Dynamic Construction Research Articles

Demand Side Perception on Success Factors for Implementing Public Road Construction Projects

Cost and schedule overruns, as well as substandard works, are common factors that fail organisations’ objectives within the construction industry. Particularly, these factors affect public road construction projects and cost taxpayers. Researchers continue scanning the environment to establish why construction projects are ever behind schedule and over budget with substandard works and contract variations to identify significant factors for successful project implementation. This study expands the debate by looking at demand side perception in establishing success factors for implementing public road construction projects motivated by governments’ high expenditure on construction projects without meeting objectives. A cross-sectional research design with a structured self-administered questionnaire was used to obtain views from three public entities representing the demand side. Results were analysed following partial least square structural modelling (PLS-SEM) in Smart-PLS3. The research design enabled statistical tests to be conducted on validity, reliability, normality, multicollinearity, correlations and regression. The PLS algorithm and bootstrapping resampling approach were employed to determine the relationship between variables by estimating path coefficients and significance. Path coefficients helped to determine strength, direction and significance, and examine the variance of dependent variables explained by combined independent variables. Results revealed that the professionalism of the staff, compliance with the public procurement regulatory framework, monitoring activities and contractors’ resistance to non-compliance are significant success factors in enhancing public road implementation. Hence, adopting these factors would be a game changer in implementing complex road construction projects. In addition, complex construction projects in a dynamic construction industry require continuous scanning to establish more factors and cope with industry dynamics.

Self-repeat as a multimodal retraction practice

Abstract The paper focuses on a particular practice of self-repeat through which participants retract their prior formulations, and explores its multimodal design and use in the dynamic construction of meaning in Hebrew conversation. Drawing on interactional approaches to language and embodied action, we show that the practice of self-repeat is used to retract a formulation judged by its producer as being inadequate and ill-calibrated in the given interactional context. This function is supported by the multimodal configuration in which the lexical repeat is cast, which involves a stable prosodic component and a variable embodied component. Through its prosodic and embodied design, the repeat is contextualized as a noticeable display of accountability for having made an ill-suited choice of words. While the self-repeat alone is sufficient in proposing a problem of calibration, it can also be followed by a lexical replacement, which makes explicit the adjusted or recalibrated term. The self-repeat practice shows how participants engage in semantic work through online and situated revision of their formulations. This exposed process of meaning construction reveals their understanding of the constitutive link between the conceptual and the normative orders as practiced in actual conversation.

Nanjing City Image Governance Based on China's Cultural Soft Power

This study focuses on the city image governance based on China's cultural soft power, taking Nanjing, China, as an example, which is crucial for China to find the path of city image governance to enhance its national strength. Methodology: The research method of "logical analysis→mechanism exploration→path optimization" is used to demonstrate the relationship between cultural soft power and urban image governance from multiple perspectives. This method, which combines technology and theory, can fill the gap of the current research method which is too theoretical and lacks targeted research. Results: The relationship between cultural soft power and Nanjing city image governance was determined in the logical analysis. In the mechanism exploration, it is found that the two are consistent with the law of action and reaction. The relationship between the two and the relationship between theory and practice are mutually applicable in the path optimization. Conclusion: First, cultural soft power and urban image governance are correlated. Second, the interaction mechanism between the two is the relationship of action and reaction. Third, it is determined that the innovative mechanism of cultural soft power and urban image governance in Nanjing can be widely applied to practice and has general applicability for other cities in China. Innovation: Propose the type, structure, approach, and effectiveness of urban cultural governance in Nanjing that is compatible with national policies. It is suggested that urban governance constructs an innovative system of multifaceted and collaborative dynamic construction that is adapted to China's local realities, has a clear urban positioning, and aims to build, govern, and share together.

Body image dissatisfaction during the first year of pediatric inflammatory bowel disease diagnosis.

Dissatisfaction with one's body can be distressing; youth with inflammatory bowel disease(IBD) may be at increased risk for body image dissatisfaction given disease symptoms and treatment side effects. Yet, no studies have examined body image dissatisfaction over time in youth with IBD and whether depressive symptoms are associated with change in dissatisfaction. Fifty-seven pediatric participants (8-17 years old) newly diagnosed with IBD were enrolled. Youth completed questionnaires assessing body image dissatisfaction and depressive symptoms shortly after diagnosis (Time 1) and 12 months later (Time 2). Multilevel longitudinal modeling was used to test the extent to which body image dissatisfaction changed across the first year of diagnosis and to test change in body image dissatisfaction as a function of depressive symptoms. Findings indicated significant between- and within-person variance in body image dissatisfaction over the 12 months, yet the sample as a whole did not report significant changes in dissatisfaction from Time 1 to Time 2. Children reporting depressive symptoms greater than their individual average over time reported greater body image dissatisfaction. Between-person variation in depressive symptoms demonstrated a significant interaction with time. As an individual's depressive symptoms exceeded the group average, their body image dissatisfaction increased, although less drastically as time since diagnosis progressed. Findings suggest that body image dissatisfaction is a complex and dynamic construct across youth and that interventions for pediatric IBD patients need to be tailored to the needs of individuals. Methods for assessing body image dissatisfaction efficiently and repeatedly across multiple visits are provided.

A Novel Dynamic S-Box Generation Scheme Based on Quantum Random Walks Controlled by a Hyper-Chaotic Map

For many years, chaotic maps have been widely used in the design of various algorithms in cryptographic systems. In this paper, a new model (compound chaotic system) of quantum random walks controlled by a hyper-chaotic map is constructed and a novel scheme for constructing a dynamic S-Box based on the new model is proposed. Through aperiodic evaluation and statistical complexity measurement, it is shown that the compound chaotic system has features such as complex structure and stronger randomness than classical chaotic systems. Based on the chaotic sequence generated by the composite system, we design a dynamic S-Box generation mechanism. The mechanism can quickly generate high-security S-Boxes. Then, an example of randomly generating S-Boxes is given alongside an analytical evaluation of S-Box standard performance criteria such as bijection, boomerang uniformity, bit independence, nonlinearity, linear approximate probability, strict avalanche effect, differential uniformity, the and generalized majority logic criterion. The evaluation results confirm that the performance of the S-Box is excellent. Thus, the proposed dynamic S-Box construction technique can be used to generate cryptographically strong substitution-boxes in practical information security systems.

Temporal assessment of emergency response and rescue capability for hybrid hydrogen-gasoline fueling stations based on dynamic scenario construction

In the event of a fire or explosion at a hybrid hydrogen-gasoline fueling station, the consequences can be significantly more severe compared to a traditional refueling station or hydrogen refueling station. It is imperative to establish an effective assessment model specific to the hybrid station to prevent and mitigate such accidents. This paper introduces a temporal dynamic assessment model designed to evaluate the emergency response and rescue capabilities of hybrid hydrogen-gasoline fueling stations. The model is grounded in dynamic scenario construction and utilizes information-driven theory. This innovative approach allows for evaluations of the risk evolution process at different stages of an accident. The proposed assessment model combines the Temporal Weighted Average (TOWA) and Temporal Weighted Geometric Average (TOWGA) operators to integrate assessment values across different stages including the initial disaster assessment, emergency action plans, effectiveness feedback, and emergency response adjustment. This integration yields a comprehensive dynamic assessment value for the emergency response and rescue capabilities of the hybrid hydrogen-gasoline fueling station. Based on the dynamic assessment of a previously occurring accident, the specialized emergency management department performed most effectively during the incident response stage, achieving a commendable assessment value of 3.184. Following closely was the government emergency response department, with assessment values of 2.865 and 2.277. This study offers a novel analytical perspective for emergency management in hybrid hydrogen-gasoline fueling stations by incorporating the findings of both dynamic and static assessments.

Optimal domain adaptive object detection with self-training and adversarial-based approach for construction site monitoring

In practice, object detection models used for construction site monitoring exhibit performance degradation owing to different monitoring settings and dynamic construction environments. Collecting and labeling images from new construction sites is necessary to mitigate these challenges; however, this requires significant resources. This paper presents an optimal domain-adaptive object detection model that employs an unsupervised domain adaptation approach to address the performance degradation challenges. Experiments were conducted at two construction sites to validate and identify the most effective domain-adaptive object detection model for construction sites. Several domain-adaptive object detection models have been developed, and our experimental results demonstrate that a hybrid approach, combining self-training and adversarial-based approaches improves the mean average precision by 12.8 and 7.1 over that of the baseline model at each construction site. These findings underscore the potential of domain adaptation-based methods to train object detection models in the construction domain, offering improved performance and reduced labeling efforts at specific target construction sites.

Enhanced visual SLAM for construction robots by efficient integration of dynamic object segmentation and scene semantics

With the increasing adoption of autonomous mobile robots in the construction industry, accurate localization and mapping in dynamic construction environments have become paramount. This is typically tackled via Simultaneous Localization and Mapping (SLAM) techniques. Primarily designed for static environments, traditional SLAM systems struggle to maintain robustness and accuracy in dynamic settings. To address this challenge, this study presents an enhanced visual SLAM system specifically tailored for dynamic construction environments. The proposed system, named vSLAM-Con, introduces an adaptive dynamic object segmentation method, utilizing an innovative AD-keyframes selection mechanism grounded on optical flow magnitude to diminish computational overhead while preserving competitive tracking accuracy. Additionally, a semantic-based feature update process is developed, leveraging scene understanding and continuous observation to augment the reliability of tracking features. This system's performance, evaluated on both an established public benchmark and a custom construction dataset, shows substantial improvements over the baseline and competitive results with the state-of-the-art algorithms. More importantly, it largely reduces the processing time compared to state-of-the-arts, demonstrating robust tracking performance even under highly dynamic conditions. The findings highlight the system's potential to contribute significantly to autonomous robotics in construction, offering more accurate navigation and interaction capabilities in complex, ever-changing environments.

Multiomics characterization of fatty acid metabolism for the clinical management of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a prevalent malignancy and there is a lack of effective biomarkers for HCC diagnosis. Living organisms are complex, and different omics molecules interact with each other to implement various biological functions. Genomics and metabolomics, which are the top and bottom of systems biology, play an important role in HCC clinical management. Fatty acid metabolism is associated with malignancy, prognosis, and immune phenotype in cancer, which is a potential hallmark in malignant tumors. In this study, the genes and metabolites related to fatty acid metabolism were thoroughly investigated by a dynamic network construction algorithm named EWS-DDA for the early diagnosis and prognosis of HCC. Three gene ratios and eight metabolite ratios were identified by EWS-DDA as potential biomarkers for HCC clinical management. Further analysis using biological analysis, statistical analysis and document validation in the discovery and validation sets suggested that the selected potential biomarkers had great clinical prognostic value and helped to achieve effective early diagnosis of HCC. Experimental results suggested that in-depth evaluation of fatty acid metabolism from different omics viewpoints can facilitate the further understanding of pathological alterations associated with HCC characteristics, improving the performance of early diagnosis and clinical prognosis.

Motherhood beliefs across sex, age, education and parenthood

Motherhood is a cultural process that has traditionally anchored women's identities in their ability to nurture infants and children (Ross, 2018). As a cultural and dynamic construct, it not only shapes women's social identity but also plays a pivotal role in influencing gender roles. The aim of the present study is to analyze the psychometric properties of the Motherhood Belief Scale, which explore Sense of Life and Social Duty as two dimensions of motherhood in a general sample (n = 6207) with different sex, age, education, and parenthood status. The reliability of the Motherhood Belief Scale in our sample showed low fitted scores, low variability, and a floor-like effect, which was more eminent for the Social Duty dimension. These findings showed differences in motherhood beliefs across sex, age, education, and parenthood. Additionally, we explored answers for two open questions about motherhood with a sentiment analysis which showed a more significant bias to positive emotional valence in people without professional studies out professional studies. These findings suggest a more stereotypical view of motherhood in populations with low education, very young or elderly, men, and with parenthood status.

DYNAMIC CAPABILITIES AND BUSINESS MODEL INNOVATION: A STUDY FOR SCALE DEVELOPMENT

Based on dynamic capability theory, this paper presents a reconceptualization of the dynamic capabilities (DC) construct within a multi-layer-structure in the context of business model innovation (BMI). A rigorous multi-stage scale development process is employed. We developed candidate DC scale items based on the literature review study and reviewed them with the participation of 5 field experts. Final DC scale items have been surveyed with 121 respondents across a wide range of small and medium scale enterprises engaging in BMI. The preliminary results of exploratory and confirmatory factor analyses highlight the multi-layer nature of dynamic capabilities construct for small and medium enterprises (SME’s) engaging in BMI in a developing country environment. Four subdimensions were clearly identified among Sensing Seizing and Reconfiguration dimensions across Owner_Manager and Employee layers: Owner_Manager_Sensing,Owner_Manager_ Seizing and Owner_Manager_Reconfiguration, Employee_Sensing_Seizing. Furthermore, Clauss’ (2017) BMI scale has been translated into local language (Turkish), validated and checked for reliability following a stepwise process. Factor analysis results paralleled the original scale. This offers a deeper and multi-layered tool for measuring subdimensions of both DC’s and BMI beyond the limitations in the prior research.

Unsupervised video object segmentation for enhanced SLAM-based localization in dynamic construction environments

Accurate robot localization in dynamic construction environments is vital for successful robotics implementation on jobsites. Dynamic Simultaneous Localization and Mapping (SLAM) methods show promise in addressing challenges by identifying and removing dynamic objects. However, existing studies struggle to accurately segment these objects, primarily due to handling unseen and temporarily static objects. This study presents a novel approach for robustly segmenting dynamic objects in construction environments. The approach captures potential objects using objectness masks and refines them by fusing masks with motion cues. The segmented information is propagated bi-directionally across frames. The method comprises four stages: objectness masks generation, motion saliency estimation, fusion of objectness masks and motion saliency, and bi-directional propagation of fused masks. Experimental results demonstrate the method's effectiveness in accurately segmenting dynamic objects and improving localization accuracy when integrated with SLAM structure, highlighting its potential for real-world applications in complex construction environments.

Complexities of gender and sex

This editorial reflects on the controversy at the American Anthropological Association and the Canadian Anthropology Society's conference, where a session on the fixity of biological sex was cancelled. Retracing developments from Simone de Beauvoir's foundational ideas to the theories of second‐wave feminism it emphasizes how the gender concept was posed as a dynamic cultural construct. rather than a fixed biological fate, then shows that biological sex is also more complex than earlier characterizations of it as given, fixed, and immutable. Underscoring the complexities of academic debate and inclusivity, the piece highlights anthropologists’ work in revealing gender diversity and spotlights the role of young LGBTQI researchers in reshaping our concepts of sex and gender, which have moved from fixed binaries to a more fluid understanding in contemporary thought. The editorial concludes with a call for an integrated, non‐dichotomous approach to (cultural) gender and (biological) sex, a nuanced understanding that recognizes their interplay.

Using intensive longitudinal methods to quantify the sources of variability for situational engagement in science learning environments

BackgroundSituational engagement in science is often described as context-sensitive and varying over time due to the impact of situational factors. But this type of engagement is often studied using data that are collected and analyzed in ways that do not readily permit an understanding of the situational nature of engagement. The purpose of this study is to understand—and quantify—the sources of variability for learners’ situational engagement in science, to better set the stage for future work that measures situational factors and accounts for these factors in models.ResultsWe examined how learners' situational cognitive, behavioral, and affective engagement varies at the situational, individual learner, and classroom levels in three science learning environments (classrooms and an out-of-school program). Through the analysis of 12,244 self-reports of engagement collected using intensive longitudinal methods from 1173 youths, we found that the greatest source of variation in situational engagement was attributable to individual learners, with less being attributable to—in order—situational and classroom sources. Cognitive engagement varied relatively more between individuals, and affective engagement varied more between situations.ConclusionsGiven the observed variability of situational engagement across learners and contexts, it is vital for studies targeting dynamic psychological and social constructs in science learning settings to appropriately account for situational fluctuations when collecting and analyzing data.

Cellular Architects at Work: Cells Building their Own Microgel Houses.

Microporous annealed particle (MAP) scaffolds are investigated for their application as injectable 3D constructs in the field of regenerative medicine and tissue repair. While available MAP scaffolds provide a stable interlinked matrix of microgels for cell culture, the infiltration depth and space for cells to grow inside the scaffolds is pre-determined by the void fraction during the assembly. In the case of MAP scaffolds fabricated from interlinked spherical microgels, a cellularity gradient can be observed with the highest cell density on the scaffold surface. Additionally, the interlinked microgel network limits the ability of cells to remodel their environment, which contradicts native tissue dynamics. In this work, a cell-induced interlinking method for MAP scaffold formation is established, which avoids the necessity of chemical crosslinkers and pre-engineered pores to achieve micro- or macropores in these 3D frameworks. This method enables cells to self-organize with microgels into dynamic tissue constructs, which can be further controlled by altering the microgel properties, the cell/microgel ratio, and well shape. To form a cell-induced interlinked scaffold, the cells are mixed with dextran-based microgels and function as a glue between the microgels, resulting in a more homogenous cell distribution throughout the scaffold with efficient cell-cell interactions.

An effective monitoring method of dynamic compaction construction quality based on time series modeling

The existing quality monitoring method of dynamic compaction construction is costly and inefficient, and the measurement results for the falling distance of the hammer and the number of tamping times may even become completely ineffective in abnormal situations during the construction process, such as empty hook rise and early release hammer. Therefore, this paper proposes an effective measurement method based on time series model. Simulate the regular periodic motion state transformation in strong ramming construction by motion state discrimination model, combined with the prediction interval of running time estimation model to assist state discrimination, realizes real-time monitoring of anomalies as well as effective measurement of important construction parameters such as the tamping times and the falling distance of the hammer. Experimental results demonstrate that the method achieves accuracy of 100% under normal tamping conditions, and accuracy of no less than 95% even in tamping conditions involving abnormal behavior.

Learning multi-granular worker intentions from incomplete visual observations for worker-robot collaboration in construction

Construction is often considered a dangerous and labor-intensive industry, with safety and workforce shortages plaguing the industry globally. Owing to its flexibility, Worker-Robot Collaboration (WRC) could combine the strengths of humans and robots, alleviating human workers from dangerous and tedious tasks and boosting productivity. WRC envisions robots assisting workers appropriately, which necessitates the ability to understand workers' intentions. However, challenges arise when robots recognize workers' intentions in construction. First, the highly unstructured and dynamic construction workplace prevents robots from capturing consistent long-term worker activities, which is detrimental to the information accumulation and disambiguation processes in intention recognition. Second, the long-horizon tasks with implicit steps make it difficult for robots to find a suitable granularity to capture workers' intentions. Considering these challenges, this study seeks to (1) explore whether it is feasible to recognize worker intentions with temporally incomplete observations and (2) investigate which granularity is best suited for recognizing worker intentions. To tackle the first challenge, a data-and-knowledge fusion method is proposed, where the contextual information from visual data is combined with task knowledge to mitigate the information loss due to incomplete observations. To address the second challenge, the best-suited granularity is determined by comparing the recognition accuracy and intention modeling ability of different granularities after defining and recognizing multi-granular worker intentions using a hierarchical task structure and Multi-Task Learning (MTL), respectively. Results show that the proposed method can recognize multi-granular worker intentions with macro-F1 scores higher than 0.85, and that the intermediate activity is the best-suited granularity as it strikes a good balance between intention recognition accuracy and intention modeling capability. This study contributes to the existing body of knowledge by recognizing multi-granular worker intentions and determining the appropriate granularity under temporally incomplete observations. The findings will help facilitate seamless WRC in construction.

An insider user authentication method based on improved temporal convolutional network

With the rapid development of information technology, information system security and insider threat detection have become important topics for organizational management. In the current network environment, user behavioral bio-data presents the characteristics of nonlinearity and temporal sequence. Most of the existing research on authentication based on user behavioral biometrics adopts the method of manual feature extraction. They do not adequately capture the nonlinear and time-sequential dependencies of behavioral bio-data, and also do not adequately reflect the personalized usage characteristics of users, leading to bottlenecks in the performance of the authentication algorithm. In order to solve the above problems, this paper proposes a Temporal Convolutional Network method based on an Efficient Channel Attention mechanism (ECA-TCN) to extract user mouse dynamics features and constructs an one-class Support Vector Machine (OCSVM) for each user for authentication. Experimental results show that compared with four existing deep learning algorithms, the method retains more adequate key information and improves the classification performance of the neural network. In the final authentication, the Area Under the Curve (AUC) can reach 96%.

An in-depth exploration of a positional subgroup in professional sport

An inevitable structural feature within sport teams involves ‘the groups within the group,’ otherwise known as subgroups. Although there is a large body of literature dedicated to group dynamics constructs at the total team level, little research has explored such topics at a positional subgroup level, particularly in professional sports. Thus, the purpose of this study was to provide an in-depth exploration of group dynamics perceptions from a specific positional subgroup in professional sports, with a particular emphasis on group cohesion and leadership. Specifically, we interviewed six experienced professional gridiron football offensive linemen to explore their perspectives of the factors influencing group cohesion within their positional subgroup. The analysis revealed environmental factors as well as internal roles and interpersonal processes that shaped group development and cohesion within the subgroup. Notably, the impact of veteran athlete leadership and how the behaviors of both the coaching staff and general managers could either positively or negatively influence group cohesion in professional football were apparent. Therefore, the present results are of interest to members of professional organizations generally, and gridiron football specifically, including general managers, head coaches, assistant coaches, support staff, and athletes regarding how the dynamics within a positional subgroup can impact individual and team outcomes. Lay summary: This article describes professional athletes’ opinions about group cohesion and leadership. All athletes were part of a positional subgroup in gridiron football. The analysis revealed environmental factors as well as internal roles and interpersonal processes that shaped group development and cohesion within the subgroup and the team as a whole.

Multi-hop community question answering based on multi-aspect heterogeneous graph

Community question answering aims to connect queries and answers based on users' community behaviors, find the most relevant solutions for newly raised questions, and improve community activity. Existing research mainly focuses on the single-hop method, which relies on a particular feature for simple question answering. However, multi-hop question answering, which depends on multi-step reasoning to solve complex questions, still faces the destitute adaptability caused by various entity features and complex entity correlations. In this work, we put forward a Multi-Hop Community Question-Answering method called MHCQA, that combines the multi-aspect features of community entities, constrains the answer retrieval process from different perspectives and improves the adaptability of multi-hop answer retrieval. Firstly, we utilize phrases for representing the semantic features of question-answering and fuse them with entity constitution properties through heterogeneous graphs to effectively indicate the diverse relationships among entities in communities. At the same time, we bring a dynamic heterogeneous network construction strategy to improve the efficiency of feature representation. Secondly, we enhance heterogeneous graph attention networks for efficiently computing local entity similarities to find relevant answers to questions based on partial graphs. Finally, we propose a multi-constraint multi-hop community question-answering method, which optimizes answer retrieval from three aspects: the hop count, the number of answers, and the relevance of responses, to improve the scene adaptability of multi-hop answer retrieving. Abundant experiments on realistic corpus (Quora, Math, Super User, and Stack Overflow) illustrate that MHCQA improves the multi-hop question answering error rate by about 2 % and is about 11 % ahead of the baselines in terms of operating efficiency. Given our present literature survey, MHCQA is a promising research idea for automatic answering services in online question-answering communities.