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Abstract Background Attentional bias is a salient manifestation of insomnia. Digital cognitive therapy for insomnia (dCBT-I) has been validated as effective in alleviating this cognitive dysfunction. However, the effect of dCBT-I on attentional bias among Chinese individuals with insomnia remains undiscussed. This research sought to investigate this effect via a pictorial dot-probe task. Methods In Study 1, the pattern of attentional bias among poor sleepers (N = 52) and normal sleepers (N = 56) was assessed by the dot-probe task. In study 2, dCBT-I and conventional education were received by the experimental group (N = 42) and control group (N = 25), respectively. The dot-probe tasks and sleep quality assessments were completed at baseline and post-test. Results The results of Study 1 indicated that poor sleepers exhibited significant attentional bias, characterized by increased attentional vigilance. Compared to normal sleepers, they showed heightened attentional vigilance toward sleep-related cues. The results of Study 2 showed that both dCBT-I and conventional education led to improvements in PSQI scores. However, only dCBT-I training alleviated attentional vigilance toward sleep-related cues. Additionally, dCBT-I was uniquely effective in reducing feelings of fatigue. Conclusions Poor sleepers had a significant attentional bias, marked by heightened vigilance toward sleep-related cues. Digital CBT-I effectively reduced attentional vigilance and fatigue, suggesting that dCBT-I targets the cognitive distortions associated with insomnia. Trial registration ChiCTR2100053172 (registered 13/11/2021).

This study addresses the integrated scheduling problem of dry bulk cargo terminal yards, which includes three components: transportation planning, yard selection optimization, and equipment scheduling. Additionally, the research integrates safety considerations and addresses the complexities of dynamic transportation planning. This work presents two innovations. Firstly, this study develops a sophisticated modeling framework that integrates graph structures for precise yard mapping with mixed-integer programming to enforce operational constraints. This integrated approach facilitates a more accurate and comprehensive representation of yard operations, capturing diverse operational aspects while maintaining model clarity and computational efficiency. Secondly, this study proposes an advanced solution methodology that employs a reinforcement learning technique integrating a Dueling Deep Q-Network and Double Deep Q-Network. This hybrid algorithm significantly enhances optimization performance and accelerates the learning process, thereby improving the efficiency of the solutions. The experimental results demonstrate that the proposed model effectively manages the integrated scheduling of bulk material ingress, storage, and egress within the yard. The operational plans generated by the approach outperform traditional first-come, first-served strategies, showcasing substantial improvements in port operational efficiency and reliability. This comprehensive solution underscores the potential for significant advancements in the overall management and performance of dry bulk cargo ports.

The dry bulk shipping network is an important carrier of global bulk commodity flow. To better understand the structural characteristics and future development trends of the global dry bulk shipping network (GDBSN), this study proposes a framework for characteristics analysis and link prediction based on complex network theory. The study integrates large-scale heterogeneous data, including automatic identification system data and port geographic information, to construct the GDBSN. The findings reveal that the network exhibits small-world properties, with the Port of Singapore identified as the most influential node. Link prediction results indicate that many potential new shipping routes exist within regions or between neighboring countries, exhibiting clear regional clustering characteristics. The added links mainly influence the local structure, with minimal impact on the overall network topology. This study provides valuable insights for shipping companies in route planning and for port authorities in developing strategic plans.

Abstract Background The purpose of this study was to create a mathematical model to precalculate the acreage change in the abdominal median sagittal plane (ac-AMSP) of patients with ankylosing spondylitis (AS) for whom two-level pedicle subtraction osteotomy (PSO) was planned. Methods A single-centre retrospective review of prospectively collected data was conducted among 11 adults with AS. Acreage of the abdominal median sagittal plane (a-AMSP) was performed. The distances and angles between the osteotomy apexes, anterosuperior edge of T12, xiphoid process, superior edge of the pubis, and anterosuperior corner of the sacrum were measured on preoperative thoracolumbar computed tomography. A mathematical model was created using basic trigonometric functions in accordance with the abdominal parameters. Planned osteotomized vertebra angles (POVAs) were substituted into the mathematical model, and the predictive ac-AMSP (P-AC) was obtained. A paired sample t test was performed to determine the differences between the P-AC and actual ac-AMSP (A-AC) and between the predictive acreage change rate (P-CR) and actual acreage change rate (A-CR). Results The mean age and GK were 44.4 ± 8.99 years and 102.9° ± 19.17°, respectively. No significant difference exists between A-CR and P-CR via mathematical modeling (p > 0.05). No statistically significant difference existed between POVA and actual osteotomized vertebra angles (AOVA) (p > 0.05). A statistically significant difference was observed between preoperative and postoperative measurements of LL, SVA, and GK variables (p

As an emerging technology, digital twin (DT) is increasingly valued in bridge management for its potential to optimize asset operation and maintenance (O&M). However, traditional bridge management systems (BMS) and existing DT applications typically rely on standalone building information modeling (BIM) or geographic information system (GIS) platforms, with limited integration between BIM and GIS or consideration for their underlying graph structures. This study addresses these limitations by developing an integrated DT system that combines WebGIS, WebBIM, and graph algorithms within a three-layer architecture. The system design includes a common data environment (CDE) to address cross-platform compatibility, enabling real-time monitoring, drone-enabled inspection, maintenance planning, traffic diversion, and logistics optimization. Additionally, it features an adaptive data structure incorporating JSON-based bridge defect information modeling and triple-based roadmap graphs to streamline data management and decision-making. This comprehensive approach demonstrates the potential of DTs to enhance bridge O&M efficiency, safety, and decision-making. Future research will focus on further improving cross-platform interoperability to expand DT applications in infrastructure management.

Urban communities have long been pivotal in wealth creation and technological innovation. In the contemporary context, their modus operandi is intricately tied to a diverse array of critical infrastructure systems (CISs). These systems—encompassing utilities, transportation, communication, and more—are indispensable for daily life; however, historical lessons underscore that the ever-growing interdependence among modern CISs has sapped their robustness. Furthermore, this vulnerability is compounded by the intensifying natural hazards catalysed by climate change, leaving urban communities with eroded resilience. Against this backdrop, pilot studies have harnessed breakthroughs in artificial intelligence (AI) to chart a new course toward resilient urban communities. This paper illuminates AI-driven resilience by reviewing the latest research in key aspects including (1) the limitation of state-of-the-art resilience assessment frameworks; (2) emergency response as a novel blueprint featuring swift response following catastrophes; (3) efficient loss assessment of CISs using AI algorithms; and (4) machine-learning-enabled autonomous emergency response planning. The remaining challenges and hardships faced on the journey toward resilient urban communities are also discussed. The findings could contribute to the ongoing discourse on enhancing urban resilience in the face of increasingly frequent and destructive climate hazards.

The realism of ocean visual systems is a key challenge in developing maritime simulators within ocean engineering. Achieving high realism in turbulence simulation is crucial for enhancing the effectiveness of these simulators. Traditional spectrum-based methods lack realism and fail to generate turbulent interaction effects. To address this, an improved Hybrid Smoothed Particle Hydrodynamics method is proposed for simulating ocean scenes, incorporating advanced micropolar fluid model techniques to enhance detail realism. The proposed algorithm introduces a density constraint solver that directly adjusts particle distribution and couples it with a divergence-free velocity solver, aiming to construct a physical-based fluid simulation framework that enhances detail realism in ocean scene simulations. The results demonstrate that the proposed method effectively accelerates the convergence of constraint conditions, reduces simulation time, and improves overall incompressibility. Additionally, the introduced turbulence model addresses high-frequency detail loss caused by numerical dissipation in the Smoothed Particle Hydrodynamics method, enabling more complex navigation scenarios. This study provides theoretical and technical references for achieving realistic ocean scene simulations in maritime simulators.

In bridge maintenance, assessing structural performance requires adherence to rules outlined in safety and regulatory standards which can be effectively and formally represented in both human and machine-readable formats using ontologies. However, ontology-based semantic inference alone falls short when faced with the complicated mathematical operations required for structural analysis. The increasing digitization of bridge engineering has opened doors to data-driven prediction methods. Machine learning (ML)-based models, in particular, have the capacity to learn from historical data and forecast future structural performance with remarkable accuracy. This paper introduces an innovative approach that integrates ML models with an ontological knowledge base for evaluating bridge corrosion. Web Ontology Language and Semantic Web Rule Language are combined to develop the knowledge base. Random forest algorithm is used to train the ML model with a good agreement (coefficient of determination of 0.989 and root mean square error of 1.200). A Python-based module is designed to seamlessly integrate ML predictions with ontology-based semantic inference. The proposed approach not only infers the corrosion ratings based on the rules defined in the Network Rail standard, but also infers the structural safety performance based on predicted structural response under the action of corrosion. To demonstrate the effectiveness of the developed method in enabling accurate and rational evaluations, a real bridge in the UK is showcased as a practical application.

In fire emergency management, a delayed execution will cause a significant number of casualties. Conventional fire drills typically only identify a certain percentage of evacuation bottlenecks after the building has been constructed, which is hard to improve. This paper proposes an innovative framework to validate fire emergency evacuation at the early design stage. According to the experience and knowledge of fire emergency evacuation design, the proposed framework also introduces a seamless two-way information channel to embed fire emergency evacuation simulations into a BIM-based design environment. Several critical factors for fire evacuation have been reviewed in relevant domain knowledge, which is used to build virtual characters to test in experimental scenarios. The results are analyzed to validate fire emergency evacuation factors, and the feedback knowledge is stored as a knowledge model for further applications.

The construction industry has increasingly adopted digital construction processes and technologies, fueled in part by governmental mandates aimed at modernizing construction workflows. While these advancements promise efficiency across different phases of a project, the efficacy of the digitally generated information often remains contingent on its alignment with the specific information exchange requirements of individual organizations. Current efforts to establish interoperable data schemas have made strides, yet challenges persist, particularly when tailoring information to meet the unique needs of organizations responsible for the operation and management of built assets. This paper dissects the outcomes of standard digital construction processes applied to a linear infrastructure project, highlighting observed shortcomings such as information overload and the difficulty of adapting the information for asset management needs. Building upon these findings, this paper introduces a framework aimed at streamlining the production of essential project information. This framework was developed through a series of expert workshops and subsequently tested on a separate infrastructure project, offering insights into its potential benefits and limitations.

In this paper, we introduce PyVT, an open-source Python toolkit for preprocessing and analysis of vessel spatio-temporal trajectories. The toolkit realizes four functions: trajectory preprocessing, trajectory data management, trajectory mining, and trajectory visualization. The toolkit is simple to use, provides satisfactory default parameter settings, and makes it easy for basic users to use and replicate its functionality in proprietary applications. More advanced users can further adjust the functions to meet their needs by optimizing parameters. At the same time, its design focuses on composability and reusability, making it functional modular and maintainable, allowing for further development.

The healthy development of railway container transport is an important part of railway freight transport and is key for promoting the green transformation of China's transport and supporting a new pattern of transport development. In this study, railway container handling station (RCHS) data, kernel density analysis, standard deviation ellipse, Herfindahl–Hirschman index (HHI), trend surface analysis (TSA), and R index were applied to examine the evolution characteristics of container transport patterns after the market-oriented reform of China's railway freight transport in 2013. The results are as follows: First, the overall scale growth trend is obvious, and the double-center effect of transport scale on the Bohai Rim region and Chengdu–Chongqing Economic Zone is evident, with the transport center of gravity moving northward. Second, the amount of attraction/occurrence is consistent in spatial distribution, and the aggregation effect of both is similar, essentially exhibiting a tendency of being high in the northwest and low in the southeast. Third, the pattern of “export-oriented in the north and import-oriented in the south” has taken shape; nearly half of cities in China have stable traffic functions, stable traffic supply, and demand relationships, and the change of functions shows that the industrial structure is constantly upgrading. This study elucidates the pattern of railway container transport in cities in China and provides empirical guidance for adjusting the functions of urban freight transport, thereby promoting the healthy development of urban freight transport and optimizing urban transport planning.

Intelligent fault diagnosis (IFD) is essential for preventative maintenance (PM) in Industry 4.0. Data-driven approaches have been widely accepted for IFD in smart manufacturing, and various deep learning (DL) models have been developed for different datasets and scenarios. However, an automatic and unified DL framework for developing IFD applications is still required. Hence, this work proposes an efficient framework integrating popular convolutional neural networks (CNNs) for IFD based on time-series data by leveraging automated machine learning (AutoML) and image-like data fusion. After normalisation, uniaxial or triaxial signals are reconstructed into -channel pseudo-images to satisfy the input requirements for CNNs and achieve data-level fusion simultaneously. Then, the model training, hyperparameter optimisation, and evaluation can be taken automatically based on AutoML. Finally, the selected model can be deployed on a cloud server or an edge device (via tiny machine learning). The proposed framework and method were validated via two case studies, demonstrating the framework’s availability for the automatic development of IFD applications and the effectiveness of the proposed data-level fusion method.

PurposeSystemic autoantibodies are important for the diagnosis of autoimmune diseases, but their roles in anti-N-methyl-D-aspartate receptor (anti-NMDAR) encephalitis are unknown. The purpose of our study is to investigate the characteristics and a prognosis of anti-NMDAR encephalitis with the prevalence of autoantibodies.MethodsSystemic autoantibodies were evaluated in 64 patients with anti-NMDAR encephalitis and 14 patients with autoimmune encephalitis with other forms. Then, according to systemic autoantibodies, patients with anti-NMDAR encephalitis were divided into an anti-nuclear antibody (ANA) positive group and an ANA negative group. The clinical outcome was assessed by a modified Rankin score at 12 months after the disease onset.ResultsA total of 64 patients with anti-NMDAR encephalitis were enrolled, of which 28.13% (18/64) were positive for ANA. The titers of a positive anti-NMDAR antibody in CSF (p = 0.041) and serum (p = 0.031) in the ANA-positive group were significantly higher than the ANA-negative group. Patients with ANA positive than those with ANA negative showed lower rates of headache (p = 0.047) and speech disorder (p = 0.049). The presence of ANA was associated with a worse clinical outcome at 12 months (p = 0.043).ConclusionANA was prevalent in patients with anti-NMDAR encephalitis, and associated with a worse prognosis and impaired neurological recovery.

As an organically interwoven system of people, government organizations and businesses, modern cities have become the engine that drives economic growth and technological revolution for each nation [...]

For the design of ship equipment and crew training, it would be useful to develop software for the three-dimensional simulation of a totally enclosed lifeboat. To improve the simulation accuracy and immersion of the software, we present a multibody dynamics model for a lifeboat lowered from a ship, accounting for the coupled motion among the ship, lifeboat slings, a cable-pulley system, and the lifeboat. The equations of the whole system are formulated using Kane's method. The model of ship manoeuvring mathematical group is used to calculate the forces and moments acting on the hull of the ship. The hydrodynamic and wind loads on the boat are modeled using the strip theory. The impact force between the ship and the boat colliding is estimated using the contact theory of Hertz and an elastoplastic model. The method of lumped mass is used to model the lifeboat slings. For the cable-pulley system, we present an efficient model for the dynamics of the pay-out/reel-in process based on the framework of Kane's method. The local load of each cable segment between two pulleys is calculated by the model of a linear spring on the basis of the amount of cable passages over pulleys and the variation of the pulley positions, conversely, the cable segment exerts force and moment on the pulleys. The motion equations of the whole system are solved using fourth-order Runge-Kutta. The model can simulate the lowering of the lifeboat, and obtain the three-dimensional motion parameters of the ship, the lifeboat, slings and pulleys, and the local tension load of the cable. The results show that the simulation curves are near the ones of the model experiment, and their trends are coincident. Thus, it can be concluded that our model is feasible. According to our model, the motion of the ship has a significant effect on the magnitude of the lifeboat's oscillation when the sea state is above level 4; it is safe when the initial clear distance is greater than 1.5 times the width of the boat and the sea conditions are below level 5. Finally, the model is applied to the software for three-dimensional simulation.

PurposeThe reported prevalence of abnormal findings by brain MRI varies from 11 to 83% among patients with anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis. Here, we investigated the prevalence of abnormal MRI findings in Chinese patients and explored whether such findings are correlated with clinical outcomes.MethodsThis retrospective study analyzed a consecutive series of 52 patients with anti-NMDAR encephalitis admitted to our hospital. The patients were assigned to the “MRI-normal” or the “MRI-abnormal” group based on brain MRI after admission. The groups were compared in terms of clinicodemographic characteristics and scores on the Mini-Mental State Examination (MMSE) and modified Rankin Scale (mRS) 3 and 12 months after admission.ResultsThirty-seven (71.15%) of the patients showed abnormalities on brain MRI; these patients were more likely to be men and showed abnormalities on electroencephalography. Patients who showed normal or abnormal MRI findings did not differ significantly in terms of clinical symptoms, rates of mortality or relapse, or mRS scores after 3 and 12 months. However, patients with abnormal MRI showed significantly lower MMSE scores than those with normal MRI after 3 and 12 months.ConclusionsWe found high prevalence of abnormal MRI findings in our sample of Chinese patients with anti-NMDAR encephalitis. We also found that the abnormal findings were associated with cognitive decline but not necessarily with mortality or functional outcomes in the short or long term.

In this study, we propose a real-time ship anomaly detection method driven by Automatic Identification System (AIS) data. The method uses ship trajectory clustering classes as a normal model and a deep learning algorithm as an anomaly detection tool. The method is divided into three main steps: (1) quality maintenance of the original AIS data, (2) extraction of normal ship trajectory clusters using Density-Based Spatial Clustering of Applications with Noise (DBSCAN), in which a segmented improved Dynamic Time Warping (DTW) algorithm is used to measure the degree of trajectory similarity, (3) the clustering results are used as a normative model to train a Bi-directional Gated Recurrent Unit (BiGRU) recurrent neural network, which is used as a trajectory predictor to achieve real-time ship anomaly detection. Experiments were conducted using real AIS data from the port of Tianjin, China. The experimental results are manifold. Firstly, the data pre-processing process effectively improves the quality of raw AIS data. Secondly, the ship trajectory clustering model can accurately classify the traffic flow of different modes in the sea area. Moreover, the trajectory prediction result of the BiGRU model has the smallest error with the actual ship trajectory and has a better trajectory prediction performance compared with the Long Short-Term Memory Network model (LSTM) and Gated Recurrent Unit (GRU). In the final anomaly detection experiment, the detection accuracy and timeliness of the BiGRU model are also higher than LSTM and GRU. Therefore, the proposed method can achieve effective and timely detection of ship anomalous behaviors in terms of position, heading and speed during ship navigation, which provides insight to enhance the intelligence of marine traffic supervision and improve the safety of marine navigation.

Traditional methodology for pile integrity evaluation usually adopts fuzzy qualitative indicators and the engineering experience of technicians to roughly estimate the integrity category of a pile, which includes many uncertainties and heavily subjective factors. Therefore, based on an analytical model for the vibration of pile and an ontology-based approach, this paper describes the development of an integrated evaluation system that can make reasonable evaluations of pile integrity where specific measured reflective wave curves are provided. First, a semi-analytical solution for the velocity response of pile with defects at the pile head was derived by analytical methodology, and then the intrinsic relationships between the quantitative indicators of pile defects and the characteristic parameters of velocity response curves were obtained according to the propagation law of elastic wave and the numerical fitting method. On this basis, a prototypical ontology-based evaluation system, ontology of pile integrity evaluation (OntoPIE), with a new ontology framework of leverage knowledge modelling was developed to create an easy-to-use tool for quantitative identification of pile defects and qualitative evaluation of pile integrity by combining ontology and semantic web rule language (SWRL) rules. A case study was also conducted to show how the developed framework can be used to demonstrate its practicability and scientific feasibility. The accuracy of the framework will be verified by comparing the quantitative indicators of pile defects inferred by OntoPIE with the preset defect indicators through designed examples.

It is urgent to improve the physical reality of ocean scenes in marine simulator for the maritime industry. In this paper, we propose a unified anisotropic particle-based ocean wave simulation framework for marine simulator. In the unified framework, a novel hybrid SPH method is applied to model the ocean waves, which combines a series of nonlinear density constraints and divergence-free velocity field constraints. By solving density constraints, the particle position distribution is directly adjusted to keep a constant density. Experiments show that the hybrid SPH method has advantages in compressibility and stability. Moreover, a novel stochastic fluctuating wind field model is integrated into the hybrid SPH method. The Perlin noise and a modified log wind profile are introduced to enrich the details of wind field. We also introduce a novel oil spill model for marine simulator based on number density, which can achieve a desired sharp density changes at interfaces between multiple fluids. For real-time surface reconstruction, we propose an improved anisotropic particle transformation method based on the distribution of the neighboring particles. The problem of particle deformation near the boundary is solved by our piecewise correction function, with the sharp features and anisotropy maintained. When calculating the covariance matrix and weighted position, the contribution of neighboring particles in other fluid phases is considered to eliminate gaps between multiple fluids in the oil spill scenes. The simulation results show that our unified anisotropic framework based on the SPH concept can easily integrate other models, has strong expansibility, and is very applicable to simulate complex ocean scenes.

Structure health inspection is the way to ensure that structures stay in optimum condition. Traditional inspection work has many disadvantages in dealing with the large workload despite using remote image-capturing devices. This research focuses on image-based concrete crack pattern recognition utilizing a deep convolutional neural network (DCNN) and an encoder–decoder module for semantic segmentation and classification tasks, thereby lightening the inspectors’ workload. To achieve this, a series of contrast experiments have been implemented. The results show that the proposed deep-learning network has competitive semantic segmentation accuracy (91.62%) and over-performs compared with other crack detection studies. This proposed advanced DCNN is split into multiple modules, including atrous convolution (AS), atrous spatial pyramid pooling (ASPP), a modified encoder–decoder module, and depthwise separable convolution (DSC). The advancement is that those modules are well-selected for this task and modified based on their characteristics and functions, exploiting their superiority to achieve robust and accurate detection globally. This application improved the overall performance of detection and can be implemented in industrial practices.

Knowledge management in construction health and safety is an intensive process involving different stakeholders. However, this domain’s information is still fragmented and stored in various disordered formats that require systematic structure for reusing and sharing. This study aims to develop a domain ontology, HSM-Onto (Health and Safety Management-Ontology), to construct health and safety knowledge and improve health and safety management decision making. The HSM-Onto could implement the organization, storage and reuse of construction health and safety knowledge. It comprises two primary domain knowledge contexts, including construction project context and risk context. Based on the conducted analyses, the findings show that the HSM-Onto’s health and safety knowledge sharing is effective and equips health and safety employees with sound recommendations for decision making.

With the development of bridge structures towards being light weight and having a large span , the overall flexibility, and, hence, wind sensitivity, of the bridge increases. Flutter is one of the pivotal factors considered in the design and operation stage for long-span cable-stayed bridges due to its devastating impact, often intrigued by relatively low instability caused by wind speed. This paper presents a reliability theory-based numerical analysis on bridge flutter stability and its influence law of key parameters using a real bridge, the Xiangshan Harbor highway cable-stayed bridge in China. The analysis starts with creating a full scale of finite element model for the bridge in service to calculate the flutter derivative and time-dominated combining rational function in order to obtain the critical-flutter wind speed, and then the aerodynamic self-excited forces on the bridge and flutter time-history response are calculated to identify the flutter critical wind speed. Further, the influence of key parameters for flutter reliability, including the stiffness of the main girder, wire breaking rate, damping ratio and cable breakage location are analyzed comprehensively to achieve the change law of critical flutter wind speed with these parameters. Considering the uncertainty of the actual parameters, these parameters are taken as random variables, and the reliability index and failure probability of bridge flutter are calculated according to their probability distribution and the Latin hypercube sampling method. On this basis, a few suggestions are put forward for flutter risk-control during the service of this cable-stayed bridge, which can further enhance the design theory for long-span flexible bridges.

Previous studies have shown that forgiveness is associated with the ability of self-control. However, whether self-control can modulate interpersonal forgiveness remains unclear. In the current study, we aimed to explore the relationship between self-control and the process of forgiveness using a behavioral measure of forgiveness during which participants distributed money between themselves and unknown others who had previously treated them fairly or unfairly in an adapted decision-making task. Seventy-two participants with low or high self-control were recruited based on their scores on the self-control scale (SCS). Results showed that participants exhibited increased anger and decreased happiness after experiencing unfair treatment. Participants with high self-control distributed more money to opponents who previously treated them unfairly compared with those with low self-control, whereas no such difference was observed to opponents who previously treated them fairly between the two groups. A significantly positive correlation was also found between the forgiveness rates and participants’ self-control scores. These findings suggest that self-control modulates interpersonal forgiveness responses. Individuals with high self-control expressed an increased prosocial response toward people who previously offended them, which is similar to the process of forgiveness.

The traditional way of designing energy pile system is mostly single domain/objective oriented, which lacks of means to coherently consider different while relevant factors across domains. The cost for life cycle design, construction and maintenance, return of investment, CO2 emission related sustainable requirements, and so on also need to be considered, in a systematic manner, along with the main functional design objective for loading capacity and robustness. This paper presents a novel multi-objective holistic approach for energy pile system design using ontology based multi-domain knowledge orchestration, which can holistically provide the designers with across domain factors regarding financial, safety, and environmental impact, for smart and holistic consideration during the early design stage. A prototypical ontology-based decision tool has been developed, aiming at the holistic optimization for energy pile system by combining ontology and Semantic Web Rule Language rules. A case study was performed to illustrate the details on how to apply knowledge query to provide a series of design alternatives autonomously by taking different design parameters into account. The method has demonstrated its practicability and scientific feasibility, it also shows the potential to be adopted and extended for other domains when dealing with multi-objective holistic design making.

Abstract Tendency to forgive refers to one’s global dispositional level of forgiveness across situations and relationships. Brain imaging studies examined activation patterns underlying forgiving response, yet focal differences in brain structures related to tendency to forgive have never been investigated. In this study, voxel-based morphometry was used to investigate relations between gray matter/white matter volume (GMV/WMV) and individual differences in tendency to forgive in a large young sample. Participants were 199 young students (60 men) who completed the tendency to forgive scale (TTF) and underwent an anatomical magnetic resonance imaging scan. Results showed that higher TTF scores were associated with larger GMV in the regions of dorsolateral prefrontal cortex, and smaller GMV in the regions of the right insular cortex and inferior frontal gyrus (IFG). Moreover, higher TTF scores were also related to smaller WMV in the regions of the left IFG. Together, these findings suggest structural variations for individual differences in the tendency to forgive, distributed across different brain regions associated with empathic response and cognitive control.

Sustainable building design has become a hot topic over the past decades. Many standards, databases, and tools have been developed for achieving a sustainable building. Not until recently have the importance of structural engineering and its contribution to sustainable building design been full recognised. However, due to the highly fragmented and diversity of knowledge across building and infrastructure domains, there is a lack of approach that can address all the sustainable issues within the structural design. This paper reviews the sustainable design from the perspective of structural engineering: (1) reviewing the current situation; (2) identifying the gaps and difficulties; and (3) making recommendations for future improvements. The strategies and indicators, as well as BIM-enabled methodology, for sustainable structural design (SSD) are also discussed in a holistic way. The results of this investigation show that most of the methods are not doing well in terms of delivering a successful sustainable structural design. It is expected that the future BIM could probably provide such a platform to address these issues.

The evolution of the construction industry is associated with the continuous implementation of new technologies. Building Information Modelling (BIM) has revolutionised the collaboration and data sharing processes in the architecture, engineering, and construction (AEC) industry. However, it needs to be supported by new technologies that can embrace digital construction by transforming the construction industry into a dynamic environment. There is a lack of understanding of the cutting-edge technologies that have emerged to help with the digital transformation of the construction industry. There is a need to understand all these technologies and how they can be leveraged holistically towards future BIM innovations. Therefore, this article conducts a literature review to evaluate how targeted cutting-edge technologies can be utilised to release the full potential of BIM from a technical perspective. A bibliometric analysis focusing on the co-occurrence of keywords related to various technologies, their links with BIM, and their related research themes was conducted based on the Web of Science (WoS) database holdings from 2010 to 2019. The findings demonstrate that one type of technology can help with solving a specific issue. However, using one technology alone does not solve an issue entirely. The current technology has been utilised independently and not as a coherent system. Thus, a weak information integration and management approach can restrict the leveraging of a smart BIM environment. This paper is not meant to be exclusive. Many new technologies, concepts, and ideas can be added to help realise BIM potentials that are not covered in this study. Furthermore, the analysis was based on the dataset retrieved from WoS and only included the literature in English. Based on those findings, the authors indicated a technology fusion to support BIM development.

Public-private partnerships (PPPs) have proliferated and adapted to public development in recent decades; within it, the value for money (VfM) assessment defines the feasibility of the project procurement model as one of the essential components of PPP. However, evaluating the VfM in PPPs remains problematic. Given concerns about PPP profitability, a more integrated VfM evaluation is urgently needed to manage multiple indicators along the project lifecycle. Building information management (BIM), popular in architecture, engineering, and construction, provides resources that could support the VfM to a great extent. This paper uses a review approach to identify the current issues that are affecting VfM assessments and suggests that BIM, functioning throughout the PPP lifecycle, could support decision-making in VfM processes in order to satisfy service targets.

Based on the theory of wave propagation in three-dimensional (3D) continuum, a new analytical approach for the longitudinal vibration characteristics of a floating pile in layered soils with radial heterogeneity is developed by employing a viscous-type damping model. Firstly, an analytical solution for the longitudinal complex impedance at the pile head is deduced by employing the Laplace transform and complex stiffness technique with the compatibility conditions of the pile and radially inhomogeneous surrounding soil. Secondly, a semi-analytical solution in the time domain is further acquired by using the inverse Fourier transform method. Furthermore, the corresponding analytical solutions are validated through contrasts with previous solutions. Finally, parametric analyses are underway to investigate the effect of radial heterogeneity of surrounding soils on longitudinal vibration characteristics of floating piles. It is indicated that the proposed approach and corresponding solutions can provide a more wide-ranging application than the simple harmonic vibration for longitudinal vibration analysis of a floating pile in soils.

In order to improve the accuracy of the freefall of lifeboat motion simulation in a ship life-saving simulation training system, a mathematical model using the strip theory and Kane’s method is established for the freefall of the lifeboat into the water from a ship. With the boat moving on a skid, the model of the ship’s maneuvering mathematical group (MMG) is used to model the motion of the ship in the waves. Based on the formula of elasticity and friction theory, the forces of the skid acting on the boat are calculated. When the boat enters the water, according to the analytical solution theory of slamming, the slamming force of water entry is solved. The simulation experiments are carried out by the established model. The results of the numerical simulation are compared with the calculation results of the hydrodynamics software Star CCM+ at water entry under initial condition A in the paper. The position and velocity of the center of gravity of the boat, the angle, and velocity and acceleration of pitch calculated by the two methods are in good agreement. There is a little difference between the values of translation acceleration calculated by the two methods, which is acceptable. This shows that our numerical algorithm has good accuracy. A qualitative analysis is performed to find the safe point of water entry under the condition of different wave heights and two situations of a ship encountering waves. Finally, the model is applied to the ship life-saving training system. The model can meet the system requirements and improve the accuracy of the simulation.

An analytical model is presented for solving the longitudinal complex impedance of a large-diameter floating pile in viscoelastic surrounding soil with radial heterogeneity and viscous-type damping, taking the effect of three-dimensional wave propagation of soil and lateral inertia of the pile shaft into account. The corresponding analytical solution for longitudinal impedance is also derived and validated via comparisons with existing solutions. The influences of the pile length, Poisson’s ratio of the pile shaft and the viscous damping coefficient, as well as the degree and radius of disturbed surrounding soil, on the longitudinal impedance of the pile shaft are examined by performing parametric analyses. It is demonstrated that the proposed analytical model and solution are suitable for the longitudinal vibration problem of a large-diameter pile and radially inhomogeneous surrounding soil, especially when the pile slenderness is low. In addition, the present solution can be easily degenerated to describe the longitudinal vibration problem relating to a large-diameter floating pile in radially homogenous soil or a pile with fixed-end supports.

As the amount of data generated by monitoring the condition of rolling bearings is increasing, it has become a research hotspot in recent years to dig valuable information from massive data and identify unknown bearing states. In Internet technology, the collaborative filtering recommendation technology provides users with an intelligent means of filtering information. Aiming at the difficulty in designing the recommendation system scoring matrix in the field of fault diagnosis, we first obtain the bearing feature matrix based on the wavelet frequency band energy and then design a scoring matrix that accurately describes the bearing state; finally, we design a joint scoring matrix for bearing state identification by combining the matrix of these two different characteristics. After that, a collaborative filtering recommendation system for bearing state identification is proposed based on matrix factorization-based collaborative filtering and gradient descent algorithm. This method is used to identify and verify two types of fault data of rolling bearing: different position faults and different types of faults on the outer ring. The results show that the accuracy of the two identifications has reached more than 90%.

The tendency to forgive (TTF) refers to one’s global dispositional level of forgiveness across situations and relationships. Previous brain imaging studies examined activation patterns underlying forgiving process, yet the association between individual differences in the TTF and spontaneous brain activity at resting-state remains unknown. In this study, resting-state functional magnetic resonance imaging (fMRI) was used to investigate the correlation between the TTF and spontaneous brain activity in a young adult sample. Participants were 178 young students (55 men) who completed the TTF scale and underwent a resting-state fMRI scan. Multiple regression analysis was conducted to assess the association between the regional amplitude of low-frequency fluctuations (ALFF) and TTF scores corrected for age and sex. Results showed that the ALFF value in the right dorsomedial prefrontal cortex (dmPFC), precuneus and inferior parietal lobule (IPL) were negatively associated with TTF scores. These findings suggest that the spontaneous brain activity of brain regions like the dmPFC, precuneus and IPL which are implicated in mentalizing and empathic response are associated with individual differences in the TTF.

Attentional bias toward negative stimuli has been observed in major depression disorders (MDDs). Imaging studies suggest the engagement of fronto-limbic regions like amygdala, anterior cingulate cortex (ACC), and lateral prefrontal cortex, is related to negatively biased attention. However, neural correlates of attentional bias for negative stimuli in individuals with subthreshold depression (SubD), that is individuals who have clinically relevant depressive symptoms but do not fulfill the criteria for MDD, remain unclear. Here, we used functional neuroimaging and the dot-probe task to elucidate the neural substrates of negatively biased attention among individuals with SubD. Behavioral results found that individuals with SubD allocated more attention toward negative stimuli relative to neutral stimuli, which were not observed among non-depressed controls (NCs). Imaging results found greater amygdala and rostral ACC activity in attentional bias toward negative stimuli among participants with SubD compared to NCs; Additionally, participants with SubD showed reduced engagement of bilateral inferior frontal gyrus compared with NCs in the attentional processing of negative stimuli. Together, these results suggest that alteration of fronto-limbic systems relative to controls, known to be related to negative detection and attentional control, is associated with negatively biased attention in individuals with SubD.

The scuffing process was in-situ observed in a sliding contact between a rotating sapphire disc and a stationary steel ball under dry condition and lubricated condition. The in-situ observations indicate that under both dry condition and lubricated condition scuffing is initiated as a result of the accumulation by wear particles. To further understand the role of wear particles in scuffing initiation, wear particles and contact area from sliding tests terminated prior to scuffing initiation and also just after scuffing initiation were examined. The shape and composition of the wear particles both prior to and after scuffing initiation were obtained. The profile, composition, and micro-hardness of the steel surface prior to scuffing initiation were also obtained with special interests on properties of the particle agglomerates formed on the steel surface. The role of wear particles in scuffing initiation was discussed based on these observations.

Ultra-high performance concrete (UHPC) has superior mechanical properties and durability to normal strength concrete. However, the high amount of cement, high environmental impact, and initial cost are regarded as disadvantages, restricting its wider application. Incorporation of supplementary cementitious materials (SCMs) in UHPC is an effective way to reduce the amount of cement needed while contributing to the sustainability and cost. This paper investigates the mechanical properties and microstructure of UHPC containing fly ash (FA) and silica fume (SF) with the aim of contributing to this issue. The results indicate that, on the basis of 30% FA replacement, the incorporation of 10% and 20% SF showed equivalent or higher mechanical properties compared to the reference samples. The microstructure and pore volume of the UHPCs were also examined. Furthermore, to minimise the experimental workload of future studies, a prediction model is developed to predict the compressive strength of the UHPC using artificial neural networks (ANNs). The results indicate that the developed ANN model has high accuracy and can be used for the prediction of the compressive strength of UHPC with these SCMs.

Smart building monitoring demands a new software infrastructure that can elaborate building domain knowledge in order to provide advanced and intelligent functionalities. Conventional facility management (FM) software tools lack semantically rich components, and that limits the capability of supporting software for automatic information sharing, resource negotiation and to assist in timely decision making. Recent hardware innovation on compact ZigBee sensor devices, software developments on ontology and intelligent software agent paradigms provide a good opportunity to develop tools that can further improve current FM practices. This paper introduces an integrated framework which includes a ZigBee based sensor network and underlying multi-agent software (MAS) components. Several different types of sensors were integrated with the ZigBee host devices to produce compact multi-functional sensor units. The MAS framework incorporates the belief-desire-intention (BDI) abstraction with ontology support (provided via explicit knowledge bases). The different software agent types have been developed to work with sensor hardware to conduct resource negotiation, to optimize battery utilization, to monitor building space in a non-intrusive way and to reason about its usage through real time ontology model queries. The deployed sensor network shows promising intelligent characteristics, and it has been applied in several on-going research projects as an underlying decision making service. More applications and larger deployments have been planned for future work.

Reducing carbon emissions and addressing environmental policies in the construction domain has been intensively explored with solutions ranging from energy efficiency techniques with building informatics to user behavior modelling and monitoring. Such strategies have managed to improve current practices in managing buildings, however decarbonizing the built environment and reducing the energy performance gap remains a complex undertaking that requires more comprehensive and sustainable solutions. In this context, building information modelling (BIM), can help the sustainability agenda as the digitalization of product and process information provides a unique opportunity to optimize energy-efficiency-related decisions across the entire lifecycle and supply chain. BIM is foreseen as a means to waste and emissions reduction, performance gap minimization, in-use energy enhancements, and total lifecycle assessment. It also targets the whole supply chain related to design, construction, as well as management and use of facilities, at the different qualifications levels (including blue-collar workers). In this paper, we present how building information modelling can be utilized to address energy efficiency in buildings in the operation phase, greatly contributing to achieving carbon emissions targets. In this paper, we provide two main contributions: (i) we present a BIM-oriented methodology for supporting building energy optimization, based on which we identify few training directions with regards to BIM, and (ii) we provide an application use case as identified in the European research project “Sporte2” to demonstrate the advantages of BIM in energy efficiency with respect to several energy metrics.

Recent building emergency management research has highlighted the need for the effective utilization of dynamically changing building information. BIM (building information modelling) can play a significant role in this process due to its comprehensive and standardized data format and integrated process. This paper introduces a BIM based virtual environment supported by virtual reality (VR) and a serious game engine to address several key issues for building emergency management, for example, timely two-way information updating and better emergency awareness training. The focus of this paper lies on how to utilize BIM as a comprehensive building information provider to work with virtual reality technologies to build an adaptable immersive serious game environment to provide real-time fire evacuation guidance. The innovation lies on the seamless integration between BIM and a serious game based virtual reality (VR) environment aiming at practical problem solving by leveraging state-of-the-art computing technologies. The system has been tested for its robustness and functionality against the development requirements, and the results showed promising potential to support more effective emergency management.