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BACKGROUND: Egg laying rate (LR) is associated with a clutch, which is defined as consecutive days of oviposition. The clutch trait can be used as a selection indicator to improve egg production in poultry breeding. However, little is known about the genetic basis of clutch traits. In this study, our aim was to estimate genetic parameters and identify quantitative trait single nucleotide polymorphisms for clutch traits in 399 purebred Laiwu Black chickens (a native Chinese breed) using a genome-wide association study (GWAS). METHODS: In this work, after estimating the genetic parameters of age at first egg, body weight at first egg, LR, longest clutch until 52 week of age, first week when the longest clutch starts, last week when the longest clutch ends, number of clutches, and longest number of days without egg-laying until 52 week of age, we identified single nucleotide polymorphisms (SNPs) and potential candidate genes associated with clutch traits in Laiwu Black chickens. The restricted maximum likelihood method was used to estimate genetic parameters of clutch pattern in 399 Laiwu Black hens, using the GCTA software. RESULTS: The results showed that SNP-based heritability estimates of clutch traits ranged from 0.06 to 0.59. Genotyping data were obtained from whole genome re-sequencing data. After quality control, a total of 10,810,544 SNPs remained to be analyzed. The GWAS revealed that 421 significant SNPs responsible for clutch traits were scattered on chicken chromosomes 1-14, 17-19, 21-25, 28 and Z. Among the annotated genes, NELL2, SMYD9, SPTLC2, SMYD3 and PLCL1 were the most promising candidates for clutch traits in Laiwu Black chickens. CONCLUSION: The findings of this research provide critical insight into the genetic basis of clutch traits. These results contribute to the identification of candidate genes and variants. Genes and SNPs potentially provide new avenues for further research and would help to establish a framework for new methods of genomic prediction, and increase the accuracy of estimated genetic merit for egg production and clutch traits.

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Sustainable management is a challenging task for large building infrastructures due to the uncertainties associated with daily events as well as the vast yet isolated functionalities. To improve the situation, a sustainable digital twin (DT) model of operation and maintenance for building infrastructures, termed SDTOM-BI, is proposed in this paper. The proposed approach is able to identify critical factors during the in-service phase and achieve sustainable operation and maintenance for building infrastructures: (1) by expanding the traditional 'factor-energy consumption' to three parts of 'factor-event-energy consumption', which enables the model to backtrack the energy consumption-related factors based on the relevance of the impact of random events; (2) by combining with the Bayesian network (BN) and random forest (RF) in order to make the correlation between factors and results more clear and forecasts more accurate. Finally, the application is illustrated and verified by the application in a real-world gymnasium.

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Follicle selection is an important step in the laying process of chicken, which is closely related to the laying performance and fecundity of hens. Follicle selection mainly depends on the regulation of follicle-stimulating hormone (FSH) secreted by pituitary gland and the expression of follicle stimulation hormone receptor. To uncover the role of FSH in chicken follicle selection, in this study, we analyzed the changes in the mRNA transcriptome profiles of FSH-treated chicken granulosa cells from prehierarchical follicles by long-read sequencing Oxford Nanopore Technologies (ONT) approach. Among the 10,764 genes detected, 31 differentially expressed (DE) transcripts of 28 DE genes were significantly upregulated by FSH treatment. These DE transcripts (DETs) were mainly related to the steroid biosynthetic process by GO analysis and enriched in pathways of ovarian steroidogenesis and aldosterone synthesis and secretion by KEGG analysis. Among these genes, the mRNA and protein expression of TNF receptor associated factor 7 (TRAF7) was upregulated after FSH treatment. Further study revealed that TRAF7 stimulated the mRNA expression of steroidogenic enzymes steroidogenic acute regulatory protein (StAR) and cytochrome P450 family 11 subfamily A member 1 (CYP11A1) genes and the proliferation of granulosa cells. This is the first study to investigate differences in chicken prehierarchical follicular granulosa cells before and after FSH treatment by using ONT transcriptome sequencing, which provides a reference for a more comprehensive understanding of the molecular mechanism of follicle selection in chicken.

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Prefabricated buildings have advantages when it comes to environmental protection. However, the dynamics and complexity of building hoisting operations bring significant safety risks. Existing research on hoisting safety risk lacks a real-time information interaction mechanism and lacks scientific control decision-making tools based on considering the correlation between safety risks. Digital twin (DT) has the advantage of real-time interaction. This paper presents a safety risk control framework for controlling prefabricated building hoisting operations based on DT. In the case of considering the correlation of the safety risk index of hoisting, the safety risk hierarchy model of hoisting is defined in the process of building the DT model. The authors have established a Bayesian network model into the process of the integrated analysis of the digital twin mechanism model and monitoring data to realize the visualization of the decision analysis process of hoisting safety risk control. The key degree of the indirect inducement variable to direct inducement variable was calculated according to probability. The key factor leading to the occurrence of risk was found. The effectiveness of the hoisting safety risk control method is verified by a large, prefabricated building project. This method provides decision tools for hoisting safety risk control, assists in formulating effective control schemes, and improves the efficiency of information integration and sharing.

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Civil infrastructure O&M requires intelligent monitoring techniques and control methods to ensure safety. Unfortunately, tedious modeling efforts and the rigorous computing requirements of large-scale civil infrastructure have hindered the development of structural research. This study proposes a method for impact response prediction of prestressed steel structures driven by digital twins (DTs) and machine learning (ML). The high-fidelity DTs of a prestressed steel structure were constructed from the perspective of both a physical entity and virtual entity. A prediction of the impact response of prestressed steel structure's key parts was established based on ML, and a structure response prediction of the parts driven by data was realized. To validate the effectiveness of the proposed prediction method, the authors carried out a case study in an experiment of a prestressed steel structure. This study provides a reference for fusion applications with DTs and ML in impact response prediction and analysis of prestressed steel structures.

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Rapid urbanization has made urban space thermal environment (USTE) problems increasingly prominent. USTE research is important for improving urban ecological environment and building energy consumption. Most studies on USTE research progress have focused on meteorological observations and remote sensing methods, and few studies on USTE are based on computational fluid dynamics (CFD). During the past two decades, with the increasing applications of CFD in USTE research, comprehensively summarizing the phased results have become necessary tasks. This paper analyzes the current research status of CFD-based USTE simulation from six perspectives. First, we summarize the current research status of USTE simulation with CFD models that integrate ground observations and remote sensing technology. Second, we define and classify the spatial scope of CFD-based USTE simulations at different scales. Third, we systematically analyze the quantitative relationships among urban land type, the underlying surface structure, water bodies, green space and the corresponding changes in CFD-based USTE simulations. Fourth, we quantitatively analyze the impact of anthropogenic heat in CFD-based USTE simulations. Fifth, we summarize the corresponding USTE mitigation measures and methods based on the CFD simulation results. Finally, the outlooks and the existing problems in current research on CFD simulations of the USTE are analyzed.

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Safety management in hoisting is the key issue to determine the development of prefabricated building construction. However, the security management in the hoisting stage lacks a truly effective method of information physical fusion, and the safety risk analysis of hoisting does not consider the interaction of risk factors. In this paper, a hoisting safety risk management framework based on digital twin (DT) is presented. The digital twin hoisting safety risk coupling model is built. The proposed model integrates the Internet of Things (IoT), Building Information Modeling (BIM), and a security risk analysis method combining the Apriori algorithm and complex network. The real-time perception and virtual-real interaction of multi-source information in the hoisting process are realized, the association rules and coupling relationship among hoisting safety risk factors are mined, and the time-varying data information is visualized. Demonstration in the construction of a large-scale prefabricated building shows that with the proposed framework, it is possible to complete the information fusion between the hoisting site and the virtual model and realize the visual management. The correlative relationship among hoisting construction safety risk factors is analyzed, and the key control factors are found. Moreover, the efficiency of information integration and sharing is improved, the gap of coupling analysis of security risk factors is filled, and effective security management and decision-making are achieved with the proposed approach.

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Prefabricated buildings are widely used because of their green environmental protection and high degree of industrialization. However, in construction process, there are some defects such as small wireless network coverage, high-energy consumption, inaccurate control, and backward blind hoisting methods in the hoisting process of prefabricated components (PC). Internet-of-Things (IoT) technology can be used to collect and transmit data to strengthen the management of construction sites. The purpose of this study was to establish an intelligent control method in the construction and hoisting process of PC by using IoT technology. Long Range Radio (LoRa) technology was used to conduct data terminal acquisition and wireless transmission in the construction site. The Inertial Measurement Unit (IMU), Global Positioning System (GPS), and other multi-sensor fusion was used to collect information during the hoisting process of PC, and multi-sensor information was fused by fusion location algorithm for location control. Finally, the feasibility of this method was verified by a project as a case. The results showed that the IoT technology can strengthen the management ability of PC in the hoisting process, and improve the visualization level of the hoisting process of PC. Analysis of the existing outdated PC hoisting management methods, LoRa, IMU, GPS and other sensors were used for data acquisition and transmission, the PC hoisting multi-level management and intelligent control.

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In this study, to address the problems of multiple dimensions, large scales, complex tension resource scheduling, and strict quality control requirements in the tensioning process of cables in prestressed steel structures, the technical characteristics of digital twins (DTs) and artificial intelligence (AI) are analyzed. An intelligent tensioning of prestressed cables method driven by the integration of DTs and AI is proposed. Based on the current research status of cable tensioning and DTs, combined with the goal of intelligent tensioning, a fusion mechanism for DTs and AI is established and their integration to drive intelligent tensioning of prestressed cables technology is analyzed. In addition, the key issues involved in the construction of an intelligent control center driven by the integration of DTs and AI are discussed. By considering the construction elements of space and time dimensions, the tensioning process is controlled at multiple levels, thereby realizing the intelligent tensioning of prestressed cables. Driven by intelligent tensioning methods, the safety performance evaluation of the intelligent tensioning process is analyzed. Combined with sensing equipment and intelligent algorithms, a high-fidelity twin model and three-dimensional integrated data model are constructed to realize closed-loop control of the intelligent tensioning safety evaluation. Through the study of digital twins and artificial intelligence fusion to drive the intelligent tensioning method for prestressed cables, this study focuses on the analysis of the intelligent evaluation of safety performance. This study provides a reference for fusion applications with DTs and AI in intelligent tensioning of prestressed cables.

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With the development of the next generation of information technology, an increasing amount of attention is being paid to smart residential spaces, including smart cities, smart buildings, and smart homes. Building indoor safety intelligence is an important research topic. However, current indoor safety management methods cannot comprehensively analyse safety data, owing to a poor combination of safety management and building information. Additionally, the judgement of danger depends significantly on the experience of the safety management staff. In this study, digital twins (DTs) are introduced to building indoor safety management. A framework for an indoor safety management system based on DT is proposed which exploits the Internet of Things (IoT), building information modelling (BIM), the Internet, and support vector machines (SVMs) to improve the level of intelligence for building indoor safety management. A DT model (DTM) is developed using BIM integrated with operation information collected by IoT sensors. The trained SVM model is used to automatically obtain the types and levels of danger by processing the data in the DTM. The Internet is a medium for interactions between people and systems. A building in the bobsleigh and sled stadium for the Beijing Winter Olympics is considered as an example; the proposed system realises the functions of the scene display of the operation status, danger warning and positioning, danger classification and level assessment, and danger handling suggestions.

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Prefabrication (PC) projects have many advantages, such as cost and energy savings and waste reduction. However, some problems still exist that hamper the development of prefabrication projects. To improve PC project performance and advance innovation in construction, this study introduces an innovative method that incorporates Radio Frequency Identification (RFID) and Long Range (LoRa) technologies, sensor networks, the BIM model and cloud computing to automatically collect, analyze and display real-time information about PC components. It can locate PC components on a construction site and monitor their structural performance during the installation process. RFID technology and strain sensors were used to collect the required data on a construction site. All the data was transmitted to a server using LoRa technology. Then, the cloud-based Building Information Modelling (BIM) model of the project was developed to store and vividly present project information and real-time onsite data. Moreover, the cloud-based BIM model enables project team members to access the project information from anywhere by using mobile devices. The proposed system was tested on a real PC project to validate its effectiveness. The results indicate that the sensor network can provide reliable data via LoRa technology, and a PC component can be accurately located on site. Also, the monitoring data of structural performance for the PC component during the installation process is acceptable. The proposed method using innovative technologies can improve PC project performance and help industry professionals by providing sufficient required information.

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In this paper, we propose a wideband dynamic behavioral model for a bulk reflective semiconductor optical amplifier (RSOA) used as a modulator in colorless radio over fiber (RoF) systems using a tapped-delay multilayer perceptron (TDMLP). 64 quadrature amplitude modulation (QAM) signals with 20 Msymbol/s were used to train, validate and test the model. Nonlinear distortion and dynamic effects induced by the RSOA modulator are demonstrated. The parameters of the model such as the number of nodes in the hidden layer and memory depth were optimized to ensure the generality and accuracy. The normalized mean square error (NMSE) is used as a figure of merit. The NMSE was up to -44.33 dB when the number of nodes in the hidden layer and memory depth were set to 20 and 3, respectively. The TDMLP model can accurately approximate to the dynamic characteristics of the RSOA modulator. The dynamic AM-AM and dynamic AM-PM distortions of the RSOA modulator are drawn. The results show that the single hidden layer TDMLP can provide accurate approximation for behaviors of the RSOA modulator.

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Reflective semiconductor optical amplifiers (RSOAs) can be used as external modulators in radio over fiber (RoF) links due to their amplification and modulation characteristics and colorless property. The nonlinear distortion of RSOA, however, limits its dynamic range. In this paper we demonstrate digital predistortion (DPD) linearization techniques to improve the linearity of RSOA external modulators. 64 quadrature amplitude modulation (QAM) signals are utilized to extract the model parameters. The dynamic AM/AM and AM/PM characteristics and power spectral densities of the modulated signals from the RSOA are demonstrated without and with DPD. Experimental results show clearly that the nonlinear distortion of RSOA external modulators in RoF links can be compensated using DPD linearization techniques.