Hasil untuk "Industries. Land use. Labor"

Zhi Wang, Wei Ma, Yunfei Lu et al.

Biochar has been widely applied as an efficiency soil additive to modify the quality of cultivated field. However, the effects of long-term biochar addition on spatial and temporal dynamics of soil compaction, and the changes in soil moisture condition and plant root growth remain unclear. Hence, an eight-year (2015/16–2023/24) consecutive field experiment on wheat was conducted in the subtropical humid region of east China, using three treatments: no N fertilizer (PK), chemical fertilizer (NPK), NPK plus biochar (5 t ha−1 yr−1, NPKB). Relative to NPK, across nine growing seasons of wheat, NPKB decreased the soil bulk density by 0.019 and 0.013 units (g cm−3 yr−1), and decreased the soil penetration resistance by 0.028 and 0.015 units (MPa yr−1) in 0–10 cm and 10–20 cm depths, respectively. Biochar addition improved soil water content from seeding to flowering, increased wheat root distribution during the whole growth period, and enhanced soil N supply capacity by promoting N adsorption, which gave rise to greater biomass and N accumulation and more biomass allocation in grain. As a result, NPKB increased wheat yield by 14.8 %, N recovery efficiency by 55.1 %, and crop water productivity by 14.9 %, relative to NPK, on average across four growing seasons of wheat. Therefore, long-term biochar addition has potential to substantially increase grain yield of post-rice wheat, water productivity, and N recovery efficiency. Hence, for the sustainable intensification cropping in the long-run, successive biochar addition could be a finable management for wheat production on the rainfed Yangtze River Region of China.

Jing Wu, Yawei Wang, Gaizhong Chen et al.

As a famous coastal tourist city in China, Sanya is facing the dual challenges of solid waste management and resource utilization while tourism is booming. To realize efficient solid waste management and innovative circular economy models, Sanya actively explores and practices the construction path of a “zero-waste city”. In this study, Pearson correlation analysis and material flow analysis were used to analyze the factors influencing the amount of municipal solid waste (MSW) generated in Sanya and the changes in the effectiveness of MSW treatment in Sanya before the construction of the “zero-waste city” (2018) and five years later (2023). The results of the study show that the construction of a “zero-waste city” in Sanya, through the implementation of a series of policy measures, including the strengthening of strategic planning and leadership, the upgrading of capacity building, and the promotion of nationwide action participation, has effectively promoted the efficient synergistic treatment of MSW, thereby realizing both environmental benefits and economic benefits.

Pablo J. Mira

"Viajar al Futuro" de Walter Sosa Escudero es un libro divulgativo que explora la ciencia detrás de los pronósticos, equilibrando rigor científico y anécdotas. El autor, experto en estadística y big data, destaca la importancia de entender la estadística en un mundo incierto y cómo su aplicación consciente impacta decisiones personales y políticas públicas. 

I. V. Florinsky, S. O. Zharnova

Geomorphometric modeling and mapping of ice-free Antarctic areas can be applied for obtaining new quantitative knowledge about the topography of these unique landscapes and for the further use of morphometric information in Antarctic research. Within the framework of a project of creating a physical geographical thematic scientific reference geomorphometric atlas of ice-free areas of Antarctica, we performed geomorphometric modeling and mapping of five key coastal oases of Enderby Land, East Antarctica. These include, from west to east, the Konovalov Oasis, Thala Hills (Molodezhny and Vecherny Oases), Fyfe Hills, and Howard Hills. As input data, we used five fragments of the Reference Elevation Model of Antarctica (REMA). For the coastal oases and adjacent ice sheet and glaciers, we derived models and maps of eleven, most scientifically important morphometric variables (i.e., slope, aspect, horizontal curvature, vertical curvature, minimal curvature, maximal curvature, catchment area, topographic wetness index, stream power index, total insolation, and wind exposition index). In total, we derived 60 maps in 1:50,000 and 1:75,000 scales. The obtained models and maps describe the coastal oases of Enderby Land in a rigorous, quantitative, and reproducible manner. New morphometric data can be useful for further geological, geomorphological, glaciological, ecological, and hydrological studies of this region.

Selidji Caroline Tossou

In this paper, I investigate the 2017 labor market reform in Benin, which reduced firing costs and allowed firms to renew short-term contracts indefinitely. Using micro-data from the Harmonized Household Living Standards Surveys and a two-way fixed effect approach with nearby countries as the control group, I assess the reform's impact on employment, worker tenure, contract types, and wages. My empirical results reveal a 2.6 percentage point (24.5 percent) increase in formal sector employment and a 2.8 percentage point (3.2 percent) reduction in informal employment. Formal sector tenure decreased by 0.23 months for short-term contract workers, reflecting higher turnover, while long-term contract tenure increased by 0.15 months. The likelihood of securing a permanent contract rose by 23.2 percentage points (41.6 percent) in the formal sector, indicating that firms used long-term contracts to retain high-productivity workers. Wages in the formal sector increased by 33.6 USD per month on average, with workers on short-term contracts experiencing a wage increase of 19.6 USD and those on long-term contracts seeing an increase of 23.4 USD. I complement these findings with a theoretical job search model, which explains the mechanisms through which lowered firing costs affected firm hiring decisions, market tightness, and the sorting of workers across sectors. This study provides robust evidence of labor market reallocation and highlights the complex trade-offs between flexibility, employment stability, and wages in a developing country context.

Salaheddin Manochehri, Fateh Habibi

Purpose: During the last two decades, housing price fluctuations in some countries including Iran have been a main challenge of the housing market and the country's economy. In one period, there was a significant increase in housing prices and, in another period, it decreased or stabilized. Relatively high and widespread, it governs the price of housing, as a result of which significant developments have occurred in the housing sector and in the entire economy. In new theories, housing prices can fluctuate over time, and housing price fluctuations can be divided into two important categories. First, minor fluctuations result from market structure based on fundamentals. The housing market is based on the housing supply and demand conditions and the endogenous factors of the housing sector. Hence, the gradual and slow changes in the housing price over time are caused by the basic and underlying factors of the housing market and through changes in the total cost. Housing production changes housing prices. Second, housing cyclical shocks or impulses, are the exogenous factors that create cyclical shocks in the housing sector, and the monetary policy's effect on asset prices, including real estate and housing, is determined. The capital market, household asset portfolio composition and macroeconomic variables are among them.Methodology: We assume thatis the probability space,  is a filter created by Brownian  and Poisson process  with  is intensity. We also assume that Brownian process, Poisson process  and price jump  are independent of one another.  housing prices are based on time . In the Black-Scholes model (BSM), housing prices at time t are modeled by the following geometric Brownian process:where  is the average and  standard deviation of housing prices. In the jump diffusion model (JDM), housing prices are calculated by the following equation:where  is the expected growth rate,  is the turbulence of the Brownian process, and  is the housing price at time t and before the jump.Results and discussion: In this research, using GEM algorithm, the five parameters of jump diffusion model were estimated and then two parameters of Black-Scholes model were estimated using the maximum likelihood method. Next, the simulation of the future housing price was done based on the Monte-Carlo method. The simulation was done in 100,000 repetitions, and then the best model was selected. The housing price was simulated based on the real price, so that the price at time t could be calculated with its next monthly price, i.e. t+1. This method was repeated until the last data. In this research, many models were simulated with random numbers generated for housing prices to get the best model with the least error. In three cases of 6 months, 12 months and 24 months, housing prices were simulated and predicted. One way to calculate the accuracy of the model was based on the confidence interval with the assumption of normal approximation. One way to check the stability of the obtained coefficients of the models was to repeat the simulation with different random numbers and calculate the average performance of each model. In this research, in order to avoid bringing a large number of estimated models, 25 models with the best performance and the least error, and among these 25 models, the best models were identified.The results of the models show that, in most of the provincial centers of Iran, the jump diffusion model yields better results than the Black-Scholes model. Also, in some provincial centers, the 6-month performance is better, and, in some others, 12-month or 24-month performance is better. On the other hand, some provincial centers perform better in 6 months, 12 months and 24 months. The results of the average jump frequency in the centers of the provinces of Iran in the housing market show that, for most of the provinces, the average jump frequency is a high number, which indicates high fluctuations and the high impact of internal and external shocks in the Iranian housing market.Conclusions and policy implications: Accurate modeling of the pricing of various assets, including the housing market, as well as its fluctuations, has always been one of the concerns of researchers and policymakers. Therefore, this research aimed at the comparative analysis of housing prices using Black-Scholes asset pricing models and jump diffusion in the provincial centers of Iran. This study used the monthly housing price data in the provincial centers of Iran for a period from March 2009 to March 2023. In addition, through the GEM algorithm, the jump diffusion model and the maximum likelihood method, the Black-Scholes model was fulfilled, and then the future housing prices in the centers of the provinces of Iran were simulated by the Monte Carlo method. The research results show that, in most provinces of Iran, the jump diffusion model has better and more accurate results than the Black-Scholes model in 6, 12 and 24 months of performance. It is worth mentioning that, in some provincial centers, the results of the Black-Scholes model were better than the jump diffusion model. According to the results of the average jump frequency, it is clear that the highest and lowest average jump frequencies belong to Khorasan Razavi and Kohgiluyeh-Boyer Ahmad Provinces with values of 0.58 and 0.09, respectively.

Omar Djoukbala, Salim Djerbouai, Saeed Alqadhi et al.

Soil erosion significantly impacts dam functionality by leading to reservoir siltation, reducing capacity, and heightening flood risks. This study aims to map soil erosion within a Geographic Information Systems (GIS) framework to estimate the siltation of the K'sob dam and compare these estimates with bathymetric observations. Focused on one of the Hodna basin’s sub-basins, the K'sob watershed (1477 km2), the assessment utilizes the Revised Universal Soil Loss Equation (RUSLE) integrated with GIS and remote sensing data to predict the spatial distribution of soil erosion. Remote sensing data were pivotal in updating land cover parameters critical for RUSLE, enhancing the precision of our erosion predictions. Our results indicate an average annual soil erosion rate of 7.83 t/ha, with variations ranging from 0 to 224 t/ha/year. With a typical relative error of about 13% in predictions, these figures confirm the robustness of our methodology. These insights are crucial for crafting mitigation strategies in areas facing high to extreme soil loss and will assist governmental agencies in prioritizing actions and formulating effective soil erosion management policies. Future studies should explore the integration of real-time data and advanced modeling techniques to further refine these predictions and expand their applicability in similar environmental assessments.

Maojian Wang, Wei Shi, Muhammad Kamran et al.

Intercropping has been widely recognized to have great advantages in terms of increasing yield, controlling pests and diseases, and saving land, particularly in developing countries. Regulated deficit irrigation reduces water consumption and improves water productivity (WP). However, it is unclear whether the combination of intercropping and deficit irrigation could improve crop yield and WP simultaneously. In this experiment, three planting modes, including forage maize (Zea mays L.) monoculture (M), lablab bean (Lablab purpureus L.) monoculture (L), and maize-lablab bean intercropping (ML) were used. Six irrigation modes were set for each planting mode, including severe water deficit (W1), late water deficit (W2), alternate water deficit (W3), late moderate water deficit (W4), early moderate water deficit (W5), and full irrigation (W6). Results showed that compared with M, the ML treatment significantly increased the fresh forage yield (9.8%–17.0%), hay yield (9.5%–13.1%), crude protein yield (22.9%–25.9%), and WP (7.8%–8.7%). The W5 treatment achieved similar fresh forage yield, hay yield, and crude protein yield as that of the W6 treatment but reduced irrigation water by 25% and increased the WP (21.9%–24.8%). Intercropping achieved a high-water equivalence ratio (WER;1.52–1.81) and land equivalence ratio (LER;1.56–1.84), indicating its advantages over monocultures. The W6 treatment had the lowest WER and LER, suggesting that excessive irrigation can reduce the efficiency of utilizing land and water resource in maze-based forage production. Among all treatments, ML–W5 achieved the highest net income and output to input ratio. Overall, intercropping of forage maize and lablab bean with moderate deficit irrigation at an early stage could be used as a high-yield and efficient forage production system in the arid areas of northwest China.

Сергій Орищенко, Віктор Орищенко

Під час робочого процесу навантажувач перемішується на майже горизонтальних майданчиках, допустимий ухил яких. Розрахунок поздовжньої стійкості навантажувачів ведеться з умови перекидання вперед з урахуванням того, що деформуються пневматичні шини, якщо пневмоколісний хід. Кут додаткового нахилу навантажувача вперед внаслідок деформації опор визначається співвідношенням сили тяжкості навантажувача з вантажем жорсткість ґрунту під переднім та заднім котками гусеничного ходу або радіальна жорсткість передніх та задніх пневматичних шин навантажувача на пневмоколісному ході; відстань між центром ваги навантажувача та вертикальною віссю, що проходить через точку перекидання. Тому при розрахунку поздовжньої стійкості гусеничного та пневмоколісного навантажувачів. Найменший запас поздовжньої стійкості має навантажувач у разі руху під ухил з одночасним гальмуванням машини та робочого обладнання при його опусканні. Положення робочого обладнання відповідає максимальному вильоту.

William Yang, Karthikeya Kona, Yashveer Jain et al.

Current mobile manipulators and high-fidelity simulators lack the ability to seamlessly operate and simulate across integrated environments spanning sea, air, and land. To address this gap, we introduce Aerial-Aquatic Manipulators (AAMs) in SEa, Air, and Land Simulator (SEALS), a comprehensive and photorealistic simulator designed for AAMs to operate and learn in these diverse environments. The development of AAM-SEALS tackles several significant challenges, including the creation of integrated controllers for flying, swimming, and manipulation, and the high-fidelity simulation of aerial dynamics and hydrodynamics leveraging particle-based hydrodynamics. Our evaluation demonstrates smooth operation and photorealistic transitions across air, water, and their interfaces. We quantitatively validate the fidelity of particle-based hydrodynamics by comparing position-tracking errors across real-world and simulated systems. AAM-SEALS benefits a broad range of robotics communities, including robot learning, aerial robotics, underwater robotics, mobile manipulation, and robotic simulators. We will open-source our code and data to foster the advancement of research in these fields. The overview video is available at https://youtu.be/MbqIIrYvR78. Visit our project website at https://aam-seals.umd.edu for more details.

Mahmoud Reshadati, Manoochehr Shirzaei

Vertical land motion (VLM) observations obtained from Interferometric Synthetic Aperture Radar (InSAR) have transformed our understanding of crustal deformation processes over the past 3 decades. However, these observations are often related to a local reference frame, posing challenges for studies that require large-scale observations within a global reference frame, such as assessments of relative sea level rise and associated hazards. Here, we present a novel approach that enables transforming InSAR-derived VLM at any location worldwide to a global (e.g., International Terrestrial Reference Frame) reference frame without a direct need for GNSS (Global Navigation Satellite System) measurements. To this end, we employ a coarse resolution model of global VLM obtained by interpolating rates of all available GNSS stations over the global land areas. Our rationale is that the high-resolution InSAR-derived VLM data do not capture the long-wavelength signals present in the global VLM model. Therefore, we employ a set of 2D polynomial models to evaluate the difference between InSAR-derived VLM and the global model and then add it back to the InSAR-derived VLM. We examined the validity of our rationale using normalized power spectrum analysis and tested the effect of polynomial order on the accuracy of transformed VLM and the overall success of our approach using two datasets from Los Angeles and New York City. This approach improves the usability of InSAR-derived VLM in geophysical applications, including monitoring regional land subsidence.

Yuval Rymon

AI is transforming human labor at an unprecedented pace - improving 10$\times$ per year in training effectiveness. This paper analyzes how society can adapt to AI-driven human-labor automation (HLA), using Bernardi et al.'s societal adaptation framework. Drawing on literature from general automation economics and recent AI developments, the paper develops a "threat model." The threat model is centered on mass unemployment and its socioeconomic consequences, and assumes a non-binary scenario between full AGI takeover and swift job creation. The analysis explores both "capability-modifying interventions" (CMIs) that shape how AI develops, and "adaptation interventions" (ADIs) that help society adjust. Key interventions analyzed include steering AI development toward human-complementing capabilities, implementing human-in-the-loop requirements, taxation of automation, comprehensive reorientation of education, and both material and social substitutes for work. While CMIs can slow the transition in the short-term, significant automation is inevitable. Long-term adaptation requires ADIs - from education reform to providing substitutes for both the income and psychological benefits of work. Success depends on upfront preparation through mechanisms like "if-then commitments", and crafting flexible and accurate regulation that avoids misspecification. This structured analysis of HLA interventions and their potential effects and challenges aims to guide holistic AI governance strategies for the AI economy.

Faria Tuz Zahura, Gautam Bisht, Zhi Li et al.

Wildfire significantly disturb ecosystems by altering forest structure, vegetation ecophysiology, and soil properties. Understanding the complex interactions between topographic and climatic conditions in post-wildfire recovery is crucial. This study investigates the interplay between topography, climate, burn severity, and years after fire on vegetation recovery across dominant land cover types (evergreen forest, shrubs, and grassland) in the Pacific Northwest region. Using Moderate Resolution Imaging Spectroradiometer data, we estimated vegetation recovery by calculating the incremental enhanced vegetation index (EVI) change during post-fire years. A machine learning technique, random forest (RF), was employed to map relationships between the input features (elevation, slope, aspect, precipitation, temperature, burn severity, and years after fire) and the target (incremental EVI recovery) for each land cover type. Variable importance analysis and partial dependence plots were generated to understand the influence of individual features. The observed and predicted incremental EVI values showed good matches, with R2 values of 0.99 for training and between 0.89 and 0.945 for testing. The study found that climate variables, specifically precipitation and temperature, were the most important features overall, while elevation played the most significant role among the topographic factors. Partial dependence plots revealed that lower precipitation tended to cause a reduction in vegetation recovery for varying temperature ranges across land cover types. These findings can aid in developing targeted strategies for post-wildfire forest management, considering the varying responses of different land cover types to topographic, climatic, and burn severity factors.

George Ayuune Akeliwira

The study examines the long-term causal relationship between public debt, governance quality, and illicit financial flows in sub-Saharan Africa. Annual time series data were gathered from the World Bank Governance Indicators, International Monetary Fund Economic Outlook, and Global Financial Integrity from 2005 to 2014. The approach adopted in the study is in the tradition of the portfolio choice framework of tax evasion, rooted in the investment theory of capital flight. The study finds that there is a negative and statistically significant long-run relationship between governance quality and illicit financial flows. The results also show a negative and statistically insignificant relationship between public debt and illicit financial flows. The findings suggest that weak institutional oversight, poor regulatory quality, corruption, and political crises are important determinants of illicit financial outflows in the region. It concludes that governments need to improve the transparency of financial transactions, including the beneficial ownership of corporate structures and tax information. The results also indicate the need to strengthen institutions such as customs, anti-corruption, and other law enforcement agencies to detect intentional trade misinvoicing as tax evaders exploit loopholes in tax administration peculiar to developing countries. The study is timely as resources are critically needed to rebuild economies in view of the global COVID-19 outbreak and its deleterious effects on low-income countries. The study is also relevant for policymakers as it presents pointers to the factors that proliferate illicit capital outflows from the region.