Technological Breakthroughs Research Articles

Spatio-temporal evolution and scenario analysis of end-recycling governance in China: based on GM-SVR-BP combined forecasting model

ABSTRACT End-recycling governance is the core link for achieving high-quality development of circular economy. This study uses data of 30 provinces in China from 2011 to 2021 as research samples, constructs a comprehensive evaluation index system, and analyzes the achievements and progress of end-recycling governance in China over the years. Based on scenario analysis, a GM-SVR-BP combined model is further used to explore the development trends of end-recycling governance performance in 30 provinces in China from 2022 to 2030. Research finding: (1) The end-recycling governance performance of Chinese provinces presents a dynamic upward trend, but overall, it is low. (2) The barycenter of end-recycling governance in China migrated to the southeast, and the transfer speed was stable, showing a northeast-southwest pattern. There are pronounced differences among different regions. (3) There are apparent spatial autocorrelation characteristics in China’s end-recycling governance. The overall spatial agglomeration degree showed a weakening trend during the observation period. (4) The development trend of end-recycling governance performance in various regions in China during 2022–2030 follows the differentiation characteristics of the observation period in this study. Among them, technological breakthrough is one of the effective ways to improve the performance of end-recycling governance. The effect of the improvement of emotional level on the performance of end-recycling governance showed apparent regional heterogeneity. This study provides a clear and reliable decision-making basis for promoting the improvement of regional end-recycling governance performance and system construction, and has certain reference value for promoting national and regional resource conservation and environmental protection.

Robotics in Manufacturing: A Review of Advances in Automation and Workforce Implications

The integration of robotics in manufacturing processes has undergone significant advancements, reshaping the landscape of industrial production and introducing transformative changes. This review explores the latest developments in robotics within the manufacturing sector, shedding light on the technological breakthroughs and their implications on the workforce. The recent surge in robotics adoption in manufacturing has been driven by the pursuit of increased efficiency, precision, and cost-effectiveness. From traditional robotic arms to cutting-edge collaborative robots (cobots) that work alongside human operators, the manufacturing industry has witnessed a paradigm shift. Advanced robotic systems equipped with artificial intelligence (AI) and machine learning algorithms enable adaptive and autonomous decision-making, further enhancing their capabilities. Automation in manufacturing, fueled by robotics, has yielded numerous benefits such as improved product quality, reduced production cycle times, and increased overall productivity. The review delves into case studies and real-world applications where robotics have proven instrumental in optimizing manufacturing processes across diverse industries. However, as the manufacturing landscape evolves, the implications for the workforce cannot be overlooked. The integration of robotics poses challenges and opportunities for human workers. While automation has the potential to eliminate routine and hazardous tasks, it also necessitates upskilling and reskilling of the workforce to operate, program, and maintain robotic systems. The review examines the socio-economic impacts of increased automation, discussing potential shifts in employment patterns and the need for a strategic approach to workforce development. This review provides a comprehensive analysis of the advances in robotics within manufacturing and their profound implications on the workforce. Striking a balance between automation and human collaboration is crucial for the future of manufacturing, emphasizing the importance of proactive strategies to harness the benefits of robotics while ensuring a resilient and adaptable workforce.

New directions for Ψ and m1A decoding in mRNA: deciphering the stoichiometry and function.

Over the past decade, advancements in epitranscriptomics have significantly enhanced our understanding of mRNA metabolism and its role in human development and diseases. This period has witnessed breakthroughs in sequencing technologies and the identification of key proteins involved in RNA modification processes. Alongside the well-studied m6A, Ψ and m1A have emerged as key epitranscriptomic markers. Initially identified through transcriptome-wide profiling, these modifications are now recognized for their broad impact on RNA metabolism and gene expression. In this Perspective, we focus on the detections and functions of Ψ and m1A modifications in mRNA and discuss previous discrepancies and future challenges. We summarize recent advances and highlight the latest sequencing technologies for stoichiometric detection and their mechanistic investigations for functional unveiling in mRNA as the new research directions.

The micro black hole cellular battery: The ultimate limits of battery energy density

With the clean energy revolution, many methods of energy production, such as solar and wind power, are quite unstable because of weather variability. However, energy consumption remains relatively stable. Therefore, efficient energy storage could be crucial for the future. In this context, we will explore the theoretical limits of battery efficiency in terms of energy density. Surprisingly, although quite speculative, a potential solution might involve a cellular battery composed of micro black holes. In fact, let us suppose hypothetically that advanced future technology can handle the formation of black holes. Then, according to the extremal solution of the Reissner–Nordström metric from general relativity, such a battery could be stable and would not collapse into a larger black hole because the electromagnetic repulsion would precisely offset the force of gravity. Additionally, although it is generally assumed that nothing can escape from a black hole, a micro black hole could possibly annihilate another micro black hole, resulting in the release of an enormous amount of clean energy. For example, a battery weighing just one kilogram could provide approximately 470 million times the energy of the most efficient 200-kilogram lithium battery at the time of writing. While achieving such a level of technological advancement is certainly not imminent, it is not inconceivable that battery technology development could follow a trajectory similar to that of computer technology. Just as breakthroughs in physics and computer engineering have led to exponential growth in computer efficiency in the last 50 years, it is possible that battery efficiency could double or even quadruple every few years following different types of breakthroughs. Nonetheless, the theoretical concept of a micro black hole battery appears to align with current predictions of fundamental physics regarding the ultimate physical limits on energy density storage. This strongly indicates we are at the very early stage of battery technology, not even close to the end.

Contribution of Science and Technology to the Country’s National Security

In the face of Nepal's constant vulnerability to major geopolitical forces, the country has yet to fully harness technology to safeguard its national interests. This paper highlights how states, in demanding times, have wielded scientific technology to safeguard their security and address their interests. This paper draws attention to the importance of investment in innovation and research for the survival of a nation. Drawing upon historical, technological, and geopolitical perspectives, this paper explores the pivotal role of scientific and technological breakthroughs in shaping military strategies, and war efforts. The paper examines key moments when innovations such as the development of bow and arrow, gunpowder, RADAR, proximity fuse, cryptography, and computer malware have provided nations, civilizations, and communities with a vital advantage in safeguarding their security. Furthermore, the paper explores the mutual relationship between scientific research, innovation, and economic growth. It highlights how investments in science bolster a country's defence capabilities and foster innovative ecosystems that drive economic prosperity. Ultimately, this paper seeks to provide a comprehensive overview of the contributions of science to national security and well-being. Analyzing the relationship between innovation, strategy, and global dynamics; the paper highlights the imperativeness of nations to adapt, collaborate, and harness scientific advancements, to safeguard their interests in an increasingly complex world. Through an enriched understanding of the past and present contributions of science in national defence, Nepalese policymakers, military leadership, and the country’s scientific community can navigate the intricate landscape of national safe guardianship to ensure a secure and prosperous future for Nepal.

DNA-Encoded Library Technology─A Catalyst for Covalent Ligand Discovery.

The identification of novel covalent ligands for therapeutic purposes has long depended on serendipity, with dedicated hit finding techniques emerging only in the early 2000s. Advances in chemoproteomics have enabled robust characterization of putative drugs to derisk the unique liabilities associated with covalent hit molecules, leading to a renewed interest in this targeting modality. DNA-encoded library (DEL) technology has similarly emerged over the past two decades as a highly efficient method to identify new chemical equity toward protein targets of interest. A number of commercial and academic groups have reported methods in covalent DEL synthesis and hit identification; however, it is evident that there is still much to be done to fully realize the power of this technology for covalent ligand discovery. This perspective will explore the current approaches in covalent DEL technology and reflect on the next steps to advance this field.

Copper ion concentration detection based on quantum weak measurement of circular dichroism

In this paper, a high precision detection method of copper ion (Cu2+) concentration based on weak measurement is proposed. The ternary complex formed by Cu2+ with L-tryptophan and phenanthroline reagents has circular dichroism (CD) signal at 605 nm in visible light. The CD signal can be obtained using the intensity contrast as a pointer in the imaginary weak-value amplification scheme. The measured sensitivity and resolution are 0.3417 mradl/mg and 0.058 mg/l, respectively, which is improved by an order of magnitude compared to the traditional CD measurement method. It is also more accurate and less costly than commonly used Cu2+ detection methods. The Cu2+ concentration detection based on the weak measurement of CD signal detection proposed in this paper not only brings technological breakthroughs in the field of heavy metal ion detection, but also is of great significance in promoting the cross-development of chemistry, biomedicine, and life sciences.

Abstract 2067: QUANTROseq, a transcriptomic based drug discovery platform using time-resolved gene expression profiling: Identification of inhibitors and degraders of transcription factors and cell signaling regulators

Abstract Integrative analyses of CRISPR/Cas9 screening datasets from different sources revealed - besides known oncogenes - a series of transcriptional master regulators as most prominent context-specific dependencies in cancer that remained largely inaccessible to conventional drug discovery approaches. New chemical entities (NCEs) that specifically interfere with a transcription factor (TF) at any level should induce and be detectable based on selective effects on their primary biochemical activity - i.e., the transcriptional activation of specific target genes. However, the definition of direct targets of a TF and, more generally, the unbiased detection of direct transcriptional effects has remained challenging due to the limited time resolution of conventional techniques for gene perturbation and transcriptional profiling. To overcome this limitation, QUANTRO Therapeutics relies on SLAMseq technology that, by metabolic labeling de novo-synthesized RNA immediately after intervention and conserving the information for later readout, provides unprecedent time-resolution in transcriptomic analysis. Aiming to target previously undruggable targets, we set out to first build definitive knowledge around their direct effect on transcription, inducing their quick protein degradation through the auxin-inducible degron (AID) technology followed by immediate transcriptional readout though SLAMseq -before any secondary, confounding effect could appear in the cell. The targets´ mRNA fingerprints produced are then used as a reference and cross-compared with existing and new chemical entities to: (i) benchmark the real mode of action of existing drugs; (ii) identify new drug candidates that specifically interfere with disease-causing transcriptional programs in cancer; (iii) interrogate the efficiency, selectivity, and potency of drug candidates during Hit-validation and Hit-to-Lead activities. Proof of concept data demonstrated that time-resolved transcriptional profiling can reveal drugs´ mode of action with unprecedent precision and sensitivity, when drugs act on Targets that have a direct effect on transcriptional regulation - such as TFs and signaling pathways. Building on this knowledge, QUANTRO Therapeutics unleashed the unique potential of mRNA fingerprinting technology for HTS-compatible drug discovery by (1) establishing scalable and automated workflows for metabolic RNA labeling, treatment of cells, and RNA extraction; (2) developing algorithms for combinatorial mRNA fingerprint-readout that allow for the simultaneous assessment of TF signatures by multiplexed targeted RNA sequencing (QUANTROseq), thus transforming the precision and scope of cell-based compound screens. Citation Format: Arianna Sabò, Nina Fasching Griesmayr, Tobias Neumann, Raphael Manzenreither, Maja Ivankovic, Rodrigo Pacheco Valamatos Costa, Sarah Rieser, Sara Scinicariello, Petr Triska, Anna Stingeder, Astrid Gruss, Ryan Cubero, Olga Frank, Ivica Sowemimo, Paul Kirchgatterer, Adriana Cantoran, Stefan Ameres, Johannes Zuber, Michael Bauer. QUANTROseq, a transcriptomic based drug discovery platform using time-resolved gene expression profiling: Identification of inhibitors and degraders of transcription factors and cell signaling regulators [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2067.

Harnessing the Techno-Scientific Progress of the 21st Century for the Preservation of Life

The impact of science and technology today cannot be relegated to the background. There is basically no area of human endeavor that has not been affected directly or indirectly by technology. Advances in scientific research have been able to make life easy for man. Notwithstanding the blessings of technology in the 21st century, there is a wide range observation of the abuses of technology. This paper notes that humanity must not lose sight of the negative effects that may come from the abuse of technology and scientific researches. It is a fact that the problems of techno-scientific inventions and researches cannot be easily resolved; however, a direct confrontation between the discovery and instrumental model of science and technology could be avoided if techno-scientists and users utilize every good technology to the service of the human person. This paper recognizes the implication of techno-scientific progress of the 21st century bearing in mind as stated above, the creation and application of knowledge, with deep consideration of the happenstance between the works of those who make discoveries on one hand and those who use those discoveries, with the moral implications in view. It pays keen attention to the many ways man could harness the technology inherent in the 21st century to preserve rather than destroy life.

Impact of Artificial Intelligence in Education

Artificial Intelligence (AI) stands out as one of the paramount technological breakthroughs of the 21st century, holding immense promise for reshaping various facets of society, including the realm of education. This systematic review aims to explore the impact of AI on education by analyzing empirical studies from various academic databases. The review identifies three main areas where AI has been implemented in education: intelligent tutoring systems, personalized learning, and grading automation.Research findings indicate that educational tools utilizing Artificial Intelligence (AI) yield favorable effects on student learning outcomes, leading to enhanced academic achievement, heightened retention rates, and increased engagement.. Moreover, AI-based tools can also benefit educators, by automating administrative tasks and providing real-time feedback to students. Nevertheless, the review underscores various concerns associated with AI in education, encompassing privacy issues, biases, and the apprehension regarding technology potentially supplanting human educators. The study concludes that while AI- based educational tools show promise, more research is needed to better understand their effectiveness, as well as their potential drawbacks, in order to fully leverage their benefits for education.

Abstract 5141: Autoantibody profiling for predictive biomarkers for immune-related adverse events and clinical benefit in rare tumors treated with anti-PD-1 therapy

Abstract Background: Immune checkpoint inhibitors (ICIs) have changed the cancer treatment paradigm; however, with high frequency of immune-related adverse events (irAEs) and limited response rates in select patients. This underscores the unmet need for the development of biomarkers predictive for the development of irAEs and ICI response. Herein, we conducted autoantibody (AAb) profiling to assess baseline (BL) AAb associations with clinical benefit (CB) and irAEs in patients (pts) with rare tumors (RT) treated with programmed cell death protein 1 (anti-PD-1) therapy. Methods: AAbs against 1168 antigens were analyzed using the SeroTag antibody discovery technology. The study included 41 pts with RT who received anti-PD-1 therapy. We conducted a Significance Analysis of Microarray (SAM) to analyze the association between BL AAbs with irAEs and CB (defined as pts achieving complete response (CR), partial response (PR), or stable disease (SD) for ≥4 months by Response Evaluation Criteria in Solid Tumors (RECIST) v1.1). AAb biomaker candidates were discovered based on filtering for SAM hits with p < 0.05 and a delta score |D| >2. Results: Table 1 outlines patients’ demographics. CB and irAEs were observed in 34% and 39% of patients, respectively. Eighteen BL AAbs were significantly associated with CB such as DNA ligase III (LIG3) and small nuclear ribonucleoprotein polypeptide (SNRPA) (p = 0.002 and p = 0.003, respectively). Likewise, 13 BL AAbs showed significant associations with any-grade irAEs, particularly AAbs to breast cancer 2 (BRCA2) DNA repair associated antigen and chromatin-modifying protein 4A (CHMP4A) (p = 0.001 and p = 0.003, respectively). Conclusion: The predictive potential of autoantibodies as biomarkers is promising. Further evaluation in larger cohorts is needed for validation of our findings and understanding the underlying mechanism. Table 1. Patients’ demographics Total patients n=41 Median age in years (range) 66 (35-84) Male n=19 Female n=22 Tumor type Carcinoma of unknown primary n=13 Squamous cell carcinoma of the skin n=9 Small cell malignancies of non-pulmonary origin n=7 Adrenocortical carcinoma n=6 Vascular sarcoma n=5 Other rare histology n=1 Clinical benefit CR, PR, or SD ≥ 4 months n=14 SD <4 months or progressive disease (PD) n=23 Not evaluable n=4 irAEs Yes n=16 No n=25 Citation Format: Mohamed H. Derbala, Joud Hajjar, Bettzy Stephen, Serdar A. Gurses, Evan Kwiatkowski, Petra Budde, Hans-Dieter Zucht, Manuel Bräutigam, Ann-Sophie Schubert, Behnaz Ahangarianabhari, Enedelia Rodriguez, Mohamed Gouda, Lilibeth Castillo, Abdulrazzak Zarifa, Jeffrey A. How, Justin T. Moyers, David S. Hong, Funda Meric-Bernstam, Aung Naing. Autoantibody profiling for predictive biomarkers for immune-related adverse events and clinical benefit in rare tumors treated with anti-PD-1 therapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5141.

Bioinspired mp20 mimicking uricase in ZIF-8: Metal ion dependent for controllable activity

Mini protein mimicking uricase (mp20) has shown significant potential as a replacement for natural enzymes in the development of uric acid biosensors. However, the design of mp20 has resulted to an inactive form of peptide, causing of loss their catalytic activity. Herein, this paper delineates the impact of various metal cofactors on the catalytic activity of mp20. The metal ion-binding site prediction and docking (MIB) web server was employed to identify the metal ion binding sites and their affinities towards mp20 residues. Among the tested metal ions, Cu2+ displayed the highest docking score, indicating its preference for interaction with Thr16 and Asp17 residues of mp20. To assess the catalytic activity of mp20 in the presence of metal ions, uric acid assays was monitored using a colorimetric method. The presence of Cu2+ in the assays promotes the activation of mp20, resulting in a color change based on quinoid production. Furthermore, the encapsulation of the mp20 within zeolitic imidazolate framework-8 (ZIF-8) notably improved the stability of the biomolecule. In comparison to the naked mp20, the encapsulated ZIFs biocomposite (mp20@ZIF-8) demonstrates superior stability, selectivity and sensitivity. ZIF’s porous shells provides excellent protection, broad detection (3–100 μM) with a low limit (4.4 μM), and optimal function across pH (3.4–11.4) and temperature (20–100°C) ranges. Cost-effective and stable mp20@ZIF-8 surpasses native uricase, marking a significant biosensor technology breakthrough. This integration of metal cofactor optimization and robust encapsulation sets new standards for biosensing applications.

From McCulloch to GPT - 4: stages of development of artificial intelligence.

The article examines the history of the development of artificial intelligence (AI), starting from its first theoretical and practical steps and tracing the evolution to modern achievements. The article provides an overview of the key milestones, scientific discoveries and technological breakthroughs made in the field of AI. The most important figures, ideas and principles that influenced its development are also discussed. In the context of this development, various definitions of artificial intelligence are given. There are several key stages in the history of AI: the early stages, the quiet period, the AI renaissance, and the era of AI in the new millennium. Each of these stages made its own unique contribution to the progress of AI. The modern period is characterized by rapid development, especially in the field of machine learning and deep learning. These methods allow artificial intelligence to learn from data and identify complex patterns. Advances in natural language processing, such as models GPT and its modifications, have shown outstanding results. However, despite linguistic advances, GPT remains limited in aspects important to creating strong AI. The article discusses the limitations of modern language models, as well as the prerequisites and prospects for the development of strong artificial intelligence. Special attention is paid to the project of Elon Musk, who, having launched the company X.AI, is engaged in research in the field of creating strong AI with the goal of “knowledge of reality.” The article also proposes an alternative approach to creating strong artificial intelligence - the development of an artificial brain based on a multidimensional multi-connected receptor-effector neuron-like growing network. Some aspects of the emergence of artificial consciousness are also considered.

A Decadal Survey Without Analogs: Earth Observation Needs for a Warming World

AbstractSince 2007, the National Academy for Sciences Engineering and Medicine (NASEM) has recommended Earth Science research and investment priorities every 10 years. The Decadal Survey balances the continuation of essential climate variable time series against unmet measurement needs and new Earth Observations made possible by technological breakthroughs. The next survey (2027–2028, DS28) will be framed by a rapidly changing world, and it will be critical to anticipate the observational needs of the 2030s–2040s, a world increasingly dominated by climate extremes and a rapidly changing Earth system. Here, we highlight some of the changes that factor into a framework for the DS28.

The Inadequacy of the Copyright System in Addressing Digitalization and Internet Challenges

At the international as well as the local level, several different legal frameworks have been developed to affirm and protect fundamental rights. Copyright law has developed in reaction to technological advancements; since its inception. The most recent breakthroughs in digital technology; such as digital communications networks like the Internet and personal computers; are generating headlines throughout the world. This study attempts to add to the current discussion on copyright law; change by doing extensive research. This research seeks to provide light on the confluence between existing intellectual property laws; and the growing digital ecosystem. Overall, the stud aims to explore all the related issues and laws associated with intellectual property in the Pakistan which is relatively in the early phases.

Internet of Things security and privacy in Higher Education Institutions in Developing Countries

Technological breakthroughs like the Internet of Things (IoT) technology have tremendously impacted today's world and have led to changes in many spheres of life, including education. With the pervasiveness of IoT devices, learning and academic institutions are already integrating IoT into educational activities. Nonetheless, despite the numerous technological advancements and the demand for high-quality education for everyone, IoT's adoption and impact on education are still in their infancy. On the other hand, with the number of connected items having reached billions and still growing, there is cause for concern regarding the Internet of Things' potential for serious privacy violations, considering it is one of the most potent technologies for creating, modifying, and sharing data. Therefore, to emphasise the thematic areas and prospects for additional research related to the security and privacy of the Internet of Things (IoT) and within Higher Education Institutions (HEIs), this study explores the security and privacy of IoT in the context of HEIs within developing countries.

Life cycle assessment and techno-economic analysis of wood-based biorefineries for cellulosic ethanol production

Poplar is widely used in the paper industry and accompanied by abundant branches waste, which is potential feedstock for bioethanol production. Acid-chlorite pretreatment can selectively remove lignin, thereby significantly increasing enzymatic efficiency. Moreover, lignin residues valorization via gasification-syngas fermentation can achieve higher fuel yield. Herein, environmental and economic aspects were conducted to assess technological routes, which guides further process optimization. Life cycle assessment results show that wood-based biorefineries especially coupling scenarios have significant advantages in reducing global warming potential in contrast to fossil-based automotive fuels. Normalization results indicate that acidification potential surpasses other indicators as the primary impact category. In terms of economic feasibility, coupling scenarios present better investment prospects. Bioethanol yield is the most critical factor affecting market competitiveness. Minimum ethanol selling price below ethanol international market price is promising with higher-levels technology. Further work should be focused on technological breakthrough, consumable reduction or replacement.

The Rise and Fall of Technological Leadership: General-Purpose Technology Diffusion and Economic Power Transitions

Abstract How do technological revolutions affect the rise and fall of great powers? Scholars have long observed that major technological breakthroughs disrupt economic power balances, yet they rarely investigate how this process occurs. Existing studies establish that a nation’s success in adapting to revolutionary technologies is determined by the fit between its institutions and the demands of these technologies. The standard explanation emphasizes institutions suited for monopolizing innovation in new, fast-growing industries (leading sectors). I outline an alternative pathway based on general-purpose technologies (GPTs), foundational advances that boost productivity only after an extended diffusion process across many sectors. Specifically, GPT diffusion demands institutional adaptations that widen the base of engineering skills associated with a GPT. To test this argument, I set the GPT mechanism against the leading-sector mechanism across three cases, which correspond to past industrial revolutions: Britain’s rise to preeminence in the early nineteenth century; the United States’s overtaking of Britain before World War I; Japan’s challenge to US technological dominance in the late twentieth century. The findings support a novel explanation for technology-driven power transitions, directly bearing on how emerging technologies like AI, which some regard as driving a fourth industrial revolution, could influence a possible US–China power transition.

Advances in AI for Protein Structure Prediction: Implications for Cancer Drug Discovery and Development.

Recent advancements in AI-driven technologies, particularly in protein structure prediction, are significantly reshaping the landscape of drug discovery and development. This review focuses on the question of how these technological breakthroughs, exemplified by AlphaFold2, are revolutionizing our understanding of protein structure and function changes underlying cancer and improve our approaches to counter them. By enhancing the precision and speed at which drug targets are identified and drug candidates can be designed and optimized, these technologies are streamlining the entire drug development process. We explore the use of AlphaFold2 in cancer drug development, scrutinizing its efficacy, limitations, and potential challenges. We also compare AlphaFold2 with other algorithms like ESMFold, explaining the diverse methodologies employed in this field and the practical effects of these differences for the application of specific algorithms. Additionally, we discuss the broader applications of these technologies, including the prediction of protein complex structures and the generative AI-driven design of novel proteins.

Optimization of isolated intersection signal timing and trajectory planning under mixed traffic environment: The flexible catalysis of connected and automated vehicles

With the development of connected and automated vehicles (CAVs) enables real-time interaction between intersection signals and vehicle trajectories. The full use of this technological breakthrough will help traffic managers improve the performance of intersections. Most previous research focuses on the 100% CAV environment and the single lateral or longitudinal optimization of CAVs. Based on the flexible characteristics of CAVs, this paper proposes that CAV is regarded as the catalyst of vehicle platoon in mixed traffic environment and considers the uncertainty of CAVs and CHVs’ interaction in the real-world, which can promote the generation of controllable platoon through the “catalytic” mode of cooperative, accelerated, or direct lane-changing, and cooperative or direct overtaking. Simultaneously, a two-stage optimization model of mixed traffic trajectory and signal timing is proposed. Stage I: Based on the predicted vehicle platoon information, a dynamic NEMA signal timing scheme without duplicate structures is generated to minimize vehicle delays. Stage II: Based on the timing scheme, the generation of controllable and stable platoon and vehicle trajectory optimization model are established to minimize vehicle emissions. Dynamic programming with NEMA signal groups as sub-states is designed to solve the proposed model. The performance of the proposed model under different scenarios is investigated through numerical experiments and compared with benchmark models. Results show that the proposed model will outperform the benchmark models regarding average vehicle delay and emission under more realistic traffic demands. The average vehicle delay can be reduced by 54.32% and 7.33%, and the average vehicle emissions can be reduced by 19.1% and 0.8%, respectively. Meanwhile, the sensitivity analysis of CAV market penetration shows that the proposed model can perform satisfactorily at 20% CAV market penetration. Notedly, with increased market penetration, the proposed model will obtain better performance.