Abstract: The deformation monitoring of long-span railway bridges is significant to ensure the safety of human life and property. The interferometric synthetic aperture radar (InSAR) technology has the advantage of high accuracy in bridge deformation monitoring. This study monitored the deformation of the Ganjiang Super Bridge based on the small baseline subsets (SBAS) InSAR technology and Sentinel-1A data. We analyzed the deformation results combined with bridge structure, temperature, and riverbed sediment scouring. The results are as follows: (1) The Ganjiang Super Bridge area is stable overall, with deformation rates ranging from −15.6 mm/yr to 10.7 mm/yr (2) The settlement of the Ganjiang Super Bridge deck gradually increases from the bridge tower toward the main span, which conforms to the typical deformation pattern of a cable-stayed bridge. (3) The sediment scouring from the riverbed cause the serious settlement on the bridge's east side compared with that on the west side. (4) The bridge deformation negatively correlates with temperature, with a faster settlement at a higher temperature and a slow rebound trend at a lower temperature. The study findings can provide scientific data support for the health monitoring of long-span railway bridges.

Abstract: The traffic scenarios that may cause extreme load effects are of great importance to the safety assessment of bridge structures. The traditional simulation method of traffic flow cannot depict the distribution pattern of vehicles on the bridge deck when the maximum effect is induced. In this paper, a probabilistic Gaussian mixture model (GMM) for heavy vehicle scenarios on the bridge deck under free-flow condition is proposed for long-span bridges based on collected Weigh in Motion (WIM) data. The scenarios of extreme response under free-flow occur more frequently than congestion scenarios and are of similar value and relevance in the daily management and safety assessment of long-span bridges. A non-stationary Poisson process is utilized to simulate the uneven occurrence of heavy vehicles in different lanes, and it is assumed that they are located within the artificially defined cells on the bridge deck. Then, Nataf transformation is employed to consider the correlation of gross vehicle weights (GVWs) within close range in the same lane. The numerical study is carried out on a long-span cable-stayed bridge to investigate the effects of correlation in GVWs and stationarity of vehicle distribution location on the structural responses. The load responses calculated by the proposed model and Monte Carlo method for different effects are compared with the values derived from code model. The results show that with the increase of the correlation level of the neighboring GVWs, the simulated responses are more prone to get extreme values, which means an increasing probability of the most unfavorable spatial distribution of on-bridge vehicles. The same results are also found under the non-stationary simulation state for vehicle location. The non-stationary Poisson process provides an efficient, highly feasible method, which is also in the safe side, for simulating the vehicle spatial distribution for specific effects.

Abstract: Abstract Long‐span suspension bridges are significantly susceptible to windstorm‐induced vibrations, leading to critical challenges of field measurements along with multicollinearity and nonlinearity between wind features and bridge dynamic responses. To address these issues, this article proposes an innovative machine learning‐assisted predictive method by integrating a predictor selector developed from regularized neighborhood components analysis and kernel regression modeling through a regularized support vector machine adjusted by Bayesian hyperparameter optimization. The crux of the proposed method lies in advanced machine learning algorithms including metric learning, kernel learning, and hybrid learning integrated in a regularized framework. Utilizing the Hardanger Bridge subjected to different windstorms, the performance of the proposed method is validated and then compared with state‐of‐the‐art regression techniques. Results highlight the effectiveness and practicality of the proposed method with the minimum and maximum R‐squared rates of 89% and 98%, respectively. It also surpasses the state‐of‐the‐art regression techniques in predicting bridge dynamics under different windstorms.

Abstract: Damage detection in bridge structures has always been challenging, particularly for long‐span bridges with complex structural forms. In this study, a partial‐model‐based damage detection method was proposed for the damage identification of long‐span steel truss bridges. The proposed method employs partial models to estimate the parameters using the stiffness separation method. This approach obviates the need to construct complete stiffness information for the structure. In contrast, it depends solely on the arrangement of the structural members and material information in the recognized area. This technique can effectively circumvent the construction of an overall structural model and reduce the complexity of damage identification in large structures. A full‐scale long‐span steel truss bridge in service was used to illustrate the feasibility of the proposed method. The locations of the three partial models were considered in the model analysis, and the parameter estimation efficiency of the Nelder–Mead simplex and quasi‐Newton algorithms were compared.

Abstract: Abstract Open innovation (OI) has acknowledged the importance of managing knowledge flows outside firm boundaries to enhance innovation. However, OI researchers have not explored the complexity of managing these knowledge flows across domains in practice. This chapter builds on the knowledge boundaries perspective to expand current understanding of knowledge work in OI contexts by nuancing the different types of knowledge flows occurring across domains and exploring the types of boundary processes needed to support the creation and development of these knowledge flows outside organizational and specialized domain boundaries. It analyzes the case of Hacking Health, a non-profit organization that developed a series of events to develop a variety of boundary processes (gathering, transferring, translating, transcending, transforming) that address different types of knowledge boundaries that emerge in the fuzzy front end of OI phenomena in the nascent industry of digital health.

Abstract: Recently, lots of works that incorporate external lexicon information into character-level Chinese named entity recognition(NER) to overcome the lackness of natural delimiters of words, have achieved many advanced performance. However, obtaining and maintaining high-quality lexicons is costly, especially in special domains. In addition, the entity boundary bias caused by high mention coverage in some boundary characters poses a significant challenge to the generalization of NER models but receives little attention in the existing literature. To address these issues, we propose SENCR, a Span Enhanced Two-stage Network with Counterfactual Rethinking for Chinese NER, that contains a boundary detector for boundary supervision, a convolution-based type classifier for better span representation and a counterfactual rethinking(CR) strategy for debiased boundary detection in inference. The proposed boundary detector and type classifier are jointly trained with the same contextual encoder and then the trained boundary detector is debiased by our proposed CR strategy without modifying any model parameters in the inference stage. Extensive experiments on four Chinese NER datasets show the effectiveness of our proposed approach.

Abstract: <span lang="EN-US">Combined with advances in sensing technologies and big data analytics, critical information can be extracted from continuous production processes for predicting the health state of equipment and safeguarding upcoming failures. This research presents a methodology for applying predictive maintenance (PdM) solutions and showcases a PdM application for health state prediction and condition monitoring, increasing the safety and productivity of centrifugal pumps for a sustainable and resilient PdM ecosystem. Measurements depicting the healthy and maintenance-prone stages of two centrifugal pumps were collected on the university campus. The dataset consists of 5,118 records and includes both running</span><span lang="EN-GB"> and standstill</span><span lang="EN-US"> values. Additionally, Spearman statistical analysis was conducted to measure the correlation of collected measurements with the predicted output of machine conditions and select the most appropriate features for model optimization. Several machine learning (ML) algorithms, namely random forest (RF), Naïve Bayes, support vector machines (SVM), and <a name="_Hlk137498016"></a>extreme gradient boosting (XGBoost) were analyzed and evaluated during the data mining process. The results indicated the effectiveness and efficiency of XGBoost for the health state prediction of centrifugal pumps. The contribution of this research is to propose an effective framework collectong multistage health data for PdM applications and showcase its effectiveness in a real-world use case.</span>

Abstract: The development tendency of lightweight and higher-speed regarding trains goes against the safety and comfort requirement of high-speed trains, especially in the scenario of the train running on bridges within crosswind environments. Wind barriers have been proven to be an effective measure to guarantee the train's operation security. In this paper, a new type of wind barrier consisting of the fence-type wind barrier form and open hole-type wind barrier form has been proposed. Then, a series of wind tunnel tests about the large-scale section model of a large-span suspension bridge installed with different wind barriers have been conducted. The effects of three different forms of wind barrier including fence-type, open hole-type, and combined-type on the aerodynamic characteristics of the train-bridge system have been analyzed. The windproofing effectiveness and the protection mechanism of the combined-type wind barrier on the train-bridge system have been revealed through the measured aerodynamic characteristics. The results show that the installation of open hole-type, fence-type, and combined-type wind barriers at a height of 4 m and a ventilation rate of 19% yields a maximum reduction in the train drag coefficient of 67.70%, 72.92%, and 97.21%, respectively. Moreover, the combined-type wind barrier can improve the train-bridge system's aerodynamic performance and trains' running safety on bridges when comparing with the open hole-type and fence-type wind barriers. The proposed combined-type wind barrier can provide a more efficiency and practical way for the high-speed train's running safety on the bridge.

Abstract: The purpose of this study is to demonstrate the validity and reliability of the scale, forming the Turkish translated version of the Successful Aging Scale (SAS) developed by Reker (2009). Survey method was used in the research. In this study, written permission was obtained from the scale owner and the institution, verbal verbal approval was obtained from the participants. Participants of the study consisted of 359 men and 151 women totally 510 people over 60 years of age living in the province of Ankara and the county of Çankaya. The data were collected using SAS consisting of 14 items. The original English form of the scale has 3 subcomponents These subcomponents and item counts are as follows: 1. Healthy life style (4 items), 2. Adaptive coping (4 items), 3. Engagement with Life (5 items), the scale is Likert type and measured as 1 (strongly disagree), 7 (strongly agree). The Cronbach's alpha reliability coefficients of the total and sub components of the original scale ranged from .72 to .84. Confirmatory Factor Analysis (Cronbach's Alpha coefficient and a Structural Equation Model for validity) was used for the reliability of the data. The linguistic validity of the scale was translated and translated by experts. Seven experts were consulted for content validity. The fit indices of the model were calculated by confirmatory factor analysis and the model fit well [χ2 (27): 64.993, p < .01; χ2/df: 2.407; SRMR: .0319; RMSEA: .053; NNFI: .960; IFI: .976; CFI: 976; GFI: .976; AGFI: .951]. In the Turkish version of the scale, when a relationship between Adaptive coping and Engagement with Life factors is found to be 0.93, a second level factor called Layout is defined according to the literature. Since no factor can be loaded predominantly, as in Item 11, the other Item 1 factor load is less than 400; Items 4 and 14 were subtracted from relevant factors as they could not make a significant contribution to being above the Common Explanatory Variance (CEV) criterion of .500 at the time of compliance. It has been determined that SAS is a valid and reliable tool to measure attitudes towards aging in the study. For future studies, it is recommended that SAS be implemented in groups with different socio-demographic characteristics and the validity and reliability of these groups should be examined.

Abstract: Welded spherical joints are critical components in large-span bridges and spatial steel structures prone to high cycle fatigue (HCF) loadings. Challenges exist for assessing fatigue life of this joints in small data from physical experiments and no detailed fatigue classification in design codes. This paper investigates the fatigue resistance of welded spherical joints and the real-time pre-damage evolution under non-stationary HCF loadings by proposing AI-based macro model learning from fine FE models and fatigue tests. Firstly, the structural features of welded spherical joints about stress concentration and weld defects were analyzed and quantified, so modified S-N curve was built. Then, based on artificial intelligence (AI-based) learning, stress concentration factors (SCF) were obtained by an artificial neural network with particle swarm optimization and the modified fatigue resistance of welded spherical joints was derived by artificial bee colony algorithms. Finally, the macro linkage element was proposed as a user element subroutine (UEL), by which non-stationary fatigue pre-damage evolution based on the time-domain incremental analytical method was realized. The results not only indicated the damage value close to the ones derived by commonly used rain flow count methods, but also provided the pre-damage evolution curve for real-time monitoring.

Abstract: In order to produce marketable yield, scientific methodologies must be used to forecast the greenhouse microclimate, which is affected by the surrounding macroclimate and crop management techniques. The MATLAB tool NARX was used in this study to predict the strawberry yield, indoor air temperature, relative humidity, and vapor pressure deficit using input parameters such as indoor air temperature, relative humidity, solar radiation, indoor roof temperature, and indoor relative humidity. The data were normalized to improve the accuracy of the model, which was developed using the Levenberg–Marquardt backpropagation algorithm. The accuracy of the models was determined using various evaluation metrics, such as the coefficient of determination, mean square error, root mean square error, mean absolute deviation, and Nash–Sutcliffe efficiency coefficient. The results showed that the models had a high level of accuracy, with no significant difference between the experimental and predicted values. The VPD model was found to be the most important as it influences crop metabolic activities and its accuracy can be used as an indoor climate control parameter.

Abstract: The seismic damage in reinforced concrete bridges is identified in this study using the &quot;M and P&quot; hybrid technique initially developed for planar frames. The proposed methodology involves a series of pushover and instantaneous modal analyses with a progressively increasing target deck displacement along the longitudinal direction of the bridge. From the results of these analyses, the diagram of the instantaneous eigenfrequency of the bridge, ranging from the health state to near collapse, is plotted against the inelastic seismic deck displacement. By pre-determining the eigen-frequency of an existing bridge along its longitudinal direction through &quot;monitoring and frequency identification&quot;, the target deck displacement corresponding to the damage state can directly be found from this diagram. Subsequently, the damage can be identified by examining the results of the pushover analysis at the step where the target deck displacement is indicated. The effectiveness of this proposed technique is evaluated in the context of multiple span bridges with unequal pier heights, illustrated through an example of a four-span bridge. The findings demonstrate that the damage potential in bridge piers can be successfully identified by combining the results of a monitoring process and pushover analysis.

Abstract: Large language models (LLMs) have shown impressive capabilities in adapting to various tasks when provided with task-specific instructions. However, LLMs using standard decoding strategies often struggle with deviations from the inputs. Intuitively, compliant LLM outputs should reflect the information present in the input, which can be measured by point-wise mutual information (PMI) scores. Therefore, we propose Diver, a novel approach that enhances LLM Decoding through span-level PMI verification. During inference, Diver first identifies divergence steps that may lead to multiple candidate spans. Subsequently, it calculates the PMI scores by assessing the log-likelihood gains of the input if the candidate spans are generated. Finally, the optimal span is selected based on the PMI re-ranked output distributions. We evaluate our method across various downstream tasks, and empirical results demonstrate that Diver significantly outperforms existing decoding methods in both performance and versatility.