Unfolding environmental γ\gamma flux spectrum with portable CZT detector

Environmental $\gamma$-rays constitute a crucial source of background in various nuclear, particle and quantum physics experiments. To evaluate the flux rate and the spectrum of $\gamma$ background, we have developed a novel and straightforward approach to reconstruct the environmental $\gamma$ flux spectrum by applying a portable CZT $\gamma$ detector and iterative Bayesian unfolding, which possesses excellent transferability for broader applications. In this paper, the calibration and GEANT4 Monte-Carlo modeling of the CZT detector, the unfolding procedure as well as the uncertainty estimation are demonstrated in detail. The reconstructed spectrum reveals an environmental $\gamma$ flux intensity of $3.3\pm 0.9\times 10^{7}$~ (m$^2\cdot$sr$\cdot$hour)$^{-1}$ ranging from 73 to 3033~keV, along with characteristic peaks primarily arising from $^{232}$Th series, $^{238}$U series and $^{40}$K. We also give an instance of background rate evaluation with the unfolded spectrum for validation of the approach

Data-Driven Parameter Selection and Modeling for Concrete Carbonation

Concrete carbonation is known as a stochastic process. Its uncertainties mainly result from parameters that are not considered in prediction models. Parameter selection, therefore, is important. In this paper, based on 8204 sets of data, statistical methods and machine learning techniques were applied to choose appropriate influence factors in terms of three aspects: (1) the correlation between factors and concrete carbonation; (2) factors’ influence on the uncertainties of carbonation depth; and (3) the correlation between factors. Both single parameters and parameter groups were evaluated quantitatively. The results showed that compressive strength had the highest correlation with carbonation depth and that using the aggregate–cement ratio as the parameter significantly reduced the dispersion of carbonation depth to a low level. Machine learning models manifested that selected parameter groups had a large potential in improving the performance of models with fewer parameters. This paper also developed machine learning carbonation models and simplified them to propose a practical model. The results showed that this concise model had a high accuracy on both accelerated and natural carbonation test datasets. For natural carbonation datasets, the mean absolute error of the practical model was 1.56 mm

Machine Learning-Based Models for Shear Strength Prediction of UHPFRC Beams

Estimating shear strength is a crucial aspect of beam design. The goal of this research is to develop a shear strength calculation technique for ultra-high performance fiber reinforced concrete (UHPFRC) beams. To begin, a shear test database of 200 UHPFRC beam specimens is established. Then, random forest (RF) is used to evaluate the importance of influence factors for the shear strength of UHPFRC beams. Subsequently, three machine learning (ML)-based models, including artificial neural network (ANN), support vector regression (SVR), and eXtreme-gradient boosting (XGBoost), are proposed to compute shear strength. Results demonstrate that the area of longitudinal reinforcement has the greatest influence on the shear capacity of UHPFRC beams, and ten parameters with high importance (e.g., the area of longitudinal reinforcement, the stirrup strength, the cross-section area, the shear span ratio, fiber volume fraction, etc.) are selected as input parameters. The models of ANN, SVR, and XGBoost have close accuracy, and their R2 are 0.8825, 0.9016, and 0.8839, respectively, which are much larger than those of existing theoretical models. In addition, the average ratios of prediction values of ANN, SVR, and XGBoost models to experimental results are 1.08, 1.02, and 1.10, respectively; the coefficients of variation are 0.28, 0.21, and 0.28, respectively. The SVR model has the best accuracy and reliability. The accuracy and reliability of ML-based models are much better than those of existing models for calculating the shear strength of UHPFRC beams.Applied Science, Faculty ofNon UBCCivil Engineering, Department ofReviewedFacult

Prediction of Neutralization Depth of R.C. Bridges Using Machine Learning Methods

Machine learning techniques have become a popular solution to prediction problems. These approaches show excellent performance without being explicitly programmed. In this paper, 448 sets of data were collected to predict the neutralization depth of concrete bridges in China. Random forest was used for parameter selection. Besides this, four machine learning methods, such as support vector machine (SVM), k-nearest neighbor (KNN) and XGBoost, were adopted to develop models. The results show that machine learning models obtain a high accuracy (>80%) and an acceptable macro recall rate (>80%) even with only four parameters. For SVM models, the radial basis function has a better performance than other kernel functions. The radial basis kernel SVM method has the highest verification accuracy (91%) and the highest macro recall rate (86%). Besides this, the preference of different methods is revealed in this study

A Novel Insecticidal Peptide SLP1 Produced by Streptomyces laindensis H008 against Lipaphis erysimi

Aphids are major insect pests for crops, causing damage by direct feeding and transmission of plant diseases. This paper was completed to discover and characterize a novel insecticidal metabolite against aphids from soil actinobacteria. An insecticidal activity assay was used to screen 180 bacterial strains from soil samples against mustard aphid, Lipaphis erysimi. The bacterial strain H008 showed the strongest activity, and it was identified by the phylogenetic analysis of the 16S rRNA gene and physiological traits as a novel species of genus Streptomyces (named S. laindensis H008). With the bioassay-guided method, the insecticidal extract from S. laindensis H008 was subjected to chromatographic separations. Finally, a novel insecticidal peptide was purified from Streptomyces laindensis H008 against L. erysimi, and it was determined to be S-E-P-A-Q-I-V-I-V-D-G-V-D-Y-W by TOF-MS and amino acid analysis

Potential association between exposure to legacy persistent organic pollutants and parasitic body burdens in Indo-Pacific finless porpoises from the Pearl River Estuary, China

A high prevalence of infectious diseases (mostly lungworms) is found in finless porpoises (genus Neophocaena) in the coastal waters of China, which is one of the most dichlorodiphenyltrichloroethane (DDT)- polluted areas worldwide, while its association with contaminant exposure remains undetermined. To address this gap, we investigated blubber levels of polychlorinated diphenyls (PCBs), organochlorine pesticides (OCPs) and polycyclic aromatic hydrocarbons in Indo- Pacific finless porpoises (Neophocaena phocaenoides) stranded in the Pearl River Estuary (PRE) of China. In the post- mortem examinations, lungworms (Halocercus species) were found to be the most common parasites, with a high density observed in lungs and bronchi. Severe infections by nematode parasites were also found in the uterus (Cystidicola species), intestine (Anisakis typica) and muscle (A. typica). For all the pollutant compounds analyzed, only the concentrations of p,p'-DDT, p,p'dichlorodiphenyldichloroethane (DDD) and o,p'-DDD were significantly higher in porpoises died of infectious diseases than in the "healthy" individuals (died from physical trauma). Contrasted accumulation pattern of DDTs and their metabolites was found between animals with different health status. The proportion of p, p'DDT in Sigma DDTs was higher than that of p,p'- dichlorodiphenyldichloroethylene (DDE) in diseased animals, whereas an opposite pattern was shown for "healthy" ones. While this study is the first to describe a significant positive correlation between parasitic diseases and high levels of DDTs in cetaceans, the direction of causality cannot be determined in our data: either a parasitic infection affected the porpoises' ability to metabolize DDTs, resulting in high levels of p,p'-DDT in their blubber, or the pollutant burden rendered them more susceptible to parasitic infection. (c) 2018 Elsevier B.V. All rights reserved

Expanding the Potential of Neoantigen Vaccines: Harnessing Bacille Calmette–Guérin Cell-Wall-Based Nanoscale Adjuvants for Enhanced Cancer Immunotherapy

Personalized antitumor immunotherapy utilizing neoantigen vaccines holds great promise. However, the limited immunogenicity of existing recognized neoantigens and the inadequate stimulation of antitumor immune responses by conventional adjuvants pose significant challenges. To address these limitations, we developed a nanovaccine that combines a BCG bacterial cell wall skeleton (BCG-CWS) based nanoscale adjuvant (BCNA) with peptide neoantigens (M27 and M30). This integrated approach provides an efficient translational strategy for cancer immunotherapy. The BCNA nanovaccine, formulated with PLGA as an emulsifier, exhibits excellent biocompatibility and superior antigen presentation compared with conventional BCG-CWS adjuvants. Subcutaneous immunization with the BCNA-based nanovaccine effectively targets lymph nodes, eliciting robust innate and tumor-specific immune responses. Importantly, our findings demonstrate that BCNAs significantly enhance neoantigen immunogenicity while minimizing acute systemic toxicity. Furthermore, when combined with a mouse PD-L1 antibody, our strategy achieves complete tumor elimination in 60% of cases and prevents 25% of tumor growth in a melanoma mouse model. In conclusion, our BCNA-based nanovaccine represents a promising avenue for advancing personalized therapeutic neoantigen vaccines and holds significant implications for enhancing personalized immunotherapy and improving patient outcomes in the field of cancer treatment