Hasil untuk "Rural industries"

R. Perlack, L. Wright, A. Turhollow et al.

Shaimuna Fareeha Sajjad

Pakistan is the one of the top ten countries affected by climate change. Floods started this year in Pakistan from June 2025. Since then, approximately two million people have been displaced and 1000 have died.1 Gender is an important determinant in disaster planning and management. Unfortunately Pakistan ranked 148 according to the Global Gender Gap Index Report 2025, hitting rock bottom.2 This is mainly due to socioeconomic and cultural disparities. Women are usually ignored or discriminated against when it comes to relief activities during disasters.3 Risk factors for gender discrimination among women include lack of education, limited access to health facilities, family system and economic dependency.2 In Pakistan the largest sector which provides employment to women (66%) is agriculture4 however their work is usually informal, unpaid and unrecognized. In our patriarchal society women remain at home and fulfill household responsibilities hence they receive no training to cope with natural disasters and are more likely to suffer in such situations. This in turn also makes them more prone to gender based violence, exploitation and child marriages as this issue surges globally during disasters.5 Small home based industries post floods should be established where women can get employed so that they become economically empowered. Women suffer from home based ailments such as anaemia, severe malnutrition, skin and gastrointestinal diseases, dengue, malaria, pneumonia, depression and post-traumatic stress disorder post floods are also common.6 An important issue which women face is of menstrual hygiene. Lack of awareness, inadequate supplies and cultural stigma are the factors which cause menstrual hygiene problems.7 Financial constraints post floods make it difficult for women to buy sanitary products due to which they start using washable cloth pads. As they may not have privacy or proper drying areas they use unclean cloths which cause urinary tract infections and bacterial vaginosis which puts them at risk of premature labour and pelvic inflammatory disease.7 Lack of segregated toilet facilities for women in flood affected areas is also a problem. During the 2022 floods 95% women reported using the same toilet as men because there was only latrine in the flood relief camp.4 In our society menstrual hygiene is a taboo topic and cannot be discussed openly. Majority of the relief workers are men so it is difficult for women to communicate with them. In addition they are also not properly trained. Efforts should be made to create a favorable environment for them to practice menstrual hygiene activities with dignity.  Pakistan’s high maternal mortality rate is worsened by inadequate reproductive health services, especially in rural areas, where floods further strain an already fragile health system. During the 2022 floods, around 650,000 pregnant women were deprived of essential antenatal and delivery services.8 The displacement of Lady Health Workers disrupted community-level care, forcing women to travel to distant facilities despite limited transport and financial constraints. Many women delivering in relief camps faced complications due to unhygienic conditions, lack of skilled birth attendants and poor postnatal care, increasing risks for both mothers and newborns, including long-term developmental issues. Implementing the Minimum Initial Service Package (MISP) is crucial in emergencies, as it provides education, hygiene supplies and reproductive health services that reduce maternal and neonatal morbidity and mortality.9 A multisectoral, gender-sensitive approach involving health, social and education sectors is essential. Strengthening structural and systemic capacities will enhance Pakistan’s preparedness for future climate-related disasters.

K. Kaygusuz

Sameera Bandaranayake, Amirreza Moradi, Tanja Suomalainen et al.

Persistent rural-urban disparities in broadband connectivity remain a major policy challenge, even in digitally advanced countries. This paper examines how these inequalities manifest in northern Finland and Sweden, where sparse populations, long distances, and seasonal variations in demand create persistent gaps in service quality and reliability. Drawing on survey data (n = 148), in-depth interviews, and spatial analysis, the study explores the lived experience of connectivity in Arctic rural communities and introduces a novel Cellular Coverage Inequality (CCI) Index. The index combines measures of rurality and network performance to quantify spatial disparities that are masked by national coverage statistics. Results reveal that headline indicators overstate inclusiveness, while local users report chronic connectivity gaps affecting work, safety, and access to services. Building on these findings, the paper outlines policy reflections in six areas: shared infrastructure and roaming frameworks, spectrum flexibility for rural operators, performance-based Quality-of-Service monitoring, standardized and transparent reporting, temporal and seasonal capacity management, and digital-skills initiatives. Together, these recommendations highlight the need for multidimensional metrics and governance mechanisms that link technical performance, spatial equity, and user experience. The analysis contributes to ongoing debates on how broadband policy in sparsely populated regions can move beyond nominal coverage targets toward genuine inclusion and reliability.

Harshita Goyal, Garima Garg, Prisha Mordia et al.

AI-driven education, particularly Large Language Models (LLMs), has the potential to address learning disparities in rural K-12 schools. However, research on AI adoption in rural India remains limited, with existing studies focusing primarily on urban settings. This study examines the perceptions of volunteer teachers on AI integration in rural education, identifying key challenges and opportunities. Through semi-structured interviews with 23 volunteer educators in Rajasthan and Delhi, we conducted a thematic analysis to explore infrastructure constraints, teacher preparedness, and digital literacy gaps. Findings indicate that while LLMs could enhance personalized learning and reduce teacher workload, barriers such as poor connectivity, lack of AI training, and parental skepticism hinder adoption. Despite concerns over over-reliance and ethical risks, volunteers emphasize that AI should be seen as a complementary tool rather than a replacement for traditional teaching. Given the potential benefits, LLM-based tutors merit further exploration in rural classrooms, with structured implementation and localized adaptations to ensure accessibility and equity.

Jean Louis Fendji Kedieng Ebongue

This study explores the shift from community networks (CNs) to community data in rural areas, focusing on combining data pools and data cooperatives to achieve data justice and foster and a just AI ecosystem. With 2.7 billion people still offline, especially in the Global South, addressing data justice is critical. While discussions related to data justice have evolved to include economic dimensions, rural areas still struggle with the challenge of being adequately represented in the datasets. This study investigates a Community Data Model (CDM) that integrates the simplicity of data pools with the structured organization of data cooperatives to generate local data for AI for good. CDM leverages CNs, which have proven effective in promoting digital inclusion, to establish a centralized data repository, ensuring accessibility through open data principles. The model emphasizes community needs, prioritizing local knowledge, education, and traditional practices, with an iterative approach starting from pilot projects. Capacity building is a core component of digital literacy training and partnership with educational institutions and NGOs. The legal and regulatory dimension ensures compliance with data privacy laws. By empowering rural communities to control and manage their data, the CDM fosters equitable access and participation and sustains local identity and knowledge. This approach can mitigate the challenges of data creation in rural areas and enhance data justice. CDM can contribute to AI by improving data quality and relevance, enabling rural areas to benefit from AI advancements.

Ahmed M. Aboseif, Nasser S. Flefil, Mostafa K.S. Taha et al.

The field of high-effective functional foods has emerged from advancements in biological pharmaceutical research, bridging pharmacology and food science. Microorganisms have been used in food production for ages. In this study, two lactobacilli were used for Azolla fermentation to be used as an economic aquafeed ingredient to identify the most suitable condition in tilapia rearing systems. The study explored the impact of using fermented Azolla and Chlorella vulgaris as a fish feed component in aquaculture systems, emphasizing their nutritional and environmental benefits. The fermentation process, involving Lactobacillus plantarum KU985433 and L. rhamnosus KU985437, enhances Azolla’s protein, phenolic, and antioxidant content while reducing carbohydrates and lipids. Comparative trials in biofloc and green water systems showed that fish fed with fermented Azolla exhibited improved growth, feed utilization, and immune response, demonstrating the potential of fermented plant-based feed as a sustainable alternative to conventional soybean meal. In silico analyses using PCA, heat maps, and network analysis identified that the optimal feed conditions were achieved using 50 % soy bean substitution with Azolla fermented with L. plantarum in both systems, highlighting the efficacy of incorporating fermented Azolla in aquaculture.

Changhao Wu, Sujing Li, Peng Hu et al.

Over the past decade, China has enacted forward-looking environmental policies that have significantly reduced air pollution. However, while there appears to be a synergy between economic development and improvements in air quality, regional imbalances in development and disparities in health risks underscore systemic challenges in environmental governance. This study employed a population-weighted exposure index to evaluate disparities in PM_2.5 exposure and its temporal and spatial trends, considering multidimensional socio-economic factors such as education, age, gender, occupation, and urban/rural backgrounds across 32 provinces and regions in China. The findings reveal that despite a notable decline in overall PM_2.5 concentrations between 2013 and 2020, improvements in air quality are uneven across regions, with less developed areas bearing a disproportionate burden of emission reductions. Urban centers exhibit lower exposure levels due to resource and industrial advantages, whereas towns experience higher risks of air pollution. Socio-economic disparities are evident, with increased exposure observed in high-pollution industries and among groups with lower educational attainment. Women are more likely to be exposed than men, and both the elderly and children face higher risks. To address these challenges, policies should focus on the economic development of underdeveloped regions, balance environmental protection with growth, prioritize heavily polluted areas and vulnerable populations, and promote the adoption of clean energy to mitigate pollution inequality.

Sukran Seker, Ertugrul Ayyildiz, Nezir Aydin

Rural-urban migration refers to the movement of people from rural to urban areas. This study strategically evaluates rural-to-urban migration trends and policies in Türkiye using Multi-Criteria Decision-Making (MCDM) methods under Fermatean fuzzy logic. The main and sub-factors influencing rural-to-urban are identified and structured under the PESTELT (Political, Economic, Social, Technological, Environmental, Legal, and Transportation) framework. It identifies key drivers of rural-to-urban migration, such as employment opportunities and educational access, rather than solely focusing on the cost of living. The goal is to develop strategies that encourage reverse migration back to rural areas. Using integrated Fermatean Fuzzy-Analytical Hierarchy Process (FF-AHP) and Fermatean Fuzzy-Evaluation based on Distance from Average Solution (FF-EDAS) methods, the study prioritizes strategies like regional employment investments and promoting village industries to strengthen rural settlement. Findings offer policy insights to balance urban-rural population distribution sustainably.

Clemens Pizzinini, Philipp Rosner, David Ziegler et al.

Transportation is a constitutional part of most supply and value chains in modern economies. Smallholder farmers in rural Ethiopia face severe challenges along their supply and value chains. In particular, suitable, affordable, and available transport services are in high demand. To develop context-specific technical solutions, a problem-to-solution methodology based on the interaction with technology is developed. With this approach, we fill the gap between proven transportation assessment frameworks and general user-centered techniques. Central to our approach is an electric test vehicle that is implemented in rural supply and value chains for research, development, and testing. Based on our objective and the derived methodological requirements, a set of existing methods is selected. Local partners are integrated in an organizational framework that executes major parts of this research endeavour in Arsi Zone, Oromia Region, Ethiopia.

Jaelyn S. Liang, Rehaan S. Mundy, Shriya Jagwayan

E-commerce is rapidly transforming economies across Africa, offering immense opportunities for economic growth, market expansion, and digital inclusion. This study investigates the effects of e-commerce on select African regions. By utilizing readiness factors, including mobile money deployment, GDP per capita, internet penetration, and digital infrastructure, the preparedness of African countries for e-commerce adoption is quantified, highlighting significant disparities. Through case studies in urban and rural areas, including Lagos, Kano, Nairobi, and the Rift Valley, the study shows e-commerce's significant effects on small and medium-sized enterprises (SMEs), employment, and market efficiency. Urban centers demonstrated significant gains in productivity and profitability, whereas rural regions experienced slower growth due to limited internet access and infrastructural barriers. Despite these challenges, localized solutions such as mobile money systems and agricultural e-commerce platforms are bridging gaps. This study highlights the significant potential of e-commerce in Africa while emphasizing the need for targeted investments and strategies to address existing regional disparities.

Amy Town Carabajal, Akoua Orsot, Marie Pelagie Elimbi Moudio et al.

This study presents the first comprehensive analysis of the social and economic effects of solar mini-grids in rural African settings, specifically in Kenya and Nigeria. A group of 2,658 household heads and business owners connected to mini-grids over the last five years were interviewed both before and one year after their connection. These interviews focused on changes in gender equality, productivity, health, safety, and economic activity. The results show notable improvements in all areas. Economic activities and productivity increased significantly among the connected households and businesses. The median income of rural Kenyan community members quadrupled. Gender equality also improved, with women gaining more opportunities in decision making and business. Health and safety enhancements were linked to reduced use of hazardous energy sources like kerosene lamps. The introduction of solar mini-grids not only transformed the energy landscape but also led to broad socioeconomic benefits in these rural areas. The research highlights the substantial impact of decentralized renewable energy on the social and economic development of rural African communities. Its findings are crucial for policymakers, development agencies, and stakeholders focused on promoting sustainable energy and development in Africa.

Bianca-Elena Mihăilă, Marian-Gabriel Hâncean, Matjaž Perc et al.

While research on adolescent smoking is extensive, little attention has been given to smoking behaviors among rural middle-aged and older adults. This study examines the role of personal networks and sociodemographic factors in predicting smoking status in a rural Romanian community. Using a link-tracing sampling method, we gathered data from 76 participants out of 83 in Leresti, Arges County. Face-to-face interviews collected sociodemographic data and network information, including smoking status and relational dynamics. We applied multilevel logistic regression models to predict smoking behaviors (current smokers, former smokers, and non-smokers) based on individual characteristics and network influences. Results indicate that social networks significantly influence smoking behaviors. For current smokers, having a smoking family member greatly increased the odds of smoking (OR = 2.51, 95% CI: 1.62, 3.91, p < 0.001). Similarly, non-smoking family members increased the likelihood of being a non-smoker (OR = 1.64, 95% CI: 1.04, 2.61, p < 0.05). Women were less likely to smoke, highlighting sex differences in behavior. These findings emphasize the critical role of social networks in shaping smoking habits, advocating for targeted interventions in rural areas.

Matthew Pierson, Zia Mehrabi

Surprisingly a number of Earth's waterways remain unmapped, with a significant number in low and middle income countries. Here we build a computer vision model (WaterNet) to learn the location of waterways in the United States, based on high resolution satellite imagery and digital elevation models, and then deploy this in novel environments in the African continent. Our outputs provide detail of waterways structures hereto unmapped. When assessed against community needs requests for rural bridge building related to access to schools, health care facilities and agricultural markets, we find these newly generated waterways capture on average 93% (country range: 88-96%) of these requests whereas Open Street Map, and the state of the art data from TDX-Hydro, capture only 36% (5-72%) and 62% (37%-85%), respectively. Because these new machine learning enabled maps are built on public and operational data acquisition this approach offers promise for capturing humanitarian needs and planning for social development in places where cartographic efforts have so far failed to deliver. The improved performance in identifying community needs missed by existing data suggests significant value for rural infrastructure development and better targeting of development interventions.

Tillmann von Carnap, Reza M. Asiyabi, Paul Dingus et al.

In many rural areas of low- and middle-income countries, weekly gatherings of buyers and sellers are the most tangible manifestation of the market economy. Knowing these markets' whereabouts and activity over time could provide insights in otherwise data-scarce environments, helping researchers and policymakers to better understand poor rural economies. But these markets are by nature informal and scattered widely across often-remote regions. As a result, data on this fundamental institution are sparse and inconsistent. We develop, test, and apply a method to fill this gap, leveraging market activity's unique temporal and visual signature in satellite imagery. Using secondary data from Kenya, Malawi, and Mozambique, we first confirm that we detect markets with high sensitivity and specificity. We then derive a map of 1,776 markets in Ethiopia and track their activity at up-to-weekly frequency between 2017 and 2024. Measured market activity exhibits seasonal patterns following local agricultural calendars and responds to weather and conflict shocks. Our approach is applicable wherever satellites can regularly acquire images of rural periodic markets and requires no ground data. Once markets are mapped, our approach can be fully automated to produce an up-to-weekly measure of economic conditions in areas where such data is otherwise generally not available.

Biplob Dey, Jannatul Ferdous, Romel Ahmed et al.

Medicinal plants have got notable attention in recent years in the field of pharmaceutical and drug research. The high demand of herbal medicine in the rural areas of developing countries and drug industries necessitates correct identification of the medicinal plant species which is challenging in absence of expert taxonomic knowledge. Against this backdrop, we attempted to assess the performance of seven advanced deep learning algorithms in the automated identification of the plants from their leaf images and to suggest the best model from a comparative study of the models. We meticulously trained VGG16, VGG19, DenseNet201, ResNet50V2, Xception, InceptionResNetV2, and InceptionV3 deep neural network models. This training utilized a dataset comprising 5878 images encompassing 30 medicinal species distributed among 20 families. Our approach involved two avenues: the utilization of public data (PI) and a blend of public and field data (PFI), the latter featuring intricate backgrounds. Our study elucidates the robustness of these models in accurately identifying and classifying both interfamily and interspecies variations. Despite variations in accuracy across diverse families and species, the models demonstrated adeptness in these classifications. Comparing the models, we unearthed a crucial insight: the Normalized leverage factor (γω) for DenseNet201 stands at 0.19, elevating it to the pinnacle position for PI with a remarkable 99.64 % accuracy and 98.31 % precision. In the PFI scenario, the same model achieves a γω of 0.15 with a commendable 97 % accuracy. These findings serve as a guiding beacon for shaping future application tools designed to automate medicinal plant identification at the user level.

Mamazhonov Abdulaziz, Sobolkin Sergey, nyakova Olga Kor

consider the technical-theoretical side of the work of neural networks on examples get acquainted with different methods of landing and understand their features, their possibilities and disadvantages compared to the standard methods of using manned vehicles. We will tell you about the method of improving the work of neural networks. We will introduce you to the features and importance of using air vehicles in rural industries and local supply organizations in order to improve the quality and speed of work.

Zhenjiang Ding, Chunmei Liu, Zihan Zhang et al.

The mitochondrial calcium uniporter (MCU) occupies a noteworthy position in the regulation of mitochondrial calcium uptake. This study investigated the effects of MCU modulator-mediated mitochondrial calcium on mitochondrial dysfunction, oxidative stress, endogenous enzyme activities, and tenderness during postmortem aging. Spermine, as an activator of MCU, resulted in an increase in mitochondrial calcium levels, not only disrupting mitochondrial morphology but also triggering mitochondrial oxidative stress and downregulation of antioxidant factors. Additionally, the spermine group underwent later activation of calpain and earlier activation of caspases, as well as the myofibril fragmentation index was initially lower and then higher compared with control group, indicating that endogenous enzymes played an indispensable role in different aging periods. Interestingly, the results of the Ru360 (an inhibitor of MCU) group were opposite to those aforementioned findings. Our data provide a novel perspective on the regulatory mechanism of mitochondrial calcium homeostasis mediated by MCU on tenderness.

Anindya Bhattacharya, Anirban Kar, Alita Nandi

In this paper we explore two intertwined issues. First, using primary data we examine the impact of asymmetric networks, built on rich relational information on several spheres of living, on access to workfare employment in rural India. We find that unidirectional relations, as opposed to reciprocal relations, and the concentration of such unidirectional relations increase access to workfare jobs. Further in-depth exploration provides evidence that patron-client relations are responsible for this differential access to such employment for rural households. Complementary to our empirical exercises, we construct and analyse a game-theoretical model supporting our findings.

Fumiko Ogushi, Chandreyee Roy, Kimmo Kaski

Human mobility and other social activity patterns influence various aspects of society such as urban planning, traffic predictions, crisis resilience, and epidemic prevention. The behaviour of individuals, like their communication frequencies and movements, are shaped by societal and socio-economic factors. In addition, the differences in the geolocation of people as well as their gender and age cast effects on their activity patterns. In this study we focus on investigating these patterns by using mobile phone data, specifically the call detail records (CDRs), to analyze the social communication and mobility patterns of people. This dataset can provide us insight into the individual and population-level behaviours in rural and urban environments on a daily, weekly and seasonal basis. The results of our analyses show that in the urban areas people have high calling activity but low mobility, while in the rural areas they show the opposite behaviour, i.e. low calling activity combined with high mobility. Overall, there is a decreasing trend in people's mobility through the year even though their calling activity remained consistent except for the holidays during which time the communication frequency drops markedly. We have also observed that there are significant differences in the mobility between the work days and free days. Finally, the age and gender of individuals have also been observed to play a role in the seasonal patterns differently in urban and rural areas.