The primary drivers of agricultural land depletion in Taiz be diagnosed quantitatively in this study, proposing for the first time a replicable conflict-sensitive urban management model. The overarching objective is to bridge the critical gap between sustainable urban expansion and the preservation of agro-ecological systems in fragile, data-scarce contexts. A combination of unplanned sprawl, crisis, and ineffective governance, Taiz City's rapid urbanization between 2000 and 2024 resulted in a 35% loss of agricultural land. This study proposes that governance reduces the primary causes of conflict escalation and the severity of sprawl. This study combines GIS spatial analysis (Landsat 8/9 and support vector machine classification), regression modeling, and global case comparisons (Medellín and Mumbai) to assess land-use trends. The findings indicate that governance diminishes the effects (β = −0.50, p < 0.01), sprawl (β = 0.85, p < 0.01), and conflict (β = 0.002, p < 0.05) explain 85% of the variance in losses. By 2024, 3.2 million residents' food security was at risk because of the urbanization of 60% of peri-urban fertile lands. Vertical expansion, tenure regularization and GIS planning will reclaim 20% of land by 2030.
Sanjit Dandapanthula, Margaret Johnson, Madeleine Pascolini-Campbell
et al.
Accurate and high-resolution estimation of land surface temperature (LST) is crucial in estimating evapotranspiration, a measure of plant water use and a central quantity in agricultural applications. In this work, we develop a novel statistical method for downscaling LST data obtained from NASA's ECOSTRESS mission, using high-resolution data from the Landsat 8 mission as a proxy for modeling agricultural field structure. Using the Landsat data, we identify the boundaries of agricultural fields through edge detection techniques, allowing us to capture the inherent block structure present in the spatial domain. We propose a block-diagonal Gaussian process (BDGP) model that captures the spatial structure of the agricultural fields, leverages independence of LST across fields for computational tractability, and accounts for the change of support present in ECOSTRESS observations. We use the resulting BDGP model to perform Gaussian process regression and obtain high-resolution estimates of LST from ECOSTRESS data, along with uncertainty quantification. Our results demonstrate the practicality of the proposed method in producing reliable high-resolution LST estimates, with potential applications in agriculture, urban planning, and climate studies.
Recent advances in remote sensing technology have made submeter resolution imagery increasingly accessible, offering remarkable detail for fine-grained land cover analysis. However, its full potential remains underutilized - particularly for large-scale land cover mapping - due to the lack of sufficient, high-quality annotated datasets. Existing labels are typically derived from pre-existing products or manual annotation, which are often unreliable or prohibitively expensive, particularly given the rich visual detail and massive data volumes of submeter imagery. Inspired by the spatial autocorrelation principle, which suggests that objects of the same class tend to co-occur with similar visual features in local neighborhoods, we propose the Mask Clustering-based Annotation Engine (MCAE), which treats semantically consistent mask groups as the minimal annotating units to enable efficient, simultaneous annotation of multiple instances. It significantly improves annotation efficiency by one to two orders of magnitude, while preserving label quality, semantic diversity, and spatial representativeness. With MCAE, we build a high-quality annotated dataset of about 14 billion labeled pixels, referred to as HiCity-LC, which supports the generation of city-scale land cover maps across five major Chinese cities with classification accuracies above 85%. It is the first publicly available submeter resolution city-level land cover benchmark, highlighting the scalability and practical utility of MCAE for large-scale, submeter resolution mapping. The dataset is available at https://github.com/chenhaocs/MCAE
Autonomous landing on sloped terrain poses significant challenges for small, lightweight spacecraft, such as rotorcraft and landers. These vehicles have limited processing capability and payload capacity, which makes advanced deep learning methods and heavy sensors impractical. Flying insects, such as bees, achieve remarkable landings with minimal neural and sensory resources, relying heavily on optical flow. By regulating flow divergence, a measure of vertical velocity divided by height, they perform smooth landings in which velocity and height decay exponentially together. However, adapting this bio-inspired strategy for spacecraft landings on sloped terrain presents two key challenges: global flow-divergence estimates obscure terrain inclination, and the nonlinear nature of divergence-based control can lead to instability when using conventional controllers. This paper proposes a nonlinear control strategy that leverages two distinct local flow divergence estimates to regulate both thrust and attitude during vertical landings. The control law is formulated based on Incremental Nonlinear Dynamic Inversion to handle the nonlinear flow divergence. The thrust control ensures a smooth vertical descent by keeping a constant average of the local flow divergence estimates, while the attitude control aligns the vehicle with the inclined surface at touchdown by exploiting their difference. The approach is evaluated in numerical simulations using a simplified 2D spacecraft model across varying slopes and divergence setpoints. Results show that regulating the average divergence yields stable landings with exponential decay of velocity and height, and using the divergence difference enables effective alignment with inclined terrain. Overall, the method offers a robust, low-resource landing strategy that enhances the feasibility of autonomous planetary missions with small spacecraft.
Junshi Xia, Hongruixuan Chen, Clifford Broni-Bediako
et al.
High-resolution land cover mapping plays a crucial role in addressing a wide range of global challenges, including urban planning, environmental monitoring, disaster response, and sustainable development. However, creating accurate, large-scale land cover datasets remains a significant challenge due to the inherent complexities of geospatial data, such as diverse terrain, varying sensor modalities, and atmospheric conditions. Synthetic Aperture Radar (SAR) imagery, with its ability to penetrate clouds and capture data in all-weather, day-and-night conditions, offers unique advantages for land cover mapping. Despite these strengths, the lack of benchmark datasets tailored for SAR imagery has limited the development of robust models specifically designed for this data modality. To bridge this gap and facilitate advancements in SAR-based geospatial analysis, we introduce OpenEarthMap-SAR, a benchmark SAR dataset, for global high-resolution land cover mapping. OpenEarthMap-SAR consists of 1.5 million segments of 5033 aerial and satellite images with the size of 1024$\times$1024 pixels, covering 35 regions from Japan, France, and the USA, with partially manually annotated and fully pseudo 8-class land cover labels at a ground sampling distance of 0.15--0.5 m. We evaluated the performance of state-of-the-art methods for semantic segmentation and present challenging problem settings suitable for further technical development. The dataset also serves the official dataset for IEEE GRSS Data Fusion Contest Track I. The dataset has been made publicly available at https://zenodo.org/records/14622048.
In agriculture, the application of plant protection products to cropland is important to prevent quality and yield reduction. The use of plant protection products implies negative effects on human health and the environment. Thus, a legal measure towards reducing the use of plant protection products is its restriction or ban especially in sensitive areas.
This is the first national study to use publicly and freely available geodata to access the area of agricultural land located in different types of sensitive areas according to the proposal for a new EU Regulation on the Sustainable Use of Plant Protection Products (SUR). We assess the impact of different scenarios for a German implementation. In this study we analyse publicly available geodata of CORINE land cover 5 ha of 2018 with geographic information systems (GIS) for different scenarios.
The results show that the impact of a pesticide ban or restriction for sensitive areas differs between regions and the type or combination of sensitive area. Using the CLC5-2018 data we estimate 19.6 million hectares of national agricultural area. Landscape Protection Area, Nature Parks and Water Protection Areas contain the largest proportion of agricultural land. A scenario which considers National Parks, Nature Reserves, Biosphere Reserves, Nature Parks, Natural Monuments, Landscape Protection Areas and Natura 2000 sites with Fauna-Flora-Habitat areas and Special Protected Areas for bird sanctuaries and Ramsar sites would affect 46.6% of the agricultural land use in Germany, ranging from 33.4% to 77.9% across different states.
Comparing our CLC5-2018 results to a similar study from 2023, which used LBM-DE as land use data, we find that there is little difference between the results of identical scenario definitions when expressed as proportions. Whereas different SUR scenario definitions can lead to significantly different outcomes.
Ognjen Radišić-Aberger, Peter Burggräf, Fabian Steinberg
et al.
By applying machine learning algorithms, predictive business process monitoring (PBPM) techniques provide an opportunity to counteract undesired outcomes of processes. An especially complex variation of business processes is the engineering change (EC) process. Here, failing to adhere to planned implementation dates can have severe impacts on assembly lines, and it is paramount that potential negative cases are identified as early as possible. Current PBPM research, however, has seldomly investigated the predictive performance of machine learning approaches and their applicability at early process steps, let alone for the EC process. In our research, we show that given adequate feature encoding, shallow learners can accurately predict schedule adherence after process initialisation. Based on EC data from an automotive manufacturer, we provide a case sensitive performance overview on algorithm-encoding combinations. For that, three algorithms (XGBoost, Random Forest, LSTM) were combined with four encoding techniques. The encoding techniques used were the two common aggregation-based and index-based last state encoding, and two new combinations of these, which we term advanced aggregation-based and complex aggregation-based encoding. The study indicates that XGBoost-index-encoded approaches outclass regarding average predictive performance, whereas Random-Forest-aggregation-encoded approaches perform better regarding temporal stability due to reduced influence by dynamic features. Our research provides a case-based reasoning approach for deciding on which algorithm-encoding combination and evaluation metrics to apply. In doing so, we provide a blueprint for an early warning and monitoring method within the EC process and other similarly complex processes.
Marketing. Distribution of products, Management. Industrial management
The statistical analysis of land clutter for Synthetic Aperture Radar (SAR) imaging has become an increasingly important subject for research and investigation. It is also absolutely necessary for designing robust algorithms capable of performing the task of target detection in the background clutter. Any attempt to extract the energy of the desired targets from the land clutter requires complete knowledge of the statistical properties of the background clutter. In this paper, the spatial as well as the temporal characteristics of the land clutter are studied. Since the data for each image has been collected based on a different aspect angle; therefore, the temporal analysis contains variation in the aspect angle. Consequently, the temporal analysis includes the characteristics of the radar cross section with respect to the aspect angle based on which the data has been collected. In order to perform the statistical analysis, several well-known and relevant distributions, namely, Weibull, Log-normal, Gamma, and Rayleigh are considered as prime candidates to model the land clutter. The goodness-of-fit test is based on the Kullback-Leibler (KL) Divergence metric. The detailed analysis presented in this paper demonstrates that the Weibull distribution is a more accurate fit for the temporal-aspect-angle statistical analysis while the Rayleigh distribution models the spatial characteristics of the background clutter with higher accuracy. Finally, based on the aforementioned statistical analyses and by utilizing the Constant False Alarm Rate (CFAR) algorithm, we perform target detection in land clutter. The overall verification of the analysis is performed by exploiting the Moving and Stationary Target Acquisition and Recognition (MSTAR) data-set, which has been collected in spotlight mode at X-band, and the results are presented.
Land-cover mapping is one of the vital applications in Earth observation, aiming at classifying each pixel's land-cover type of remote-sensing images. As natural and human activities change the landscape, the land-cover map needs to be rapidly updated. However, discovering newly appeared land-cover types in existing classification systems is still a non-trivial task hindered by various scales of complex land objects and insufficient labeled data over a wide-span geographic area. In this paper, we propose a generalized few-shot segmentation-based framework, named SegLand, to update novel classes in high-resolution land-cover mapping. Specifically, the proposed framework is designed in three parts: (a) Data pre-processing: the base training set and the few-shot support sets of novel classes are analyzed and augmented; (b) Hybrid segmentation structure; Multiple base learners and a modified Projection onto Orthogonal Prototypes (POP) network are combined to enhance the base-class recognition and to dig novel classes from insufficient labels data; (c) Ultimate fusion: the semantic segmentation results of the base learners and POP network are reasonably fused. The proposed framework has won first place in the leaderboard of the OpenEarthMap Land Cover Mapping Few-Shot Challenge. Experiments demonstrate the superiority of the framework for automatically updating novel land-cover classes with limited labeled data.
The disconnect between tokenizer creation and model training in language models allows for specific inputs, such as the infamous SolidGoldMagikarp token, to induce unwanted model behaviour. Although such `glitch tokens', tokens present in the tokenizer vocabulary but that are nearly or entirely absent during model training, have been observed across various models, a reliable method to identify and address them has been missing. We present a comprehensive analysis of Large Language Model tokenizers, specifically targeting this issue of detecting under-trained tokens. Through a combination of tokenizer analysis, model weight-based indicators, and prompting techniques, we develop novel and effective methods for automatically detecting these problematic tokens. Our findings demonstrate the prevalence of such tokens across a diverse set of models and provide insights into improving the efficiency and safety of language models.
The work examines the unique nanostructure of carbon nanoparticles deposited from sooting premixed flames with flame temperatures exceeding 2200 K. This flame temperature regime has previously been shown to transition from typical soot formation conditions to a regime whereby the flame-form carbon adopts a nanostructure considerably more ordered than soot. Graphenic carbon deposits observed by High-resolution TEM (HRTEM) are reported here corroborating previous Raman spectroscopy evidence. The use of premixed stretch-stabilized flames enables particle production in the high-temperature regime under a flow field amenable to low-dimensional flame modeling. Although the flame flow configuration is relatively simple, three sample preparation methods are used to assess the representation of true carbon properties as they exist in the flame. HRTEM imaging is carried out on carbon particle samples prepared by rapid-insertion deposition, aerosol dilution probe deposition and carbon particle film deposition. Images from rapid-insertion samples show amorphous particles in the lightly sooting flame and turbostratic particles in the heavy sooting flame. There is trace evidence of graphenic structure in rapid-insertion samples but the most striking particles on the TEM grid are graphite nanocrystals presumably formed by a new artificial crystallization process. HRTEM images of particles collected over time by diluted aerosol deposition and film deposition show clear graphenic structures. Overall, the carbon nanostructure observed by HRTEM is a mixture of amorphous, turbostratic and graphenic carbon lattices depending on the flame condition and sampling method. The current work highlights potential impacts of higher flame temperatures and higher equivalence ratio on deposited flame-formed carbon. Namely, graphenic particle structure is observed in rapid-insertion deposition samples but graphene portions are most abundant in aerosol dilution and carbon particle film deposition samples. This may indicate that graphene structures grow on the deposition surface over time.
Fuel, Energy industries. Energy policy. Fuel trade
The assessment of ecological assets is of great significance for protecting and using ecological resources. Traditional methods of ecological assets assessment, which adopt the planar area as the standard, often ignore the impact of the surface area, resulting in a large difference between the evaluation and the actual result. To fill this gap, this paper conducted research on ecological assets assessment based on surface area. Taking mainland China into consideration, this paper constructed a triangulation network based on 30 m resolution DEM data to simulate the real land surface form and calculate its surface area. Then, land use/cover data from 1995, 2000, 2005, 2010, and 2015 were used to estimate the ecosystem service value (ESV) and analyze its spatial variation. This paper found that: (1) The surface area of mainland China is 1.04 × 107 km2, which is 8.2 × 105 km2 larger than the planar area; (2) A huge difference was found between the total ESV based on the surface and planar area, with an absolute difference of ∼$141.66-$144.14 billion and a relative difference of ∼ 10%. For different ecosystem types, the largest difference was found in the forest ecosystem, followed by the grassland ecosystem, while the wetland ecosystem showed the smallest difference; (3) The high value of absolute difference between the ESV based on the surface and planar area was concentrated in Tibet and Northeast China. The high value of relative difference was mainly distributed in Central and Southern China. On the provincial level, the absolute difference in Tibet ranked in the first place. There was a total of 14 provinces showing a relative difference above 10%; (4) The total ESV based on surface area was basically unchanged, while various ecosystems underwent significant changes. The ESV of wetland increased by nearly 50%, while the ESV of grassland decreased by more than 10%; and (5) Change in the ESV based on surface area showed obvious spatial heterogeneity. High-High cluster was located in Tibet and the Northeast China while the Low-Low cluster was distributed in the North China Plain and Xinjiang. This paper emphasized the importance of the surface area in resource survey and asset estimation and gave more effective suggestions for ecological protection.
Nowadays, the society faces with financial and economic risks which play a special role in the diversity of risks. In the most general form, they affect the amount of available financial resources that can meet the current needs of the population and spread new living standards. The purpose of the study is to analyze the affection of financial and economic risks on social growth and to develop recommendations for creating a mechanism for managing them in the Russian health care system. For this goal achievement, the author has identified several tasks clarifying the approach to determining financial and economic risks in this paradigm, as well as identifying measures to change financing Russian health care. The methodological base: systemic; comparative analysis; synthesis; socio-economic and statistical methods of data analysis. The theoretical and practical significance of the study lies in an integrated system growth for managing financial and economic risks, which unites different economic entities, as well as in determining measures to change the financing mechanisms of the Russian health care system. The specialists can use the results obtained in subsequent work on the problems of risk management at the level of corporate organizations, state agencies, and society.
Sausar Karaf, Aleksey Fedoseev, Mikhail Martynov
et al.
This paper focuses on a novel robotic system MorphoLander representing heterogeneous swarm of drones for exploring rough terrain environments. The morphogenetic leader drone is capable of landing on uneven terrain, traversing it, and maintaining horizontal position to deploy smaller drones for extensive area exploration. After completing their tasks, these drones return and land back on the landing pads of MorphoGear. The reinforcement learning algorithm was developed for a precise landing of drones on the leader robot that either remains static during their mission or relocates to the new position. Several experiments were conducted to evaluate the performance of the developed landing algorithm under both even and uneven terrain conditions. The experiments revealed that the proposed system results in high landing accuracy of 0.5 cm when landing on the leader drone under even terrain conditions and 2.35 cm under uneven terrain conditions. MorphoLander has the potential to significantly enhance the efficiency of the industrial inspections, seismic surveys, and rescue missions in highly cluttered and unstructured environments.
Given an allotment of land divided into parcels, government decision-makers, private developers, and conservation biologists can collaborate to select which parcels to protect, in order to accomplish sustainable ecological goals with various constraints. In this paper, we propose a mixed-integer optimization model that considers the presence of multiple species on these parcels, subject to predator-prey relationships and crowding effects.
During few last years, climate change including global warming which is attributed to human activities and also its long-term adverse effects on the planet's functions have been identified as the most challenging discussion topics which have arisen many concerns and efforts to find the possible solutions. Since the warmth arising from Earth's landscapes affects the world's weather and climate patterns, we decided to study the changes in the Land Surface Temperature (LST) patterns in different seasons through non-linear methods. Here, we particularly want to estimate the non-integer dimension and fractal structure of the land surface temperature. For this study, the (LST) data has been obtained during the daytime by the Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA's Terra satellite. Depending on what time of the year data has been collected, temperatures change in different ranges. Since equatorial regions remain warm, and Antarctica and Greenland remain cold, and also because altitude affects temperature, we selected Riley County in the U.S. state of Kansas, which does not belong to any of this type locations and we are interested to observe the seasonal changes in temperature in this county. The results of the present study show that the Land Surface Temperature (LST) belongs to the class of fractal process with non-integer dimension.
Ryan J. McNeilly, Jennifer A. Schwanekamp, Logan S. Hyder
et al.
Abstract Background Since the introduction of copper based, lead-free frangible (LFF) ammunition to Air Force small arms firing ranges, instructors have reported symptoms including chest tightness, respiratory irritation, and metallic taste. These symptoms have been reported despite measurements determining that instructor exposure does not exceed established occupational exposure limits (OELs). The disconnect between reported symptoms and exposure limits may be due to a limited understanding of LFF firing byproducts and subsequent health effects. A comprehensive characterization of exposure to instructors was completed, including ventilation system evaluation, personal monitoring, symptom tracking, and biomarker analysis, at both a partially enclosed and fully enclosed range. Results Instructors reported symptoms more frequently after M4 rifle classes compared to classes firing only the M9 pistol. Ventilation measurements demonstrated that airflow velocities at the firing line were highly variable and often outside established standards at both ranges. Personal breathing zone air monitoring showed exposure to carbon monoxide, ultrafine particulate, and metals. In general, exposure to instructors was higher at the partially enclosed range compared to the fully enclosed range. Copper measured in the breathing zone of instructors, on rare occasions, approached OELs for copper fume (0.1 mg/m3). Peak carbon monoxide concentrations were 4–5 times higher at the partially enclosed range compared to the enclosed range and occasionally exceeded the ceiling limit (125 ppm). Biological monitoring showed that lung function was maintained in instructors despite respiratory symptoms. However, urinary oxidative stress biomarkers and urinary copper measurements were increased in instructors compared to control groups. Conclusions Consistent with prior work, this study demonstrates that symptoms still occurred despite exposures below OELs. Routine monitoring of symptoms, urinary metals, and oxidative stress biomarkers can help identify instructors who are particularly affected by exposures. These results can assist in guiding protective measures to reduce exposure and protect instructor health. Further, a longitudinal study is needed to determine the long-term health consequences of LFF firing emissions exposure.
Con motivo y ocasión del número cien de Sociología del Trabajo, se revisan las aportaciones e influencias más sobresalientes en el estudio de las mujeres y los trabajos, de sus experiencias como trabajadoras dentro y fuera de los hogares. Un recorrido escogido y discontinuo que reconstruye una amplitud de problemas y desafíos en torno a los cuidados, la conciliación de la vida personal y familiar, los impactos de las transformaciones productivas, el género y su intersección con la clase, la etnicidad y la edad en la esfera laboral, el alcance de las políticas de igualdad, etc. a la luz de la renovación teórica del feminismo, desde su esfuerzos de redefinición de conceptos esenciales como el trabajo que permitieron renovar y crear nuevas formas de abordar e interpretar la compleja realidad del trabajo de las mujeres.