This study examines the impact of TFP shock, world interest rate shock, domestic deposit rate shock, and fiscal policy shock on China's macroeconomic variables—such as capital credit scale and output growth rate—under the framework of endogenous and exogenous capital controls. Quantitative analysis reveals that an increase in the world interest rates has a negative impact on the domestic credit market, leading to a simultaneous decline in both household savings and capital inflows. Endogenous capital control can mitigate the adverse effect, playing a macroprudential role, whereas exogenous capital controls tend to amplify the negative shock. Expansionary fiscal policy through tax cutting proves effective in stimulating output growth rate. When facing economic downturns, priority should be given to implementing proactive fiscal measures, complemented by appropriate monetary easing with endogenous capital controls, to achieve output growth with less fluctuations.
Economic growth, development, planning, Economic history and conditions
Abstract Background Recent studies emphasize the significance of copper dyshomeostasis in neurodegenerative diseases, such as Alzheimer’s and Parkinson’s, thereby highlighting the role of copper in neurotoxicity. Cuproptosis, a novel mechanism of copper-dependent cell death, remains underexplored, particularly concerning environmental pollutants like polystyrene nanoplastics (PS-NPs). While PS-NPs are recognized for inducing neurotoxicity through various forms of cell death, including apoptosis and ferroptosis, their potential to trigger neuronal cuproptosis has not yet been investigated. This study aims to determine whether exposure to PS-NPs induces neurotoxicity via cuproptosis and to explore the preliminary molecular mechanisms involved, thereby addressing this significant knowledge gap. Methods Seven-week-old male C57BL/6 mice were exposed to PS-NPs at dose of 12.5 mg/kg, and were co-treated with the antioxidant N-acetylcysteine (NAC). Complementary in vitro experiments were conducted using SH-SY5Y neuronal cells exposed to PS-NPs at a concentration of 0.75 mg/mL, with interventions that included the copper chelator tetrathiomolybdate (TTM), NAC, and the MAPK inhibitor PD98059. Results Exposure to PS-NPs significantly increased cerebral copper accumulation (P < 0.05) and induced cuproptosis, characterized by lipid-acylated DLAT oligomerization, dysregulation of cuproptosis regulators (FDX1, LIAS, HSP70), and mitochondrial damage. In murine models, PS-NPs elicited neurotoxicity, as evidenced by neuronal loss, decreased Nissl body density, impaired synaptic plasticity, and suppressed oxidative stress markers (GSH, SOD, Nrf2), alongside activation of the ERK-MAPK pathway, ultimately resulting in deficits in learning and memory. Treatment with NAC alleviated these adverse effects. In SH-SY5Y cells, exposure to PS-NPs resulted in reduced cell viability (p < 0.01), an effect that was mitigated by TTM. Furthermore, NAC and PD98059 were found to reverse elevated copper levels, cuproptosis markers, and mitochondrial anomalies (p < 0.05). Conclusion This study presents preliminary evidence indicating that PS-NPs may induce neuronal cuproptosis, potentially through the oxidative stress-mediated activation of the ERK-MAPK pathway, which contributes to cognitive dysfunction in mice. These findings provide insights into the potential mechanisms underlying PS-NPs neurotoxicity and highlight possible therapeutic targets, such as copper chelation or MAPK inhibition, for mitigating the neurological risks associated with nanoplastic exposure, pending further validation in human-relevant models.
This thesis studies the effectiveness of Long Short Term Memory model in forecasting future Job Openings and Labor Turnover Survey data in the United States. Drawing on multiple economic indicators from various sources, the data are fed directly into LSTM model to predict JOLT job openings in subsequent periods. The performance of the LSTM model is compared with conventional autoregressive approaches, including ARIMA, SARIMA, and Holt-Winters. Findings suggest that the LSTM model outperforms these traditional models in predicting JOLT job openings, as it not only captures the dependent variables trends but also harmonized with key economic factors. These results highlight the potential of deep learning techniques in capturing complex temporal dependencies in economic data, offering valuable insights for policymakers and stakeholders in developing data-driven labor market strategies
Sabab Aosaf, Muhammad Ali Nayeem, Afsana Haque
et al.
Urban land-use allocation represents a complex multi-objective optimization problem critical for sustainable urban development policy. This paper presents novel computational intelligence approaches for optimizing land-use allocation in mixed-use areas, addressing inherent trade-offs between land-use compatibility and economic objectives. We develop multiple optimization algorithms, including custom variants integrating differential evolution with multi-objective genetic algorithms. Key contributions include: (1) CR+DES algorithm leveraging scaled difference vectors for enhanced exploration, (2) systematic constraint relaxation strategy improving solution quality while maintaining feasibility, and (3) statistical validation using Kruskal-Wallis tests with compact letter displays. Applied to a real-world case study with 1,290 plots, CR+DES achieves 3.16\% improvement in land-use compatibility compared to state-of-the-art methods, while MSBX+MO excels in price optimization with 3.3\% improvement. Statistical analysis confirms algorithms incorporating difference vectors significantly outperform traditional approaches across multiple metrics. The constraint relaxation technique enables broader solution space exploration while maintaining practical constraints. These findings provide urban planners and policymakers with evidence-based computational tools for balancing competing objectives in land-use allocation, supporting more effective urban development policies in rapidly urbanizing regions.
This paper examines gender stratification in the Latin American data annotation gig economy, with a particular focus on the "triple burden" shouldered by women: unpaid care responsibilities, economic precarity, and the volatility of platform-mediated labor. Data annotation, once lauded as a democratizing force within the global gig economy, has evolved into a segmented labor market characterized by low wages, limited protections, and unequal access to higher-skilled annotation tasks. Drawing on an exploratory survey of 30 Latin American data annotators, supplemented by qualitative accounts and comparative secondary literature, this study situates female annotators within broader debates in labor economics, including segmentation theory, monopsony power in platform labor, and the reserve army of labor. Findings indicate that women are disproportionately drawn into annotation due to caregiving obligations and political-economic instability in countries such as Venezuela, Colombia, and Peru. Respondents highlight low pay, irregular access to tasks, and lack of benefits as central challenges, while also expressing ambivalence about whether their work is valued relative to male counterparts. By framing annotation as both a gendered survival strategy and a critical input in the global artificial intelligence supply chain, this paper argues for the recognition of annotation as skilled labor and for regulatory interventions that address platform accountability, wage suppression, and regional inequalities.
This study investigates the impact of artificial intelligence (AI) adoption on job loss rates using the Global AI Content Impact Dataset (2020--2025). The panel comprises 200 industry-country-year observations across Australia, China, France, Japan, and the United Kingdom in ten industries. A three-stage ordinary least squares (OLS) framework is applied. First, a full-sample regression finds no significant linear association between AI adoption rate and job loss rate ($β\approx -0.0026$, $p = 0.949$). Second, industry-specific regressions identify the marketing and retail sectors as closest to significance. Third, interaction-term models quantify marginal effects in those two sectors, revealing a significant retail interaction effect ($-0.138$, $p < 0.05$), showing that higher AI adoption is linked to lower job loss in retail. These findings extend empirical evidence on AI's labor market impact, emphasize AI's productivity-enhancing role in retail, and support targeted policy measures such as intelligent replenishment systems and cashierless checkout implementations.
The rapid advancement of Large Language Models (LLMs) has generated considerable speculation regarding their transformative potential for labor markets. However, existing approaches to measuring AI exposure in the workforce predominantly rely on concurrent market conditions, offering limited predictive capacity for anticipating future disruptions. This paper presents a predictive study examining whether online discussions about LLMs can function as early indicators of labor market shifts. We employ four distinct analytical approaches to identify the domains and timeframes in which public discourse serves as a leading signal for employment changes, thereby demonstrating its predictive validity for labor market dynamics. Drawing on a comprehensive dataset that integrates the REALM corpus of LLM discussions, LinkedIn job postings, Indeed employment indices, and over 4 million LinkedIn user profiles, we analyze the relationship between discussion intensity across news media and Reddit forums and subsequent variations in job posting volumes, occupational net change ratios, job tenure patterns, unemployment duration, and transitions to GenAI-related roles across thirteen occupational categories. Our findings reveal that discussion intensity predicts employment changes 1-7 months in advance across multiple indicators, including job postings, net hiring rates, tenure patterns, and unemployment duration. These findings suggest that monitoring online discourse can provide actionable intelligence for workers making reskilling decisions and organizations anticipating skill requirements, offering a real-time complement to traditional labor statistics in navigating technological disruption.
Claire Marie Guimond, Tilman Spohn, Svetlana Berdyugina
et al.
Water and land surfaces on a planet interact with gases in the atmosphere and with radiation from the star. These interactions define the environments that prevail on the planet, some of which may be more amenable to prebiotic chemistry, some to the evolution of more complex life. This review article covers (i) the physical conditions that determine the ratio of land to sea on a rocky planet, (ii) how this ratio would affect climatic and biologic processes, and (iii) whether future astronomical observations might constrain this ratio on exoplanets. Water can be delivered in multiple ways to a growing rocky planet -- and although we may not agree on the contribution of different mechanism(s) to Earth's bulk water, hydrated building blocks and nebular ingassing could at least in principle supply several oceans' worth. The water that planets sequester over eons in their solid deep mantles is limited by the water concentration at water saturation of nominally anhydrous mantle minerals, likely less than 2000 ppm of the planet mass. Water is cycled between mantle and surface through outgassing and ingassing mechanisms that, while tightly linked to tectonics, do not necessarily require plate tectonics in every case. The actual water/land ratio at a given time emerges from the balance between the volume of surface water on the one hand, and on the other hand, the shape of the planet (its ocean basin volume) that is carved out by dynamic topography, the petrologic evolution of continents, impact cratering, and other surface-sculpting processes. By leveraging the contrast in reflectance properties of water and land surfaces, spatially resolved 2D maps of Earth-as-an-exoplanet have been retrieved from models using real Earth observations, demonstrating that water/land ratios of rocky exoplanets may be determined from data delivered by large-aperture, high-contrast imaging telescopes in the future.
Land-cover underpins ecosystem services, hydrologic regulation, disaster-risk reduction, and evidence-based land planning; timely, accurate land-cover maps are therefore critical for environmental stewardship. Remote sensing-based land-cover classification offers a scalable route to such maps but is hindered by scarce and imbalanced annotations and by geometric distortions in high-resolution scenes. We propose LC4-DViT (Land-cover Creation for Land-cover Classification with Deformable Vision Transformer), a framework that combines generative data creation with a deformation-aware Vision Transformer. A text-guided diffusion pipeline uses GPT-4o-generated scene descriptions and super-resolved exemplars to synthesize class-balanced, high-fidelity training images, while DViT couples a DCNv4 deformable convolutional backbone with a Vision Transformer encoder to jointly capture fine-scale geometry and global context. On eight classes from the Aerial Image Dataset (AID)-Beach, Bridge, Desert, Forest, Mountain, Pond, Port, and River-DViT achieves 0.9572 overall accuracy, 0.9576 macro F1-score, and 0.9510 Cohen' s Kappa, improving over a vanilla ViT baseline (0.9274 OA, 0.9300 macro F1, 0.9169 Kappa) and outperforming ResNet50, MobileNetV2, and FlashInternImage. Cross-dataset experiments on a three-class SIRI-WHU subset (Harbor, Pond, River) yield 0.9333 overall accuracy, 0.9316 macro F1, and 0.8989 Kappa, indicating good transferability. An LLM-based judge using GPT-4o to score Grad-CAM heatmaps further shows that DViT' s attention aligns best with hydrologically meaningful structures. These results suggest that description-driven generative augmentation combined with deformation-aware transformers is a promising approach for high-resolution land-cover mapping.
Aiming at the problems of reduced winter wheat yield and aggravation of nitrogen leaching pollution caused by the waterlogging in the Middle-Lower Yangtze Plain, China, a two-year field experiment with three farmland water levels (W40, W60, W80) and three nitrogen application rates (N150, N225, N300) as well as a non-waterlogged treatment (CK) was carried out, to investigate the coupling effects of farmland water level and nitrogen application rate on the plant growth, grain yield, crop water productivity (WPC) and nitrogen load with waterlogging conditions. Three man-made waterlogging events were applied at winter wheat jointing-booting stage, heading-flowering stage and grain filling stage, respectively. The results indicated that with the farmland water level decreased from −40 cm to −60 cm and the nitrogen application rate increased from 150 kg∙ha−1 to 225 kg∙ha−1, the plant height, aboveground dry matter, leaf area index, spike length, grain yield, effective panicles, grain number per ear, 1000-grain weight and WPC in the waterlogging field increased significantly. However, since the nitrogen application rate exceeded 225 kg∙ha−1 and farmland water level lowered more than −60 cm, the favorable effects of nitrogen application rate and farmland water level for winter wheat growth and production reduced. Additionally, both the nitrogen load and partial factor productivity of nitrogen (PFPN) increased with the decline of farmland water level, while the nitrogen load increased and the PFPN decreased with the increasing nitrogen application rate. The raise of nitrogen rate from 150 kg∙ha−1 to 225 kg∙ha−1 was beneficial to plant growth, however, the increase of nitrogen application resulted in the decrease of PFPN and increase of drainage nitrogen loads. Compared with the water farmland water level of −40 cm and nitrogen application rate of 150 kg∙ha−1, the increase of nitrogen application rate and the decrease of farmland water level in the range of 50%-100% resulted in yield raise by 5.78%-32.29% approximately and the increase of nitrogen load by 36.20%-178.44% approximately. The comprehensive evaluation with TOPSIS-Entropy method for plant growth, grain yield, WPC, PFPN and nitrogen loads suggested that, the appropriate nitrogen application rate for winter wheat in the waterlogging areas of Middle-Lower Yangtze Plain in China was 225 kg∙ha−1, and the proper farmland water level was lowering to −80 cm in wet year and −60 cm in dry year within 3 days after waterlogging.
Rayees Ahmed, Taha Shamim, Joshal Kumar Bansal
et al.
Climate change poses significant challenges to the Himalayas, a region characterised by its fragile ecosystems and vulnerable communities dependent on environmental resources. Accurate climate data are crucial for understanding regional climatic variations and assessing climate change impacts, particularly in areas with limited observational networks. This study represents a pioneering effort in evaluating climatic fluctuations in the Jhelum basin, located in the North Western Himalayas, by utilising a diverse range of gridded meteorological datasets (APHRODITE, CHIRPS, CRU, and IMDAA) alongside observed climate data from the Indian Meteorological Department. The primary goal is to identify the most effective gridded climate data product for regions with limited data and to explore the potential of combining gridded data sets with observed data to understand climatic variability. Findings indicate a consistent upward trend in temperature across all datasets, with varying rates of increase. CRU records a rise of 1 °C in Tmax and 1.6 °C in Tmin, while APHRODITE shows a Tmean increase of approximately 1 °C. IMDAA reports increases in Tmax and Tmin. Observed mean annual Tmax and Tmin show net increases of 1 °C and 0.6 °C, respectively. Regarding precipitation, all datasets except IMDAA exhibit an increasing trend, contrary to observed data, which decreases from 1266 mm to 1068 mm over 40 years. CHIRPS, CRU, and APHRODITE display increasing trends, while IMDAA aligns closely with observed data but tends to overestimate precipitation by about 30%. Our research identifies IMDAA as the most suitable gridded climate data for the Jhelum basin in the North-western Himalayas. Despite some discrepancies in precipitation trends, IMDAA closely aligns with observed data, providing valuable insights for scholars and policymakers navigating climate data uncertainties in complex environments. Our findings contribute to informed decision-making and effective climate change mitigation strategies in the region.
Kukuh Setyo Priyanto, Prasadja Ricardianto, Aang Gunawan
et al.
This research aimed to study the opinion and perspectives of Commuter Line passengers in Indonesia by using 18 attributes of service quality. There still needed to be more understanding about which service attributes were less satisfying and which were more pleasing to the Commuter Line passengers in the area of Jakarta and its surroundings. This research used factor analysis and Principal Component Analysis to select among the 18 Commuter Line service quality variables with the Varimax and Ordered Logit model rotation method. The number of samples used was 384 respondents from Commuter Line passengers in Jakarta and its surroundings. The result of factor analysis stated that the 18 attributes of service quality with three factors were the main attributes of service quality being used, namely the factor of station facilities and passenger behavior, the factor of ticket and security system, and they had reasonably strong correlations. The key finding of this research was that some service quality attributes, such as the crowd or density of trains, station stair facility, station lift facility, station seat facility, and shelter, were perceived as the attributes of service that were less satisfying. This research provided valuable insights into important factors affecting the opinion and perspective of Commuter Line passengers in Jakarta and its surroundings.
Social Sciences, Management. Industrial management
Individual participants in human society collectively exhibit aggregation behavior. In this study, we present a simple microscopic model of labor force migration based on the active Brownian particles framework. In particular, agent-based simulations show that the model produces clusters of agents from a random initial distribution. Furthermore, two empirical regularities called Zipf's and Okun's laws were observed. To reveal the mechanism underlying the reproduced aggregation phenomena, we use our microscopic model to derive an extended Keller--Segel system, which is a classic model describing the aggregation behavior of biological organisms called taxis. The obtained macroscopic system indicates that the concentration of the workforce in the real world can be explained through a new type of taxis central to human behavior, highlighting the relevance of urbanization to blow-up phenomena in the derived PDE system. We then characterize the transition between the aggregation and diffusion regimes both analytically and computationally. The predicted long-term dynamics of urbanization -- originating in the asymmetric natures of employed and unemployed agents -- are compared with global empirical data, particularly in the realms of labor statistics and urban indicators.
ABSTRACTUrban land change and transportation infrastructure development often interact and collectively lead to significant socioeconomic and biophysical impacts. Here, we performed a systematic review to identify how urban land change modeling studies account for transportation infrastructure. We found that less than one-fifth of urban land modeling studies explicitly incorporated a transportation component. Of these, most incorporated transportation based on relatively simple distance-based variables. Despite the recognized influence of transportation on urban growth, only a few studies attempted to capture the dynamic interaction between the two. We present a conceptual modeling framework and argue for a renewed focus on capturing the dynamic interaction between urban land change and transportation development in modeling studies. Such focus is essential to develop a well-informed understanding of the implications for urban forms and landscapes of the wide-ranging changes in transportation systems that accompany rapid urbanization around the world.
António Cardoso, Augustė Paulauskaitė, Hajar Hachki
et al.
ABSTRACT: In this study, Airbnb’s brand personality is explored in relation to its effects on consumer involvement and institutional trust. The objective of this paper is to fill a gap in marketing research by building up a solid understanding of the relationship between those constructs in the context of hospitality brands. The results of the study revealed that Airbnb’s brand personality is mostly associated with excitement, sincerity, and competence. Brand personality was shown to have effects on both consumer involvement and institutional trust, with competence having the biggest impact on consumer involvement, and institutional trust being under the most significant influence of sincerity. The results of this study present meaningful implications not only for the academic community, but also for marketing specialists focusing on branding strategies in the innovative context of sharing economy businesses.
J. Brandon Carter, Christopher R. Browning, Bethany Boettner
et al.
Collective efficacy -- the capacity of communities to exert social control toward the realization of their shared goals -- is a foundational concept in the urban sociology and neighborhood effects literature. Traditionally, empirical studies of collective efficacy use large sample surveys to estimate collective efficacy of different neighborhoods within an urban setting. Such studies have demonstrated an association between collective efficacy and local variation in community violence, educational achievement, and health. Unlike traditional collective efficacy measurement strategies, the Adolescent Health and Development in Context (AHDC) Study implemented a new approach, obtaining spatially-referenced, place-based ratings of collective efficacy from a representative sample of individuals residing in Columbus, OH. In this paper, we introduce a novel nonstationary spatial model for interpolation of the AHDC collective efficacy ratings across the study area which leverages administrative data on land use. Our constructive model specification strategy involves dimension expansion of a latent spatial process and the use of a filter defined by the land-use partition of the study region to connect the latent multivariate spatial process to the observed ordinal ratings of collective efficacy. Careful consideration is given to the issues of parameter identifiability, computational efficiency of an MCMC algorithm for model fitting, and fine-scale spatial prediction of collective efficacy.
Novice pilots find it difficult to operate and land unmanned aerial vehicles (UAVs), due to the complex UAV dynamics, challenges in depth perception, lack of expertise with the control interface and additional disturbances from the ground effect. Therefore we propose a shared autonomy approach to assist pilots in safely landing a UAV under conditions where depth perception is difficult and safe landing zones are limited. Our approach comprises of two modules: a perception module that encodes information onto a compressed latent representation using two RGB-D cameras and a policy module that is trained with the reinforcement learning algorithm TD3 to discern the pilot's intent and to provide control inputs that augment the user's input to safely land the UAV. The policy module is trained in simulation using a population of simulated users. Simulated users are sampled from a parametric model with four parameters, which model a pilot's tendency to conform to the assistant, proficiency, aggressiveness and speed. We conduct a user study (n = 28) where human participants were tasked with landing a physical UAV on one of several platforms under challenging viewing conditions. The assistant, trained with only simulated user data, improved task success rate from 51.4% to 98.2% despite being unaware of the human participants' goal or the structure of the environment a priori. With the proposed assistant, regardless of prior piloting experience, participants performed with a proficiency greater than the most experienced unassisted participants.
The paper investigates gender differences in entrepreneurship by exploiting a large-scale land lottery in Oklahoma at the turn of the 20$^{\text{th}}$ century. Lottery winners claimed land in the order in which their names were drawn, so the draw number is an approximate rank ordering of lottery wealth. This mechanism allows for the estimation of a dose-response function, which relates each draw number to the expected outcome under each draw. I estimate dose-response functions on a linked dataset of lottery winners and land patent records, and find the probability of purchasing land from the government to be decreasing as a function of lottery wealth, which is evidence for the presence of liquidity constraints. I find female winners were more effective in leveraging lottery wealth to purchase additional land, as evidenced by significantly higher median dose-responses compared to those of male winners. For a sample of winners linked to the 1910 Census, I find that male winners have higher median dose-responses compared to female winners in terms of farm or home ownership. These results suggest that liquidity constraints may have been more binding for female entrepreneurs in the market economy.