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$1024 pixels, covering 35 regions from Japan, France, and the USA, with partially manually annotated and fully pseudo 8-class land cover labels at a ground sampling distance of 0.15--0.5 m. We evaluated the performance of state-of-the-art methods for semantic segmentation and present challenging problem settings suitable for further technical development. The dataset also serves the official dataset for IEEE GRSS Data Fusion Contest Track I. The dataset has been made publicly available at https://zenodo.org/records/14622048.
K. L. van der Enden, R. Kirschner, M. Krumtünger
et al.
A future quantum internet brings promising applications related to security, privacy and enabling distributed quantum computing. Integration of these concepts into the future trends of the automotive sector is of considerable interest, as it enables both the development of practical quantum internet use cases and the adoption of innovative technologies in the automotive sector. In this work we analyze cross-platform megatrends in both the quantum internet and the automotive industry, identifying mutually beneficial regions of interest. In the short-term ($<10$ years) hardware miniaturization and automation of quantum internet technology provides a synergy interface between the two domains. For the long-term ($\geq10$ years) we develop a comprehensive list of use cases for the quantum internet within the automotive sector. We find considerable relevancy of augmenting autonomous driving, vehicle ad hoc networks and sensor fusion with blind quantum computing, anonymous transmission and quantum cryptographic tools. These results can be used to target future research, engineering and venture developments for both domains. Furthermore, our approach can be applied to other industries, enabling a structured methodology for identifying and developing feasible use cases for the quantum internet in diverse domains.
This study investigates the labor market consequences of AI by analyzing near real-time changes in employment status and work hours across occupations in relation to advances in AI capabilities. We construct a dynamic Occupational AI Exposure Score based on a task-level assessment using state-of-the-art AI models, including ChatGPT 4o and Anthropic Claude 3.5 Sonnet. We introduce a five-stage framework that evaluates how AI's capability to perform tasks in occupations changes as technology advances from traditional machine learning to agentic AI. The Occupational AI Exposure Scores are then linked to the US Current Population Survey, allowing for near real-time analysis of employment, unemployment, work hours, and full-time status. We conduct a first-differenced analysis comparing the period from October 2022 to March 2023 with the period from October 2024 to March 2025. Higher exposure to AI is associated with reduced employment, higher unemployment rates, and shorter work hours. We also observe some evidence of increased secondary job holding and a decrease in full-time employment among certain demographics. These associations are more pronounced among older and younger workers, men, and college-educated individuals. College-educated workers tend to experience smaller declines in employment but are more likely to see changes in work intensity and job structure. In addition, occupations that rely heavily on complex reasoning and problem-solving tend to experience larger declines in full-time work and overall employment in association with rising AI exposure. In contrast, those involving manual physical tasks appear less affected. Overall, the results suggest that AI-driven shifts in labor are occurring along both the extensive margin (unemployment) and the intensive margin (work hours), with varying effects across occupational task content and demographics.
Varun Nagaraj Rao, Samantha Dalal, Andrew Schwartz
et al.
What happens when a rideshare driver is suddenly locked out of the platform connecting them to riders, wages, and daily work? Deactivation-the abrupt removal of gig workers' platform access-typically occurs through arbitrary AI and algorithmic decisions with little explanation or recourse. This represents one of the most severe forms of algorithmic control and often devastates workers' financial stability. Recent U.S. state policies now mandate appeals processes and recovering compensation during the period of wrongful deactivation based on past earnings. Yet, labor organizers still lack effective tools to support these complex, error-prone workflows. We designed FareShare, a computational tool automating lost wage estimation for deactivated drivers, through a 6 month partnership with the State of Washington's largest rideshare labor union. Over the following 3 months, our field deployment of FareShare registered 178 account signups. We observed that the tool could reduce lost wage calculation time by over 95%, eliminate manual data entry errors, and enable legal teams to generate arbitration-ready reports more efficiently. Beyond these gains, the deployment also surfaced important socio-technical challenges around trust, consent, and tool adoption in high-stakes labor contexts.
<p>The research and development department (R&D) is a necessary and vital organ for all organizations that intend to be active in domestic and foreign markets, and it is of undeniable importance for domestic and international competition as one of the most important factors for achieving the goals of organizations and industries in economic progress and access to commercial markets. Hence, in the present study, the intelligent R&D management model was evaluated with an agility approach, and to this end, the data was collected from 270 participants using a questionnaire, including managers, professors, senior experts, and experts of petrochemical companies. Then, the fitted data, obtained from the structural equation model, was analyzed with the help of partial least squares method using PLS statistical software. The results of the path coefficients showed that there is a significant relationship between the research variables and the evaluation indices of the model fit. Also, it was found that the relevant model has a good fit. Therefore, it can be stated that intelligent research and development management with an agility approach has improved processes, innovation, optimized communication, and also has financial and competitive consequences for the organization.</p>
The principal function of skin is to form an effective barrier between the human body and its environment. Impaired barrier function represents a precondition for the development of skin diseases such as atopic dermatitis (AD), which is the most common inflammatory skin disease characterized by skin barrier dysfunction. AD significantly affects patients’ quality of life, thus, there is a growing interest in the development of novel delivery systems that would improve therapeutic outcomes. Herein, eight novel lyotropic liquid crystals (LCCs) were investigated for the first time in a double-blind, interventional, before-after, single-group trial with healthy adult subjects and a twice-daily application regimen. LCCs consisted of constituents with skin regenerative properties and exhibited lamellar micro-structure, especially suitable for dermal application. The short- and long-term effects of LCCs on TEWL, SC hydration, erythema index, melanin index, and tolerability were determined and compared with baseline. LCCs with the highest oil content and lecithin/Tween 80 mixture stood out by providing a remarkable 2-fold reduction in TEWL values and showing the most distinctive decrease in skin erythema levels in both the short- and long-term exposure. Therefore, they exhibit great potential for clinical use as novel delivery systems for AD treatment, capable of repairing skin barrier function.
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.
Flame spray pyrolysis (FSP) is a technique for the synthesis of metal oxide nanoparticles by combusting precursor solutions in a spray flame. The combustion of certain precursor solutions is known to lead to severe droplet disruptions (μ-explosions) in the spray flame that are linked to the synthesis of homogeneous and phase-pure nanoparticles. In this work, a broad spectrum of suitable subsonic operating conditions for the synthesis of iron oxide nanoparticles by FSP is investigated to understand the influence of the jet Reynolds number and turbulence on the onset of μ-explosions and droplet dynamics in spray flames. In order to enable a coherent comparison between differently operated spray flames using an iron(III) nitrate nonahydrate solution, the gas-to-liquid mass ratio and, hence, the oxygen/fuel ratio have been kept constant in order to identify the influence of flow conditions on the droplet dynamics. From the analysis of the droplet sizes in the spray and in the spray flame, it is found that in all combusting sprays, the droplet sizes convert from unimodal (after atomization) to bimodal droplet size distribution (DSD) due to the presence of μ-explosions. The occurrence and evolution of the bimodal DSD reveal that high jet Reynolds numbers result in narrower DSD and in a sharper separation of both DSD probability peaks (modal values). A straightforward 1-step kinematic model is presented to describe the conversion of unimodal to bimodal DSD considering the evaporation of droplets as well as the disruption of droplets to mimic the effect of μ-explosions. The temporal evolution of droplets in FSP is investigated by spatially resolved velocity data that reveal the formation of a temporal self-similarity. The resulting iron oxide nanoparticle size decreases with increasing jet Reynolds number. The turbulent mixing and residence times in the flame, primarily set by the jet Reynolds number, are identified as key design parameters for FSP.
Fuel, Energy industries. Energy policy. Fuel trade
The cultural rituals of any society are the birth certificates of that society’s culture, history, religion, and ethics. These rites and rituals are an important factor in fostering cohesion and unity among the people of a society who gather together in various ceremonies. Mourning ceremonies and rituals vary in different provinces, cities, and villages across the country, particularly during the first decade of Muharram. Iran is considered one of the inheritors of world culture due to its diverse and rich cultural rituals. The purpose of this research is to do a comparative and documented study of Muharram rituals in these two provinces so that by revealing the manifestations of cultural rituals and their differences and similarities, we can reach the deep and rich layers of the culture of these two provinces. This research intends to investigate the differences and similarities between the rituals of the first decade of Muharram and their cultural differences in the two provinces of East Azarbaijan and Khuzestan, using a descriptive-comparative study method. These two provinces have a deep cultural and religious background and an ancient civilization. The results of this research show that despite the great similarity in the performance of Muharram rituals in the two provinces in question, the diversity of mourning rituals is also visible in these two provinces. The varieties and differences are closely related to cultural, climatic, and linguistic differences in these two provinces. These two provinces’ people have chosen to mourn in accordance with their religion, vegetation, culture, and language. The results of this research show that, in addition to these differences, the common points of this cultural ritual in the two provinces are the love of the Prophet’s family and devotion to Ashura culture.
Economic growth, development, planning, Ethnology. Social and cultural anthropology
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.
The use of steel is essential in many industries, including infrastructure, transportation, and modern architecture. Predicting power consumption in the steel industry is crucial to meet the rising demand for steel and promoting city development. However, predicting energy consumption in the steel industry is challenging due to several factors, such as the type of steel produced, the manufacturing process, and the efficiency of the manufacturing facility. This research aims to contribute by creating a predictive model that estimates power consumption in the steel industry. The unique approach combines linear regression to predict a continuous variable related to power consumption and the KNN clustering method to identify the demanding load type. This study's novelty lies in the development of a model that accurately predicts energy consumption in the steel industry, leading to more sustainable and efficient practices. This research contributes to enabling industries to anticipate and optimize their energy consumption, leading to more sustainable practices and economic development.
Ben-Hur Francisco Cardoso, Eva Yamila da Silva Catela, Guilherme Viegas
et al.
Research on productive structures has shown that economic complexity conditions economic growth. However, little is known about which type of complexity, e.g., export or industrial complexity, matters more for regional economic growth in a large emerging country like Brazil. Brazil exports natural resources and agricultural goods, but a large share of the employment derives from services, non-tradables, and within-country manufacturing trade. Here, we use a large dataset on Brazil's formal labor market, including approximately 100 million workers and 581 industries, to reveal the patterns of export complexity, industrial complexity, and economic growth of 558 micro-regions between 2003 and 2019. Our results show that export complexity is more evenly spread than industrial complexity. Only a few -- mainly developed urban places -- have comparative advantages in sophisticated services. Regressions show that a region's industrial complexity is a significant predictor for 3-year growth prospects, but export complexity is not. Moreover, economic complexity in neighboring regions is significantly associated with economic growth. The results show export complexity does not appropriately depict Brazil's knowledge base and growth opportunities. Instead, promoting the sophistication of the heterogeneous regional industrial structures and development spillovers is a key to growth.
An important problem for pharmaceutical and toxicological laboratories is quick and accurate determination of mixtures of active pharmaceutical ingredients in various samples, including blood samples. This article shows the simultaneous quantitative analysis and identification of two common pharmaceutical ingredients (paracetamol and ibuprofen) on a SCION LC 6000 liquid chromatograph with a diode array detector. As a result of the experiment, it was shown that with the help of a diode-array detector with multi-wavelength function, it is possible to determine both substances in one injection. Excellent linearity and repeatability of the SCION HPLC-DAD was demonstrated with low RSD values and high R2 values.
U ovom radu je obrađena jedna relativno nova metoda za procenu mesta nastanka kvara na vodu, koja se bazira na primeni neuralnih mreža. Kako bi se izvršila provera efikasnosti ove metode, u programskom paketu MATLAB/Simulink je formiran model jednostavnog EES-a, koji čine dve visokonaponske mreže (ekvivalentirane Tevenenovim generatorom) povezane 100 km dugačkim dalekovodom. Pomoću ovog programskog paketa su simulirani različiti tipovi kvarova na različitim pozicijama na vodu. Vrednosti faznih struja i međufaznih napona koje generišu ovi kvarovi predstavljaju podatke koji se dovode na ulaz neuralne mreže. Na osnovu ovih ulaznih veličina i izlazne vrednosti koja je jednaka stvarnom rastojanju mesta kvara od početka voda (odnosno pozicije lokatora kvara), moguće je izvršiti obučavanje neuralne mreže. Obučavanje neuralne mreže je izvršeno pomoću softverskog alata nntool (eng. Neural Network Toolbox), koji je sastavni deo programskog paketa MATLAB.
Energy industries. Energy policy. Fuel trade, Economics as a science