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[Introduction] Offshore wind power generation is gradually being widely applied in the global energy structure transformation due to its advantages of high annual power generation and stable power generation. However, due to the complex and ever-changing marine environment for offshore wind turbine construction, this poses significant obstacles to the construction process and technical solutions of offshore wind turbine foundations. [Method] In the context of the construction project of a certain offshore wind farm, this paper analyzed the type selection criteria and feasibility of pile driving construction ships, pile stabilizing platforms and hydraulic pile hammers. At the same time, in response to the construction requirements for wind turbine foundation pile driving, the preparation work before the operation, the construction plan for the pile stabilizing platform, and the wind turbine foundation pile driving operation with the cooperation of the main-auxiliary crane ships were emphasized. Furthermore, the control and correction measures for verticality in wind turbine foundation pile driving were analyzed. [Result] Steel pipe pile driving construction process can effectively ensure the project quality and expected progress, and has significant economic benefits, safety and reliability. During the pile driving process, it's necessary to complete preliminary and final adjustments to the verticality accural control. [Conclusion] Through the study of pile driving construction technology for offshore wind turbine foundations, it will provide useful reference and inspiration for similar offshore wind turbine projects.

Abstract Background Cryo-electron microscopy (Cryo-EM) plays an increasingly important role in the determination of the three-dimensional (3D) structure of macromolecules. In order to achieve 3D reconstruction results close to atomic resolution, 2D single-particle image classification is not only conducive to single-particle selection, but also a key step that affects 3D reconstruction. The main task is to cluster and align 2D single-grain images into non-heterogeneous groups to obtain sharper single-grain images by averaging calculations. The main difficulties are that the cryo-EM single-particle image has a low signal-to-noise ratio (SNR), cannot manually label the data, and the projection direction is random and the distribution is unknown. Therefore, in the low SNR scenario, how to obtain the characteristic information of the effective particles, improve the clustering accuracy, and thus improve the reconstruction accuracy, is a key problem in the 2D image analysis of single particles of cryo-EM. Results Aiming at the above problems, we propose a learnable deep clustering method and a fast alignment weighted averaging method based on frequency domain space to effectively improve the class averaging results and improve the reconstruction accuracy. In particular, it is very prominent in the feature extraction and dimensionality reduction module. Compared with the classification method based on Bayesian and great likelihood, a large amount of single particle data is required to estimate the relative angle orientation of macromolecular single particles in the 3D structure, and we propose that the clustering method shows good results. Conclusions SimcryoCluster can use the contrastive learning method to perform well in the unlabeled high-noise cryo-EM single particle image classification task, making it an important tool for cryo-EM protein structure determination

Standing and lying are the fundamental behaviours of quadrupedal animals, and the ratio of their durations is a significant indicator of calf health. In this study, we proposed a computer vision method for non-invasively monitoring of calves’ behaviours. Cameras were deployed at four viewpoints to monitor six calves on six consecutive days. YOLOv8n was trained to detect standing and lying calves. Daily behavioural budget was then summarised and analysed based on automatic inference on untrained data. The results show a mean average precision of 0.995 and an average inference speed of 333 frames per second. The maximum error in the estimated daily standing and lying time for a total of 8 calf-days is less than 14 min. Calves with diarrhoea had about 2 h more daily lying time (p < 0.002), 2.65 more daily lying bouts (p < 0.049), and 4.3 min less daily lying bout duration (p = 0.5) compared to healthy calves. The proposed method can help in understanding calves’ health status based on automatically measured standing and lying time, thereby improving their welfare and management on the farm.

Online multi-object tracking (MOT) techniques are instrumental in monitoring workers’ positions and identities in construction settings. Traditional approaches, which employ deep neural networks (DNNs) for detection followed by body similarity matching, often overlook the significance of clear head features and stable head motions. This study presents a novel bidirectional tracking method that integrates intra-frame processing, which combines head and body analysis to minimize false positives and inter-frame matching to control ID assignment. By leveraging head information for enhanced body tracking, the method generates smoother trajectories with reduced ID errors. The proposed method achieved a state-of-the-art (SOTA) performance, with a multiple-object tracking accuracy (MOTA) of 95.191%, higher-order tracking accuracy (HOTA) of 78.884% and an identity switch (IDSW) count of 0, making it a strong baseline for future research.

Real-time detection of workers is crucial in construction safety management. Deep learning-based detecting methods are valuable, but always challenged by the possibility of target missing or identity errors under complex scenarios. To address these limitations, previous research depended on re-training for new models or datasets, which are prohibitively time-consuming and incur high computing demands. However, we demonstrate that the better detecting model might not rely on more re-training of weights; instead, a training-free model can achieve even better performance by integrating head information. In this paper, a new head-detecting branch (55 MB) is added to the Keypoint Region-based Convolutional Network (Keypoint R-CNN, 226 MB) without altering its original weights, allowing for a less occluded head to aid in body detection. We also deployed motion information and anthropometric data through a post-processing module to calculate movement relationships. This study achieved an identity F1-score (IDF1) of 97.609%, recall (Rcll) of 98.173%, precision (Prcn) of 97.052%, and accuracy of 95.329% as a state-of-the-art (SOTA) method for worker detection. This exploration breaks the inertial attitudes of re-training dependency and accelerates the application of universal models, in addition to reducing the computational difficulty for most construction sites, especially in scenarios with an insufficient graphics processing unit (GPU). More importantly, this study can address occlusion challenges effectively in the worker detection field, making it of practical significance.

Monitoring workers’ safety compliance is critical to construction accident prevention. However, most of the previous research focuses on checking the entry of workers wearing safety belts and the monitoring of the fastening action of safety rope screw buckles has not yet been considered. In this paper, a reconstruction method for the safety rope fastening behaviour of workers is proposed based on inertial measurement units (IMU) and a time series approach to monitor safety belt use. The proposed method was applied and evaluated through on-site construction experiments. The experimental results show that the acceleration, angular velocity, and magnetic induction intensity data obtained by the inertial measurement unit exhibit clear behavioural characteristics during safety rope fastening. The trajectory of the safety rope can be reconstructed and monitored through inertial measurement units and a time series approach. The results of this study will contribute to the reconstruction and monitoring of safety rope attachment trajectories for scaffolding workers working at heights in order to prevent falls at construction sites.

Establishing the National Comprehensive Disaster-Reduction Demonstration Community (NCDDC) is crucial for enhancing comprehensive disaster risk reduction at the grassroots level in China. Studying the distribution characteristics and influencing factors of NCDDCs can guide future NCDDC layout optimization and related policy adjustments. Using the standard deviation ellipse, nearest neighbor index, kernel density, spatial autocorrelation, and Geodetector, we analyzed the spatiotemporal distribution characteristics of NCDDCs in China from 2008 to 2021 and detected their influencing factors. The findings are as follows: (1) NCDDCs exhibit an uneven distribution at different scales, including spatial, urban–rural, and county scales. (2) The spatial distribution of NCDDCs mainly follows a northwest–southeast pattern during 2008–2014 and shows a northeast–southwest trend after 2014. (3) The positive spatial correlation and spatial agglomeration of NCDDCs increase annually. (4) NCDDCs show a concentrated and contiguous distribution pattern in 2021, based on “core density zone–ring-core decreasing area–ring-core expansion group–Ɔ-shaped area–belt-shaped area”. (5) The main factors affecting the NCDDC distribution are hospital density, road density, GDP density, and population density, with factors’ interactions exhibiting bilinear and nonlinear enhancement effects. This study reveals the NCDDC spatiotemporal distribution characteristics and its influence mechanism, providing a scientific basis for future NCDDC layout optimization and related policy adjustments.

Metallic Mg is an important strategic metal and its properties are greatly affected by impurities. Silicothermic reduction and electrolysis are the most used approaches to prepare metallic Mg. The products of these processes need to be further refined to obtain high-purity Mg metal. However, previous research has mainly focused on refining the crude Mg (CM) produced via silicothermic reduction, whereas no in-depth investigations have been conducted on refining the CM produced via electrolysis. Here, vacuum distillation was used to refine electrolytically produced CM. The content and morphological characteristics of the impurity elements in CM were studied via glow discharge mass spectrometry, mineral dissociation analysis, and electron probe microanalysis. The effect of different distillation temperatures and times on the quality of the refined Mg was investigated. The results show that the main impurity elements are Al, Fe, Si, Ti, Cr, S, Cl, and Ni. The content of impurities, such as Si, Al, Fe, Ni, Ti, and Cr, in the refined Mg is significantly reduced at a temperature of 1023 K and a time of 120 min, and the purity of the refined Mg reaches 99.99%, which meets the Mg9999 national standard for primary Mg ingots in China (GB/3499-2011).

In construction planning, decision making has a great impact on final project performance. Hence, it is essential for project managers to assess the construction planning and make informed decisions. However, disproportionately large uncertainties occur during the construction planning stage; in the worst case, reliable probability distributions of uncertainties are sometimes unavailable due to a lack of information before construction implementation. This situation constitutes a deep uncertainty problem, making it a challenge to perform a probability-based uncertainty assessment. The current study proposes a modeling approach that applies prediction intervals for construction planning via the integration of discrete-event simulation (DES), fuzzy C-means clustering (FCM), Bayesian regularization backpropagation neural networks (BRBNNs), and particle swarm optimization (PSO). The DES is used to perform data sampling of the construction alternatives and assess their performances under uncertainty. Based on the generated samples, the FCM, BRBNN, and PSO are integrated in a machine learning algorithm to model the prediction intervals that represent relationships between construction planning schemes, performances, and the corresponding uncertainties. The proposed approach was applied to a case project, with the results indicating that it is capable of modeling construction performance and deep uncertainties with a defined 95% confidence level and fluctuation within 1~9%. The presented research contributes a new and innovative option, using prediction intervals to solve deep uncertainty problems, without relying on the probability of the uncertainty. This study demonstrates the effectiveness of the proposed approach in construction planning.

Load conductive component onto textiles via coating method is a simple and facile fabrication process to prepare flexible smart wearable device. However, this functional textile will suffer a significant damage after washing or mechanical wear, which hindered its application. In this study, polydopamine (PDA) was used as an interfacial agent to enhance the combination between carbon nanotubes (CNTs) and cotton fiber to fabricated a highly conductive fabric. Furthermore, a hydrophobic material 1H,1H,2H,2H-perfluorooctyltriethoxysilane (PFOTES) was introduced to the conductive fabric via dip-coating process, which effectively fixed the CNTs and simultaneously realize the water resistance of the composite fabric, as the contact angle for the fabric was up to 138°. Owning to the formation of a stable conductive path, the composite conductive fabric always possessed a surface resistance lower than 110 Ω/sq. Even after going through the repeated mechanical deformation or multiple stripping cycles, this conductive pathway of the composite fabric was still well maintained. Benefit from the excellent stability and high electrical conductivity, this fabric shows an attractive electromagnetic (EMI) shielding performance, as its EMI shielding effectiveness was 22 dB under 6.57–9.99 GHz and 23 dB under 11.9–18 GHz. Furthermore, the fabric exhibited an outstanding electric heating performance with the heating temperature reaches more than 85 °C at 6 V. Along with the facile preparation technique and stable functional performance, we expect that this hydrophobic conductive cotton fabric can be used as wearable electronics and for the design of various smart flexible devices.

Understanding the characteristics of the traditional village distribution contributes to the formulation of relevant protection and development strategies. We adopted a series of spatial analysis methods to investigate the characteristics of the traditional village distribution in China by using the watershed as the research unit. Moreover, we conducted quantitative and qualitative analyses of the relevant influencing factors affecting the distribution pattern using Geodetector and mathematical statistics. The findings indicate that traditional villages are distributed unevenly across watershed units. High–High clusters tend to occur at the boundaries of first-level watersheds. Traditional villages have a clear agglomeration trend in space, with a concentrated and contiguous distribution pattern based on the “core density area–ring-core expansion group–belt area”. The key factors affecting the traditional village distribution are annual precipitation, annual average temperature, and river density. The traditional village number has a clear inverted U-shaped relationship with the annual average temperature, river density, and road density. The study reveals the complex and various characteristics of the traditional village distribution and its influence mechanism and offers scientific advice for traditional villages’ future protection and development.

The building performance simulation (BPS) based on physical models is a popular method to estimate the expected energy-savings of energy-efficient building retrofitting. However, many buildings, especially the older building constructed several decades ago, do not have full access to complete information for a BPS method. Incomplete information generally comes from the information that is missing, such as the U-value of part building components, due to incomplete documentation or component deterioration over time. It also comes from the case-specific incomplete information due to different documentation systems. Motivated by the available big data of real-life building performance datasets (BPDs), a data-driven approach is proposed to support the decision-making of building retrofitting selections under incomplete information conditions. The data-driven approach constructed a Performance Modelling with Data Imputation (PMDI) with integrated backpropagation neural networks, fuzzy C-means clustering, principal component analysis, and trimmed scores regression. An empirical study was conducted on real-life buildings in Sweden, and the results validated that the PMDI method can model the performance ranges of energy-efficient retrofitting for family house buildings with more than 90% confidence. For a target building in Stockholm, the suggested retrofitting measure is expected to save energy by 12,017~17,292 KWh/year.

The current research focuses on the mechanism of the surfactant polyethylene glycol (PEG) in the preparation of magnesium hydroxide by electrolysis of a salt lake bischite aqueous solution. The samples were analyzed by a scanning electron microscope (SEM), X-ray diffraction (XRD), Fourier transform infrared spectrometer (FT-IR) and laser particle size analyzer. The characterization results show that PEG plays an important role in adjusting the growth mode and morphology of Mg(OH)2 crystals. The ether group of the PEG molecular chain and the hydroxyl group of Mg(OH)2 can be combined by a hydrogen bond, which provides a "template" for the growth of Mg(OH)2. At the same time, the difference in growth mode and morphology will also affect the economic performance of electrolytic reactions. When the PEG content reaches 0.4 g/L, the particle size of the product is uniform, which can well reduce the polarization of the electrode plate. The cell potential of electrolytic reaction is small, and the economic benefit is high. When the content of PEG is low, it has a low impact on the product and the economic benefits of electrolytic reaction. When the PEG content is higher than 0.4 g/L, the electrode reaction is hindered, resulting in an increase in cell potential.

Stochastic Brownian dynamics is an extremely powerful way to simulate the polymer dynamics in solutions and melts. Mathematically, these models are described by stochastic differential equations. The most challenging problems are the Monte Carlo algorithm, which simulates the motion of a large number of model particles and hence requires an enormous amount of computer time. It is therefore necessary to develop an efficient numerical method in operational emergency response applications. In this paper, we give an improved multilevel Monte Carlo (improved-MLMC) method based on equilibrium control variables at each level to calculate the propagation of polymers. The improved-MLMC method can be shown to result in asymptotically optimal random errors and reduce total cost when compared to the standard Monte Carlo and MLMC methods. Finally, the effect of the Wi number (dimensionless parameter) on the total cost of the presented MLMC method is also discussed in detail.

Component factories are experiencing the problems associated with lean production, especially the accuracy of production time prediction and the unnecessary waste in terms of time and resource utilization. In order to solve these problems, a discrete event simulation- (DES-) based lean planning and optimization method for precast component production is proposed by integrating the complexity assessment (CS), discrete event simulation (DES), and lean management (LM). The method includes three submodels: improved production planning, DES, and lean analysis and optimization. In the submodel of improved production planning, a complexity evaluation index system for precast components is established through investigating five component factories, consulting seven domain experts and analysing relevant literature. In the submodel of DES, the DES technique is adopted to simulate and analyse the production activities of precast components. The submodel of lean analysis and optimization provides multidimensional analysis, comparative analysis, and suggestions. Finally, a detailed production case is selected to simulate and test the proposed method. The important findings are as follows: (1) this method can minimize the difference between the processing time of each workstation to avoid bottleneck stations as much as possible; (2) this method can capture the uncertainty during precast component production, and the most likely production time calculated by the method is 12.05 hours instead of the 11.50 hours originally estimated by the component factory; (3) this method can identify some unnecessary waste in the production process of precast components, including less than 50% utilization of workstations and unnecessary equipment purchases; (4) this method also provides some suggestions regarding production optimization. Due to the particularity of precast component production, it further expands the boundary of lean production methodology from the perspective of the construction industry rather than the manufacturing industry. The proposed method assists component factories in planning and optimizing the precast component production when they make detailed production plans.

Individual identification plays an important part in disease prevention and control, traceability of meat products, and improvement of agricultural false insurance claims. Automatic and accurate detection of cattle face is prior to individual identification and facial expression recognition based on image analysis technology. This paper evaluated the possibility of the cutting-edge object detection algorithm, RetinaNet, performing multi-view cattle face detection in housing farms with fluctuating illumination, overlapping, and occlusion. Seven different pretrained CNN models (ResNet 50, ResNet 101, ResNet 152, VGG 16, VGG 19, Densenet 121 and Densenet 169) were fine-tuned by transfer learning and re-trained on the dataset in the paper. Experimental results showed that RetinaNet incorporating the ResNet 50 was superior in accuracy and speed through performance evaluation, which yielded an average precision score of 99.8% and an average processing time of 0.0438 s per image. Compared with the typical competing algorithms, the proposed method was preferable for cattle face detection, especially in particularly challenging scenarios. This research work demonstrated the potential of artificial intelligence towards the incorporation of computer vision systems for individual identification and other animal welfare improvements.

A selective liquation process to extract Al from a coarse Al–Si alloy, produced by carbothermal reduction, was investigated on the laboratory scale. The products obtained by selective liquation–vacuum distillation were analyzed by X-ray diffraction, inductively coupled plasma optical emission spectrometry and scanning electron microscopy. During the selective liquation process with the use of zinc as the solvent, the pure aluminum in the coarse Al–Si alloy dissolved in the zinc melt to form an α-solid solution with zinc, and most of the silicon and iron-rich phases and Al–Si–Fe intermetallics precipitated and grew into massive grains that entered into the slag and separated with the Zn–Al alloy melt. However, some fine silicon particles remained in the Zn–Al alloy. Thus, Al–Si alloys conforming to industrial application standards were obtained when the Zn–Al alloys were separated by a distillation process.

An effective method is proposed in this paper for calculating the transient magnetic field and induced voltage in the photovoltaic bracket system under lightning stroke. Considering the need for the lightning current responses on various branches of the photovoltaic bracket system, a brief outline is given to the equivalent circuit model of the photovoltaic bracket system. The analytic formulas of the transient magnetic field are derived from the vector potential for the tilted, vertical and horizontal branches in the photovoltaic bracket system. With a time–space discretization scheme put forward for theses formulas, the magnetic field distribution in an assigned spatial domain is determined on the basis of the lightning current responses. The magnetic linkage passing through a conductor loop is evaluated by the surface integral of the magnetic flux density and the induced voltage is obtained from the time derivative of the magnetic linkage. In order to check the validity of the proposed method, an experiment is made on a reduced-scale photovoltaic bracket system. Then, the proposed method is applied to an actual photovoltaic bracket system. The calculations are performed for the magnetic field distributions and induced voltages under positive and negative lightning strokes.

The potential of emerging information technology has been proposed by many researchers and practitioners in the construction industry, including smart construction. Meanwhile, emerging information technology acceptance and use is one of the major subjects for current smart construction study and practice. Furthermore, although there are many potential applications for and benefits of emerging information technology in the development of smart construction system, the current issue is that it is unclear why this technology is adopted, and that the factors that enhance its implementation are unknown. Therefore, an emerging information technology acceptance model (EITAM) was proposed, and our hypotheses were tested by structural equation modeling (SEM) based on an open-ended questionnaire survey. This study identified the factors that affect emerging information technology acceptance from engineering construction technology and innovation professionals. The EITAM evaluation results can be used to develop an emerging information technology acceptance strategy that is suitable for continual smart construction promotion. Finally, this study can provide guidance to smart construction developers to establish an effective technological integration plan.

Due to more complex structure and increasing prefabrication rate of precast concrete buildings, the assembly order between their constituent components is getting more and more attention. In order to solve the assembly sequence planning and optimization (ASPO) problem in precast concrete buildings, Building Information Modelling (BIM) and Improved Genetic Algorithm (IGA) are organically combined to propose a new method called BIM-IGA-based ASPO method. This method uses BIM for parametric modelling, uses IGA to search for an optimal assembly sequence, and then uses BIM again for visual simulation to further test the assembly sequence. Besides, IGA, which is improved in coding mode, crossover operation and mutation operation, is also used to achieve the dynamic adjustment of assembly sequence in construction process. A full-text example is used to explain the detailed operating principle of BIM-IGA-based ASPO method. The results indicate that the method can effectively find an optimal assembly sequence to reduce the assembly difficulty of a precast concrete building.

Efficiency and equity have always been the two points of focus of transport projects. Compared with efficiency, equity is easily overlooked in the evaluation of transport projects. Many studies emphasize that defining and operationalizing costs and benefits and the distributive principle are critical parts in the assessment of transportation equity. However, the scope and time frame of the assessment target are also critical. In this paper, we took China’s fastest urbanizing city, Shenzhen, as a case study to assess transport equity by comparing accessibility among groups. First, the public transport system was divided into bus and subway, and the residents were divided into two groups: urban village and nonurban village. Second, we adopted an enhanced potential opportunity model to measure residents’ bus and subway accessibility and summarized them as transit opportunity. Third, we used the Dagum Gini coefficient decomposition and kernel density estimation method to explore the fair distribution of transit opportunity among groups and districts from 2011 to 2020. Decade-long changes in disparity and distribution of transit opportunity gave us a clear picture. On the one hand, the development of Shenzhen public transport system had a positive effect. All populations are benefiting, and their accessibility is increasing. On the other hand, it also had a negative effect to exacerbate inequality between populations. For the absolute value of the opportunity, Shenzhen’s urban village populations do have fewer transportation opportunities than nonurban villages, and this gap between them will be wider more and more. The public transport system is more inclined to improve the population with high initial opportunity and make them higher. The results illustrated the importance of examining transportation equity over an extended period and could provide information on urban development strategies.

Like DIP-STR markers (deletion/insertion polymorphism-short tandem repeat combinations), SNP-STR markers (single nucleotide polymorphism-STR combinations) are also valuable in forensic DNA mixture analysis. In this study, eight SNP-STRs were selected, and a stable and sensitive multiplex polymerase chain reaction (PCR) assay was developed for amplifying these SNP-STRs and the Amelogenin gender marker according to the principle of amplification refractory mutation system (ARMS). This novel multiplex set allows detection of the minor DNA contributor in a DNA mixture of any gender and cellular origin with high resolution (beyond a DNA ratio of 1:20). In addition, SNP-STR haplotype frequencies were estimated based on a survey of 350 unrelated individuals from Chinese Han population, and the combined power of discrimination (PD) and power of exclusion (PE) of the eight SNP-STRs were calculated as 0.99999999965 and 0.9996, which were obviously higher than that of the eight STR loci: 0.9999999954 and 0.9989 respectively. The results indicated that the SNP-STR compound markers have higher application value in forensic identification compared to standard autosomal STRs, especially in the analysis of imbalanced DNA mixtures.

Purpose: The purpose of this article is to study the description and extension of the Industry Foundation Classes (IFC) standard in construction schedule management, which achieves the information exchange and sharing among the different information systems and stakeholders, and facilitates the collaborative construction in the construction projects. Design/methodology/approach: The schedule information processing and coordination are difficult in the complex construction project. Building Information Modeling (BIM) provides the platform for exchanging and sharing information among information systems and stakeholders based on the IFC standard. Through analyzing the schedule plan, implementing, check and control, the information flow in the schedule management is reflected based on the IDEF. According to the IFC4, the information model for the schedule management is established, which not only includes the each aspect of the schedule management, but also includes the cost management, the resource management, the quality management and the risk management. Findings: The information requirement for the construction schedule management can be summarized into three aspects: the schedule plan information, the implementing information and the check and control information. The three aspects can be described through the existing and extended entities of IFC4, and the information models are established. Originality/value: The main contribution of the article is to establish the construction schedule management information model, which achieves the information exchange and share in the construction project, and facilitates the development of the application software to meet the requirements of the construction project.