S. Hosseinzadeh, Azizollah Jafarikukhdan, A. Hosseini
et al.
Medicinal plants have played an essential role in the development of human culture. Medicinal plants are resources of traditional medicines and many of the modern medicines are produced indirectly from plants. This study illustrates the importance of traditional and modern medicines in the treatment and management of human diseases and ailments. It has been confirmed by WHO that herbal medicines serve the health needs of about 80 percent of the world’s population; especially for millions of people in the vast rural areas of developing countries. Meanwhile, consumers in developed countries are becoming disillusioned with modern health care and are seeking alternatives. Thymus vulgaris is a species of flowering plant in the mint family Lamiacea. Thymus is a widely used medicinal plant in food and pharmaceutical industries. Among different species of Thymus, Thymus vulgaris is used more than other species in therapeutic dosage forms. In Traditional medicine T. vulgaris is cultivated in many countries by most people especially in rural areas depending on herbal medicines to treat many diseases including inflammation-related ailments such as rheumatism, muscle swelling, insect bites, pains, etc. Also the modern medicine in essential oil of thyme has demonstrated that the compounds have shown anti-inflammatory, antioxidant, antibacterial and antifungal properties. In this review the objective is to consider the past and present value of medicinal plants such as Thymus vulgar is used in traditional and modern medical practices as bioactive natural compounds.
In this paper we analyze the Finnish road network as a graph in order to measure whether the "rurality" or "urbanity" of an area correlates with local geometrical properties of the graph. Our primary motivation is the observation that the road systems in rural areas look similar to hyperbolic graphs, while in large cities they resemble more the Cayley graph of $\mathbb{Z}^2$. We do not aim for a comprehensive analysis, but rather wish to demonstrate that this observation can be measured and analyzed through looking at various "hyperbolicity measures" of randomly sampled geodesic triangles in the road graph.
With economic development and changes in the industrial structure of cities in China, the transformation of industries from agricultural to non-agricultural has brought about significant changes in the living spaces, economic growth, ideology, and cultural attitudes of rural people. Placing people first and responding to rural residents’ wishes in the process of rapid development could become an important issue. This study focuses on the relationship between the well-being experience of relocated residents in rural areas and the public realm of new communities, and constructs a framework for understanding the relationship between the two. By doing so, it aims to resolve the conflict between the design of new settlements and the residents’ needs, thus providing a reference for similar designs in the future.
Solar energy is one of the promising renewable energies; it is clean, green, and accepted worldwide for targeting sustainable development through applications such as power generation, desalination, food preservation, etc. Solar-powered desalination has received more attention in recent times to meet the demand of pure water in the rural places of many countries where solar energy is abundant. In the present work, a double-slope passive solar desalination system was fabricated with readily available materials that can be installed and used in rural places, either for domestic purposes or in small-scale industries. The capacity of the desalination system fabricated to be filled with saline water is ~15 L. The performance of the desalination system is continuously monitored by recording the temperatures at various locations around the system, such as the outer surface of the glass, the inner surface of the glass, inside the basin, and outside the basin, through DHT11 sensors controlled by Arduino programming fed in the Arduino UNO board. The influence of solar radiation intensity and temperatures at various locations on the solar still on the thermal performance and production of desalination unit is analyzed by the data recorded by the Arduino program. A cumulative yield of fresh water of around 0.7–0.9 L is recorded every day, and the lowest yield of around 0.55 L was obtained on the third day of experimentation.
The Yellow River Basin (YRB), a vital region for agricultural production in China, is currently grappling with severe rural population shrinkage and variations in the carbon emission intensity across the basin. Based on census data from 2010 to 2020, this study categorized 320 counties by population shrinkage type and applied baseline regression and upper–middle–lower reach heterogeneity analysis to explore population shrinkage’s impact on carbon intensity. The results indicated that population shrinkage in the Yellow River Basin during 2010–2020 was primarily characterized by a rural population decline, which exerted divergent impacts on carbon emissions across the basin. Consequently, the upper reaches were identified as a critical problem area where severe population shrinkage coexisted with a high carbon emission intensity. Based on these findings, targeted and region-specific strategies and policies are proposed. Specifically, High Shrinkage-High Emission (H-H) regions need to focus on promoting ecological migration and the coordinated transformation of industries; High Shrinkage-Low Emission (H-L) regions should strengthen policy coordination in the border areas of the middle and upper reaches; Low Shrinkage-High Emission (L-H) regions should promote the low-carbon technological transformation of traditional industries in downstream counties; and Low Shrinkage-Low Emission (L-L) regions should refine the low-carbon transformation model in the core downstream areas.
Angisenit Reyes-Prezas, Marieli Lavoignet-Ruiz, Gregorio Fernández-Lambert
et al.
In a global context marked by the rising demand for sustainable and biodegradable materials, natural fibres have gained unprecedented significance in scientific and technological research. Known for their low environmental impact and versatility, natural fibres offer innovative solutions to replace non-renewable synthetic materials. This study explores the trends from 2010 to 2023, focusing on the use of Luffa cylindrica, an alternative crop particularly relevant for emerging economies and rural communities. A total of 589 articles and 632 patents were analyzed using a bibliometric and patent review approach to identify industrial advancements related to this plant.The findings reveal a 35 % increase in publications and a 40 % increase in patents related to sectors such as construction, pharmaceutical, biotechnology, and agriculture between 2010 and 2023, reflecting a significant growth in interest. Literature highlights its applications in lightweight construction materials, such as eco-friendly panels; acoustic insulation, including soundproofing in urban housing; and reinforced composites for automotive interior components, demonstrating its adaptability to diverse industries. Patent data underscores advancements in biotechnological applications, environmental engineering for water and air filtration, and agricultural innovations. Its potential for pollutant remediation, with absorption efficiencies reaching up to 90 % for heavy metals, and sustainable packaging solutions that could reduce reliance on synthetic plastics by an estimated 25 % in specific markets, positions this plant as a key renewable resource for promoting sustainability.The evidence suggests that Luffa cylindrica has the potential not only to replace non-renewable materials but also to drive technological advancements in both emerging and established markets. This study emphasizes the importance of developing new applications while addressing these issues to support the transition to a circular economy and mitigate environmental impacts. The research contributes significantly to the development of cleaner, more sustainable industrial processes.
Renewable energy sources, Environmental engineering
Connected braking can reduce fatal collisions in connected and autonomous vehicles (CAVs) by using reliable, low-latency 5G New Radio (NR) links, especially NR Sidelink Vehicle-to-Everything (V2X). In rural areas, road side units are sparse and power-constrained, so energy efficiency must be considered alongside safety. This paper studies how three communication control factors including subcarrier spacing ($\mathrm{SCS}$), modulation and coding scheme ($\mathrm{MCS}$), and transmit power ($P_{\mathrm{t}}$) should be configured to balance safety and energy consumption in rural scenarios in light and heavy traffic scenarios. Safety is quantified by the packet receive ratio ($\mathrm{PRR}$) against the minimum communication distance $D_{\mathrm{comm}}$, defined as the distance that the vehicle travels during the transmission of the safety message. Results show that, under heavy traffic, increasing $P_{\mathrm{t}}$ and selecting a low-rate $\mathrm{MCS}$ at $\mathrm{SCS} = 30$ kHz sustains high $\mathrm{PRR}$ at $D_{\mathrm{comm}}$, albeit with higher energy cost. In light traffic, maintaining lower $P_\mathrm{t}$ with low $\mathrm{MCS}$ levels achieves a favorable reliability-energy trade-off while preserving acceptable $\mathrm{PRR}$ at $D_{\mathrm{comm}}$. These findings demonstrate the necessity of adaptive, energy-aware strategy to guarantee both safety and energy efficiency in rural V2X systems.
Anselme Ndikumana, Kim Khoa Nguyen, Adel Larabi
et al.
Deploying fiber optics as a last-mile solution in rural areas is not economically viable due to low population density. Nevertheless, providing high-speed internet access in these regions is essential to promote digital inclusion. 5G Fixed Wireless Access (5G FWA) has emerged as a promising alternative; however, its one-hop topology limits coverage. To overcome this limitation, a multi-hop architecture is required. This work proposes a unified multi-hop framework that integrates long-haul microwave, Integrated Access and Backhaul (IAB), and FWA to provide wide coverage and high capacity in rural areas. As the number of hops increases, total energy consumption also rises, a challenge often overlooked in existing literature. To address this, we propose an energy-efficient multi-radio microwave and IAB-based FWA framework for rural area connectivity. When the network is underutilized, the proposed approach dynamically operates at reduced capacity to minimize energy consumption. We optimize the off, start-up, serving, deep sleep, and wake-up sates of microwave radios to balance energy use and satisfying data rate requirements. Additionally, we optimize resource block allocation for IAB-based FWA nodes connected to microwave backhaul. The formulated optimization problems aim to minimize the energy consumption of long-haul microwave and multi-hop IAB-based network while satisfying data rate constraints. These problems are solved using dual decomposition and multi-convex programming, supported by dynamic programming. Simulation results demonstrates
Tzu-Yun Tseng, Alexey Nekrasov, Malcolm Burdorf
et al.
Existing autonomous driving datasets are predominantly oriented towards well-structured urban settings and favourable weather conditions, leaving the complexities of rural environments and adverse weather conditions largely unaddressed. Although some datasets encompass variations in weather and lighting, bad weather scenarios do not appear often. Rainfall can significantly impair sensor functionality, introducing noise and reflections in LiDAR and camera data and reducing the system's capabilities for reliable environmental perception and safe navigation. This paper introduces the Panoptic-CUDAL dataset, a novel dataset purpose-built for panoptic segmentation in rural areas subject to rain. By recording high-resolution LiDAR, camera, and pose data, Panoptic-CUDAL offers a diverse, information-rich dataset in a challenging scenario. We present the analysis of the recorded data and provide baseline results for panoptic, semantic segmentation, and 3D occupancy prediction methods on LiDAR point clouds. The dataset can be found here: https://robotics.sydney.edu.au/our-research/intelligent-transportation-systems, https://vision.rwth-aachen.de/panoptic-cudal
Srividya Sheshadri, Unnikrishnan Radhakrishnan, Aswathi Padmavilochanan
et al.
This study explores the novel application of Generative Artificial Intelligence (GenAI) in analyzing unstructured visual data generated through Participatory Rural Appraisal (PRA), specifically focusing on women's empowerment research in rural communities. Using the "Ideal Village" PRA activity as a case study, we evaluate three state-of-the-art Large Language Models (LLMs) - GPT-4o, Claude 3.5 Sonnet, and Gemini 1.5 Pro - in their ability to interpret hand-drawn artifacts containing multilingual content from various Indian states. Through comparative analysis, we assess the models' performance across critical dimensions including visual interpretation, language translation, and data classification. Our findings reveal significant challenges in AI's current capabilities to process such unstructured data, particularly in handling multilingual content, maintaining contextual accuracy, and avoiding hallucinations. While the models showed promise in basic visual interpretation, they struggled with nuanced cultural contexts and consistent classification of empowerment-related elements. This study contributes to both AI and gender research by highlighting the potential and limitations of AI in analyzing participatory research data, while emphasizing the need for human oversight and improved contextual understanding. Our findings suggest future directions for developing more inclusive AI models that can better serve community-based participatory research, particularly in gender studies and rural development contexts.
Urban-rural development is an important driving force for regional economic growth. The existing researches have studied this issue from various perspectives, but they ignore the impact of big data on the economy. In the post pandemic era, big data, as an emerging production factor, has a significant indicative effect in promoting urban-rural economic recovery and fostering new business forms. Therefore, fully considering the factor of big data can help reveal its impact mechanism on urban-rural economic growth in the post-epidemic period. Based on the data of 30 provinces and cities in China, this paper introduced big data on the basis of traditional models and constructed a multi-dimensional factor indicator system. At the same time, the panel regression model was established by using unit root test, Hausman test and precision test. Through benchmark regression and heterogeneity analysis, the impact of urban-rural development factors on economic growth was discussed. The results showed that the panel model passed all tests, and its regression error was stable below 5 %. Transportation, technology, and the three major industries can all promote positive economic growth, with a significance of 1 %. The three industries' contribution to economic growth ranks the third, second and first industries in order. In addition, the good ecological environment contributes to the benign economic growth during the study period. A 1 % increase in forest cover would drive economic growth by 0.215 %. But the impact of public's attention on the overall economy was an indirect effect manifested through its physical industries.The regional heterogeneity indicated that each element had different effects on economic development in eastern, central and western regions. Based on its results, this paper proposed suggestions for each region. In addition, this study found that the Internet attention reflected by big data did not directly drive economic growth, but affected economic growth through indirect channels such as information flow and resource allocation of real industries. This study provided data support for the existing theoretical review, and provided policy reference for the rational planning and industrial layout of China's regional economy.
IntroductionAlthough rural industrial integration is a crucial pathway for advancing the revitalization of rural economies, it continues to grapple with financial challenges. This paper delves into the theoretical underpinnings of how capital marketization influences rural industrial integration.MethodsUsing panel data from China’s provinces spanning the years 2010 to 2020, a comprehensive index of rural industrial integration is constructed from the vantage point of a new development paradigm. The paper employs the system GMM method to empirically investigate the impact of capital marketization on rural industrial integration and to dissect its transmission mechanisms. Additionally, a threshold regression model is applied to explore the specific patterns of the nonlinear relationship between the two variables.Results and discussionThe study’s findings reveal that the degree of rural industrial integration is significantly and positively influenced by its previous level, demonstrating an accumulative effect wherein the prior level of integration lays the groundwork for future advancements. The influence of capital marketization on the degree of rural industrial integration is characterized by a non-linear relationship, adhering to a “U-shaped” curve. Below the inflection point, the development of capital marketization is detrimental to rural industrial integration, whereas above this point, it exerts a positive influence. Currently, China’s overall level of capital marketization is positioned beyond the inflection point, indicating substantial potential for enhancing industry integration in rural China. In addition, the study indicates that at very low levels of economic development, capital marketization does not affect the development of rural industries. As the economic development level rises, so does the impact of capital marketization on rural industrial integration.
Nutrition. Foods and food supply, Food processing and manufacture
<i>Background:</i> China’s rural revitalisation policy has promoted the transformation of rural industries, which always neglect the “dual-carbon” goal in rural. Rural industrial upgrading in Europe can inspire sustainable rural development in China. <i>Methods:</i> Based on EDGAR and NEP data, the carbon emission intensity of rural ecosystems was calculated in terms of area. By Isodata cluster algorithm and k-means, the Chinese and European rural regions were classified based on agricultural areas. Pearson’s coefficient and geographical convergent cross-mapping (GCCM) were used to explore the correlation and causality between carbon intensity and development patterns in rural China and Europe. <i>Results:</i> The expansion of the land share of the primary industry and land consolidation will lead to more carbon emissions in the study areas. The proportion of land used for tertiary industry increases carbon emission intensity in rural China, but not in European study areas. The area carbon emission intensity shows that the fragmented industrial layout may hinder the development of rural industries in Europe, but not in China, from a productivity perspective. <i>Conclusions:</i> Carbon emission distribution and industrial development patterns vary spatially. GCCM can help identify the interactions for this variation between China and Europe, providing insights into China’s sustainable development.
Introduction: In both of China and other industrializing countries, improving the efficiency of degraded industrial land use will help control urban sprawl brought about by rapid urbanization. The redevelopment of industrial parks in the countryside is becoming a starting point for phasing out high-polluting industries and an important source of land supply for high-end and green industries. The objective of this paper is to identify how the local state of China determines the necessity for the demolition of rural industrial parks (RIPs) and how this process reflects the underlying decision-making mechanisms.Methodology: This paper carries out descriptive spatial analysis by combining the economic and social development cross-sectional data in 2019 and extracts data from the Baidu Map to calculate the traffic network density. Cluster analysis is also used to group the RIPs according to their data characteristics. In order to provide an in-depth discussion of the cases, the authors also overlay the results of the spatial and cluster analyses.Results: The spatial distribution of RIPs is closely related to their location and transportation conditions. Failure of the market has resulted in large tracts of advantageous land being taken up by inefficient industrial parks. Cluster analysis and overlay analysis have evaluated the difficulty of redevelopment and divided the industrial parks into three clusters: retained RIPs, medium-term removed RIPs, and near-term-removed RIPs. The authors put forward that different strategies should be adopted for the future renovation of medium-term-removed and near-term-removed RIPs.Discussion: This paper argues that proper categorization is the beginning of feasible RIP redevelopment. Local governments should resist the temptation of short-term land transfer revenues to achieve long-term growth. The significant differences in concerns between the grassroots and the higher levels of government also require that the effects of bottom-up influence and top-down intervention should be balanced.
Mohammad Kakooei, James Bailie, Markus B. Pettersson
et al.
Accurate and consistent mapping of urban and rural areas is crucial for sustainable development, spatial planning, and policy design. It is particularly important in simulating the complex interactions between human activities and natural resources. Existing global urban-rural datasets such as such as GHSL-SMOD, GHS Degree of Urbanisation, and GRUMP are often spatially coarse, methodologically inconsistent, and poorly adapted to heterogeneous regions such as Africa, which limits their usefulness for policy and research. Their coarse grids and rule-based classification methods obscure small or informal settlements, and produce inconsistencies between countries. In this study, we develop a DeepLabV3-based deep learning framework that integrates multi-source data, including Landsat-8 imagery, VIIRS nighttime lights, ESRI Land Use Land Cover (LULC), and GHS-SMOD, to produce a 10m resolution urban-rural map across the African continent from 2016 to 2022. The use of Landsat data also highlights the potential to extend this mapping approach historically, reaching back to the 1990s. The model employs semantic segmentation to capture fine-scale settlement morphology, and its outputs are validated using the Demographic and Health Surveys (DHS) dataset, which provides independent, survey-based urban-rural labels. The model achieves an overall accuracy of 65% and a Kappa coefficient of 0.47 at the continental scale, outperforming existing global products such as SMOD. The resulting High-Resolution Urban-Rural (HUR) dataset provides an open and reproducible framework for mapping human settlements, enabling more context-aware analyses of Africa's rapidly evolving settlement systems. We release a continent-wide urban-rural dataset covering the period from 2016 to 2022, offering a new source for high-resolution settlement mapping in Africa.
Mukaram Shahid, Kunal Das, Taimoor Ul Islam
et al.
Due to factors such as low population density and expansive geographical distances, network deployment falls behind in rural regions, leading to a broadband divide. Wireless spectrum serves as the blood and flesh of wireless communications. Shared white spaces such as those in the TVWS and CBRS spectrum bands offer opportunities to expand connectivity, innovate, and provide affordable access to high-speed Internet in under-served areas without additional cost to expensive licensed spectrum. However, the current methods to utilize these white spaces are inefficient due to very conservative models and spectrum policies, causing under-utilization of valuable spectrum resources. This hampers the full potential of innovative wireless technologies that could benefit farmers, small Internet Service Providers (ISPs) or Mobile Network Operators (MNOs) operating in rural regions. This study explores the challenges faced by farmers and service providers when using shared spectrum bands to deploy their networks while ensuring maximum system performance and minimizing interference with other users. Additionally, we discuss how spatiotemporal spectrum models, in conjunction with database-driven spectrum-sharing solutions, can enhance the allocation and management of spectrum resources, ultimately improving the efficiency and reliability of wireless networks operating in shared spectrum bands.