Digital labor, as a new theoretical form of "audience commodity theory" in the digital media era, represents a new form of production and labor. This paper explores the unique features of digital labor in labor form, labor products and labor time, and combining Marx's theory, it further reveals the alienation and exploitation of human social relations, emotional value and social class in the process of digital labor, and finally makes suggestions on the unequal relationship between platform and workers in the process of digital labor.

Polymers obtained from renewable sources are gaining popularity over their petroleum based counter parts in recent years due to their capability to address the environmental pollution related concerns emanating from the widespread usage of synthetic polymers. Even though the polymers from renewable sources are attractive in an environmental point of view, some of the property limitations and the high cost of these materials pose limitations for their extensive commercial applications. These aspects opened the door for a large chunk of research activities in development of polyblends and composites containing polymers from renewable sources as one of the components. Poly (lactic acid) (PLA) is one of the most discussed and commercialized polymer originated from renewable resources. Even though it has many useful properties, certain disadvantages like high brittleness, low impact resistance etc. limit the wide spread commercialization of PLA. In this review article, the recent research activities which are aimed to fill this gap by various modifications of PLA are discussed with special emphasis on the latest research advancements in the field of biodegradable and non biodegradable systems containing PLA.

Infrastructure development is critical for sustaining Asia’s economic growth. Unfortunately, huge financing gaps—estimated by a recent Asian Development Bank study to be USD22.5 trillion—constrain the ability of most emerging Asian countries to fully realize the benefits of infrastructure development. For instance, over 70% of infrastructure investments in Asia are still funded by public resources, which pose acute financing challenges for many countries with limited budgets and fiscal constraints. This paper discusses some of the challenges associated with public financing of infrastructure projects in emerging Asian countries, before introducing some new options for alleviating their infrastructure investment needs. In particular, it proposes a new approach to infrastructure financing by utilizing the spillover effects of infrastructure investment, where additional revenues generated from such investment can be channeled back to investors as subsidy to increase the returns to their investment. The paper also argues the need for Asian countries to implement fiscal reforms and to develop a more balanced approach to financing, one that involves both the private and public sector. 

The present study demonstrates the effect of direct solar drying (DSD) and hot air drying (HAD) on the quality attributes of Fuji apple slices. DSD samples took a longer time (150–180 min) to dry and simultaneously reached higher equilibrium moisture content at the end of rehydration than HAD samples. DSD samples have higher rehydration ability, dry matter holding capacity, and water absorption capacity than HAD samples. Among several empirical models, the Weibull model is the best fit with higher R² (0.9977), lower root mean square (0.0029), and chi-square error (0.0031) for describing the rehydration kinetics. Rehydrated HAD samples showed better color characteristics than DSD in terms of overall color change, chroma, and hue angle values. Whereas the hardness and chewiness of rehydrated DSD samples were better than HAD samples because of higher dry matter holding capacity in DSD. Apart from color retention, the DSD samples showed better rehydration capacity and a good texture upon rehydration than HAD slices.

Over the past decade, Ontario has seen a renewal in efforts to stimulate economic growth by investing in infrastructures. In this paper, we analyze the impact of public infrastructure investment on economic performance in this province. We use a multivariate dynamic time series methodological approach, based on the use of vector autoregressive models to estimate the elasticities and marginal products of six different types of public infrastructure assets on private investment, employment and output. We find that all types of public investment crowd in private investment while investment in highways, roads, and bridges crowds out employment. We also find that all types of public investment, with the exception of highways, roads and bridges, have a positive effect on output. The relatively large range of results estimated for the impact of each of the different public infrastructure types suggests that a targeted approach to the design of infrastructure investment policy is required. Infrastructure investment in transit systems and health facilities display the highest returns for output and the largest effects on employment and labor productivity. In terms of the nature of the empirical results presented here it would be important to highlight the fact that investments in health infrastructures as well as investments in education infrastructures are of great relevance. This is a pattern consistent with the mounting international evidence on the importance of human capital for long term economic performance.

This study aims to predict whether university students will make efficient use of Artificial Intelligence (AI) in the coming years, using a statistical analysis that predicts the outcome of a binary dependent variable (in this case, the efficient use of AI). Several independent variables, such as digital skills management or the use of Chat GPT, are considered.The results obtained allow us to know that inefficient use is linked to the lack of digital skills or age, among other factors, whereas Social Sciences students have the least probability of using Chat GPT efficiently, and the youngest students are the ones who make the worst use of AI.