Construction Method Research Articles

Application and Empirical Analysis of Random Volatility Model in Financial Markets

The Stochastic Volatility Model (SVM), as a key innovation in modern financial engineering, has profoundly changed our understanding of the volatility characteristics of financial markets. This model not only considers the volatility of asset prices as a time-varying random variable, but also simulates this uncertainty by introducing stochastic processes such as Brownian motion or more complex stochastic differential equations, thereby achieving a precise characterization of the dynamic characteristics of volatility. Compared to traditional fixed or historical average volatility assumptions, SVM can capture sudden changes in market sentiment, the impact of news events, and the influence of macroeconomic factors on volatility, providing investors with a more realistic perspective on the market. This article not only deeply analyzes the theoretical basis and construction methods of SVM, but also verifies its effectiveness and applicability in different financial market environments through empirical analysis and actual market data, providing valuable references and insights for financial practitioners, scholars, and policy makers.

Synergetic construction of color and multifunction for sustainable lyocell fabric by Coptis chinensis and BTCA

In the context of escalating standards of living, the demand for healthy and multifunctional textiles is increasing. As a kind of cellulose macromolecular-based material, lyocell fiber has low carbon, is environmentally friendly, and demonstrates superb performance. The utilization of some Chinese herb dyes solves the pollution problem in the color and functionality construction of lyocell fabric by synthetic dyes and finishing agents. However, problems such as low dye utilization rate, light apparent color, and weak functionality of dyed fabrics remain, thus limiting the further application of the powerful combination of lyocell fabric and Chinese herb dyes. Here, a color and multifunction construction method of lyocell fabric with Coptis chinensis and 1,2,3,4-butanetetracarboxylic acid was proposed. Under the optimal color construction condition, the color depth increased remarkably, and the dye exhaustion rate of the modified fabric enhanced by 332.3 % compared with the unmodified one. The multifunction construction imparted outstanding fuzz and pilling inhibition, fibrillation resistance, and antiwrinkle performance for lyocell fabrics. Moreover, the dyed lyocell fabric exhibited considerable UV protective activity and antibacterial property against Staphylococcus aureus. This work provided an efficient color and multifunction construction technology for lyocell fabric with high value added.

Health assessment method of front landing gear hydraulic retraction and extension system based on GRNN

With the continuous development of civil aircraft health management technology, the status monitoring data of important aircraft systems and components are constantly enriched. The health status of the front landing gear hydraulic retraction system has a great impact on the take-off and landing of the aircraft. Although the system has multi-dimensional monitoring parameters, it is difficult to effectively use the system monitoring data to accurately evaluate its health status. Aiming at the health assessment problem of the front landing gear hydraulic retraction system, this paper builds a simulation model of the system through AMESim software modeling, and studies the influence of the performance changes of hydraulic component parameters on the front landing gear retraction performance. The performance data of the front landing gear retraction actuator at different levels of failure are used as the original data, and the characterization parameters such as retraction time and maximum flow rate are extracted. A health index construction method based on generalized regression neural network (GRNN) is proposed, which can more effectively evaluate the health status of the system. Its effectiveness and accuracy are proved by method comparison.

Advancements in Battery Management through Multi-Scale Digital Twin Technology: Principles and Applications

To achieve peak carbon and carbon neutral goals, the development of electric cars has become strategically important. It is necessary to have precise battery management technology because the lifespan and safety of power batteries change dynamically as they are used. This leads to rapid capacity degradation brought on by the inconsistent performance of single cells and thermal runaway brought on by short board batteries or internal defects. New battery management capabilities have been made possible by the development of the digital twin model, which is now one of the technical trends in the industry. Based on the development trend of battery management technology , the article concentrates on the analysis of basic principles of battery digital twin modeling from the aspects of system modeling to management and control requirements. The article systematically introduces the construction method of multi-dimensional, multi- scale and multi-physical field fusion of digital twin battery. Combined with the previous research of the team, the practical case of the digital twin battery was analyzed. Finally, the application perspective of digital twin battery is discussed in production design, life cycle management and other scenarios, which provided ideas and references for the development of battery management technology.

Cluster-efficient pangenome graph construction with nf-core/pangenome.

Pangenome graphs offer a comprehensive way of capturing genomic variability across multiple genomes. However, current construction methods often introduce biases, excluding complex sequences or relying on references. The PanGenome Graph Builder (PGGB) addresses these issues. To date, though, there is no state-of-the-art pipeline allowing for easy deployment, efficient and dynamic use of available resources, and scalable usage at the same time. To overcome these limitations, we present nf-core/pangenome, a reference-unbiased approach implemented in Nextflow following nf-core's best practices. Leveraging biocontainers ensures portability and seamless deployment in HPC environments. Unlike PGGB, nf-core/pangenome distributes alignments across cluster nodes, enabling scalability. Demonstrating its efficiency, we constructed pangenome graphs for 1000 human chromosome 19 haplotypes and 2146 E. coli sequences, achieving a two to threefold speedup compared to PGGB without increasing greenhouse gas emissions. Nf-core/pangenome is released under the MIT open-source license, available on GitHub and Zenodo, with documentation accessible at https://nf-co.re/pangenome/1.1.2/docs/usage. Supplementary data are available at Bioinformatics online.

A Comparative Study of Women Empowerment among Rural and Urban Female Secondary School Teachers in Punjab State

Teacher empowerment means a teacher who has the power of taking decisions, opportunities to get knowledge and professional growth, and also have the recognition and autonomous power. The present research designed to study and compare the women empowerment of female secondary school teachers. The descriptive survey research method was used for the present study. A representative sample of 500 (250 rural and 250 urban) female secondary school teachers selected randomly from twenty (10 rural and 10 urban) randomly selected school from five randomly selected districts of Punjab. A self-constructed tool in the form of scale using Likert method of scale construction for measuring empowerment of female secondary school teacher. Mean, S.D. and t- test were used to draw the conclusions. Finding of the study shows that no significant difference was found between rural and urban female secondary school teachers on women empowerment.

A Novel Kind of Knowledge Graph Construction Method for Intelligent Machine as a Service Modeling

With the development of Intelligent Machine as a Service (IMaaS), devices increasingly require personalization, intelligence, and service orientation, making resource modeling a key challenge. Knowledge graph (KG) technology, known for unifying heterogeneous data, has become an essential tool for modeling and analyzing manufacturing resources. On this basis, this study proposes a novel resource KG construction method for IMaaS. First, an E-R diagram is used to divide the constant and variable entities and set the constant attributes and the constant relationships. Then, the triplets are named, the value space is set, and the schema layer is constructed. Finally, the related information about devices is used to fill the data layer, and then, the knowledge graph is generated. Meanwhile, this study utilizes desktop FDM 3D printing devices as a case example for validation. The method proposed in this study can enhance the accuracy and maintainability of equipment resource management in the manufacturing sector, effectively promoting subsequent activities such as management, analysis, and decision-making.

ACT‐GAN: Radio map construction based on generative adversarial networks with ACT blocks

AbstractThe radio map serves as a vital tool in assessing wireless communication networks and monitoring radio coverage, providing a visual representation of electromagnetic spatial characteristics. To address the limitation of low accuracy in current radio map construction method, this article presents a novel method based on Generative Adversarial Network (GAN), called ACT‐GAN. This method incorporates the aggregated contextual‐transformation block, the convolutional block attention module, and the transposed convolutional block into the generator, significantly enhancing the construction accuracy of radio map. The performance of ACT‐GAN is validated in three distinct scenarios. The results indicate that, in scenario 1, where the transmitter locations are known, the average reduction in Root Mean Square Error (RMSE) is 14.6%. In scenario 2, where the transmitter locations are known and supplemented with sparse measurement maps, the average reduction in RMSE is 13.3%. Finally, in scenario 3, where the transmitter locations are unknown, the average reduction in RMSE is 7.1%. Moreover, the proposed model exhibits clearer predictive results and can accurately capture multi‐scale shadow fading.

Research on the Construction Method of Curriculum Teaching Knowledge Graph Based on Bi-LSTM and CNN Algorithm

The aim of the paper is to explore a method of constructing a curriculum teaching knowledge graph by combining Bi-LSTM and convolutional neural network (CNN) algorithm. The field of education is constantly seeking innovation to improve teaching results and student learning experience. Knowledge graph, as an advanced technology of structured representation of knowledge, is expected to provide effective support for teaching management and personalized learning. First, this paper introduces the background and significance of the curriculum teaching knowledge graph. By establishing knowledge maps, we can more clearly present the knowledge system and correlation in the curriculum, which helps teachers to design more targeted teaching content and provide personalized learning paths for students. However, traditional knowledge graph construction methods are often faced with problems such as incomplete information capture and inaccurate semantic association, so it is necessary to introduce advanced deep learning algorithms to improve the quality of knowledge graph. Secondly, this paper elaborates on the construction method of fusion Bi-LSTM and CNN algorithm. Bi-LSTM, as a recurrent neural network capable of capturing sequence information, can better model the evolution process of knowledge in the course. As a CNN is good at extracting local features, CNN can effectively capture the spatial structure information in the knowledge graph. By integrating two, we can improve the expression ability and reasoning accuracy of knowledge graph. Further, the experimental results show that the fusion Bi-LSTM and the CNN algorithm have significantly improved the accuracy of information capture and inference compared with the traditional method. In summary, this paper proposes an innovative construction method of curriculum teaching knowledge graph by integrating Bi-LSTM and CNN algorithm, which provides new ideas and solutions for informatization and personalized teaching in the field of education. In the future, the applicability of this method in different disciplines and teaching scenarios can be further discussed, and more advanced technologies can be combined to continuously improve and expand the research.

Construction, assessment, and protection of green infrastructure networks from a dynamic perspective: A case study of Dalian City, Liaoning Province, China

The green infrastructure (GI) network, an important nature-based solution (NBS) strategy, is pivotal for sustainable urban development. However, current research perspectives focus on constructing a static GI network, and research on evaluating and protecting GI networks in the context of spatiotemporal changes has been limited. This research aims to comprehensively characterise the spatio-temporal changes in the GI network and protect its efficiency. We selected Dalian City as a case study and applied the Future Land-use Simulation (FLUS) model to predict its land use for 2030 and 2040. Based on predicted and historical land-use data, the 1990–2040 GI network of Dalian City was constructed utilising the Integrated valuation of ecosystem services and trade-offs (InVEST), morphological spatial pattern analysis (MSPA) and minimum cumulative resistance (MCR) model, while its spatiotemporal changes were evaluated. The results revealed that the hub area exhibited positive growth over time, whereas the link lengths showed an opposite trend. Furthermore, the overall position of the GI network shifted toward the southwest. The flatness indicator detected that the direction of the GI network shape gradually became less pronounced. Consequently, the structure of the GI network shifted from multi-centre to mono-centre. Additionally, priority protection for hubs is mainly in the northeast, while links are primarily in the coastal regions. The urgency of protection for both increases over time. This research developed a dynamic GI network construction and assessment method, providing a scientific basis and reference for future rational GI network planning, ecological protection planning, and related land policy formulation.

Study on the characteristics particles of coarse grain material in high bench dump

Particle shape is an important factor affecting the strength and deformation of coarse materials in dumps. However, conducting research on particle shape in current laboratory experiments, and there exists no classification standard for the particle shape of the dump. Considering the project on high-bench dump in Jiangxi Province, based on the random particle construction method, this study determined the characteristic particles of coarse grain material in the dumping site by combining field investigation results, large-scale direct shear tests, and numerical analysis. Further, the influence mechanism of coarse grain content and characteristic particles on the mechanical characteristics of the dump were analyzed. The results showed that the increase in coarse grain content enhanced the irregularity of particle shape, increased the internal friction angle between particles, and reduced the cohesion of the dump. Moreover, with increase in the normal stress, the shear strength changed from insignificant to gradually enhanced, although the increasing trend slowed down. The constructed random particle model can well solve the effects of occlusion, nesting, and friction between particles that is neglected by the standard spherical particle model, and the fitting degree is better. The particle characteristics of coarse-grain materials in the dump were primarily block, strip, and flake. The three types of shape characteristics under low normal stress did not affect the mechanical properties of the dump. Further, with the increase in normal stress, the stress-strain curve reflected the shape characteristics and content of the dominant characteristics in the dump. Specifically, the block sample exhibited the dominant characteristics and the most content under the same normal stress, followed by the strip sample, with the flake sample exerting the least influence. The research results provide a theoretical basis for long-term safe operation, disaster monitoring, and early warning of high bench dump.

Influence of macroeconomic factors on construction costs: an analysis of project cases

This study explores the relationship between price increase and project cost increase under macroeconomic shocks by considering the hierarchical structure within price categories, diversification of the cost breakdown structure, infrastructure sector, delivery system, construction methods, and type of bid. The analysis of a large construction company with five large projects as embedded units of analysis reveals significant variations in the relative influence of specific factors across the projects. Unit price categories located higher in the hierarchical breakdown structure display lower vulnerability to macroeconomic changes due to their unique and group-specific influencing factors while price categories influenced by macroeconomic factors depart at lower values in the hierarchical structure. Projects with low-cost breakdown structure diversification can either mitigate or exacerbate cost increases depending on construction methods (off-site or on-site), price drivers of most representative materials (global or local), and delivery systems risk allocation. Under macroeconomic crisis periods, public entities may consider giving high priority to projects more reliant on materials driven by regional dynamics (e.g., concrete pouring) over those driven by global market conditions (e.g., steel, aluminum) while prioritizing integrated delivery systems (e.g., design-build) that promote enhanced collaboration, proactive risk management, aligned incentives, and adaptability for managing complexities and volatilities. Complementary, for projects with higher reliance on global market-driven materials, the results suggest that contractors should prioritize early procurement and acquisition of highly volatile resources in projects under macroeconomic crises.

Main Stages and Features of the Development of Husserl’s Conception of Metaphysics: Or How Might We Thematize the “Supreme and Ultimate Questions” in a Phenomenologically Legitimate Manner?

In this paper, we provide an overview of the main stages in the development of Edmund Husserl’s conception of metaphysics, highlighting its most significant characteristics. We propose that Husserl’s views on metaphysics traversed three main stages: (1) from the early 1890s until his so-called “transcendental turn” around 1906/07; (2) from his transcendental turn until the late 1920s, and (3) the metaphysical conceptualization during the 1930s, aptly characterized as—following the interpretation of László Tengelyi—a “metaphysics of primal facts” (Urfakta, Urtatsache). We further demonstrate that Husserl’s considerations concerning metaphysics—throughout his entire opus—span three essential levels: (1) the epistemological level, in particular, phenomenological preparations to establish a foundation for metaphysics; (2) a level concerning metaphysics as the universal and ultimate science of reality, and (3) a level addressing metaphysics as a proper field of phenomenological investigations into the highest and ultimate questions (such as God and immortality). We argue in detail that Husserl attempted to render this field—which radically transcends the domain of possible intuitions (Anschauungen) and direct experience—accessible to a legitimate phenomenological treatment via the method of phenomenological constructions, prior to the efforts of Eugen Fink.

Deep Learning for Epileptic Seizure Detection Using a Causal-Spatio-Temporal Model Based on Transfer Entropy.

Drug-resistant epilepsy is frequent, persistent, and brings a heavy economic burden to patients and their families. Traditional epilepsy detection methods ignore the causal relationship of seizures and focus on a single time or spatial dimension, and the effect varies greatly in different patients. Therefore, it is necessary to research accurate automatic detection technology of epilepsy in different patients. We propose a causal-spatio-temporal graph attention network (CSTGAT), which uses transfer entropy (TE) to construct a causal graph between multiple channels, combining graph attention network (GAT) and bi-directional long short-term memory (BiLSTM) to capture temporal dynamic correlation and spatial topological structure information. The accuracy, specificity, and sensitivity of the SWEZ dataset were 97.24%, 97.92%, and 98.11%. The accuracy of the private dataset reached 98.55%. The effectiveness of each module was proven through ablation experiments and the impact of different network construction methods was compared. The experimental results indicate that the causal relationship network constructed by TE could accurately capture the information flow of epileptic seizures, and GAT and BiLSTM could capture spatiotemporal dynamic correlations. This model accurately captures causal relationships and spatiotemporal correlations on two datasets, and it overcomes the variability of epileptic seizures in different patients, which may contribute to clinical surgical planning.

Safety-risk assessment system for prefabricated building construction in China

PurposePrefabricated building (PB) uses factory production and onsite assembly, which differs from traditional construction methods. This special construction approach may lead to dissimilar safety risks and challenges. Traditional safety assessment methods may not adequately and accurately assess the safety risks of PB construction. This paper aims to develop a new concept and methodology for targeted improvement in assessing PB safety risks.Design/methodology/approachRisk factors and indicators were established based on literature review and expert inputs. A structural equation model (SEM) was developed to investigate the relationships among three main risk categories: objects, workers and management. SEM analyzed the intricate associations between indicators and deepened understanding of safety risks. The model was tailored for China’s PB construction projects to enhance safety-risk management.FindingsThe cloud model evaluation validated the SEM model. A PB case study project tested and verified the model, evaluated its efficacy and quantified its safety performance and grade. We identified significant safety risk impacts across the three risk categories, safety-control level and specific areas that require improvement. The SEM model established a robust safety evaluation indicator system for comprehensive safety assessment of PB construction.Practical implicationsPractical recommendations provide valuable insights for decision-makers to enhance construction efficiency without compromising safety. This study contributed to the conceptual foundation and devised a novel method for evaluating safety performance in PB construction for safer and more efficient practices.Originality/valueThis study departed from the traditional method of calculating weights, opting instead for the SEM method to determine the weights of individual risk indicators. Additionally, we leveraged the cloud model to mitigate the influence of subjective factors in analyzing questionnaire survey responses. The feasibility and reliability of our proposed method were rigorously tested and verified by applying it to the PB case.

Contribution of the large-scale population cohort in disease risk prediction model study: taking United Kingdom Biobank as an example

The disease risk prediction model is the basis of precision prevention and an essential reference for clinical treatment decisions. The development of risk prediction models requires the support of a large amount of high-quality data. A large population cohort study is an important basis for this study. The United Kingdom Biobank (UKB), as a mega-population cohort and biobank, has played an essential role in the exploration of disease etiology and research related to disease prevention and control, with its rich baseline and follow-up data and concepts and mechanisms shared globally. This study followed PRISMA guidelines and included 210 articles with corresponding authors from 18 countries, of which 58 (27.62%) were from the UKB. A total of 491 disease risk prediction models were extracted for cancer, cardiovascular and cerebrovascular diseases, endocrine and metabolic diseases, respiratory diseases, and other diseases and their subgroups, of which 132 were developed by UKB without validation, 183 were developed by UKB with internal validation, 17 were developed by UKB with external validation, and 159 were developed by external development with UKB validation. A total of 188 models used only macro variables (38.29%), and 303 models combined macro and micro variables (61.71%). Model construction methods included survival outcome models, logistic regression, and machine learning. Survival outcome models were dominated by Cox proportional risk regression models and a few models considering competitive risk, accelerated failure models, or different baseline risk functions. Machine learning models included random forest, XGBoost, CatBoost, support vector machine, convolutional neural network, and other methods. The UKB is an essential resource for multiple disease risk prediction modeling studies.

Comparative analysis of embodied carbon in modular and conventional construction methods in Hong Kong

Addressing the rise in global temperatures and the associated increase in greenhouse gases, particularly carbon dioxide, is a critical challenge necessitating innovative approaches within the building sector, a significant contributor to worldwide carbon emissions. While previous studies have demonstrated the prefabrication’s potential in reducing emissions, comprehensive assessments using actual project data for buildings constructed entirely with modular methods in Hong Kong are lacking. This study bridges this gap by evaluating the modular integrated construction (MiC) method through an embodied carbon assessment of the Kai Tak Community Isolation Facility. Using comprehensive project data from China State Construction (HK) Limited, the research conducts a comparative analysis between the actual emissions of the MiC method and those of a hypothetical conventional construction approach. Quantitative analysis reveals that MiC achieves a 20.7% reduction in embodied carbon, primarily due to shortened construction timelines, decreased waste generation, and optimized material usage. This significant reduction suggests substantial potential for decreasing the construction industry’s carbon footprint. The study provides empirical evidence supporting the environmental benefits of MiC in Hong Kong construction industry, promoting its broader adoption of MiC as a strategy for achieving carbon reduction targets. The findings align with Hong Kong’s carbon neutrality goals and contribute to the global initiative to mitigate the effects of climate change.

Pushing boundaries in slurry microtunneling: Innovations in microtunnel boring machine design for hard rock conditions

AbstractFor microtunneling in hard rock, as it is encountered very often in alpine tunneling, the continuous further development of technologies plays a key role to overcome the limitations in drive length and to improve performance. During the last decades, the application range of slurry microtunneling has been considerably extended and more demanding projects have been designed for underground utilities installations worldwide, including many sewage tunnels and hydropower plants. The prevailing ground conditions still play a key role and determine the limits for alignment design and the possible use of trenchless construction methods. In hard rock conditions with high rock strength and/or abrasivity, the applicability of slurry microtunneling has been especially limited in the non‐accessible diameter range in the past. With the new AVN 800 HR for hard rock microtunneling, Herrenknecht has developed a machine concept, enabling drives of up to 200 m, with precise steering of the MTBM. Longer drives, even through hard rock, make microtunneling a more economic, flexible, and more environmentally friendly construction method. New opportunities for clients and consultants in the planning of tunnel routes in alpine environments for different infrastructure installations can be considered, making trenchless installations even more cost‐effective, while improving public acceptance.

Evaluation of Cumulative Damage and Safety of Large-Diameter Pipelines under Ultra-Small Clear Distance Multiple Blasting Using Non-Electric and Electronic Detonators

The safety assessment and control of large-diameter pipelines under tunnel blasting at ultrasmall clear distances is a significant problem faced in construction. However, there has been no reference case for the quantitative comparison of the disturbance degree of surrounding rock by using two blasting schemes of non-electric detonator design and electronic detonator design under a similar total blasting charge consumption. In this study, the blasting test was carried out based on the engineering background of drilling and blasting methods to excavate the tunnel under the water pipeline at a close distance. The peak particle velocity (PPV), stress, and deformation responses of the pipeline under the two construction methods of non-electric and electronic detonators were comparatively analyzed. The PPV can be remarkably reduced by 64.2% using the hole-by-hole initiation of the electronic detonators. For the large-diameter pipeline, the PPV on the blasting side was much larger than that on the opposite side because the blasting seismic wave propagated a longer distance and attenuated more rapidly, owing to its greater cavity vibration reduction effect. The PPV of the electronic detonators decayed more slowly than that of the non-electric detonators. The cumulative damage caused by consecutive hole-by-hole blasting using electronic detonators was less than that caused by simultaneous multi-hole initiation using non-electric detonators, with a reduction of about 50.5%. When the nearest peripheral holes away from the pipeline are detonated, the cumulative damage variable D and damage range increase rapidly. The PPV, dynamic tensile strength, and cumulative damage variables were used to evaluate the safety of the pipelines.

Life cycle assessment and generative design: development of a national LCA tool for exterior walls

PurposeThe promotion of sustainable design is demanded globally. The life cycle assessment (LCA) proved its reliability in this mission, but the difficulty and time required to apply it discouraged designers. This research aims to integrate LCA into the building design process through a software tool, taking advantage of generative design features. This will facilitate decision-making by architects and construction professionals.Design/methodology/approachThe study develops the EGY-LCA (http://egy-lca.com/). This prototype tool suggests exterior wall design alternatives for residential buildings in Egypt, using the environmental impact indicators of LCA data and other criteria related to national codes, materials, construction methods and required thermal resistance. Within a generative design process, the algorithm tests every possible wall method with materials and thickness combinations for each layer in compliance with inputs. The paper begins by explaining the tool’s working method. Afterward, different sets of inputs are examined and the values of the resultant environmental impacts of several suggested wall solutions are statistically analyzed. The application demonstrates the importance of the generative design tool. Proposing several solutions based on a set of inputs facilitates the selection of sustainable choices and allows comparisons between alternatives.FindingsThe prototype experiment confirms the research hypothesis. Unlike the available LCA tools, architects can make decisions with limited LCA experience if the data and equations are integrated into a generative design tool. The prototype proves its applicability for exterior wall alternatives.Research limitations/implicationsThe prototype is the initial step toward a whole-building LCA tool. It includes limited LCA stages and materials for the external wall. Future research is required to expand this parametric tool concept to include all the building components. The framework in Section 5 proposes a visualization.Practical implicationsThe prototype tool: EGY-LCA (http://egy-lca.com/). The value added to the design and construction sectors through the uncomplicated LCA application is fostering sustainable design, generative design tools can achieve this.Originality/valueThe novelty of this work is that it is the first initiative offering a parametric LCA tool. It promotes the application of LCA at the design stage using generative design, which contributes to sustainable development.