Hasil untuk "Rural industries"

G. Shaw, Allan M. Williams

Felipe M. Suplicy, Emanuela R. Meneghetti, Natalie A. Moltschaniwskyj et al.

This study investigated the feeding behavior of the Pacific oyster (Crassostrea gigas) in response to natural variations in seston availability in Santa Catarina, Brazil, to inform assessments of ecological carrying capacity and sustainability certification. Field experiments were conducted at two sites using a flow-through biodeposition system to measure clearance, filtration, ingestion, rejection, and absorption rates under variable seston conditions. Organic and inorganic contents of feces and pseudofeces were quantified to estimate feeding efficiencies. A deterministic, nonlinear model was developed using STELLA software to simulate oyster feeding processes, integrating 18 equations and 26 variables driven by time series of total, organic, and inorganic particulate matter. Clearance and filtration rates decreased as seston concentration and organic content increased, showing a parabolic relationship. Net organic ingestion and absorption rates were highest at intermediate seston quality and quantity, with absorption efficiency declining at very low or high filtration rates. The model accurately reproduced observed trends and predicted annual biodeposit production of 74.15 tons of pseudofeces and 18.13 tons of feces for a typical 3-ha farm. A sensitivity analysis revealed feces production was the most responsive output to changes in seston properties. The model provides a robust framework for predicting the ecological effects of oyster aquaculture and can be applied to evaluate farm-scale impacts on water quality and sediment enrichment. This approach supports evidence-based certification and management of bivalve farming by quantifying biological processes and their environmental interactions under realistic field conditions.

Lydia Jade Makonda, Lydia Jade Makonda, Orlando Nipassa et al.

Food fortification is a public health strategy for tackling micronutrient deficiencies in sub-Saharan Africa. This systematic review explores the policies governing food fortification in Malawi and Mozambique. By comparing policy implementation, regulatory mechanisms and outcomes, this review aims to identify best practices, challenges, and opportunities to strengthen fortification programs across the region. All applicable evidence collated from reports and articles published between 2000 and 2025, databases from key organizations, and reports/literature received from key informants were systematically reviewed. A total of 29 reports were selected based on the inclusion and exclusion criteria and subjected to risk of bias assessment. The key issues include high fortification costs, lack of technical knowledge and expertise, equipment limitations, quality control issues and regulatory compliance. Malawi’s mandatory fortification, enacted in 2011, has demonstrated higher compliance rates, supported by robust monitoring mechanisms, albeit with limited reach into informal food processing sectors that serve rural populations. Mozambique’s voluntary fortification guidelines, which became mandatory in 2016, exhibit lower compliance but greater flexibility in accommodating small and medium-scale processors. This review recommends monitoring quality and reporting, results-based implementation, stakeholder and community engagement, support and incentives to the food industries, and reduction of bureaucratic burdens to achieve effective fortification programs.Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO/view/CRD420251113300, identifier PROSPERO (CRD420251113300).

Zhengpeng Luo, Bo Chai, Bo Chai et al.

IntroductionEcosystem stability confers more abundant and comprehensive ecosystem service values. However, current valuation methods often simplify these ecosystems as undisturbed, ideal, and standardized—neglecting inherent variations in structure and quality—and thereby risk skewing service valuations.MethodsWe propose a coupled calculation model based on patch stability that integrates land cover dynamics with transitions in the Remote Sensing-based Ecological Index (RSEI). From this model, we derive a novel valuation metric, the Effective Forest Area (EFA). We validate both the model and the metric using Heshan City—a coal mining city facing resource depletion—as a case study.ResultsBetween 2010 and 2020, despite a net increase in total forest area, management practices driven by fast-growing forestry industries degraded the quality of stable forests and resulted in a persistent decline in their extent. Even in regions with intensive ecological compensation, achieving desired restoration outcomes proved challenging, a situation that ultimately reduced the overall function and service value of the regional forest ecosystem. The results show that, compared to the EFA model, traditional calculation methods overestimated the forest ecosystem service value in all regions, with the overestimation being highest in the Spontaneously Developed Rural Area (35%), followed by the Industrial Heritage Tourism Area (29%), and the Urbanization Area (26%).DiscussionThe EFA model underscores the critical impact of structural and quality changes on ecosystem service value, thereby enabling more comprehensive evaluations—assessments that are essential for developing nature-based solutions and strategies to enhance ecosystem quality.

Md. Rahat Khan, Asmaul Nupur, Jannatul Jany et al.

Purpose: The current study examined the impacts of postharvest treatments with different coating solutions to enhance the shelf life of papaya at the least nutrient loss. Research method: The study was carried out with mature and fresh shahi papayas (BARI Papaya-1) using Complete Randomized Design. The experiment comprised four treatments namely control (T1), coating with 2% pectin solution (T2), 2% cellulose acetate solution (T3), and 2% sodium alginate solution (T4). Findings: Significant variations among the treatments regarding physicochemical characteristics like color, weight loss (%), moisture content (%), pH, titratable acidity, total soluble solids (°Brix), vitamin C content, and biological parameters like total viable count (TVC), and shelf life were observed for the 12 day storage periods. It was observed that vitamin C content, moisture content, and titratable acidity gave higher values in the treated samples (T2, T3, T4) with the lowest color score, weight loss, total soluble solids, and pH. Among the samples, the papaya treated with 2% sodium alginate solution obtained the longest shelf life with the lowest TVC value. Conversely, the control papaya had the highest microbial load with the shortest shelf life. Research limitations: There was no limitation. Originality/Value: Among the treatments, 2% sodium alginate solution increased the shelf life of papaya by 16% and decreased post-harvest loss. Therefore, 2% sodium alginate solution treatment seems to be a good substitute for preservation and an effective way to retain the quality of papaya.

Zijuan Ding, Ke Liu, Sabine Grunwald Ph.D. et al.

Abstract Measurement, monitoring, and prediction of soil organic carbon (SOC) are fundamental to supporting climate change mitigation efforts and promoting sustainable agricultural management practices. This review discusses recent advances in methodologies and technologies for SOC quantification, including remote sensing (RS), proximal soil sensing (PSS), artificial intelligence (AI) for SOC modelling (in particular, machine learning (ML) and deep learning (DL)), biogeochemical modelling, and data fusion. Integrating data from RS, PSS, and other sensors usually leads to good SOC predictions, provided it is supported by careful calibration, validation across diverse pedo‐climatic and land management, and the use of data processing and modelling frameworks. We also found that the accuracy of AI‐driven SOC prediction improves when RS covariates are included. Although DL often outperforms classical ML, there is no single best AI algorithm. By incorporating simulated outputs from biogeochemical model as additional training data for AI, causal relationships in SOC turnover can be incorporated into empirical modelling, while maintaining predictive accuracy. In conclusion, SOC prediction can be enhanced through 1) integrating sensing technologies, 2) applying AI, notably DL, 3) addressing biogeochemical model limitations (assumptions, parameterization, structure), 4) expanding SOC data availability, 5) improving mathematical representation of microbial influences on SOC, and 6) strengthening interdisciplinary cooperation between soil scientists and model developers.

Benjamin L. Allen, Benjamin L. Allen, Andrew J. Abraham et al.

Killing animals is a ubiquitous human activity consistent with our predatory and competitive ecological roles within the global food web. However, this reality does not automatically justify the moral permissibility of the various ways and reasons why humans kill animals – additional ethical arguments are required. Multiple ethical theories or frameworks provide guidance on this subject, and here we explore the permissibility of intentional animal killing within (1) consequentialism, (2) natural law or deontology, (3) religious ethics or divine command theory, (4) virtue ethics, (5) care ethics, (6) contractarianism or social contract theory, (7) ethical particularism, and (8) environmental ethics. These frameworks are most often used to argue that intentional animal killing is morally impermissible, bad, incorrect, or wrong, yet here we show that these same ethical frameworks can be used to argue that many forms of intentional animal killing are morally permissible, good, correct, or right. Each of these ethical frameworks support constrained positions where intentional animal killing is morally permissible in a variety of common contexts, and we further address and dispel typical ethical objections to this view. Given the demonstrably widespread and consistent ways that intentional animal killing can be ethically supported across multiple frameworks, we show that it is incorrect to label such killing as categorically unethical. We encourage deeper consideration of the many ethical arguments that support intentional animal killing and the contexts in which they apply.

Mohammad Saddam Hosen, MD Shahidul Islam Fakir, Shamal Chandra Hawlader et al.

Rural Bangladesh is confronted with substantial healthcare obstacles, such as inadequate infrastructure, inadequate information systems, and restricted access to medical personnel. These obstacles impede effective disease control and pandemic preparedness. This investigation employs a structured methodology to develop and analyze numerous plausible scenarios systematically. A purposive sampling strategy was implemented, which involved the administration of a questionnaire survey to 264 rural residents in the Rangamati district of Bangladesh and the completion of a distinct questionnaire by 103 healthcare and medical personnel. The impact and effectiveness of the study are assessed through logistic regression analysis and a pre-post comparison that employs the Wilcoxon Signed-Rank test and Kendall's coefficient for non-parametric paired and categorical variables. This analysis evaluates the evolution of disease control and preparedness prior to and subsequent to the implementation of the Generative AI-Based Model 4.0. The results indicate that trust in AI (\b{eta} = 1.20, p = 0.020) and confidence in sharing health data (\b{eta} = 9.049, p = 0.020) are the most significant predictors of AI adoption. At the same time, infrastructure limitations and digital access constraints continue to be significant constraints. The study concludes that the health resilience and pandemic preparedness of marginalized rural populations can be improved through AI-driven, localized disease control strategies. The integration of Generative AI into rural healthcare systems offers a transformative opportunity, but it is contingent upon active community engagement, enhanced digital literacy, and strong government involvement.

Mathieu Chateauvert, Jonathan Ethier, Adrian Florea

Accurate radio wave propagation modeling is essential for effective spectrum management by regulators and network deployment by operators. This paper investigates the ITU-R P.1812-7 (P.1812) propagation model's reliance on geospatial inputs, particularly clutter information, to improve path loss estimation, with an emphasis on rural geographic regions. The research evaluates the impact of geospatial elevation and land cover datasets, including Global Forest Canopy Height (GFCH), European Space Agency WorldCover, and Natural Resources Canada LandCover, on P.1812 propagation model prediction accuracy. Results highlight the trade-offs between dataset resolution, geospatial data availability, and representative clutter height assignments. Simulations reveal that high-resolution data do not always yield better results and that global datasets such as the GFCH provide a robust alternative when high-resolution data are unavailable or out-of-date. This study provides a set of guidelines for geospatial dataset integration to enhance P.1812's rural path loss predictions.

Xuxin Yang, Xue Yuan, Donghan Feng et al.

Constructing clean and low-carbon rural integrated energy system (RIES) is a fundamental requirement for supporting China's rural modernization and new-type urbanization. Existing research on RIES decarbonization primarily focuses on the optimal low-carbon operation of system-level energy devices at the macro level, while the synergistic carbon-reduction effects of demand-side flexible loads and external carbon trading mechanisms have not been fully explored. Meanwhile, at the micro level, the carbon sensitivity of device parameters and their potential contribution to emission reduction remain insufficiently investigated. To address these gaps, this study integrates macro- and micro-level analyses. At the macro level, a multi-energy-coupled low-carbon optimal operation framework is developed, incorporating coordinated electric-thermal demand response (DR) and carbon trading. At the micro level, a carbon emission model for RIES components is established, and sensitivity analysis is conducted on 28 carbon-related parameters to identify highly sensitive determinants of emission reduction. Case studies based on typical operation data from a rural region in northern China demonstrate that coordinated electric-thermal DR and carbon trading can achieve maximum carbon-reduction potential. Furthermore, the identified high-sensitivity parameters provide essential theoretical guidance for enhancing the decarbonization potential of RIES.

Kausutua Tjikundi, Jackie Kleynhans, Stefano Tempia et al.

Households play a crucial role in the propagation of infectious diseases due to the frequent and prolonged interactions that typically occur between their members. Recent studies have emphasized the need to include socioeconomic variables in epidemic models to account for the heterogeneity induced by human behavior. While sub-Saharan Africa suffers the highest burden of infectious disease diffusion, few studies have investigated the mixing patterns in the countries and their relation with social indicators. This work analyzes household contact matrices measured with wearable proximity sensors in a rural and an urban village in South Africa. Leveraging a rich data collection describing additional individual and household attributes, we investigate how the household contact matrix varies according to the household type (whether it is composed only of a familiar nucleus or by a larger group), the gender of its head (the primary decision-maker), the rural or urban context, and the season in which it was measured. We show the household type and the gender of its head induce differences in the interaction patterns between household members, particularly regarding child caregiving, suggesting they are relevant attributes to include in epidemic modeling.

K. K. Kim, S. P. McGrath, D. Lindeman

Chronic illnesses are a global concern with essential hypertension and diabetes mellitus among the most common conditions. Remote patient monitoring has shown promising results on clinical and health outcomes. However, access to care and digital health solutions is limited among rural, lower-income, and older adult populations. This paper repots on a pre-post study of a comprehensive care coordination program including connected, wearable blood pressure and glucometer devices, tablets, and medical assistant-provided health coaching in a community health center in rural California. The participants (n=221) had a mean age of 54.6 years, were majority female, two-thirds spoke Spanish, 19.9% had hypertension, 49.8% diabetic, and 30.3% both conditions. Participants with hypertension achieved a mean reduction in systolic blood pressure of 20.24 (95% CI: 13.61, 26.87) at six months while those with diabetes achieved a mean reduction of 3.85 points (95% CI: 3.73, 4.88). These outcomes compare favorably to the small but growing body of evidence supporting digital care coordination and remote monitoring. These results also support the feasibility of well-designed digital health solutions yielding improved health outcomes among underserved communities.

Stefano M Iacus, Giuseppe Porro

We analyze a novel large-scale social-media-based measure of U.S. job satisfaction, constructed by applying a fine-tuned large language model to 2.6 billion georeferenced tweets, and link it to county-level labor market conditions (2013-2023). Logistic regressions show that rural counties consistently report lower job satisfaction sentiment than urban ones, but this gap decreases under tight labor markets. In contrast to widening rural-urban income disparities, perceived job quality converges when unemployment is low, suggesting that labor market slack, not income alone, drives spatial inequality in subjective work-related well-being.

K. Chan

Maksim Papenkov

Accurate industry classification is critical for many areas of portfolio management, yet the traditional single-industry framework of the Global Industry Classification Standard (GICS) struggles to comprehensively represent risk for highly diversified multi-sector conglomerates like Amazon. Previously, we introduced the Multi-Industry Simplex (MIS), a probabilistic extension of GICS that utilizes topic modeling, a natural language processing approach. Although our initial version, MIS-1, was able to improve upon GICS by providing multi-industry representations, it relied on an overly simple architecture that required prior knowledge about the number of industries and relied on the unrealistic assumption that industries are uncorrelated and independent over time. We improve upon this model with MIS-2, which addresses three key limitations of MIS-1 : we utilize Bayesian Non-Parametrics to automatically infer the number of industries from data, we employ Markov Updating to account for industries that change over time, and we adjust for correlated and hierarchical industries allowing for both broad and niche industries (similar to GICS). Further, we provide an out-of-sample test directly comparing MIS-2 and GICS on the basis of future correlation prediction, where we find evidence that MIS-2 provides a measurable improvement over GICS. MIS-2 provides portfolio managers with a more robust tool for industry classification, empowering them to more effectively identify and manage risk, particularly around multi-sector conglomerates in a rapidly evolving market in which new industries periodically emerge.

Jinlin Xue

De-gen Wang, Yujia Zhu, Mei-feng Zhao et al.

Abstract The hollowing of traditional villages not only causes the waste of land resources, loss of population, aging and weakening of rural population, and the decline of rural industries, but also threatens the protection of tangible cultural heritage and traditional folklore. Taking the case of Dongcun Village, a traditional village in Jinting town in Suzhou, this paper measures the degree of hollowing from three dimensions of land, population and industry, and uses GIS technology to analyze the rural hollowing characteristics. It builds regression models with the rural households as the study units and provides a micro-scale analysis of the formation mechanism of traditional village hollowing. The results are shown as follows. (1) The land hollowing rate of this traditional village is 20.19 % in Dongcun Village. Vacant and abandoned residential land is concentrated at the core of the village, while new houses increase on the periphery. Many families have more than one plot of housing land, accounting for 67.97 %. (2) The population hollowing of Dongcun Village is not only manifested in the large proportion of out-migrants (20.3 %), but also in the unbalanced structure of the resident population. The proportion of remaining labor has decreased to 42.31 % and is lower than the average level of rural China. (3) More than half of the households only had the elderly and weak farming laborers, and a few households even abandoned it. Industry hollowing was particularly severe in households along the town road, indicating that the periphery of the village was not solid and some deeper problems of population and industry hollowing occurred in there. (4) The hollowing of traditional villages is influenced by various factors including family economics, location and transportation, natural resource endowment, family demographic structure, housing situation, and land management. Among them, family economy is the main influence factor of rural hollowing, since non-agricultural employment transformation enhances the ability of farmers to build houses, contributing to land and industry hollowing. The location and transportation factor is the guiding force for the hollowing of land and industry. The relative lack of cultivated land resources and the low efficiency of farming are the root causes of population and industry hollowing. The family demographic structure provides the basic driving force for rural hollowing. The housing situation, especially the building year of houses, affects the demand for house renewal and becomes the direct driving force of land hollowing. The land management factor also contributes to hollowing.

Nattanicha Chairassamee, Oudom Hean

We investigate the offshoring effect on local productivity, physical and intellectual capital investment at the U.S. county level from 1999 to 2006. By using regression with fixed effects and instrumental variable to account for possible endogeneity, we find that offshoring can increase overall local productivity and capital investment. Through industry linkages, an increase in productivity and capital investment from offshoring enhances those increases in non-offshoring industries. Industries in both MSA (urban) and non-MSA (rural) counties receive benefits of productivity expansion and capital investment from offshoring. The increased capital investment from offshoring could be a channel of local productivity and capital investment expansion.

Daniel Chirilă, Mariana Chirilă, Claudia Sîrbulescu

The paper presents the forms and activities from the hospitality industry, including conventional tourism of table, cultural tourism, business tourism, rural tourism, cruise tourism, religious tourism, sports tourism and urban tourism. The process of direction to the sustainability should normally be coordinated at national level by governmental factors and supported by local factors, at community level. Sustainability, for tourism, as well as for other industries, has three independent aspects: economic, social, cultural and environmental. Sustainable development involves permanency, meaning that sustainable tourism requires optimal use of resources (including biological diversity), minimizing economic, socio-cultural and ecological negative impacts, and maximizing benefits for local communities, national economies, and conservation of nature. As a natural consequence, sustainability also refers to the managerial structures needed to meet these desires.

Aman Shreshtha, Smruti R Sarangi

5G connectivity has become essential to integrate rural communities into the broader digital economy and support critical applications like remote education and remote surgery. A major hindrance to expanding rural broadband coverage, especially in developing countries, is the high cost of installing 5G base stations. Hence, there is a need to reduce the cost of a 5G base station without degrading its performance. Our work proposes a novel approach to efficiently utilize the polar code encoders in a 5G base station. The idea is to use the idle time of the polar encoders during downlink transmission for error correction in the 5G data plane. Polar codes have conventionally been used in the 5G control plane, while LDPC codes are used in the data plane. We perform detailed characterization experiments to show the advantages of using polar codes in the data plane as well. Further, to intelligently distribute the user data packets among the available compute nodes, we propose a set of novel resource allocation algorithms and compare their performance with other algorithms in the literature. Using our proposed optimization techniques, we achieve a 17% reduction in the cost of a 5G base station. Simultaneously, we are able to improve the performance by 24% compared to a conventional base station.