"The Great Divergence" brings new insight to one of the classic questions of history: Why did sustained industrial growth begin in Northwest Europe, despite surprising similarities between advanced areas of Europe and East Asia? As Ken Pomeranz shows, as recently as 1750, parallels between these two parts of the world were very high in life expectancy, consumption, product and factor markets, and the strategies of households. Perhaps most surprisingly, Pomeranz demonstrates that the Chinese and Japanese cores were no worse off ecologically than Western Europe. Core areas throughout the eighteenth-century Old World faced comparable local shortages of land-intensive products, shortages that were only partly resolved by trade.Pomeranz argues that Europe's nineteenth-century divergence from the Old World owes much to the fortunate location of coal, which substituted for timber. This made Europe's failure to use its land intensively much less of a problem, while allowing growth in energy-intensive industries. Another crucial difference that he notes has to do with trade. Fortuitous global conjunctures made the Americas a greater source of needed primary products for Europe than any Asian periphery. This allowed Northwest Europe to grow dramatically in population, specialize further in manufactures, and remove labor from the land, using increased imports rather than maximizing yields. Together, coal and the New World allowed Europe to grow along resource-intensive, labor-saving paths.Meanwhile, Asia hit a cul-de-sac. Although the East Asian hinterlands boomed after 1750, both in population and in manufacturing, this growth prevented these peripheral regions from exporting vital resources to the cloth-producing Yangzi Delta. As a result, growth in the core of East Asia's economy essentially stopped, and what growth did exist was forced along labor-intensive, resource-saving paths--paths Europe could have been forced down, too, had it not been for favorable resource stocks from underground and overseas.
Urban traffic flow is governed by the complex, nonlinear interaction between land use configuration and spatiotemporally heterogeneous mobility demand. Conventional global regression and time-series models cannot simultaneously capture these multi-scale dynamics across multiple travel modes. This study proposes a GeoAI Hybrid analytical framework that sequentially integrates Multiscale Geographically Weighted Regression (MGWR), Random Forest (RF), and Spatio-Temporal Graph Convolutional Networks (ST-GCN) to model the spatiotemporal heterogeneity of traffic flow patterns and their interaction with land use across three mobility modes: motor vehicle, public transit, and active transport. Applying the framework to an empirically calibrated dataset of 350 traffic analysis zones across six cities spanning two contrasting urban morphologies, four key findings emerge: (i) the GeoAI Hybrid achieves a root mean squared error (RMSE) of 0.119 and an R^2 of 0.891, outperforming all benchmarks by 23-62%; (ii) SHAP analysis identifies land use mix as the strongest predictor for motor vehicle flows and transit stop density as the strongest predictor for public transit; (iii) DBSCAN clustering identifies five functionally distinct urban traffic typologies with a silhouette score of 0.71, and GeoAI Hybrid residuals exhibit Moran's I=0.218 (p<0.001), a 72% reduction relative to OLS baselines; and (iv) cross-city transfer experiments reveal moderate within-cluster transferability (R^2>=0.78) and limited cross-cluster generalisability, underscoring the primacy of urban morphological context. The framework offers planners and transportation engineers an interpretable, scalable toolkit for evidence-based multimodal mobility management and land use policy design.
U.S. urban industry has experienced remarkable transformations. At least two and a half centuries of evolution have changed location, productive processes, technology, sources of energy, and outputs. This article analyzes an attempt to help retrofit an aging urban industrial district in South Phoenix. It focuses specifically on mounting pressures to convert remaining, centrally located, industrial land to non-productive commercial and office uses. It is argued that the Wedge South Mountain industrial area presents advantages to the local community in terms of urban structure, proximity to labor pools, and good transport networks in the core of the Phoenix metropolis. The methods comprised an Advanced Urban Planning Studio at Arizona State University, multiple inventories and site visits, presentations, and feedback from guest speakers and economic development specialists at the City of Phoenix and at the Greater Phoenix Economic Council. The main policy recommendations are to preserve small-business activities, modernize neighborhoods along industrial park settings with green and environmental strategies (i.e., parasols and solar panels), and encourage growth in the technology manufacturing sector. The key finding is a series of implications for the retrofitting of other urban industrial areas in the Global North.
Harmini Harmini, Harianto Harianto, Nunung Kusnadi
et al.
Most small-scale rice farming households in Indonesia face land ownership constraints that result in low on-farm income levels, often falling below the national poverty line. In such circumstances, off-farm income serves as a vital alternative source to meet household needs. This study aims to identify the key determinants of off-farm income among rice farming households in Indonesia using the two-step Heckman selection model to address potential sample selection bias. The analysis is based on household survey data collected in 2016 by the Ministry of Agriculture of the Republic of Indonesia, comprising 321 farming households across 14 major rice-producing districts. The estimation results indicate that off-farm income is significantly influenced by macro-level factors, such as the proportion of non-agricultural labor in the district and geographical proximity to economic centers. At the micro level, the number of working household members and the educational attainment of the household head are positively associated with off-farm income. Conversely, limited access to land and low levels of non-labor income serve as push factors driving participation in low-productivity off-farm activities. The findings suggest that policy interventions should focus on promoting the development of non-agricultural industries in rural areas and improving farmers' access to education and vocational training in order to enhance income diversification and strengthen the economic resilience of farming households.
Florida ranks second in the United States for vegetable production, and the second largest industry in Florida is agriculture. However, the on-going loss of agricultural land to development, increasing labor costs, and decreasing water resources creates a need for alternative agricultural systems. Hydroponic food production can produce similar yields as traditional agriculture using less water, land, and labor. Equipping urban audiences with tools to grow food hydroponically provides new options for feeding the growing urban population in non-traditional environments. "Set it and forget it" hydroponics is a simple, affordable, and successful introductory hydroponic method. This technique is great for growers new and experienced and is especially well adapted for classroom experiments.
Digital workers on crowdsourcing platforms (e.g., Amazon Mechanical Turk, Appen, Clickworker, Prolific) play a crucial role in training and improving AI systems, yet they often face low pay, unfair conditions, and a lack of recognition for their contributions. To map these issues in the existing literature of computer science, AI, and related scholarship, we selected over 300 research papers on digital labor published between 2015 and 2024, narrowing them down to 143 on digital gig-labor for a detailed analysis. This analysis provides a broad overview of the key challenges, concerns, and trends in the field. Our synthesis reveals how the persistent patterns of representation and voices of gig workers in digital labor are structured and governed. We offer new insights for researchers, platform designers, and policymakers, helping them better understand the experiences of digital workers and pointing to key areas where interventions and future investigations are promptly needed. By mapping the findings from the past ten years' growth of the domain and possible implications, this paper contributes to a more coherent and critical understanding of digital labor in contemporary and future AI ecosystems.
Land cover classification involves the production of land cover maps, which determine the type of land through remote sensing imagery. Over recent years, such classification is being performed by machine learning classification models, which can give highly accurate predictions on land cover per pixel using large quantities of input training data. However, such models do not currently take account of input measurement uncertainty, which is vital for traceability in metrology. In this work we propose a Bayesian classification framework using generative modelling to take account of input measurement uncertainty. We take the specific case of Bayesian quadratic discriminant analysis, and apply it to land cover datasets from Copernicus Sentinel-2 in 2020 and 2021. We benchmark the performance of the model against more popular classification models used in land cover maps such as random forests and neural networks. We find that such Bayesian models are more trustworthy, in the sense that they are more interpretable, explicitly model the input measurement uncertainty, and maintain predictive performance of class probability outputs across datasets of different years and sizes, whilst also being computationally efficient.
Artificial intelligence (AI) is reshaping the labor market by changing the task content of occupations. This study investigates the impact of AI development on the emergence of new work, employment, and wages in the United States from 2015 to 2022. I develop innovative methods to measure occupational and industry exposure to AI technologies that substitute labor (automation AI ) or enhance workers' output (augmentation AI), and to identify new work (i.e., new job titles). To address endogeneity, I use instrumental variable estimators, leveraging AI development in countries with limited economic ties to the United States. The findings indicate that automation AI negatively impacts new work, employment, and wages in low-skilled occupations, while augmentation AI fosters the emergence of new work and raises wages for high-skilled occupations. These results suggest that AI may contribute to rising wage inequality.
The environment is something that cannot be separated from human life. Because where someone lives, another environment is created and vice versa. The industry is a sectoral economy or activity that involves the processing of raw materials or the production of products, i.e., the use of skills and labor in the factory and the use of field tools to process the results on the ground and distribute them as the main work. Therefore, as an eye chain, the industry is also responsible for meeting land-related (economic) needs, i.e., agriculture, cultivation, and mining. To place the sector far from the country, the basis of economy, culture, and politics. Study law is carried out in a manner juridical normative is Conceptual research as what is written in regulation legislation or law conceptualized as a rule or the norm, which is benchmark behavior considered human deserves. Study law normative This is based on material primary and secondary laws, i.e., referring research to existing norms in regulation legislation. Development is a planned change process as one effort man in increasing the quality of his life. However, the development industry in Indonesia has a significant impact on the environment, resulting in pollution, damage to the climate forests, and depletion of source Power nature. The impact development industry on the environment in Indonesia has been attention for many years. This leads to an increased need for measures and legislation to reduce the negative impact of industrial development on the environment.
Agroforestry is often seen as a sustainable land-use system for agricultural production providing ecosystem services. Intercropping with food crops leads to equal or higher productivity than monoculture and results in food production for industry and subsistence. Low rubber price and low labor productivity in smallholdings have led to a dramatic conversion of rubber plantations to more profitable crops. The literature analysis performed in this paper aimed at better understanding the ins and outs that could make rubber-based agroforestry more attractive for farmers. A comprehensive search of references was conducted in March 2023 using several international databases and search engines. A Zotero library was set up consisting of 415 scientific references. Each reference was carefully read and tagged in several categories: cropping system, country, main tree species, intercrop type, intercrop product, level of product use, discipline of the study, research topic, and intercrop species. Of the 232 journal articles, 141 studies were carried out on rubber agroforestry. Since 2011, the number of studies per year has increased. Studies on rubber-based agroforestry systems are performed in most rubber-producing countries, in particular in Indonesia, Thailand, China, and Brazil. These studies focus more or less equally on perennials (forest species and fruit trees), annual intercrops, and mixed plantations. Of the 47 annual crops associated with rubber in the literature, 20 studies dealt with rice, maize, banana, and cassava. Agronomy is the main discipline in the literature followed by socio-economy and then ecology. Only four papers are devoted to plant physiology and breeding. The Discussion Section has attempted to analyze the evolution of rubber agroforestry research, progress in the selection of food crop varieties adapted to agroforestry systems, and to draw some recommendations for rubber-based agroforestry systems associated with food crops.
Fuxing Liu, Muhammad Aamir Shahzad, Lianfen Wang
et al.
This study was conducted to examine the effects of agricultural subsidies on the technical efficiency of agricultural production technology and on factor input. It utilized a random frontier production function, instrumental variable method, and threshold regression model. The data used for this analysis consisted of 609 field yield measurements from the National Rapeseed Industry Technology System in 2020. The findings indicate that agricultural subsidies have a substantial impacts and it increases the technical efficiency of production process. Specifically, these subsidies encourage the use of land resources while inhibiting the use of chemical fertilizers. However, this does not have a significant effect on the utilization of labor and capital resources. Furthermore, the impact of agricultural subsidies on production technology efficiency varies depending on the scale of the farming operation. The subsidies significantly enhance the production technology efficiency of farmers with a business scale of less than 0.67 ha, but do not significantly improve the production technology efficiency of farmers with a business scale exceeding 0.67 ha. To optimize the effectiveness of agricultural subsidy policy, three methods and recommendations are proposed: increasing the overall amount of subsidies, expanding and diversifying the types of subsidies, and refining the process of disbursing subsidies.
Vertical farming, a sustainable key for urban agriculture, has garnered attention for its land use optimization and enhanced food production capabilities. The adoption of automation in vertical farming is a pivotal response to labor shortages, addressing the need for increased efficiency, particularly in labor-intensive tasks like harvesting. Although soft robotic grippers offer a significant promise for delicately handling fragile objects, the absence of sensors has hindered their full potential to execute precise and secure grasping. To address this challenge, we present a new solution: a detachable Fiber Bragg Grating-based flexible contact force sensor to capture gripper-vegetable interactions. The sensing module was 3D printed using soft material, and the FBG fiber was attached to the module using epoxy. From evaluation tests, this lightweight sensor demonstrated a wide measurement range of up to 9.87 N, with a high sensitivity of 141.7 pm/N, good repeatability, and a hysteresis of 7.96%. Compared to commercial load cells, our sensor achieves a small measurement RMSE of 0.41 N and a percentage error of 4.15%. The sensor was integrated into two robotic 3D-printed soft grippers to enable real-time monitoring of dynamic contact force during vegetable harvesting in vertical farming scenarios. By reflecting contact status, this sensor provides a promising glimpse into the future of agricultural automation, enhancing operational efficiency and strengthening situation awareness and decision-making capabilities in vertical farms. Beyond agriculture, the versatility of this sensor extends to application in areas such as warehousing, logistics, and the food and beverage industry.
As the need and demand for sustainability come to the forefront of innovative efforts by technology companies, the use of rechargeable batteries has only become more prominent. A critical mineral in the manufacture of such batteries is cobalt. Looking deeper into how manufacturers get their hands on cobalt exposes the troubling cobalt-mining practices largely taking place within the Democratic Republic of Congo (DRC). This article dives into the underbelly of the cobalt-mining industry, revealing the egregious human-rights abuses occurring in the DRC and examining the current legal and ethical landscape surrounding cobalt mining around the world. In both small-scale artisanal mines and larger industrial mines, child labor, physical and verbal abuse, and non-livable, low wages are commonplace. As the mines expand, and the land, homes, and farms of Congolese residents are destroyed in the process, Congolese people wind up with little to no choice but to work in the mines. This article addresses how current legislation and initiatives in the United States and internationally miss the mark in responding to the increasing volume of problems in the DRC’s cobalt mines, and how past cases involving human rights abuses in the supply chains of United States companies have panned out. Finally, this article emphasizes the need for change and reform as innovation efforts continue to increase worldwide.
Ognjen Radišić-Aberger, Peter Burggräf, Fabian Steinberg
et al.
By applying machine learning algorithms, predictive business process monitoring (PBPM) techniques provide an opportunity to counteract undesired outcomes of processes. An especially complex variation of business processes is the engineering change (EC) process. Here, failing to adhere to planned implementation dates can have severe impacts on assembly lines, and it is paramount that potential negative cases are identified as early as possible. Current PBPM research, however, has seldomly investigated the predictive performance of machine learning approaches and their applicability at early process steps, let alone for the EC process. In our research, we show that given adequate feature encoding, shallow learners can accurately predict schedule adherence after process initialisation. Based on EC data from an automotive manufacturer, we provide a case sensitive performance overview on algorithm-encoding combinations. For that, three algorithms (XGBoost, Random Forest, LSTM) were combined with four encoding techniques. The encoding techniques used were the two common aggregation-based and index-based last state encoding, and two new combinations of these, which we term advanced aggregation-based and complex aggregation-based encoding. The study indicates that XGBoost-index-encoded approaches outclass regarding average predictive performance, whereas Random-Forest-aggregation-encoded approaches perform better regarding temporal stability due to reduced influence by dynamic features. Our research provides a case-based reasoning approach for deciding on which algorithm-encoding combination and evaluation metrics to apply. In doing so, we provide a blueprint for an early warning and monitoring method within the EC process and other similarly complex processes.
Marketing. Distribution of products, Management. Industrial management
This paper evaluates the impact of the German minimum wage policy on firms' financial leverage. By using a comprehensive firm-establishment-employee linked dataset and a difference-in-differences estimation with firm-level variation in treatment intensity, the analysis shows that the average minimum wage level reduces firms' financial leverage by about 0.5 to 0.9 percentage points, corresponding to 1 to 2 percent of the mean of financial leverage. Further investigation of the mechanism shows that the minimum wage does not lead to significant capital-labor substitution; therefore, the labor share increases. Firms react to the increased labor share by deleveraging. The results suggest that while the minimum wage benefits workers by allocating more earnings to the labor force, it also introduces greater operating risks and encourages conservative financial behavior among firms.
Accurate and timely modeling of labor migration is crucial for various urban governance and commercial tasks, such as local policy-making and business site selection. However, existing studies on labor migration largely rely on limited survey data with statistical methods, which fail to deliver timely and fine-grained insights for time-varying regional trends. To this end, we propose a deep learning-based spatial-temporal labor migration analysis framework, DHG-SIL, by leveraging large-scale job query data. Specifically, we first acquire labor migration intention as a proxy of labor migration via job queries from one of the world's largest search engines. Then, a Disprepant Homophily co-preserved Graph Convolutional Network (DH-GCN) and an interpretable temporal module are respectively proposed to capture cross-city and sequential labor migration dependencies. Besides, we introduce four interpretable variables to quantify city migration properties, which are co-optimized with city representations via tailor-designed contrastive losses. Extensive experiments on three real-world datasets demonstrate the superiority of our DHG-SIL. Notably, DHG-SIL has been deployed as a core component of a cooperative partner's intelligent human resource system, and the system supported a series of city talent attraction reports.
The work examines the unique nanostructure of carbon nanoparticles deposited from sooting premixed flames with flame temperatures exceeding 2200 K. This flame temperature regime has previously been shown to transition from typical soot formation conditions to a regime whereby the flame-form carbon adopts a nanostructure considerably more ordered than soot. Graphenic carbon deposits observed by High-resolution TEM (HRTEM) are reported here corroborating previous Raman spectroscopy evidence. The use of premixed stretch-stabilized flames enables particle production in the high-temperature regime under a flow field amenable to low-dimensional flame modeling. Although the flame flow configuration is relatively simple, three sample preparation methods are used to assess the representation of true carbon properties as they exist in the flame. HRTEM imaging is carried out on carbon particle samples prepared by rapid-insertion deposition, aerosol dilution probe deposition and carbon particle film deposition. Images from rapid-insertion samples show amorphous particles in the lightly sooting flame and turbostratic particles in the heavy sooting flame. There is trace evidence of graphenic structure in rapid-insertion samples but the most striking particles on the TEM grid are graphite nanocrystals presumably formed by a new artificial crystallization process. HRTEM images of particles collected over time by diluted aerosol deposition and film deposition show clear graphenic structures. Overall, the carbon nanostructure observed by HRTEM is a mixture of amorphous, turbostratic and graphenic carbon lattices depending on the flame condition and sampling method. The current work highlights potential impacts of higher flame temperatures and higher equivalence ratio on deposited flame-formed carbon. Namely, graphenic particle structure is observed in rapid-insertion deposition samples but graphene portions are most abundant in aerosol dilution and carbon particle film deposition samples. This may indicate that graphene structures grow on the deposition surface over time.
Fuel, Energy industries. Energy policy. Fuel trade
Nowadays, the society faces with financial and economic risks which play a special role in the diversity of risks. In the most general form, they affect the amount of available financial resources that can meet the current needs of the population and spread new living standards. The purpose of the study is to analyze the affection of financial and economic risks on social growth and to develop recommendations for creating a mechanism for managing them in the Russian health care system. For this goal achievement, the author has identified several tasks clarifying the approach to determining financial and economic risks in this paradigm, as well as identifying measures to change financing Russian health care. The methodological base: systemic; comparative analysis; synthesis; socio-economic and statistical methods of data analysis. The theoretical and practical significance of the study lies in an integrated system growth for managing financial and economic risks, which unites different economic entities, as well as in determining measures to change the financing mechanisms of the Russian health care system. The specialists can use the results obtained in subsequent work on the problems of risk management at the level of corporate organizations, state agencies, and society.