Waste Heat Recovery from Diesel Engine Exhaust Using a Single-Screw Expander Organic Rankine Cycle System: Experimental Investigation of Exergy Destruction

The organic Rankine cycle is a mature small-scale power generation technology for harnessing low- to mid-temperature heat sources. However, the low efficiency of the cycle still hinders its widespread implementation. To optimize the cycle’s performance, it is crucial to identify the source and magnitude of losses within each component of the cycle. This study, thus, aims to investigate the irreversible losses and their effect on the performance of the system. A prototype organic Rankine cycle (ORC) with the exhaust of a diesel engine as the heat source was developed to experimentally investigate the system and ascertain the losses. The experiments were performed at steady-state conditions at different evaporation pressures from 1300 kPa to 1600 kPa. The exergy loss and exergetic efficiency of the individual component and the overall system was estimated from the experimentally measurement of the pressure, temperature, and mass flow rate. The results indicate that the exergy losses of the evaporator are almost 60 kW at different evaporation pressures and the exergy loss rate is from 69.1% to 65.1%, which accounted for most of the total exergy loss rate in the organic Rankine cycle system. Meanwhile, the highest shaft efficiency and exergetic efficiency of the screw expander are 49.8% and 38.4%, respectively, and the exergy losses and exergy loss rate of the pump and pipe are less than 0.5 kW and 1%. Due to the relatively higher exergy loss of the evaporator and the low efficiency of expander, the highest exergetic efficiency of the organic Rankine cycle system is about 10.8%. The study concludes that the maximum improvement potential lies in the evaporator, followed by the expander

Rewarding Chatbots for Real-World Engagement with Millions of Users

The emergence of pretrained large language models has led to the deployment of a range of social chatbots for chitchat. Although these chatbots demonstrate language ability and fluency, they are not guaranteed to be engaging and can struggle to retain users. This work investigates the development of social chatbots that prioritize user engagement to enhance retention, specifically examining the use of human feedback to efficiently develop highly engaging chatbots. The proposed approach uses automatic pseudo-labels collected from user interactions to train a reward model that can be used to reject low-scoring sample responses generated by the chatbot model at inference time. Intuitive evaluation metrics, such as mean conversation length (MCL), are introduced as proxies to measure the level of engagement of deployed chatbots. A/B testing on groups of 10,000 new daily chatbot users on the Chai Research platform shows that this approach increases the MCL by up to 70%, which translates to a more than 30% increase in user retention for a GPT-J 6B model. Future work aims to use the reward model to realise a data fly-wheel, where the latest user conversations can be used to alternately fine-tune the language model and the reward model

Novel NIR-II organic fluorophores for bioimaging beyond 1550 nm

This work was partially supported by grants from NSFC (81773674, 81573383, and 21473041), NSFHP (2017CFA024, 2017CFB711, and 2016ACA126), the Applied Basic Research Program of WMBST (2019020701011429), Tibet Autonomous Region Science and Technology Plan Project Key Project (XZ201901-GB-11), Project First-Class Disciplines Development Supported by Chengdu University of Traditional Chinese Medicine (CZYJC1903), and Health Commission of Hubei Province Scientific Research Project (WJ2019M177 and WJ2019M178).Peer reviewedPublisher PD

Small molecular inhibitors reverse cancer metastasis by blockading oncogenic PITPNM3

Most cancer‐related deaths are a result of metastasis. The development of small molecular inhibitors reversing cancer metastasis represents a promising therapeutic opportunity for cancer patients. This pan‐cancer analysis identifies oncogenic roles of membrane‐associated phosphatidylinositol transfer protein 3 (PITPNM3), which is crucial for cancer metastasis. Small molecules targeting PITPNM3 must be explored further. Here, PITPNM3‐selective small molecular inhibitors are reported. These compounds exhibit target‐specific inhibition of PITPNM3 signaling, thereby reducing metastasis of breast cancer cells. Besides, by using nanoparticle‐based delivery systems, these PITPNM3‐selective compounds loaded nanoparticles significantly repress metastasis of breast cancer in mouse xenograft models and organoid models. Notably, the results establish an important metastatic‐promoting role for PITPNM3 and offer PITPNM3 inhibition as a therapeutic strategy in metastatic breast cancer

Sequencing and Genetic Variation of Multidrug Resistance Plasmids in Klebsiella pneumoniae

BACKGROUND: The development of multidrug resistance is a major problem in the treatment of pathogenic microorganisms by distinct antimicrobial agents. Characterizing the genetic variation among plasmids from different bacterial species or strains is a key step towards understanding the mechanism of virulence and their evolution. RESULTS: We applied a deep sequencing approach to 206 clinical strains of Klebsiella pneumoniae collected from 2002 to 2008 to understand the genetic variation of multidrug resistance plasmids, and to reveal the dynamic change of drug resistance over time. First, we sequenced three plasmids (70 Kb, 94 Kb, and 147 Kb) from a clonal strain of K. pneumoniae using Sanger sequencing. Using the Illumina sequencing technology, we obtained more than 17 million of short reads from two pooled plasmid samples. We mapped these short reads to the three reference plasmid sequences, and identified a large number of single nucleotide polymorphisms (SNPs) in these pooled plasmids. Many of these SNPs are present in drug-resistance genes. We also found that a significant fraction of short reads could not be mapped to the reference sequences, indicating a high degree of genetic variation among the collection of K. pneumoniae isolates. Moreover, we identified that plasmid conjugative transfer genes and antibiotic resistance genes are more likely to suffer from positive selection, as indicated by the elevated rates of nonsynonymous substitution. CONCLUSION: These data represent the first large-scale study of genetic variation in multidrug resistance plasmids and provide insight into the mechanisms of plasmid diversification and the genetic basis of antibiotic resistance

Aggregation-Induced Emission (AIE), Life and Health

Light has profoundly impacted modern medicine and healthcare, with numerous luminescent agents and imaging techniques currently being used to assess health and treat diseases. As an emerging concept in luminescence, aggregation-induced emission (AIE) has shown great potential in biological applications due to its advantages in terms of brightness, biocompatibility, photostability, and positive correlation with concentration. This review provides a comprehensive summary of AIE luminogens applied in imaging of biological structure and dynamic physiological processes, disease diagnosis and treatment, and detection and monitoring of specific analytes, followed by representative works. Discussions on critical issues and perspectives on future directions are also included. This review aims to stimulate the interest of researchers from different fields, including chemistry, biology, materials science, medicine, etc., thus promoting the development of AIE in the fields of life and health

Initial Public Offerings and the Firm Location

The firm geographic location matters in IPOs because investors have a strong preference for newly issued local stocks and provide abnormal demand in local offerings. Using equity holdings data for more than 53,000 households, we show the probability to participate to the stock market and the proportion of the equity wealth is abnormally increasing with the volume of the IPOs inside the investor region. Upon nearly the universe of the 167,515 going public and private domestic manufacturing firms, we provide consistent evidence that the isolated private firms have higher probability to go public, larger IPO underpricing cross-sectional average and volatility, and less pronounced long-run under-performance. Similar but opposite evidence holds for the local concentration of the investor wealth. These effects are economically relevant and robust to local delistings, IPO market timing, agglomeration economies, firm location endogeneity, self-selection bias, and information asymmetries, among others. Findings suggest IPO waves have a strong geographic component, highlight that underwriters significantly under-estimate the local demand component thus leaving unexpected money on the table, and support state-contingent but constant investor propensity for risk

The Economic Consequences of IPO Spinning

Using a sample of fifty-six companies going public in 1996--2000 in which top executives received allocations of other hot initial public offerings (IPOs) from the bookrunner, a practice known as spinning, we examine the consequences of spinning. The fifty-six IPOs had first-day returns that were, on average, 23% higher than similar IPOs. The profits collected by these executives were only a small fraction of the incremental amount of money left on the table by their companies when they went public. These companies were dramatically less likely to switch investment bankers in a follow-on offer: only 6% of issuers whose executives were spun switched underwriters, whereas 31% of other issuers switched. These findings suggest that the spinning of executives accomplished its goal of affecting corporate decisions. The Author 2010. Published by Oxford University Press on behalf of The Society for Financial Studies. All rights reserved. For Permissions, please e-mail: [email protected]., Oxford University Press.