haiyang Ten Thousand Meters Beach of China from three perspectives: point, line, and surface. This method integrates LiDAR, GNSS, and RS technologies. The research results indicate that between 2016 and 2018, 68% of the GNSS point measurements in 22 survey profiles of coastal dunes were eroded along the foreshore. During 2011-2020, the coastline based on the RS data basically remained stable; only the estuarine district underwent significant changes, and the overall average change rate was 1.32 m/a. The overall morphological evolution of coastal dunes obtained by LiDAR in 2018 was relatively stable, but there was a weak erosion trend in the foreshores of the coastal dunes. This study is beneficial for providing precise targets for remediating and restoring eroded coastal areas in the future. Furthermore, it can provide technical support and informational information for ecological environment protection, coastal erosion prevention and environmental evolution in the Haiyang Ten Thousand Meters Beach Marine Nature Reserve. [ABSTRACT FROM AUTHOR]

This research aimed at evaluation of a given land resource suitable for selected rain fed crops production (malt barley, wheat and teff) in Ambesh watershed. It also quantified suitable land and classified into the land mapping units (LMUs), and presents a land suitability map. Land suitability assessment (LSA) made using climatic condition, topography, soil physical and chemical properties as a major factor integrated with a multi criteria decision making (MCDM) analysis in Ambesh watershed. Fourteen composite soil samples were taken by categorizing the watershed into different land mapping units (LMUs) and analyzed in a soil laboratory. Climatic data, rainfall was obtained from two meteorological stations nearby to the study area. Temperature data derived from Landsat 8 satellite thermal bands data. Data obtained from the soil laboratory and others were finally analyzed using ArcGIS environment and priority estimation tool (PriEsT) software's. Weighted Sum Overlay was implemented to investigate the final LSA map of the watershed. Results revealed that LMUs, VRe–LPq and LPK.Pq–FLc LMUs has higher overall suitability for all the selected rain fed crops. However, LMUs (VRe–NTu and NTu–VRe) has lower overall suitability values particularly for S1 suitability class (0.05% and 10.6%, respectively). The least suitable LMU is VRe–NTu with 0.05% S1 suitability class and above 99% of the land laid under the suitability classes of moderately suitable, marginally suitable and not suitable for the selected land utilization types. Moreover, about 219.06 ha (17.76%), 217.6 ha (17.64%), 168.9 ha (13.7%), of land are highly suitable for malt barley, teff and wheat crop production, respectively. In conclusion, during MCDM, classifying the land into closer homogeneities (LMU) an important application of LSA integrated with remote sensing and GIS for a better decision making. Meanwhile, majority (above two third's) of the land in the watershed is under moderate and marginally suitable, it needs intensive land management activities to increase the land qualities and obtain high yields. LSA recommended before land utilization decision has to be made. It is also important to classifying the land into LMUs to make it more homogeneous for sample taking and reducing the prestigious soil laboratory analysis costs. [ABSTRACT FROM AUTHOR]

The topographical and geological conditions in China are complicated, with frequent geological disasters, which have been aggravated by various human engineering geological activities. The prevention and management of geological disasters are the premise and foundation of ensuring the safety of people's lives and property. Geological disaster susceptibility evaluation is the premise of hazard evaluation and risk assessment, and the basis of geohazard zoning and prevention as well. Based on related researches on geological disaster susceptibility at home and abroad, the paper discusses the concept of geohazard susceptibility, evaluation index system, weight calculation method and evaluation model, and points out the main problems to be solved in geohazard susceptibility evaluation. [ABSTRACT FROM AUTHOR]

Land suitability assessment is essential for planned land management strategies aimed at preserving soil and increasing productivity while ensuring sustainable agricultural production. Land degradation resulting from poor land management and fallowing practices typically leads to low land productivity in Iraq. To maintain agricultural productivity in the targeted area, agricultural requirements must align with available resources through land suitability analysis. In the northern region of Basrah Governorate in Iraq, the study focused on integrating GIS-based land suitability analysis with the fuzzy-analytical hierarchy process (F-AHP) approach. The analysis revealed varying suitability categories throughout the study area, with the largest proportion of unsuitable areas found in category N2, covering 31,202.36 hectares (37.76%), and category N1, currently unsuitable, covering an area of 19,956.24 hectares (24.15%). On the other hand, the moderately suitable category (S3) covered 8,297.26 hectares (10.04%), while the moderately suitable category (S2) covered 23,177.79 hectares (28.05%) of the total study area. No highly suitable lands were identified. The key determining factors for the suitability of lands for wheat cultivation were high values of electrical conductivity, carbonate minerals, bulk density, and low organic carbon content. Most agricultural lands are being used in a manner that contradicts their suitable potentials in the study area. Therefore, the pattern of agricultural land use needs to be adjusted based on their current potentials to reduce soil degradation. [ABSTRACT FROM AUTHOR]

Determination of total impervious area (TIA) or effective impervious area (EIA) is mandatory for hydrological modelling of water quantity and quality in urban areas. In this study, a multilayer deep learning model Convolutional Neural Network (CNN) is implemented for estimating TIA. A more realistic automated method is suggested to determine EIA by integrating the remote sensing data, the digital format of the drainage network, and a digital elevation model (DEM). A graphical user interface (GUI) called EIA estimator is developed for automatic creation of EIA maps. An effort is made to derive a relationship between TIA and EIA. Several power relationships are obtained for easily measurable TIA and hydraulically relevant EIA in urban catchments of India. These relationships would aid planners and decision-makers with quick initial estimates for surface water quantity and quality problems. [ABSTRACT FROM AUTHOR]

Water is a power source, and its lack is an important and strategic issue that affects the social and economic development of societies and thus threatens their political authority. Lack of water in international rivers causes its politicization and, as a result, water tensions. Helmand River is one of the international rivers whose division has led to hydro-political tension. Helmand River, most of which is located in Afghanistan, is the main water supplier of Sistan region in Iran. The main goal of this research is to evaluate the hydro political analysis of the Helmand River by examining the changes in the use of the river catchment in three classes of water, vegetation, and soil using the virtual engine of satellite image processing (Google Earth Engine). The Kappa coefficient and Overall Accuracy were exerted to validate the research data. The results show that measures such as building new dams and closing old dams, neglecting bilateral agreements and not respecting Iran's water rights by the upstream country due to the unfavorable environmental conditions prevailing in the region have aggravated the water tension in the basin. [ABSTRACT FROM AUTHOR]

Due to the rapid exploitation of water resources in the Huixian karst wetland in the southwest of China in the past decades, the wetland has suffered from shrinkage in size and serious degradation of ecological functioning. To assess how much water within the wetland could still be taken out for local supply purposes while the wetland can still be kept in its normal ecological functioning. Through the use of multi-methods, including field surveys by a multi-disciplinary team, water balance, Remote Sensing, GIS and numerical simulation, this paper characterizes the wetland regime of the study area and finally determines scenarios of water resource utilization for local water supply within acceptable parameters of wetland ecological health. Through the analysis of the methods, it was found that the hydrological characteristics of the study area were conditioned by not only the karst water but also the regional precipitation fluctuations. A zone of mobile watersheds for Lake Mudong was established as opposed to a conventional single watershed. If the wetland ecosystem is kept at the current status of class III, a scenario of withdrawal of up to 20% of lake inflows could be accommodated. The results and their approaches would provide much-needed information for the protection of the wetland and its sustainable water utilization per se. It would offer a basic reference for similar problems in karst areas of southwest China and other areas alike. [ABSTRACT FROM AUTHOR]

The exponential growth of online information has necessitated effective solutions to combat information overload and optimize network resources. Recommender systems (RS) have emerged as critical tools in this context, opening new avenues for research. However, RS encounters formidable challenges in understanding user behavior and preferences, reducing redundancy in recommendations within social networks (SN), and ensuring scalability and accuracy. To address these issues, this study introduces a novel approach that harnesses the power of two neural networks: Bidirectional long short-term memory (BILSTM) for SN behavior analysis and graph neural network (GNN) for modelling consumer behaviour, both represent a powerful Neuroevolution network. The proposed RS, tailored for SNs, demonstrates significant performance enhancements when compared to traditional deep learning and deep reinforcement learning algorithms. The methodology involves a rigorous training process with a 70% training set and 10% validation set to mitigate overfitting, with final evaluation on a previously unseen 20% testing set. Optimization techniques, including momentum and adaptive learning rates, are applied to GNN-BiLSTM, ensuring computational efficiency. The results unequivocally showcase the effectiveness of this approach in generating more precise and contextually relevant recommendations. By leveraging BILSTM and GNN, the RS gains a deeper understanding of user preferences and item relationships, resulting in superior recommendation quality. Performance metrics such as root mean squared error (RMSE) and mean absolute error (MAE) unequivocally demonstrate the superiority of the proposed model over traditional deep learning and deep reinforcement learning algorithms. In conclusion, the integration of BILSTM and GNN in RS offers a promising solution to the pressing challenges faced by existing systems. This hybrid approach significantly elevates the accuracy and efficiency of recommendations in SNs, paving the way for valuable insights and potential enhancements in future recommendation systems which depends on Neuroevolution approach. [ABSTRACT FROM AUTHOR]

Geological hazards significantly threaten the safety of China's railway network. As the railway system continues to expand, particularly with the effects of accelerated climate change, approximately 70% of the newly encountered geohazards occur outside of known areas. This study proposes a novel approach that can be applied to railway systems to identify potential geohazards, analyze risk areas, and assess section vulnerability. The methodology uses integrated remote sensing technology to effectively enhance potential railway hazard identification timeliness. It combines kernel density, hotspot, and inverse distance-weighted analysis methods to enhance applicability and accuracy in the risk assessment of railway networks. Using a case study in southeastern Gansu as an example, we identified 3976 potential hazards in the study area, analyzed five areas with high concentrations of hazards, and 11 districts and counties prone to disasters that could threaten the railway network. We accurately located 16 sections and 20 significant landslide hazards on eight railway lines that pose operational risks. The effectiveness of the methodology proposed in this paper has been confirmed through field investigations of significant landslide hazards. This study can provide a scientific basis for the sustainability of the railway network and disaster risk management. [ABSTRACT FROM AUTHOR]

This study aims to delineate groundwater potential zones using an integrated approach of remote sensing (RS), geographical information system (GIS), and analytical hierarchy process (AHP) method in the middle and high Cheliff basin, Algeria. Multiple data such as lithology, lineament density, geomorphology, slope, soil, rainfall, drainage density, and land use/land cover were considered for delineating the groundwater potential zones. Spatially distributed maps/thematic layers of all the aforementioned parameters were created using remotely sensed data as well as ground data in a GIS environment. The assigned weights of the thematic layers and their features were then normalized by using the AHP technique. The delineated groundwater potential zones in this study area were categorized as very good, good, moderate, and poor, respectively. The results showed that the area along the Chlef River which is approximately 6% of the total study area was delineated as an area having "very good" potential for groundwater. The "good zone" delineated encompassed approximately 31% of the study area and was found in the pediment-pediplain complex zone. The moderate zones encompassed approximately 58% of the area. The "poor zones" encompassed approximately 5% of the area which included the cities of Ramka, El Hadjadj, Moussadek, and certain parts of Mekhatria. The groundwater potential zones map was compared with the actual discharge data from various wells within the study area and was found reasonable. Overall, this study provides a convenient approach of delineating the potential of groundwater availability which ultimately will aid in better planning and managing of groundwater resources. [ABSTRACT FROM AUTHOR]

Land suitability assessment is essential for planned land management strategies aimed at preserving soil and increasing productivity while ensuring sustainable agricultural production. Land degradation resulting from poor land management and fallowing practices typically leads to low land productivity in Iraq. To maintain agricultural productivity in the targeted area, agricultural requirements must align with available resources through land suitability analysis. In the northern region of Basrah Governorate in Iraq, the study focused on integrating GIS-based land suitability analysis with the fuzzy-analytical hierarchy process (F-AHP) approach. The analysis revealed varying suitability categories throughout the study area, with the largest proportion of unsuitable areas found in category N2, covering 31,202.36 hectares (37.76%), and category N1, currently unsuitable, covering an area of 19,956.24 hectares (24.15%). On the other hand, the moderately suitable category (S3) covered 8,297.26 hectares (10.04%), while the moderately suitable category (S2) covered 23,177.79 hectares (28.05%) of the total study area. No highly suitable lands were identified. The key determining factors for the suitability of lands for wheat cultivation were high values of electrical conductivity, carbonate minerals, bulk density, and low organic carbon content. Most agricultural lands are being used in a manner that contradicts their suitable potentials in the study area. Therefore, the pattern of agricultural land use needs to be adjusted based on their current potentials to reduce soil degradation.

AbstractClimate changes in increasing at alarming rates which caused outward changes in the hydrological cycle parameters such as temperature and precipitation. The spatial and temporal distribution of rainfall has changed all over the world. Rainfall forecasting is considered an important tool that could help decisionmakers for managing water resources. Syria is facing several drought events that may affect its water resources. Due to the lack of available rainfall data from land stations in Syria after the civil war started in 2010 till now, one of the aims of this study is to depend on rainfall data from satellite images after correcting these data using the available land stations data. This study also aims to assess rainfall trends in Syria due to climate change and forecasting rainfall. The rainfall data were collected from 71 land stations from 1991 to 2009. Satellite images from Climate Data Record (CDR) were downloaded from 1983 to 2020 and ArcMap was used to extract rainfall data at locations of land stations. The extracted rainfall data from satellite images were corrected by land stations data from 1991 to 2009. Then Statistical Package for the Social Sciences (SPSS) program with Auto-Regressive Integrated Moving Average (ARIMA) models are used to fit and forecasting rainfall in Syria. The rainfall trends at the studied stations were analyzed for 40 years (1983–2022) and a forecast of one year is conducted. The results showed decreasing trends at all the stations that lead to a decrease the quantity of water received every year which could affect the water resources in Syria and agriculture and food security will be also affected. The results are consistent with Intergovernmental Panel on Climate Change (IPCC) reports that concluded a decrease in rainfall at southern and eastern Mediterranean regions. The results of the current study could help in the management of water resources in Syria and forecasting using ARIMA is recommended for rainfall predictions.

Abstract Flood and drought are natural phenomena whose probability of occurrence increases with changing weather conditions. Climate change has affected the hydrologic cycle's parameters and led to changes in temperature and precipitation pattern, which may increase the probability of occurrence of floods and droughts. Arid and semi‐arid areas are subject to extreme weather conditions and water resources scarcity. This study aims to assess drought and flood in Syria using remote sensing data. Monthly precipitation data have been collected from 10 land stations in the western part of Syria for the period (1991–2009) and satellite images for monthly precipitation from 1983 to 2020 were collected. The land stations data were used to correct the satellite images and correction equation was developed for each station. Standardized Precipitation Index (SPI) is used for analysis of drought and flood. Also, the main parameters for drought and flood, severity, intensity, duration, and the average return time were estimated. The results showed extreme flood events were recorded in 1988, 2002, 2012, and 2019 and extreme drought events were recorded in 1990, 2014, and 2016. The highest severity of drought and flood were recorded in 2014 and 1988 respectively. However, the highest intensity of droughts and floods was recorded in 2016 and 1988, respectively. If land station data are unavailable, as in the case of Syria owing to the protracted war, satellite images may be a useful source of data for drought and flood analysis after correction. This methodology can help in drought/flood analysis and estimate severity, intensity, duration, and the average return time of flood and drought in other locations of the world. The results proved that SPI is a useful tool to predict the time of occurrence of both flood and drought that could help decision‐makers for putting efficient plans for flood and drought risk management that could mitigate the impacts of such risks.

Abstract Background The Republic of Serbia (RS) is strategically oriented towards sustainable development, but the implementation thereof faces different limitations and problems. RS emits substantial pollution caused by fossil fuels, and pollution from agricultural sector characterized by inefficient use of energy and intensive use of fertilizers. Bearing in mind the significant agricultural capacities and the orientation towards rural development, a special green transformation must be implemented in this domain. Taking into account the specificities and tradition, the introduction of the circularity concept can be considered the most acceptable. Research on readiness for transition to circularity in RS rural areas has not been conducted; therefore, the goal of the paper is to develop a concept for assessing the capacity of rural areas for circularity. This study is the first scientific proposal that aims to provide input for policymakers, thus contributing to the creation of a new identity of RS, whose development is based on the principles of sustainability. Methods The study was conducted as conceptual research, with the objective of examining an undiscovered phenomenon without empirical evidence and incorporating the targeted phenomenon into a conceptual framework, while providing a proposal for a solution model based on an interdisciplinary approach—the application of qualitative and quantitative methods (aggregation of composite indicators and Delphi method). Results Paper results can be summarized as follows: (a) qualitative analysis of policy framework related to RS transition towards circularity (which shows that regulation is insufficient or non-existent, so conceptual research at this stage is necessary and only possible); (b) research questionnaire; (c) original set of indicators for measuring capacity for circularity (derived from the questionnaire); (d) concept of index of capacity of agriculture and rural areas for circularity (based on a set of indicators); and (e) concept of the monitoring of circularity implementation. Conclusions The main research findings presented in this paper could be beneficial for countries at early stages of introducing circularity, having both low and high agricultural potential. With slight modifications, they can also be applied to other economic activities.