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Existing approaches to automatic data transformation are insufficient to meet the requirements in many real-world scenarios, such as the building sector. First, there is no convenient interface for domain experts to provide domain knowledge easily. Second, they require significant training data collection overheads. Third, the accuracy suffers from complicated schema changes. To address these shortcomings, we present a novel approach that leverages the unique capabilities of large language models (LLMs) in coding, complex reasoning, and zero-shot learning to generate SQL code that transforms the source datasets into the target datasets. We demonstrate the viability of this approach by designing an LLM-based framework, termed SQLMorpher, which comprises a prompt generator that integrates the initial prompt with optional domain knowledge and historical patterns in external databases. It also implements an iterative prompt optimization mechanism that automatically improves the prompt based on flaw detection. The key contributions of this work include (1) pioneering an end-to-end LLM-based solution for data transformation, (2) developing a benchmark dataset of 105 real-world building energy data transformation problems, and (3) conducting an extensive empirical evaluation where our approach achieved 96% accuracy in all 105 problems. SQLMorpher demonstrates the effectiveness of utilizing LLMs in complex, domain-specific challenges, highlighting the potential of their potential to drive sustainable solutions.

The immersion weakening effect of natural soil has always been a difficult problem encountered in geotechnical engineering practice. The bond dissolution is a common cause of soil strength deterioration, which is not yet fully understood. In this study, a thermodynamic-based constitutive model of structural soils based on the [alpha] model is first established, considering the bond strength by modifying the yield surface size and gradually reducing the bond strength with the development of plastic strain. Furthermore, by taking the meso-mechanisms of bond dissolution into account, the evolution rule of the free energy during the bond dissolution process is derived based on a homogenization approach, and a thermodynamic-based constitutive model of structural soil with bond dissolution is thereafter developed. By comparing with the results of one-dimensional compression tests and conventional triaxial tests, the model is verified to be capable of reflecting the gradual destructuration process of soil while loading. The comparison with triaxial test results of completely decomposed granite after different immersion durations and parametric studies show that based on the cross-scale energy equivalence, the model can well reflect the strength deterioration characteristics of completely decomposed granite with bond dissolution mechanisms at the mesoscale fully considered. Key words: immersion weakening, bond dissolution, structural soil, thermodynamics, constitutive model. L'effet d'affaiblissement par immersion du sol naturel a toujours ete un probleme difficile rencontre dans la pratique de l'ingenierie geotechnique. La dissolution des liaisons est une cause frequente de la deterioration de la resistance des sols, qui reste encore mal comprise. Dans cette etude, un modele constitutif thermodynamique des sols structurels base sur le modele a est d'abord etabli, en considerant la force de liaison en modifiant la taille de la surface d'elasticite et en reduisant progressivement la force de liaison avec le developpement de la deformation plastique. En outre, en tenant compte des meso-mecanismes de dissolution des liaisons, la regle d'evolution de l'energie libre pendant le processus de dissolution des liaisons est derivee sur la base d'une approche d'homogeneisation, et un modele constitutif du sol structurel avec dissolution des liaisons, base sur la thermodynamique, est ensuite developpe. En comparant avec les resultats des essais de compression unidimensionnelle et des essais triaxiaux conventionnels, on verifie que le modele est capable de refleter le processus de destructuration progressive des sols en charge. La comparaison avec les resultats d'essais triaxiaux du granit completement decompose apres differentes durees d'immersion et les etudes parametriques montrent que, sur la base de l'equivalence energetique a l'echelle croisee, le modele peut bien refleter les caracteristiques de deterioration de la resistance du granit completement decompose avec des mecanismes de dissolution des liaisons a la meso-echelle entierement pris en compte. [Traduit par la Redaction] Mots-cles: affaiblissement par immersion, dissolution des liaisons, sol structurel, thermodynamique, modele constitutif., 1. Introduction The physical and chemical changes of soil after immersion in water are important factors affecting the mechanical properties of soil. The immersion weakening effect of some particular kinds [...]

This document provides a summary of various methods for the synthesis of tetrasubstituted axially chiral alkenes. The methods discussed include urea group-directed organocatalytic dihalogenation, Rh(III)-catalyzed C-H activation and directing group migration, cationic rhodium(I)/H8-BINAP-catalyzed chelation-controlled cycloaddition, Cu/Pd dual catalysis for arylboration, nickel-catalyzed cross-electrophile trans-aryl-benzylation, palladium-catalyzed C-H functionalization, Pd(II)-catalyzed thioether-directed alkenyl C-H olefination, Suzuki-Miyaura cross-coupling, asymmetric N-alkylation, chiral phosphoric acid catalyzed kinetic resolution, and asymmetric allylic substitution-isomerization. The methods demonstrate broad substrate scope and high enantioselectivities, but further research is needed to explore stereodivergent synthesis and applications in pharmaceutical chemistry. [Extracted from the article]

Fast radio bursts (FRBs) have been found in great numbers, but the physical mechanism of these sources is still a mystery. The redshift evolutions of the FRB energy distribution function and the volumetric rate shed light on the origin of FRBs. However, such estimations rely on the dispersion measurement (DM)–redshift (z) relationship. A few FRBs that have been detected recently show large excess DMs beyond the expectation from the cosmological and Milky Way contributions, which indicates large spread of DMs from their host galaxies. In this work, we adopt two lognormal-distributed DM host models and estimate the energy function using the non-repeating FRBs selected from the Canadian Hydrogen Intensity Mapping Experiment (CHIME)/FRB Catalog 1. By comparing the lognormal-distributed DM host models to a constant DM host model, the FRB energy function results are consistent within the measurement uncertainty. We also estimate the volumetric rate of the non-repeating FRBs in three different redshift bins. The volumetric rate shows that the trend is consistent with the stellar-mass density redshift evolution. Since the lognormal-distributed DM host model increases the measurement errors, the inference of FRBs tracking the stellar-mass density is nonetheless undermined. [ABSTRACT FROM AUTHOR]

Human-like automatic deductive reasoning has always been one of the most challenging open problems in the interdisciplinary field of mathematics and artificial intelligence. This paper is the third in a series of our works. We built a neural-symbolic system, named FGeo-DRL, to automatically perform human-like geometric deductive reasoning. The neural part is an AI agent based on deep reinforcement learning, capable of autonomously learning problem-solving methods from the feedback of a formalized environment, without the need for human supervision. It leverages a pre-trained natural language model to establish a policy network for theorem selection and employ Monte Carlo Tree Search for heuristic exploration. The symbolic part is a reinforcement learning environment based on geometry formalization theory and FormalGeo, which models geometric problem solving (GPS) as a Markov Decision Process (MDP). In the formal symbolic system, the symmetry of plane geometric transformations ensures the uniqueness of geometric problems when converted into states. Finally, the known conditions and objectives of the problem form the state space, while the set of theorems forms the action space. Leveraging FGeo-DRL, we have achieved readable and verifiable automated solutions to geometric problems. Experiments conducted on the formalgeo7k dataset have achieved a problem-solving success rate of 86.40%. [ABSTRACT FROM AUTHOR]

The application of contemporary artificial intelligence techniques to address geometric problems and automated deductive proofs has always been a grand challenge to the interdisciplinary field of mathematics and artificial intelligence. This is the fourth article in a series of our works, in our previous work, we established a geometric formalized system known as FormalGeo. Moreover, we annotated approximately 7000 geometric problems, forming the FormalGeo7k dataset. Despite the fact that FGPS (Formal Geometry Problem Solver) can achieve interpretable algebraic equation solving and human-like deductive reasoning, it often experiences timeouts due to the complexity of the search strategy. In this paper, we introduced FGeo-TP (theorem predictor), which utilizes the language model to predict the theorem sequences for solving geometry problems. The encoder and decoder components in the transformer architecture naturally establish a mapping between the sequences and embedding vectors, exhibiting inherent symmetry. We compare the effectiveness of various transformer architectures, such as BART or T5, in theorem prediction, and implement pruning in the search process of FGPS, thereby improving its performance when solving geometry problems. Our results demonstrate a significant increase in the problem-solving rate of the language model-enhanced FGeo-TP on the FormalGeo7k dataset, rising from 39.7% to 80.86%. Furthermore, FGeo-TP exhibits notable reductions in solution times and search steps across problems of varying difficulty levels. [ABSTRACT FROM AUTHOR]

Geometric problem solving (GPS) has always been a long-standing challenge in the fields of automated reasoning. Its problem representation and solution process embody rich symmetry. This paper is the second in a series of our works. Based on the Geometry Formalization Theory and the FormalGeo geometric formal system, we have developed the Formal Geometric Problem Solver (FGPS) in Python 3.10, which can serve as an interactive assistant or as an automated problem solver. FGPS is capable of executing geometric predicate logic and performing relational reasoning and algebraic computation, ultimately achieving readable, traceable, and verifiable automated solutions for geometric problems. We observed that symmetry phenomena exist at various levels within FGPS and utilized these symmetries to further refine the system's design. FGPS employs symbols to represent geometric shapes and transforms various geometric patterns into a set of symbolic operation rules. This maintains symmetry in basic transformations, shape constructions, and the application of theorems. Moreover, we also have annotated the formalgeo7k dataset, which contains 6981 geometry problems with detailed formal language descriptions and solutions. Experiments on formalgeo7k validate the correctness and utility of the FGPS. The forward search method with random strategy achieved a 39.71% problem-solving success rate. [ABSTRACT FROM AUTHOR]

A Janus layered metamaterial (JLM) operating in the terahertz (THz) region is proposed, which increases the direction dimension to manipulate the propagation of electromagnetic waves (EWs) through the introduction of nonlinear Kerr media. It achieves multi-scale multitasking with different logic gates and mode-switching biosensing, making a breakthrough on traditional single-function and single-scale THz devices. Due to the electro-optical effect, the liquid crystal (LC) is adjusted by the electric field, and the sharp transmission peak generated by the located defect mode resonance can be used to realize the forward scale logic product gate and the backward scale exclusive NOR gate. In addition, by locking the sharp transmission peak, the biosensing can have different detection ranges under the LC modulation. Therefore, when EWs propagate forward and backward, the mode switching of different biosensing categories with a glucose concentration of 300–550 g/l, creatinine concentration of 80.9–85.28 μmol/l, and the blood component in refractive index of 1.33–1.38 can be realized. The JLM proposed provides a possibility for the design of the chip-level compact optical integrated circuit and offers a new idea for multi-purpose biomedical detection, owning certain research value. [ABSTRACT FROM AUTHOR]

With the continuous construction of urban traffic roads, more and more new roads are cut off by existing roads to form "dead end roads". There is an urgent need for a trenchless method suitable for urban ultra-shallow overburden to build the undercrossing tunnel. To solve this problem, this paper proposed the micro pipe jacking and joint assembly structure (MPJ & JAS) method, which has the characteristics of shallow burial depth, low cost, short construction time, flexible cross-section setting and high space utilization. The MPJ & JAS method construct a large cross-section tunnel through assembling small cross-section elements, quite different from traditional methods. Therefore, this paper designed a CT-shaped integrated joint, the mechanical performance of which was verified and clarified by tensile test. The bending test and finite element (FE) analysis proved the reliability of MPJ & JAS tunnel structure, and confirmed the structure performances such as the failure models, crack behaviors, load–deflection response and stress–strain distribution. Moreover, the influences of the steel plate thickness, concrete strength and shear connector spacing were determined by the FE analysis. On the basis of test results and reasonable assumptions, a theoretical design method considering the influence of the CT-shaped integrated joint was proposed, which can effectively predict the bending strength of the MPJ & JAS tunnel structure with an error of less than 10%. Finally, in view of the characteristics of the MPJ & JAS method, the suitable micro pipe jacking machine, soil reinforcement measure, hydraulic traction construction technology, high-precision guidance system and concrete construction quality detection method based on the phased array ultrasonic imaging technology were developed, supporting the accurate and efficient construction of the MPJ & JAS tunnel. [ABSTRACT FROM AUTHOR]

A one-dimensional optical biosensing metastructure (OBM) with graphene layers is presented in this paper. It is realized by coherent perfect absorption (CPA) and operates in the transverse electric mode. It shows a strong linear fitting relationship between the refractive index (RI) of the analysis layer and the frequency corresponding to the absorption peak, and the R-square is up to 1. Additionally, based on the principle of CPA, the OBM can realize the function of multiple cancer cell detection by adjusting the detection range by controlling the phase difference of coherent electromagnetic waves. Its detection ranges are 1.34–1.355 and 1.658–1.662. Thanks to its high-quality factor, great figure of merit, and low detection limit, whose best values are, respectively, 6.9 × 104, 1.2 × 104 RIU−1, and 3.6 × 10−6 RIU, the detection of weak changes in the RI of a cancer cell is possible. Additionally, its sensitivity can reach 26.57 THz RIU−1. This OBM based on CPA has major implications for advancing the study and investigation into the application of CPA. It also provides a simple and efficient approach to distinguish cancer cells and may be widely used in the biomedical field. [ABSTRACT FROM AUTHOR]

In this study, the form factors of B (s) to light D-wave tensor mesons with J PC = 2 - - ( ρ 2 , ω 2 , K 2 and ϕ 2 ) are calculated via the light cone sum rules (LCSR) in the framework of heavy quark effective eld theory (HQEFT). Firstly, the expressions of form factors in terms of the light cone distribution amplitudes (LCDAs) of tensor mesons are derived via the LCSR at the leading order of heavy quark expansion. Similar to the case of 2 + + tensor mesons, the penguin type form factors can be obtained directly from the corresponding semileptonic ones. Considering the light tensor meson LCDAs to twist-3 and quark mass corrections, we give the numerical results of form factors systematically. Based on the form factors given here, we investigate the branching ratios, longitudinal polarization fractions and forward-backward asymmetries of relevant charged current induced semileptonic decays. Our results may be tested by more precise experiments in the future. [ABSTRACT FROM AUTHOR]

Many SARS-CoV-2 neutralizing antibodies (nAbs) lose potency against variants of concern. In this study, we developed 2 strategies to produce mutation-resistant antibodies. First, a yeast library expressing mutant receptor binding domains (RBDs) of the spike protein was utilized to screen for potent nAbs that are least susceptible to viral escape. Among the candidate antibodies, P5-22 displayed ultrahigh potency for virus neutralization as well as an outstanding mutation resistance profile. Additionally, P14-44 and P15-16 were recognized as mutation-resistant antibodies with broad betacoronavirus neutralization properties. P15-16 has only 1 binding hotspot, which is K378 in the RBD of SARS-CoV-2. The crystal structure of the P5-22, P14-44, and RBD ternary complex clarified the unique mechanisms that underlie the excellent mutation resistance profiles of these antibodies. Secondly, polymeric IgG enhanced antibody avidity by eliminating P5-22's only hotspot, residue F486 in the RBD, thereby potently blocking cell entry by mutant viruses. Structural and functional analyses of antibodies screened using both potency assays and the yeast RBD library revealed rare, ultrapotent, mutation-resistant nAbs against SARS-CoV-2., Introduction Neutralizing antibodies (nAbs), which show efficacy for prophylactic as well as therapeutic use, are being developed against the spike (S) protein of SARS-CoV-2 (1-8). These nAbs have shown efficacy [...]

Biodiversity drives ecosystem processes, but its influence on deadwood decomposition is poorly understood. To test the effects of insect diversity on wood decomposition, we conducted a mesocosm experiment manipulating the species richness and functional diversity of beetles. We applied a novel approach using computed tomography scanning to quantify decomposition by insects and recorded fungal and bacterial communities. Decomposition rates increased with both species richness and functional diversity of beetles, but the effects of functional diversity were linked to beetle biomass, and to the presence of one large‐bodied species in particular. This suggests that mechanisms behind observed biodiversity effects are the selection effect, which is linked to the occurrence probability of large species, and the complementarity effect, which is driven by functional differentiation among species. Additionally, beetles had significant indirect effects on wood decomposition via bacterial diversity, fungal community composition, and fungal biomass. Our experiment shows that wood decomposition is driven by beetle diversity and its interactions with bacteria and fungi. This highlights that both insect and microbial biodiversity are critical to maintaining ecosystem functioning. [ABSTRACT FROM AUTHOR]

Background: In the past two years, studies have found a significant increase in neutrophil extracellular traps (NETs) in patients with IgA vasculitis (IgAV), which is correlated with the severity of the disease. NETs have been reported as an intervention target in inflammatory and autoimmune diseases. This study aimed to investigate the effect of targeted degradation of NETs using DNase I in IgAV rat model. Methods: Twenty-four Sprague-Dawley rats were randomly divided into three groups: the IgAV model group, the DNase I intervention group and the normal control group, with an average of 8 rats in each group. The model group was established by using Indian ink, ovalbumin, and Freund's complete adjuvant. In the intervention group, DNase I was injected through tail vein 3 days before the end of established model. The circulating cell free-DNA (cf-DNA) and myeloperoxidase-DNA (MPO-DNA) were analyzed. The presence of NETs in the kidney, gastric antrum and descending duodenum were detected using multiple fluorescences immunohistochemistry and Western blots. Morphological changes of the tissues were observed. Results: After the intervention of DNase I, there was a significant reduction in cf-DNA and MPO-DNA levels in the intervention group compared to the IgAV model group (all P<0.001). The presence of NETs in renal, gastric, and duodenal tissues of the intervention group exhibited a significant decrease compared to the IgAV model group (P < 0.01). Moreover, the intervention group demonstrated significantly lower levels of renal MPO and citrullinated histone H3 (citH3) protein expression when compared to the IgAV model group (all P < 0.05). The HE staining results of intervention group demonstrated a significant reduction in congestion within glomerular and interstitial capillaries. Moreover, there was a notable improvement in gastric and intestinal mucosa necrosis, congestion and bleeding. Additionally, there was a substantial decrease in inflammatory cells infiltration. Conclusion: The degradation of NETs can be targeted by DNase I to mitigate tissue damage in IgAV rat models. Targeted regulation of NETs holds potential as a therapeutic approach for IgAV. [ABSTRACT FROM AUTHOR]