This paper introduces the AI “next to normal”-thesis, suggesting that as Artificial Intelligence becomes more ingrained in our daily lives, it will transition from a sensationalized entity to a regular tool. However, this normalization has psychosocial implications, particularly when it comes to AI safety concerns. The “next to normal”-thesis proposes that AI will soon be perceived as a standard component of our technological interactions, with its sensationalized nature diminishing over time. As AI’s integration becomes more seamless, many users may not even recognize their interactions with AI systems. The paper delves into the psychology of AI safety concerns, discussing the “Mere Exposure Effect” and the “Black Box Effect”. While the former suggests that increased exposure to AI leads to a more positive perception, the latter highlights the unease stemming from not fully understanding its capabilities. These effects can be seen as two opposing forces shaping the public’s perception of the technology. The central claim of the thesis is that as AI progresses to become normal, human psychology will evolve alongside with it and safety concerns will diminish, which may have practical consequences. The paper concludes by discussing the implications of the “next to normal”-thesis and offers recommendations for the industry and policymakers, emphasizing the need for increased transparency, continuous education, robust regulation, and empirical research. The future of AI is envisioned as one that is seamlessly integrated into society, yet it is imperative to address the associated safety concerns proactively and not take the normalization effects take ahold of it.
Turing the electronic structure by inserting certain functional groups in graphitic carbon nitride (g-C3N4, CN for short) skeleton through molecular doping is an effective way to improve its photocatalytic performance. Herein, we prepare a benzene bridged carbon nitride (BCN) by calcining urea and 1,3,5-tribromobenzene at elevated temperature. The introduction of benzene ring in g-C3N4 layers improves the separation efficiency and lifetime of photogenerated carriers, inhibits the recombination rate of electron/hole pairs, thus the performance of photocatalytic hydrogen evolution improves. The optimal hydrogen evolution rate of 1.5BCN reaches 1800 µmol/h·g, which is nine times that of the pure g-C3N4. DFT calculation proved the benzene bridged CN increased the distance of charge transfer (DCT) and the push-pull electronic effect of intramolecular electrons. This work may provide a pathway for preparing molecular doped g-C3N4 with improved photocatalytic performance.
In the manufacturing process, in addition to the properties of material itself, the quality of a product is directly related to the cutting process. Cutting force and cutting heat are two crucial factors in cutting processing. Researchers can analyze various signals during cutting process, such as cutting force signal, vibration signal, temperature signal, etc., which can regulate force and temperature, optimize the cutting process, and improve product quality. Therefore, it is very important to pay attention to various signals in cutting process. Meanwhile, good-quality signal data sets will greatly reduce time, resource and labor costs for subsequent use or analysis of researchers. Therefore, how to collect high-quality signals effectively and accurately is the first step. At present, researchers prefer to use various sensors to collect signals. With the advancement of science and technology, intelligent tool holder appears in researchers’ vision. It integrates multiple systems such as sensors, data collection, data transmission, and power supply on the tool holder. It replaces traditional wired sensors, and it is highly interactive with CNC machine tools. This paper will carry out a systematic review and prospect from three aspects: the structural design of the intelligent tool holder, the signal monitoring technology of the intelligent tool holder, and the tool condition monitoring of the intelligent tool holder.
Pattern formation is a fundamental process in biological development, enabling the transformation of initially uniform or random states into spatially ordered structures. A comprehensive understanding of the formation and function of these patterns is crucial for unraveling the underlying principles of biological design and engineering. In recent years, synthetic biology has emerged as a powerful discipline for investigating and manipulating pattern formation in biological systems, involving the design and construction of novel biological components, circuits, and networks with specific functionalities. The integration of computational simulations (in silico) and experimental techniques (wet lab) in synthetic biology has significantly advanced our knowledge of pattern formation and its implications in biological design and engineering. This review provides an overview of the computational simulations employed in studying pattern formation and introduces the representative and cutting-edge experimental methods utilized in wet labs.
The endoplasmic reticulum (ER) to Golgi secretory pathway is an elegantly complex process whereby protein cargoes are manufactured, folded, and distributed from the ER to the cisternal layers of the Golgi stack before they are delivered to their final destinations. The export of large bulky cargoes such as procollagen and its trafficking to the Golgi is a sophisticated mechanism requiring TANGO1 (Transport ANd Golgi Organization protein 1. It is also called MIA3 (Melanoma Inhibitory Activity protein 3). TANGO1 has two prominent isoforms, TANGO1-Long and TANGO1-Short, and each isoform has specific functions. On the luminal side, TANGO1-Long has an HSP47 recruitment domain and uses this protein to collect collagen. It can also tether its paralog isoforms cTAGE5 and TALI and along with these proteins enlarges the vesicle to accommodate procollagen. Recent studies show that TANGO1-Long combines retrograde membrane flow with anterograde cargo transport. This complex mechanism is highly activated in fibrosis and promotes the excessive deposition of collagen in the tissues. The therapeutic targeting of TANGO1 may prove successful in the control of fibrotic disorders. This review focuses on TANGO1 and its complex interaction with other procollagen export factors that modulate increased vesicle size to accommodate the export of procollagen.
Serine integrases are emerging as one of the powerful tools for synthetic biology. They have been widely developed across genome engineering, biological part construction, genetic circuit design, and in vitro DNA assembly. However, the strategy of in vivo DNA assembly by serine integrases has not yet been reported. To address this opportunity, here we developed a serine integrase-based in vivo DNA (plasmid) assembly approach. First, we demonstrated that the engineered “Acceptor” plasmids could be assembled with diverse “Donor” plasmids by serine integrases (Bxb1 and phiC31) in Escherichia coli (E. coli). Then, by programming the “Donor” plasmids and the host E. coli cells, we established an assembly cascade procedure and finally constructed plasmids that could constitutively express three different fluorescent proteins. Moreover, we used this approach to assemble different chromoprotein genes and generated colored E. coli cells. We anticipate that this approach will enrich the serine integrase-based biotechnology toolbox, and accelerate multiple plasmid assembly for synthetic biology with broad applications.