Unsustainable land-use practices in ecologically sensitive regions threaten biodiversity, water resources, and the livelihoods of millions. This paper presents a deep reinforcement learning (RL) framework for optimizing land-use allocation in the Lake Malawi Basin to maximize total ecosystem service value (ESV). Drawing on the benefit transfer methodology of Costanza et al., we assign biome-specific ESV coefficients -- locally anchored to a Malawi wetland valuation -- to nine land-cover classes derived from Sentinel-2 imagery. The RL environment models a 50x50 cell grid at 500m resolution, where a Proximal Policy Optimization (PPO) agent with action masking iteratively transfers land-use pixels between modifiable classes. The reward function combines per-cell ecological value with spatial coherence objectives: contiguity bonuses for ecologically connected land-use patches (forest, cropland, built area etc.) and buffer zone penalties for high-impact development adjacent to water bodies. We evaluate the framework across three scenarios: (i) pure ESV maximization, (ii) ESV with spatial reward shaping, and (iii) a regenerative agriculture policy scenario. Results demonstrate that the agent effectively learns to increase total ESV; that spatial reward shaping successfully steers allocations toward ecologically sound patterns, including homogeneous land-use clustering and slight forest consolidation near water bodies; and that the framework responds meaningfully to policy parameter changes, establishing its utility as a scenario-analysis tool for environmental planning.
Ardaneswari Dyah Pitaloka Citraresmi, Sri Gunani Partiwi, Ratna Sari Dewi
The creative industry has experienced rapid expansion in emerging economies, substantially contributing to employment and economic growth. However, despite this expansion, understanding how multiple workforce-related factors jointly influence creative performance remains limited. This study’s main contribution is to offer an integrated perspective on how workforce resilience, sustainability, and digital readiness collectively shape the creative output of Micro, Small, and Medium Enterprises (MSMEs). We used a mixed-methods design to collect data through surveys and in-depth interviews with owners and employees to capture insights on adaptability, well-being, and digital competencies. Results derived from Partial Least Squares Structural Equation Modeling (PLS-SEM) reveal that resilient and sustainable workforces positively affect creative performance, with digital readiness as a crucial mediator. This study highlights the importance of digital adoption strategies and workforce preparedness in an evolving industry landscape. Importance-Performance Map Analysis further identifies psychosocial risk management, employee well-being, and workplace safety as high-priority yet underdeveloped areas requiring immediate attention. By clearly articulating how an integrated approach to resilience, sustainability, and digital readiness advances theoretical and practical discourse, this work provides actionable insights for policymakers and MSMEs practitioners seeking to enhance innovation and maintain competitiveness in the face of ongoing digital disruption.
Debvrat Varshney, Vibhas Vats, Bhartendu Pandey
et al.
Land cover, both present and future, has a significant effect on several important Earth system processes. For example, impervious surfaces heat up and speed up surface water runoff and reduce groundwater infiltration, with concomitant effects on regional hydrology and flood risk. While regional Earth System models have increasing skill at forecasting hydrologic and atmospheric processes at high resolution in future climate scenarios, our ability to forecast land-use and land-cover change (LULC), a critical input to risk and consequences assessment for these scenarios, has lagged behind. In this paper, we propose a new paradigm exploiting Generative AI (GenAI) for land cover change forecasting by framing LULC forecasting as a data synthesis problem conditioned on historical and auxiliary data-sources. We discuss desirable properties of generative models that fundament our research premise, and demonstrate the feasibility of our methodology through experiments on imperviousness forecasting using historical data covering the entire conterminous United States. Specifically, we train a diffusion model for decadal forecasting of imperviousness and compare its performance to a baseline that assumes no change at all. Evaluation across 12 metropolitan areas for a year held-out during training indicate that for average resolutions $\geq 0.7\times0.7km^2$ our model yields MAE lower than such a baseline. This finding corroborates that such a generative model can capture spatiotemporal patterns from historical data that are significant for projecting future change. Finally, we discuss future research to incorporate auxiliary information on physical properties about the Earth, as well as supporting simulation of different scenarios by means of driver variables.
Christopher Ummerle, Antonio Giganti, Sara Mandelli
et al.
Remote sensing plays a crucial role in monitoring Earth's ecosystems, yet satellite-derived data often suffer from limited spatial resolution, restricting their applicability in atmospheric modeling and climate research. In this work, we propose a deep learning-based Super-Resolution (SR) framework that leverages land cover information to enhance the spatial accuracy of Biogenic Volatile Organic Compounds (BVOCs) emissions, with a particular focus on isoprene. Our approach integrates land cover priors as emission drivers, capturing spatial patterns more effectively than traditional methods. We evaluate the model's performance across various climate conditions and analyze statistical correlations between isoprene emissions and key environmental information such as cropland and tree cover data. Additionally, we assess the generalization capabilities of our SR model by applying it to unseen climate zones and geographical regions. Experimental results demonstrate that incorporating land cover data significantly improves emission SR accuracy, particularly in heterogeneous landscapes. This study contributes to atmospheric chemistry and climate modeling by providing a cost-effective, data-driven approach to refining BVOC emission maps. The proposed method enhances the usability of satellite-based emissions data, supporting applications in air quality forecasting, climate impact assessments, and environmental studies.
Mantas Mazeika, Alice Gatti, Cristina Menghini
et al.
AIs have made rapid progress on research-oriented benchmarks of knowledge and reasoning, but it remains unclear how these gains translate into economic value and automation. To measure this, we introduce the Remote Labor Index (RLI), a broadly multi-sector benchmark comprising real-world, economically valuable projects designed to evaluate end-to-end agent performance in practical settings. AI agents perform near the floor on RLI, with the highest-performing agent achieving an automation rate of 2.5%. These results help ground discussions of AI automation in empirical evidence, setting a common basis for tracking AI impacts and enabling stakeholders to proactively navigate AI-driven labor automation.
Mofijul Hoq Masum, Mohammad Faridul Alam, Md. Shariful Alam
Corporate governance is one of the key factors in corporate performance for the economy. In particular, for a transition economy, which is on the way of developing economies from the least developing economy, the relevant attributes of corporate governance are a vital issue. This study explores the most important board and ownership attributes that affect corporate performance in a transitional economy. A static panel fixed effects model is used to identify the most significant board and ownership attributes that affect corporate performance. It is found that board independence, board size, inclusion of women on the board, foreign shareholding and institutional shareholding significantly influence corporate performance, whereas executive shareholding has an adverse impact on corporate performance in the context of a transition economy. There is a paradoxical finding representing that although the foreign shareholdings significantly influenced the corporate performance in the transitional economy the inclusion of foreign members on the board has no significant impact on corporate performance. In addition, the government shareholding has no significant role in earning profit. These diversified findings implied that not all corporate governance attributes have the same effect on corporate performance. Based on the outcomes of this study, the regulatory body of the transitional economy can design its corporate governance policy.
As Islamic banks grow and evolve, pricing methods for their services have become essential to study and implement. This study highlights the significance of understanding the factors influencing Islamic banking service pricing in Algeria. The study aims to analyze how Islamic banks price their services, with a focus on cost, market, and value strategies. Additionally, it seeks to evaluate and recommend ways to enhance the current practices of banks operating in the national market. Algeria is experiencing rapid growth in Islamic banking, making it an ideal location to study this subject. The country is home to two Islamic banks, Al Baraka Bank and Al Salam Bank. Algeria was selected as a new market to allow the findings to be applicable to similar situations elsewhere. The research utilizes secondary data obtained from available information on Islamic bank service fees, comparing them with those of traditional banks. It also conducts financing simulations in both banks and compares them with the traditional theoretical framework. Data was gathered from various sources, including bank websites, annual reports, and previous studies. The research reveals that Algerian Islamic banks do not prioritize scientific methods in pricing their services. The results suggest that these banks operate within a traditional framework under the oversight of the central bank. The central bank's rules depend on the prices of services conventional banks offer. This shapes how customers perceive these banks as representatives of Islamic banking. Islamic banks can utilize the study's results to develop pricing strategies that are more effective and compliant with Islamic law. Regulators can utilize these findings to formulate enhanced policies to bolster the Islamic banking sector. The results also assist researchers in delving deeper into the realm of Islamic banking service pricing. This study refutes the hypothesis that Algerian Islamic banks have enhanced the efficiency of their service pricing by adopting models in line with Islamic finance principles, such as profit-sharing, while considering market conditions and service value. They should embrace more pragmatic and beneficial pricing strategies that align with Islamic law, cater to customer needs, and enhance their competitiveness and value in the national banking market.
F. Sainsbury-Martinez, C. Walsh, G. J. Cooke
et al.
Interpretation of the ongoing efforts to simulate the atmospheres of potentially-habitable terrestrial exoplanets requires that we understand the underlying dynamics and chemistry of such objects to a much greater degree than 1D or even simple 3D models enable. Here, for the tidally-locked habitable-zone planet TRAPPIST-1e, we explore one effect which can shape the dynamics and chemistry of terrestrial planets: the inclusion of an Earth-like land-ocean distribution with orography. To do this we use the Earth-system model WACCM6/CESM2 to run a pair of TRAPPIST-1e models with N$_2$-O$_2$ atmospheres and with the sub-stellar point fixed over either land or ocean. The presence of orography shapes atmospheric transport, and in the case of Earth-like orography, breaks the symmetry between the northern and southern hemispheres which was previously found in slab ocean models. For example, peak zonal jet speeds in the southern hemisphere are $50\rightarrow100\%$ faster than similar jets in the northern hemisphere. This also affects the meridional circulation, transporting equatorial material towards the south-pole. As a result we also find significant changes in the atmospheric chemistry, including the accumulation of potentially lethal quantities of ozone at both the south pole and the surface. Future studies which investigate the effects of land-mass distribution on the dynamics of exoplanetary atmospheres should pay close attention to both the day-side land-fraction as well as the orography of the land. Simply modelling a flat land-mass will not give a complete picture of its dynamical impact.
Unsupervised domain adaptation (UDA) is a challenging open problem in land cover mapping. Previous studies show encouraging progress in addressing cross-domain distribution shifts on remote sensing benchmarks for land cover mapping. The existing works are mainly built on large neural network architectures, which makes them resource-hungry systems, limiting their practical impact for many real-world applications in resource-constrained environments. Thus, we proposed a simple yet effective framework to search for lightweight neural networks automatically for land cover mapping tasks under domain shifts. This is achieved by integrating Markov random field neural architecture search (MRF-NAS) into a self-training UDA framework to search for efficient and effective networks under a limited computation budget. This is the first attempt to combine NAS with self-training UDA as a single framework for land cover mapping. We also investigate two different pseudo-labelling approaches (confidence-based and energy-based) in self-training scheme. Experimental results on two recent datasets (OpenEarthMap & FLAIR #1) for remote sensing UDA demonstrate a satisfactory performance. With only less than 2M parameters and 30.16 GFLOPs, the best-discovered lightweight network reaches state-of-the-art performance on the regional target domain of OpenEarthMap (59.38% mIoU) and the considered target domain of FLAIR #1 (51.19% mIoU). The code is at https://github.com/cliffbb/UDA-NAS}{https://github.com/cliffbb/UDA-NAS.
This paper explores the potential impacts of large language models (LLMs) on the Chinese labor market. We analyze occupational exposure to LLM capabilities by incorporating human expertise and LLM classifications, following the methodology of Eloundou et al. (2023). The results indicate a positive correlation between occupational exposure and both wage levels and experience premiums at the occupation level. This suggests that higher-paying and experience-intensive jobs may face greater exposure risks from LLM-powered software. We then aggregate occupational exposure at the industry level to obtain industrial exposure scores. Both occupational and industrial exposure scores align with expert assessments. Our empirical analysis also demonstrates a distinct impact of LLMs, which deviates from the routinization hypothesis. We present a stylized theoretical framework to better understand this deviation from previous digital technologies. By incorporating entropy-based information theory into the task-based framework, we propose an AI learning theory that reveals a different pattern of LLM impacts compared to the routinization hypothesis.
Forest fires are one of the most frequently occurring natural hazards, causing substantial economic loss and destruction of forest cover. As the Gangwon-do region in Korea has abundant forest resources and ecological diversity as Korea's largest forest area, spatial data on forest fire susceptibility of the region are urgently required. In this study, a forest fire susceptibility map (FFSM) of Gangwon-do was constructed using Google Earth Engine (GEE) and three machine learning algorithms: Classification and Regression Trees (CART), Random Forest (RF), and Boosted Regression Trees (BRT). The factors related to climate, topography, hydrology, and human activity were constructed. To verify the accuracy, the area under the receiver operating characteristic curve (AUC) was used. The AUC values were 0.846 (BRT), 0.835 (RF), 0.751 (CART). Factor importance analysis was performed to identify the important factors of the occurrence of forest fires in Gangwon-do. The results show that the most important factor in the Gangwon-do region is slope. A slope of approximately 17° (moderately steep) has a considerable impact on the occurrence of forest fires. Human activity and interference are the other important factors that affect forest fires. The established FFSM can support future efforts on forest resource protection and environmental management planning in Gangwon-do.
Svetlana V. Domnina, Elena V. Savoskina, Oksana А. Guzhova
The aim of the study is to identify the role of the innovative component in the system of quality management of highways. An analysis of the proportion of new technologies and materials used to improve the quality of roads, the dynamics of accidents on sections of federal highways, where new technologies and materials were used, as well as the condition of roads was carried out. The data became the basis for the formation of factors that improve road safety. A regression model of the dependence of road safety on the quality of roads (with three variables) was developed. With the help of the model it is possible to predict the impact of changes in the variables on the number of road accidents.
The study developed a system of quality management of roads and highlighted the innovative components, such as controlling effects, management tools, factors, innovative technologies and elements of the integral indicator of innovation. New horizons in the system of quality management of roads – innovation, which forms the need to improve all of its elements (planning, construction management, construction technology, its organization, organization of labor of employees), taking into account the practice of quality management of roads based on the standards of project management of the International Association of Project Management. This will improve the quality of the road object. As a result, there will be an economic effect from the introduction of the quality management system of road works.
Roads are among the institutional support processes for the formation of public welfare and security of the country. However, there is still a problem of low satisfaction with the condition of roads by the population. Despite the active participation of the state in the development of the transport industry, quality management systems in Russian road organizations are not always effective. Most of them focus their attention on solving technical problems and issues of operational management. New horizons in the system of quality management of roads should open the program of innovative development of the state company “Russian Highways”. This program focuses on improving the quality of roads through the introduction of technological initiatives at all stages of road construction. Application of the principle “open innovations” strengthens the need to include the block of innovations in the standardized model of the quality management system (QMS). In this regard, it becomes obvious to develop a road quality management system taking into account the innovation component.
The purpose of this study is to develop a system of quality management of roads, taking into account the innovative component.
Method and methodology of work: in the process of work were used general scientific methods of research: analysis of scientific and educational literature; process approach; comparative analysis; system analysis, synthesis; method of correlation and regression analysis; methods of tabular and graphical representation of data, as well as standards of project management.
Results:
– A correlation and regression model of the dependence of road traffic safety on the quality of roads (on three variables) was developed;
– developed a quality management system at all stages of road construction, taking into account the innovative component.
Scope of the results: the proposed results can be used to improve the quality management system of roads in the Russian Federation.
Working with others is key to professionalism but little attention has been given to how specific actions contribute to collective practices to secure shared ends in work. This essay considers how professionals’ actions connect with one another in distributed (multi-participant) work practices. Recently, Hopwood, Blomberg, Dahlberg and Abrant Dahlgren identified a new way of viewing how professionals in distributed practices coordinate their actions to accomplish shared ends, in terms of phenomena they describe as “connective enactments” and “collective accomplishments”. In this essay, we explore the possibility that these phenomena have far more general application than the cases studied by Hopwood et al. We use the theory of practice architectures to outline this more general account and test its viability in by examining a case of culinary services practices. This more generalised account may offer new ways to understand features of distributed work practices and enhance professional practice and learning.
We describe chromatic localisations of genuine L-spectra of discrete rings and deduce that the purity property of $K(1)$-local $K$-theory of rings established by Bhatt-Clausen-Mathew also holds in Grothendieck-Witt theory. In addition, we collect some results on higher chromatic localisations of various L-theory spectra and their consequences for Grothendieck-Witt theory.