Step In Construction Research Articles

Public-private partnership strategies for the implementation of innovation centers: the ciat case (Santarém - Pará)

As a strategy to promote regional development, leverage new business niches and disseminate a culture of entrepreneurship and innovation in its area of operation, the Santarém Commercial and Business Association - ACES, in association with the Guamá Foundation, presented to the Government of the State of Pará a proposal to create an Innovation Center in the municipality. Based on this case, this article revisits the fundamental concepts for the differentiation of innovation environments and presents the state of the art of the project by pervading the construction steps of the ACES Tapajós - CIAT Innovation Center and the integration process between universities, companies, governments, and social organizations for its execution. ACES, as a backbone organization, identified 43 potentially relevant external partners, of which four are essential for space consolidation. As a counterpart, the Government of Pará allocated more than three million reais for the CIAT's implementation. The initiative, inaugurated in March 2023, involves more than 350 associated members, groups of researchers and university students, companies, and independent professionals. This essay, therefore, records the first steps for these partnerships and the network's ability to deal effectively with the current socioeconomic, environmental, and technological transformations underway in the Amazon.

Application of discrete event simulation model in analysis on cost-effectiveness of epidemiology screening

Discrete event simulation (DES) model is based on individual data, by which discrete events over time are simulated to reflect disease progression. The effects of individual characteristics on disease progression could be considered in the DES model. Moreover, unlike state-transition models, DES model without setting of fixed cycle can contribute to more accurate estimation of event time, especially in the evaluation of the long-term effectiveness of screening strategies for complex diseases in which time dimension needs to be considered. This article introduces the general principles, construction steps, analytic methods and other relevant issues of the DES model. Based on a research case of estimating the cost-effectiveness of screening for abdominal aortic aneurysms in women aged 65 years and above in the United Kingdom, key points in applications of the DES model in analysis on effectiveness of complex disease screening are discussed in detail, including model construction and analysis and interpretation of the results. DES model can predict occurring time of discrete events accurately by establishing the distribution function of their occurring time and is increasingly used to evaluate the screening strategies for complex diseases in which time dimension needs to be considered. In the construction of DES model, it is necessary to pay close attention to the clear presentation of model structure and simulation process and follow the relevant reporting specification to conduct cost-effectiveness analysis to ensure the transparency and repeatability of the research.

Microfluidic Electrochemical Glucose Biosensor with In Situ Enzyme Immobilization

The development and characterization of a microfluidic electrochemical glucose biosensor are presented herein. The transducer part is based on thin-film metal electrodes on a glass substrate. The biological recognition element of the biosensor is the pyrroloquinoline quinone-glucose dehydrogenase (PQQ-GdhB) enzyme, selectively in situ immobilized via microcontact printing of a mixed self-assembling monolayer (SAM) on a gold working electrode, while the microfluidic part of the device comprises microchannel and microfluidic connections formed in a polydimethylsiloxane (PDMS) elastomer. The electrode properties throughout all steps of biosensor construction and the biosensor response to glucose concentration and analyte flow rate were characterized by cyclic voltammetry and chronoamperometry. A measurement range of up to 10 mM in glucose concentration with a linear range up to 200 μM was determined. A detection limit of 30 µM in glucose concentration was obtained. Respective biosensor sensitivities of 0.79 nA/µM/mm2 and 0.61 nA/µM/mm2 were estimated with and without a flow at 20 µL/min. The developed approach of in situ enzyme immobilization can find a wide number of applications in the development of microfluidic biosensors, offering a path towards continuous and time-independent detection.

Monitoring Method of Large Section Bifurcated Small Clear Distance Tunnel during Information Construction Control

Constructing a large section bifurcated small clear distance tunnel poses significant challenges due to its large section and small clear distance, complex and overlapping construction steps, unsynchronized excavation and support processes, and disturbance of surrounding rock caused by blasting. Timely and effective construction monitoring is crucial to ensure construction safety and quality. However, traditional monitoring methods are unable to fully satisfy requirements and realize real-time monitoring. Therefore, this study proposes an automatic monitoring and control method for a large section bifurcated small clear distance tunnel to leverage the positive role of information construction. Using the Xiamen Haicang Evacuate Channel project as a case study, this paper introduces the tunnel monitoring and layout scheme of monitoring points. The monitoring data were analyzed from different perspectives, including cumulative deformation, deformation rate, cumulative deformation control value, and deformation rate control value. The follow-up excavation and support measures were optimized based on the measurement results. Additionally, an automatic monitoring and control method of the middle rock pillar combined with the criterion of stability of the middle rock pillar was proposed, constructing a working idea and framework for timely feedback and three-dimensional and dynamic monitoring. Site monitoring predicted the danger before construction and provided feedback on construction information to improve the specific construction method of subsequent excavation and support. The proposed automatic monitoring and control method can solve the problem of accidental errors in traditional monitoring methods and enable real-time automatic monitoring. The research results provide valuable experience for constructing a bifurcated small clear distance tunnel.

Application for calculating the motor control frequency for vehicle speed reading system test bench

The present work describes the construction steps of an application on the MIT App online platform Inventor 2. It is able to calculate command frequencies for stepper motors, which are equivalent to vehicle speed values. These frequency values ​​can be entered in programming codes within controllers such as Raspberry Pi, Arduino, ESP 32, among others, which makes possible the development of a test bench for the speed reading system of vehicles for transporting loads and people.

Analysis of surface settlements induced by tunnel excavation with EPB-TBM

Purpose. To investigate the efficiency of various approaches to predict surface settlements due to tunnel excavation. Methodology. To appreciate the surface displacements, our study is focalized on the case of a real tunnel in a layered ground (Algiers’s Metro), where a tunnel boring machine was driven for the first time in this country. Firstly, the surface settlement trough was calculated with empirical, analytical, and numerical (FEM) methods. Secondly, a set of numerical analyses was carried out to inspect the evolution of surface settlement as the TBM progresses. Finally, a parametric study was performed to examine the construction step most productive for surface settlement. Findings. FEM is a useful tool for predicting surface displacements due to tunnelling, especially when assigning an adequate and sophisticated behaviour model. Originality. A reference numerical model which represents well the construction procedures of the Algiers tunnel has been established. Practical value. This study illustrates that the results obtained by FEM with the use of Hardening Soil as a constitutive model to represent the soil are almost identical to those measured during the tunnel excavation. On the other hand, the empirical formulas available in the literature are not always efficient to predict surface movements.

Construction of discontinuous enrichment functions for enriched or generalized FEM’s for interface elliptic problems in 1D

We introduce an enriched unfitted finite element method to solve 1D elliptic interface problems with discontinuous solutions, including those having implicit or Robin-type interface jump conditions. We present a novel approach to construct a one-parameter family of discontinuous enrichment functions by finding an optimal order interpolating function to the discontinuous solutions. In the literature, an enrichment function is usually given beforehand, not related to the construction step of an interpolation operator. Furthermore, we recover the well-known continuous enrichment function when the parameter is set to zero. To prove its efficiency, the enriched linear and quadratic elements are applied to a multi-layer wall model for drug-eluting stents in which zero-flux jump conditions and implicit concentration interface conditions are both present.

Gold-binding peptide as a selective layer for electrochemical detection of SARS-CoV-2 antibodies

Electrochemical immunosensors are excellent alternatives to prepare portable platforms used for rapid and inexpensive diagnostic of infectious diseases such as the recently emerged COVID-19. Incorporating synthetic peptides as selective recognition layers combined with nanomaterials such as gold nanoparticles (AuNPs) can significantly enhance the analytical performance of immunosensors. In the present study, an electrochemical immunosensor based on solid-binding peptide was built and evaluated towards SARS-CoV-2 Anti-S antibodies detection. The peptide used as recognition site has two important portions: one based on the viral receptor binding domain (RBD), capable of recognizing antibodies of the spike protein (Anti-S), and another suitable for interacting with gold nanoparticles. Gold-binding peptide (Pept/AuNP) dispersion was used directly to modify a screen-printed carbon electrode (SPE). The voltammetric behavior of the [Fe(CN)6]3-/4- probe after every construction and detection step was recorded using cyclic voltammetry by assessing the stability of the Pept/AuNP as a recognition layer onto the electrode surface. Differential pulse voltammetry was used as a detection technique, and a linear working range from 75 ng mL−1 to 15 μg mL−1 was established, with 1.059 μA dec−1 of sensitivity and R2 = 0.984. The response selectivity against SARS-CoV-2 Anti-S antibodies was investigated in presence of concomitant species. The immunosensor was used to detect SARS-CoV-2 Anti-spike protein (Anti-S) antibodies in human serum samples, successfully differentiating between negative and positive responses of samples at a 95% confidence level. Therefore, the gold-binding peptide is a promising tool to be applied as a selective layer for antibody detection.

Bacterial nanocellulose membrane as novel substrate for biomimetic structural color materials: Application to lysozyme sensing

The development of optical biosensors based on structural colors generated by short-range ordered colloidal particles is attracting growing interest due to their non-iridescent and non-fading features. In this study, a biomimetic approach using biopolymers for the various steps of sensor construction is presented. Bacterial nanocellulose (BNC) has many foreseen applications in biomedical engineering because of its biocompatibility, good mechanical strength, and large modifiable surface area. Herein, a novel approach is taken by using functionalized BNC as a substrate to build a molecularly imprinted photonic sensing layer. BNC was modified with polydopamine (PDA), which improved the adhesion and mechanical properties of the BNC substrate while providing simultaneously a black background for color saturation. A molecularly imprinted polymer (MIP) also made of PDA was used to create the recognition sites for the biomarker lysozyme. A monodisperse colloidal suspension of silica particles was first synthesized and used as core of the MIP shell, and then the photonic structure was assembled on the PDA-BNC membrane. The biosensor showed a detection limit of about 0.8 nmol L−1 of lysozyme in spiked human serum and demonstrated to be selective against cystatin C. These properties, combined with biocompatible, eco-friendly, and low-cost materials, offer a sustainable sensing platform with great potential for healthcare applications.

Network Optimization based on Genetic Algorithm for High-Level Data Classification

High-level data classification techniques are capable of considering not only physical aspects of the data, such as space, distance, proximity, distribution, but can also consider their functional, topological and structural aspects. High-level techniques are commonly defined in two major steps: the construction of a network from the feature vector data and the uncovering of its underlying patterns using complex networks properties. In the network construction step, heuristics based on k-nearest neighbors strategies have been widely adopted, while several complex network measures (e.g. PageRank) have been modeled to learn high-level patterns of the input data. As both steps are directly related, i.e., the network configuration impacts directly the results obtained by the classifier, in this paper we develop a genetic algorithm (GA) to optimize the network construction step. To be specific, we hypothesize that the salient features of GAs, such as their robust search mechanism and binary representation, may provide a more powerful network representation in the context of the high-level classification based on importance characterization. In summary, extensive experiments with real data sets demonstrate that the networks provided by our GA strategy achieved higher predictive accuracy than those of a widely adopted method based on the nearest neighbors heuristic and competitive results against state-of-the-art ones.

A Cross-Level Requirement Trace Link Update Model Based on Bidirectional Encoder Representations from Transformers

Cross-level requirement trace links (i.e., links between high-level requirements (HLRs) and low-level requirements (LLRs)) record the top-down decomposition process of requirements and support various development and management activities (e.g., requirement validation). Undoubtedly, updating trace links synchronously with requirement changes is critical for their constant availability. However, large-scale open-source software that is rapidly iterative and continually released has numerous requirements that are dynamic. These requirements render timely update of trace links challenging. To address these problems, in this study, a novel deep-learning-based method, deep requirement trace analyzer fusing heterogeneous features (DRAFT), was proposed for updating trace links between various levels of requirements. Considering both the semantic information of requirement text descriptions and the process features based on metadata, trace link data accumulated in the early stage are comprehensively used to train the trace link identification model. Particularly, first, we performed second-phase pre-training for the bidirectional encoder representations from transformers (BERT) language model based on the project document corpus to realize project-related knowledge transfer, which yields superior text embedding. Second, we designed 11 heuristic features based on the requirement metadata in the open-source system. Based on these features and semantic similarity between HLRs and LLRs, we designed a cross-level requirement tracing model for new requirements. The superiority of DRAFT was verified based on the requirement datasets of eight open-source projects. The average F1 and F2 scores of DRAFT were 69.3% and 76.9%, respectively, which were 16.5% and 22.3% higher than baselines. An ablation experiment proved the positive role of two key steps in trace link construction.

Analysis of Air Quality Evolution Trends in the Chinese Air Pollution Transmission Channel Cities under Socioeconomic Development Scenarios

In this paper, based on the traditional grey multivariate convolutional model, the concept of a buffer operator is introduced to construct a single-indicator buffered grey multivariate convolutional model applicable to air quality prediction research. The construction steps of the model are described in detail in this paper, and the stability of the model is analyzed based on perturbation theory. Furthermore, the model was applied to predict the air quality composite index of the “2 + 26” Chinese air pollution transmission corridor cities based on different socioeconomic development scenarios in a multidimensional manner. The results show that the single-indicator buffered grey multivariate convolutional model constructed in this paper has better stability in predicting with a small amount of sample data. From 2020 to 2025, the air quality of the target cities selected in this paper follows an improving trend. The population density, secondary industry, and urbanization will not have a significant negative impact on the improvement of air quality if they are kept stable. In the case of steady development of secondary industry, air quality maintained a stable improvement in 96.4% of the “2 + 26” cities. The growth rate of population density will have an inverted U-shaped relationship with the decline in the city air quality composite index. In addition, with the steady development of urbanization, air quality would keep improving steadily in 71.4% of the “2 + 26” cities.

Construction and application of a refined model for the optimal allocation of water resources — Taking Guantao County, China as an example

The traditional optimal allocation of water resources mostly focuses on the allocation of different water supply sources to various types of water users in life, production, and ecology, and cannot reflect the differences in time and space of a certain type of water users. Therefore, this paper constructs a refined model for the optimal allocation of water resources, and further explores the optimal allocation of water resources with multiple water sources, multiple users, and multiple objectives. The main steps of the model construction are: (1) Regional gridding. The planting structure map and land use type map of the same resolution in the study area are selected and superimposed on them, the application attributes of each cell can be directly obtained. (2) Water demand gridding. On the basis of gridding in the study area, according to the application attributes of each cell, the quota method is used to predict the water demand of each cell. Then the water demand forecast result for the entire research area is obtained. (3) Water supply gridding. It is possible to reflect the types of water supply sources that a certain or a certain type of cell can accept, as well as the types of water supply sources that can be supplied by a certain type of water supply sources, by determining the types of water supply sources in the research area. The supply ranges that various water supply sources can reach, followed by the completion of the water supply grid. (4) According to the results of water demand gridding and water supply gridding, the water demand of each cell and the corresponding water supply source and water supply topological relationship are established, and the objective function is to minimize water shortage and maximize economic benefits to build a refined model for the optimal allocation of water resources.(5) The particle swarm algorithm is introduced to solve it, and the Pareto optimal solutions is obtained to determine the optimal water distribution for each cell. (6) Taking Guantao County, China as an example, the model was applied. In terms of minimum water shortage, the total water demand in Guantao County is predicted to be 118.8719 million m3 in the planning year (P = 50 %), while the total water supply is 125.2818 million m3. The water supply can meet the demand for water. In terms of financial advantages, the planned level of water Annual economic benefits will amount to 4.0865 million yuan.

Efficient gene–environment interaction testing through bootstrap aggregating

Gene–environment (GxE) interactions are an important and sophisticated component in the manifestation of complex phenotypes. Simple univariate tests lack statistical power due to the need for multiple testing adjustment and not incorporating potential interplay between several genetic loci. Approaches based on internally constructed genetic risk scores (GRS) require the partitioning of the available sample into training and testing data sets, thus, lowering the effective sample size for testing the GxE interaction itself. To overcome these issues, we propose a statistical test that employs bagging (bootstrap aggregating) in the GRS construction step and utilizes its out-of-bag prediction mechanism. This approach has the key advantage that the full available data set can be used for both constructing the GRS and testing the GxE interaction. To also incorporate interactions between genetic loci, we, furthermore, investigate if using random forests as the GRS construction method in GxE interaction testing further increases the statistical power. In a simulation study, we show that both novel procedures lead to a higher statistical power for detecting GxE interactions, while still controlling the type I error. The random-forests-based test outperforms a bagging-based test that uses the elastic net as its base learner in most scenarios. An application of the testing procedures to a real data set from a German cohort study suggests that there might be a GxE interaction involving exposure to air pollution regarding rheumatoid arthritis.

Investigation on the Influence of Active Underpinning Process on Bridge Substructures during Shield Tunnelling: Numerical Simulation and Field Monitoring

The pile foundation cutting and underpinning process during shield tunnelling significantly impacts the stability of bridge substructures. In this paper, the shield tunnel area from Hongguzhong Avenue Station to Yangming Park Station of Nanchang Metro Line 2 was taken as the research subject, which crosses the pile foundation underpinning project of the south approach section of Bayi Bridge. Through numerical simulation and on-site monitoring analysis, the influence of the active underpinning process of shield tunnelling pile foundation on the deformation of bridge substructure was studied. First, through analyzing on-site conditions and comparing technical solutions, an active gantry bridge pile foundation underpinning technology was proposed, and the specific construction steps were determined. On this basis, for the C15 pile foundation with the most complex working conditions, ABAQUS software was applied to simulate the jack-up, unloading and pile-cutting process during the pile foundation underpinning construction, and the displacement development of the bridge pier, underpinning beam and new pile during the whole construction process were analyzed. Finally, through on-site monitoring data analysis, the technology’s feasibility and safety were further verified. At the same time, according to the analysis of the monitoring results of the bridge piers, underpinning beams and new piles, the results from the finite element software were nearly the same as the trend shown by the monitoring results, and the displacement of the main structures of the lower part of the bridge was small and within the control range. The above research work verified the applicability of the active gantry type bridge pile foundation underpinning technology in the pile foundation underpinning condition of the single-column single-pile bridge in the narrow space curved bridge section, and is worthy of further promotion and application.

Algorytmy konstrukcji obrazów w systemach tomografii procesowej

Tomography is one of the methods used to obtain cross-sectional images of the analysed object. This imaging technique has been used for many years in medicine, providing significantly higher efficiency of patient’s diagnosis. In recent years, the imaging method has also found application for visualization of industrial processes. The article describes the tomographic image construction methods used in the modules of monitoring of dynamic industrial processes. Introduction to methods of the inverse problems solutions in tomography systems, from a practical point of view, was presented, with particular emphasis on the electrical capacitance tomography. It was shown how based on the mathematical theorems and knowledge about physics rules, concerning the behaviour of measurement signal in tomography sensor, is possible to construct cross-sectional image of industrial process. In article the image construction steps were explained and discussion on the impact of various factors on the quality and speed of imaging was conducted.

Predictable forward performance processes in complete markets

We establish existence of Predictable Forward Performance Processes (PFPPs) in conditionally complete markets, which has been previously shown only in the binomial setting. Our market model can be a discrete-time or a continuous-time model, and the investment horizon can be finite or infinite. We show that the main step in construction of PFPPs is solving a one-period problem involving an integral equation, which is the counterpart of the functional equation found in the binomial case. Although this integral equation has been partially studied in the existing literature, we provide a new solution method using the Fourier transform for tempered distributions. We also provide closed-form solutions for PFPPs with inverse marginal functions that are completely monotonic and establish uniqueness of PFPPs within this class. We apply our results to two special cases. The first one is the binomial market and is included to relate our work to the existing literature. The second example considers a generalized Black–Scholes model which, to the best of our knowledge, is a new result.

EuclidNet: Deep Visual Reasoning for Constructible Problems in Geometry

In this paper, we present a visual reasoning framework driven by deep learning for solving constructible problems in geometry that is useful for automated geometry theorem proving. Constructible problems in geometry often ask for the sequence of straightedge-and-compass constructions to construct a given goal given some initial setup. Our EuclidNet framework leverages the neural network architecture Mask R-CNN to extract the visual features from the initial setup and goal configuration with extra points of intersection, and then generate possible construction steps as intermediary data models that are used as feedback in the training process for further refinement of the construction step sequence. This process is repeated recursively until either a solution is found, in which case we backtrack the path for a step-by-step construction guide, or the problem is identified as unsolvable. Our EuclidNet framework is validated on the problem set of Euclidea with an average of 75% accuracy without prior knowledge and complex Japanese Sangaku geometry problems, demonstrating its capacity to leverage backtracking for deep visual reasoning of challenging problems.

Study on the Compaction Performance and Its Key Influencing Factors of Asphalt Mixture Based on Intelligent Sensing Techniques

Real‐time and accurate compaction quality monitoring is one of the key steps in the pavement construction. In this study, the effect of roller passes and temperature on the compactness of asphalt mixtures and its uniformity is discussed using the semivariance function. Further, the indoor asphalt mixture gyratory compaction test was carried out based on the embedded SmartRock sensor, with the aim of monitoring the changes in rotation and contact forces between aggregates. In addition, the effect of temperature on the dynamic response of SmartRock is discussed, and the compaction mechanism of asphalt mixtures is investigated from a microscopic scale. It is found that compaction degree and uniformity are not always positively correlated with the number of roller passes and that excessive compaction leads to a decrease in compaction effect; meanwhile, the variation in the compactness of asphalt mixtures can be characterized by the dynamic response of SmartRock, which providing a feasible method for real‐time monitoring of the compaction process and optimization of the construction plan.

Compaction Prediction for Asphalt Mixtures Using Wireless Sensor and Machine Learning Algorithms

Compaction is a critical step in asphalt roadway construction to determine the pavement’s quality and service life. Current field compactions are mainly based on test strips and engineers’ experiences. Such empirical-based approaches sometimes cause compaction problems especially when new materials are implemented. Intelligent compaction (IC) technology was invented to improve compaction quality. The real-time collection and visualization of the pavement responses and temperature can greatly improve compaction uniformity. However, the material viscoelastic property and the complex pavement structure still hinder the establishment of the correlation between the Intelligent Compaction Measurement Value (ICMV) and the compactability of asphalt pavement. This paper aims to establish compaction prediction models based on machine learning (ML) algorithms and sensing technology. Given the strong correlation between the mixture’s compactability and the particle compaction characteristics, a particle-size wireless sensor, SmartRock, was used to collect the particle kinematic behaviors during compaction. 11 asphalt mixtures were compacted with an embedded SmartRock sensor. Three ML models and three dataset scenarios were applied to predict the compaction condition, and a higher than 98% prediction accuracy was achieved. The results revealed that combining the ML approach and sensing data is appropriate and practical for predicting the compaction condition of the asphalt mixture. Future studies shall further evaluate the algorithm based on field compaction data so that intelligent sensing technology can be implemented to improve compaction quality.