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The market for industrial enzymes has witnessed constant growth, which is currently around 7% a year, projected to reach $10.5 billion in 2024. Lipases are hydrolase enzymes naturally responsible for triglyceride hydrolysis. They are the most expansively used industrial biocatalysts, with wide application in a broad range of industries. However, these biocatalytic processes are usually limited by the low stability of the enzyme, the half-life time, and the processes required to solve these problems are complex and lack application feasibility at the industrial scale. Emerging technologies create new materials for enzyme carriers and sophisticate the well-known immobilization principles to produce more robust, eco-friendlier, and cheaper biocatalysts. Therefore, this review discusses the trending studies and industrial applications of the materials and protocols for lipase immobilization, analyzing their advantages and disadvantages. Finally, it summarizes the current challenges and potential alternatives for lipases at the industrial level.

We design a self-exploratory reinforcement learning (RL) framework, based on the Q-learning algorithm, that enables the base station (BS) to choose a suitable modulation and coding scheme (MCS) that maximizes the spectral efficiency while maintaining a low block error rate (BLER). In this framework, the BS chooses the MCS based on the channel quality indicator (CQI) reported by the user equipment (UE). A transmission is made with the chosen MCS and the results of this transmission are converted by the BS into rewards that the BS uses to learn the suitable mapping from CQI to MCS. Comparing with a conventional fixed look-up table and the outer loop link adaptation, the proposed framework achieves superior performance in terms of spectral efficiency and BLER.

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Lignocellulosic biomasses are used in several applications, such as energy production, materials, and biofuels. These applications result in increased consumption and waste generation of these materials. However, alternative uses are being developed to solve the problem of waste generated in the industry. Thus, research is carried out to ensure the use of these biomasses as enzymatic support. These surveys can be accompanied using the advanced bibliometric analysis tool that can help determine the biomasses used and other perspectives on the subject. With this, the present work aims to carry out an advanced bibliometric analysis approaching the main studies related to the use of lignocellulosic biomass as an enzymatic support. This study will be carried out by highlighting the main countries/regions that carry out productions, research areas that involve the theme, and future trends in these areas. It was observed that there is a cooperation between China, USA, and India, where China holds 28.07% of publications in this area, being the country with the greatest impact in the area. Finally, it is possible to define that the use of these new supports is a trend in the field of biotechnology.