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Abstract: Owing to growing uncontrolled land-use change and urban expansion, farmers in urban fringes are struggling to sustain their livelihood. Farmers have been expressing their dissatisfaction at different times. This study analyzes the stakeholders' perspectives on the causes and outcomes of farmers' resistance to land-use change and urban expansion processes by zooming in on Bahir Dar, Ethiopia. The paper is based on focus group discussions with farmers in the neighboring villages, local agricultural extension experts, and, subsequently, key informant interviews of local government officials. Juxtaposing farmers' and local experts' positions reveals that inadequate compensations during land expropriation, lack of good governance in the urban expansion process, and inaccessibility of infrastructures are primary reasons for the farmers' struggle against urban expansion in the urban fringes. This study provides insights into the consequences of unplanned urban development challenges and may inform research and policymaking on sustainable urban development in the area and beyond.

Managing the rapid growth of deprived areas (commonly known as slums, informal settlements, etc.) in cities of Low- to Middle-Income Countries (LMICs) demands detailed and consistent information that is often unavailable. Recent Earth Observation (EO) mapping approaches with supervised classification models overlook the diversity of deprived areas and require resource-intensive training sets. In this study, we analyse the potential of unsupervised machine learning (ML) models to capture intra-urban diversity of deprived areas in São Paulo, using solely open geodata. We provide a workflow of characterising deprivation at a city scale with a disaggregated approach, offering scalability and transferability potential. First, we extract a pool of spatial features from open geospatial datasets to characterise the morphological and environmental conditions of the study area. After input preparation, we train and optimise a k-means model, including a coupled feature importance tool. Four cluster types emerged with different deprivation aspects such as higher and lower accessibility to services and infrastructure, sparser and denser occupation; regular and complex morphology; flat and steep terrain. This alternative methodology to capture diversity of deprived areas with open EO-based features can inform locally targeted, thus more efficient, urban policies and interventions.

Remote sensing data is a cheap form of surficial geoscientific data, and in terms of veracity, velocity and volume, can sometimes be considered big data. Its spatial and spectral resolution continues to improve over time, and some modern satellites, such as the Copernicus Programme's Sentinel-2 remote sensing satellites, offer a spatial resolution of 10 m across many of their spectral bands. The abundance and quality of remote sensing data combined with accumulated primary geochemical data has provided an unprecedented opportunity to inferentially invert remote sensing data into geochemical data. The ability to derive geochemical data from remote sensing data would provide a form of secondary big geochemical data, which can be used for numerous downstream activities, particularly where data timeliness, volume and velocity are important. Major benefactors of secondary geochemical data would be environmental monitoring and applications of artificial intelligence and machine learning in geochemistry, which currently entirely relies on manually derived data that is primarily guided by scientific reduction. Furthermore, it permits the usage of well-established data analysis techniques from geochemistry to remote sensing that allows useable insights to be extracted beyond those typically associated with strictly remote sensing data analysis. Currently, no generally applicable and systematic method to derive chemical elemental concentrations from large-scale remote sensing data have been documented in geosciences. In this paper, we demonstrate that fusing geostatistically-augmented geochemical and remote sensing data produces an abundance of data that enables a more generalized machine learning-based geochemical data generation. We use gold grade data from a South African tailing storage facility (TSF) and data from both the Landsat-8 and Sentinel remote sensing satellites. We show that various machine learning algorithms can be used given the abundance of training data. Consequently, we are able to produce a high resolution (10 m grid size) gold concentration map of the TSF, which demonstrates the potential of our method to be used to guide extraction planning, online resource exploration, environmental monitoring and resource estimation. Validerad;2023;Nivå 1;2023-04-12 (hanlid);Funder: Department of Scienceand Innovation (DSI) - National Research Foundation (NRF) ThuthukaGrant (121,973); SI-NRF CIMERA; Centre for Advanced Miningand Metallurgy (CAMM), Luleå University of Technology; Sibanye-Stillwater Ltd

Summary Surgeons and their patients recognise that one of the major advances in surgical technique over the last 20 years has been the growth of minimal access surgery by means of laparoscopic and robotic approaches. Partnerships with industry have facilitated the development of advanced technical instruments, light sources, recording devices and optics which are almost out of date by the time they are introduced to surgical practice. However, lest we think that technological innovation is entirely a modern concept, we should remember that our predecessors were masters of their craft and able to apply new technologies to surgical practice. The history of minimal access surgery can be traced back to approximately 5000 years ago and this review aims to remind us of the achievements of historical doctors and engineers, as well as bring more modern developments to wider attention. This review will comprise a three-part series: Part I 3000BC to 1850 Early instruments for viewing body cavities Part II 1850 to 1990 Technological developments Part III 1990 to present Organisational issues and the rise of the robots

In this work, an internet of things (IoT) sensing and monitoring box has been developed. The proposed low-cost system is a portable device for smart buildings to measure vibrations, monitor, and control noise caused by the industrial machines. We will present an instrument and a method to measure the vibration and tilt of a mechanical system (air conditioner). The primary goal is to create a signal acquisition and monitoring system that is both userfriendly and affordable, while also delivering exceptional precision. The key concept is centered around acquiring and processing signals through the Raspberry Pi. We will use for the first time as an application, which does not exist before, a conversion method to control and monitor remotely the noise generated by the machines. Once the noise reaches a high value or the air conditioner is too much tilted, the system sends an alert in the form of an email. We will use the Python language to acquire and process the signal and send the alerts. The proposed approach is straightforward to implement, and the obtained results demonstrate a high level of accuracy that is consistent with the existing literature.

The Thrace Basin in northwestern Turkey is a deep Eocene–Oligocene hydrocarbon-bearing sedimentary basin. The basin has potential for geothermal energy utilization in the future due to its favorable geological conditions. In this study, we combined the available bottom hole temperature (BHT) data from 70 points with the thermal conductivity and radiogenic heat productions of the basin formations, and generated a detailed thermal model of the northern part of the basin. For heat flow determinations from the BHT data, we applied Bullard’s thermal resistance method on formation thermal conductivities and thicknesses. The results give an average surface heat flow of 65.8 ± 11.3 mW/m2. We obtained high heat flow values (75–80 mW/m2) in the eastern and western sides, and the central part of the study area. These relatively high heat flow values can be explained by the combined effect of basement topography and the variations in the radiogenic heat production of the basement rocks. The calculated subsurface temperatures in selected hydrocarbon fields vary in the range of 45–64 °C at 1 km depth, 99–136 °C at 3 km depth, and 155–208 °C at 5 km depth as a result of local variations of the surface heat flow and formation thermal resistances. These variations in subsurface temperatures can have significant effects on the cost of geothermal energy production in future.

Key message Authors have analyzed the possible correlation between measurements/indicators of forest structure and species richness of many taxonomic or functional groups over three regions of Germany. Results show the potential to use structural attributes as a surrogate for species richness of most of the analyzed taxonomic and functional groups. This information can be transferred to large-scale forest inventories to support biodiversity monitoring. Context We are currently facing a dramatic loss in biodiversity worldwide and this initiated many monitoring programs aiming at documenting further trends. However, monitoring species diversity directly is very resource demanding, in particular in highly diverse forest ecosystems. Aims We investigated whether variables applied in an index of stand structural diversity, which was developed based on forest attributes assessed in the German National Forest Inventory, can be calibrated against richness of forest-dwelling species within a wide range of taxonomic and functional groups. Methods We used information on forest structure and species richness that has been comprehensively assessed on 150 forest plots of the German biodiversity exploratories project, comprising a large range of management intensities in three regions. We tested, whether the forest structure index calculated for these forest plots well correlate with the number of species across 29 taxonomic and functional groups, assuming that the structural attributes applied in the index represent their habitat requirements. Results The strength of correlations between the structural variables applied in the index and number of species within taxonomic or functional groups was highly variable. For some groups such as Aves, Formicidae or vascular plants, structural variables had a high explanatory power for species richness across forest types. Species richness in other taxonomic and functional groups (e.g., soil and root-associated fungi) was not explained by individual structural attributes of the index. Results indicate that some taxonomic and functional groups depend on a high structural diversity, whereas others seem to be insensitive to it or even prefer structurally poor stands. Conclusion Therefore, combinations of forest stands with different degrees of structural diversity most likely optimize taxonomic diversity at the landscape level. Our results can support biodiversity monitoring through quantification of forest structure in large-scale forest inventories. Changes in structural variables over inventory periods can indicate changes in habitat quality for individual taxonomic groups and thus points towards national forest inventories being an effective tool to detect unintended effects of changes in forest management on biodiversity. Annals of Forest Science, 80 (1) ISSN:1286-4560 ISSN:1297-966X

The North Carolina Farmworker Health Program (NCFHP) implemented an emergency program in response to North Carolina migrant and seasonal farmworkers’ urgent need for Internet access for health information, family connections, and telehealth services during COVID-19 isolation and quarantine. This article describes the NCFHP Internet Connectivity Project implementation and evaluation from June 2020 to December 2021. The project placed 448 devices across the state and provided Internet access to more than 3184 farmworkers during the 2021 peak farming season. (Am J Public Health. 2022;112(11):1551–1555. https://doi.org/10.2105/AJPH.2022.307017 )