Towards an Understanding of Large Language Models in Software Engineering Tasks

Large Language Models (LLMs) have drawn widespread attention and research due to their astounding performance in tasks such as text generation and reasoning. Derivative products, like ChatGPT, have been extensively deployed and highly sought after. Meanwhile, the evaluation and optimization of LLMs in software engineering tasks, such as code generation, have become a research focus. However, there is still a lack of systematic research on the application and evaluation of LLMs in the field of software engineering. Therefore, this paper is the first to comprehensively investigate and collate the research and products combining LLMs with software engineering, aiming to answer two questions: (1) What are the current integrations of LLMs with software engineering? (2) Can LLMs effectively handle software engineering tasks? To find the answers, we have collected related literature as extensively as possible from seven mainstream databases, and selected 123 papers for analysis. We have categorized these papers in detail and reviewed the current research status of LLMs from the perspective of seven major software engineering tasks, hoping this will help researchers better grasp the research trends and address the issues when applying LLMs. Meanwhile, we have also organized and presented papers with evaluation content to reveal the performance and effectiveness of LLMs in various software engineering tasks, providing guidance for researchers and developers to optimize

Polyamine Function in Plants: Metabolism, Regulation on Development, and Roles in Abiotic Stress Responses

Polyamines (PAs) are low molecular weight aliphatic nitrogenous bases containing two or more amino groups. They are produced by organisms during metabolism and are present in almost all cells. Because they play important roles in diverse plant growth and developmental processes and in environmental stress responses, they are considered as a new kind of plant biostimulant. With the development of molecular biotechnology techniques, there is increasing evidence that PAs, whether applied exogenously or produced endogenously via genetic engineering, can positively affect plant growth, productivity, and stress tolerance. However, it is still not fully understood how PAs regulate plant growth and stress responses. In this review, we attempt to cover these information gaps and provide a comprehensive and critical assessment of the published literature on the relationships between PAs and plant flowering, embryo development, senescence, and responses to several (mainly abiotic) stresses. The aim of this review is to summarize how PAs improve plants' productivity, and to provide a basis for future research on the mechanism of action of PAs in plant growth and development. Future perspectives for PA research are also suggested

An Inhibitory Effect of Extracellular Ca2+ on Ca2+-Dependent Exocytosis

Aim: Neurotransmitter release is elicited by an elevation of intracellular Ca 2+ concentration ([Ca 2+] i). The action potential triggers Ca 2+ influx through Ca 2+ channels which causes local changes of [Ca 2+] i for vesicle release. However, any direct role of extracellular Ca 2+ (besides Ca 2+ influx) on Ca 2+-dependent exocytosis remains elusive. Here we set out to investigate this possibility on rat dorsal root ganglion (DRG) neurons and chromaffin cells, widely used models for studying vesicle exocytosis. Results: Using photolysis of caged Ca 2+ and caffeine-induced release of stored Ca 2+, we found that extracellular Ca 2+ inhibited exocytosis following moderate [Ca 2+]i rises (2–3 mM). The IC50 for extracellular Ca 2+ inhibition of exocytosis (ECIE) was 1.38 mM and a physiological reduction (,30%) of extracellular Ca 2+ concentration ([Ca 2+]o) significantly increased the evoked exocytosis. At the single vesicle level, quantal size and release frequency were also altered by physiological [Ca 2+] o. The calcimimetics Mg 2+,Cd 2+, G418, and neomycin all inhibited exocytosis. The extracellular Ca 2+-sensing receptor (CaSR) was not involved because specific drugs and knockdown of CaSR in DRG neurons did not affect ECIE. Conclusion/Significance: As an extension of the classic Ca 2+ hypothesis of synaptic release, physiological levels of extracellular Ca 2+ play dual roles in evoked exocytosis by providing a source of Ca 2+ influx, and by directly regulatin

Potential of Core-Collapse Supernova Neutrino Detection at JUNO

JUNO is an underground neutrino observatory under construction in Jiangmen, China. It uses 20kton liquid scintillator as target, which enables it to detect supernova burst neutrinos of a large statistics for the next galactic core-collapse supernova (CCSN) and also pre-supernova neutrinos from the nearby CCSN progenitors. All flavors of supernova burst neutrinos can be detected by JUNO via several interaction channels, including inverse beta decay, elastic scattering on electron and proton, interactions on C12 nuclei, etc. This retains the possibility for JUNO to reconstruct the energy spectra of supernova burst neutrinos of all flavors. The real time monitoring systems based on FPGA and DAQ are under development in JUNO, which allow prompt alert and trigger-less data acquisition of CCSN events. The alert performances of both monitoring systems have been thoroughly studied using simulations. Moreover, once a CCSN is tagged, the system can give fast characterizations, such as directionality and light curve

Detection of the Diffuse Supernova Neutrino Background with JUNO

As an underground multi-purpose neutrino detector with 20 kton liquid scintillator, Jiangmen Underground Neutrino Observatory (JUNO) is competitive with and complementary to the water-Cherenkov detectors on the search for the diffuse supernova neutrino background (DSNB). Typical supernova models predict 2-4 events per year within the optimal observation window in the JUNO detector. The dominant background is from the neutral-current (NC) interaction of atmospheric neutrinos with 12C nuclei, which surpasses the DSNB by more than one order of magnitude. We evaluated the systematic uncertainty of NC background from the spread of a variety of data-driven models and further developed a method to determine NC background within 15\% with {\it{in}} {\it{situ}} measurements after ten years of running. Besides, the NC-like backgrounds can be effectively suppressed by the intrinsic pulse-shape discrimination (PSD) capabilities of liquid scintillators. In this talk, I will present in detail the improvements on NC background uncertainty evaluation, PSD discriminator development, and finally, the potential of DSNB sensitivity in JUNO

Sol-gel preparation of self-cleaning SiO2-TiO2/SiO2-TiO2 double-layer antireflective coating for solar glass

Self-cleaning SiO2-TiO2/SiO2-TiO2 double-layer antireflective (AR) coating is prepared by sol-gel process. SiO2 sol is prepared by using tetraethyl orthosilicate (TEOS) as precursor and ammonia as catalyst, while TiO2 sol was prepared by using tetrabutyl orthotitanate (TBOT) as precursor and hydrochloric acid as catalyst. The effect of TiO2 content on refractive index, abrasion-resistance and photo-catalytic activity of SiO2-TiO2 hybrid thin films or powders is systematically investigated. It is found that the refractive index of SiO2-TiO2 hybrid thin films increases gradually from 1.18 to 1.53 as the weight ratio of TiO2 to SiO2 increased from 0 to 1.0. The SiO2-TiO2 hybrid thin film and powder possesses good abrasion-resistance and photo-catalytic activity, respectively, as the weight ratio of TiO2 to SiO2 is 0.4. The degradation degree of Rhodamine B by SiO2-TiO2 hybrid powder is 88.3%. Finally, SiO2-TiO2/SiO2-TiO2 double-layer AR coating with high transmittance, abrasion-resistance and self-cleaning property is realized. Keywords: Sol-gel process, Antireflective coating, Silica, Titania, Self-cleanin