Complex Construction Research Articles

Turbulence throughout teacher education: a case study of negotiating teacher identity

ABSTRACT Becoming a teacher is a tricky road paved with struggles. The purpose of this reconstructive educational case study is to exemplify the complex construction of teacher identity by following the five-year journey of one student teacher in the process of qualifying as a primary schoolteacher in Finland. With the heuristic analysis of longitudinal thematic narrative interviews in the frame of Dialogical Self Theory (DST), the key results of the study make a substantial contribution by illustrating how struggles are entrenched in various I-positions of teacher identity. According to our results, we conclude that four I-positions should be carefully supported in teacher education: I as a person, I as a foreigner, I as an academic student and I as a teacher. The role of teacher education in supporting student teachers in this respect is discussed in light of the results.

Specific Features of the Functional Activity of Human Adipose Stromal Cells in the Structure of a Partial Skin-Equivalent.

Mesenchymal adipose stromal cells (ASCs) are considered the most promising and accessible material for translational medicine. ASCs can be used independently or within the structure of scaffold-based constructs, as these not only ensure mechanical support, but can also optimize conditions for cell activity, as specific features of the scaffold structure have an impact on the vital activity of the cells. This manuscript presents a study of the secretion and accumulation that occur in a conditioned medium during the cultivation of human ASCs within the structure of such a partial skin-equivalent that is in contact with it. It is demonstrated that the ASCs retain their functional activity during cultivation both within this partial skin-equivalent structure and, separately, on plastic substrates: they proliferate and secrete various proteins that can then accumulate in the conditioned media. Our comparative study of changes in the conditioned media during cultivation of ASCs on plastic and within the partial skin-equivalent structure reveals the different dynamics of the release and accumulation of such secretory factors in the media under a variety of conditions of cell functioning. It is also demonstrated that the optimal markers for assessment of the ASCs' secretory functions in the studied partial skin-equivalent structure are the trophic factors VEGF-A, HGF, MCP, SDF-1α, IL-6 and IL-8. The results will help with the development of an algorithm for preclinical studies of this skin-equivalent in vitro and may be useful in studying various other complex constructs that include ASCs.

Construction of fatty acid-ovalbumin binary complexes to improve the water dispersibility, thermal/digestive stability and bioaccessibility of lutein: A comparative study of different fatty acids

Lipids are increasingly being incorporated into delivery systems due to their ability to facilitate intestinal absorption of lipid-soluble nutrients through molecular solubilization and micellization. In this work, self-assembled complexes of ovalbumin (OVA) and nine dietary fatty acids (FAs) were constructed to improve the processability and absorbability of lutein (LUT). Results showed that all FAs could form stable hydrophilic particles with OVA under the optimized ultrasound-coupled pH conditions. Fourier infrared spectroscopy and transmission electron microscopy analysis showed that these binary complexes effectively encapsulated LUT with an encapsulation rate > 90.0 %. Stability experiments showed that these complexes protected LUT well, which could improve thermal stability and in vitro digestive stability by 1.66–3.58-fold and 1.27–2.74-fold, respectively. Besides, the bioaccessibility of LUT was also enhanced by 7.16–24.99-fold. The chain length and saturation of FAs affected the stability and absorption of LUT. Therefore, these results provided some reference for the selection of FAs for efficient delivery of lipid-soluble nutrients.

Light-Responsive and Antibacterial Graphenic Materials as a Holistic Approach to Tissue Engineering.

While the continuous development of advanced bioprinting technologies is under fervent study, enhancing the regenerative potential of hydrogel-based constructs using external stimuli for wound dressing has yet to be tackled. Fibroblasts play a significant role in wound healing and tissue implants at different stages, including extracellular matrix production, collagen synthesis, and wound and tissue remodeling. This study explores the synergistic interplay between photothermal activity and nanomaterial-mediated cell proliferation. The use of different graphene-based materials (GBM) in the development of photoactive bioinks is investigated. In particular, we report the creation of a skin-inspired dressing for wound healing and regenerative medicine. Three distinct GBM, namely, graphene oxide (GO), reduced graphene oxide (rGO), and graphene platelets (GP), were rigorously characterized, and their photothermal capabilities were elucidated. Our investigations revealed that rGO exhibited the highest photothermal efficiency and antibacterial properties when irradiated, even at a concentration as low as 0.05 mg/mL, without compromising human fibroblast viability. Alginate-based bioinks alongside human fibroblasts were employed for the bioprinting with rGO. The scaffold did not affect the survival of fibroblasts for 3 days after bioprinting, as cell viability was not affected. Remarkably, the inclusion of rGO did not compromise the printability of the hydrogel, ensuring the successful fabrication of complex constructs. Furthermore, the presence of rGO in the final scaffold continued to provide the benefits of photothermal antimicrobial therapy without detrimentally affecting fibroblast growth. This outcome underscores the potential of rGO-enhanced hydrogels in tissue engineering and regenerative medicine applications. Our findings hold promise for developing game-changer strategies in 4D bioprinting to create smart and functional tissue constructs with high fibroblast proliferation and promising therapeutic capabilities in drug delivery and bactericidal skin-inspired dressings.

Research on Rural Environments’ Effects on Well-Being: The Huizhou Area in China

The Huizhou region is an important area of traditional Chinese culture, and currently, the state of the village’s surroundings in this area is still not perfect. In this study, seven districts (counties) in the Huizhou region were selected for research. The Rural Habitat Environment (RHES) Indicator Program is based on the concept of Socio-Economic-Natural Complex Ecosystems (SENCE) and constructs 18 metrics in three dimensions. Trends and influencing factors were analyzed using entropy weight TOPSIS and a Grey Relational Analysis (GRA) for the years 2013–2022, and spatial and temporal evolution was measured using Geographic Information Systems (GISs). The findings show that the composite index for the Huizhou region grew from 2013 (0.3197) to 2022 (0.6806). Second, the Tunxi District belongs to the high index–high economy category. The Shexian, Xiuning, and Qimen counties belong to the high index–low economy category. Huizhou District and Huangshan District belong to the low index–high economy category. Yixian County belongs to the low index–low economy category. Third, all districts (counties) show an upward trend, and Huangshan District has the best RHES condition. Shexian County ranks relatively low in the comprehensive index.

Entropy-based syntactic tree analysis for text classification: a novel approach to distinguishing between original and translated Chinese texts

Abstract This research focuses on classifying translated and non-translated Chinese texts by analyzing syntactic rule features, using an integrated approach of machine learning and entropy analysis. The methodology employs information entropy to gauge the complexity of syntactic rules in both text types. The methodology is based on the concept of information entropy, which serves as a quantitative measure for the complexity inherent in syntactic rules as manifested from tree-based annotations. The goal of the study is to explore whether translated Chinese texts demonstrate syntactic characteristics that are significantly different from those of non-translated texts, thereby permitting a reliable classification between the two. To do this, the research calculates information entropy values for syntactic rules in two comparable corpora, one of translated and the other of non-translated Chinese texts. Then, various machine learning models are applied to these entropy metrics to identify any significant differences between the two groups. The results show significant differences in the syntactic structures. Translated texts have a higher degree of entropy, indicating more complex syntactic constructs compared to non-translated texts. These findings contribute to our understanding of the effect of translation on language syntax, with implications for text classification and translation studies.

DESIGN AND FABRICATION OF CIVIL 3D PRINTER

The advent of 3D printing technology has revolution- ized the construction industry, offering innovative so- lutions for rapid, cost-effective, and sustainable build- ing practices. This research paper presents a compre- hensive study on the manufacturing and fabrication of a 3D concrete printer tailored for large-scale construc- tion applications. The paper outlines the design princi- ples, mechanical and electrical systems, material con- siderations, and software integration necessary for the development of an efficient and reliable 3D concrete printer. Key components of the printer, including the frame, extrusion system, and control mechanisms, are meticulously designed to ensure precision, stability, and durability. Advanced materials such as high- performance concrete mixes are formulated to meet the unique requirements of 3D printing, balancing workability, strength, and setting time. The integration of sensors and automation technology facilitates real- time monitoring and control, enhancing the accuracy and consistency of the printed structures. The implementation phase involved extensive testing and validation, highlighting the printer’s capability to construct complex geometries and large-scale ele- ments with minimal human intervention. Results indi- cate significant improvements in construction speed, cost savings, and material efficiency compared to tra- ditional methods. The paper concludes with a discus- sion on potential applications, challenges, and future directions for 3D concrete printing technology in the construction industry.

Seismic response control of large-span suspend-dome structures using multidimensional isolation systems

Three isolation strategies have been developed for seismic response control of suspend-domes: bidirectional isolation, bidirectional isolation with additional restrainers, and three-dimensional (3D) isolation. The first and second control strategies use pendulum sliding isolation bearings (PSIBs) and shape memory alloy (SMA)-restrained PSIBs (SPSIBs), respectively, as high-position bearings to support the roof of a structure. The third strategy uses of prepressed spring bearings with additional dampers and SPSIBs with slack SMA strands to form a vertical isolation layer at the roof support and a horizontal isolation layer at the base of the substructure. This paper describes the configurations, working mechanisms, and numerical simulations of these isolation systems in detail and presents their parameter design based on the related design objectives of a prototype suspend-dome structure. In this study, an extensive numerical modeling investigation was performed on isolated and non-isolated suspend-domes to examine the feasibility and effectiveness of the seismic control systems. The results showed that the segmental 3D isolation system provides superior seismic protection performance for the target suspend-dome compared with the bidirectional isolation systems, although the former involves a complex design, modeling, and construction.

Construction Activity Recognition Method Based on Object Detection, Attention Orientation Estimation, and Person Re-Identification

Recognition and classification for construction activities help to monitor and manage construction workers. Deep learning and computer vision technologies have addressed many limitations of traditional manual methods in complex construction environments. However, distinguishing different workers and establishing a clear recognition logic remain challenging. To address these issues, we propose a novel construction activity recognition method that integrates multiple deep learning algorithms. To complete this research, we created three datasets: 727 images for construction entities, 2546 for posture and orientation estimation, and 5455 for worker re-identification. First, a YOLO v5-based model is trained for worker posture and orientation detection. A person re-identification algorithm is then introduced to distinguish workers by tracking their coordinates, body and head orientations, and postures over time, then estimating their attention direction. Additionally, a YOLO v5-based object detection model is developed to identify ten common construction entity objects. The worker’s activity is determined by combining their attentional orientation, positional information, and interaction with detected construction entities. Ten video clips are selected for testing, and a total of 745 instances of workers are detected, achieving an accuracy rate of 88.5%. With further refinement, this method shows promise for a broader application in construction activity recognition, enhancing site management efficiency.

Digital identity: an approach to its nature, concept, and functionalities

Abstract Digital identity is a basic component of the knowledge economy and society. It is the key for accessing the digital world and for carrying out commercial, economic, or any kind of transactions and communications. Far from being a merely digital version of the physical identity, digital identity is a singular and complex construct which poses three main dilemmas that provide the framework for its analysis. The first arises from the context in which it is located, the digital ecosystem, that changes its scope and nature. The second, conceptual, is a consequence of the lack of agreement about its definition but also of the different legal framework derived from it. A third dilemma, functional, is due to the fact that digital identity can fulfil different, even contradictory, functionalities. An analysis of these dilemmas can contribute to a better understanding of this category leading to a proposal for its definition and legal framework.

Joint Anchor Graph Embedding and Discrete Feature Scoring for Unsupervised Feature Selection.

The success of existing unsupervised feature selection (UFS) methods heavily relies on the assumption that the intrinsic relationships among original high-dimensional (HD) data samples exist in the discriminative low-dimension (LD) subspace. However, previous UFS methods commonly construct pairwise graphs and employ l2,1 -norm regularization to severally preserve the local structure and calculate the score of features, which is computationally complex and easy to get stuck into local optimum, so that those approaches cannot be applied in dealing with large-scale datasets in practice. To overcome this challenge, we propose a novel UFS method, in which a novel anchor graph embedding paradigm is designed to extract the local neighborhood relationships among data samples by reducing the computational complexity of graph construction to be linear in the number of data. Moreover, to improve the optimality of selected features as well as the performance of downstream tasks, we propose a discrete feature scoring mechanism, which imposes orthogonal l2,0 -norm constraints on learned projections, in order to enhance the distinction of feature scores as well as reduce the probability of falling into local optimum. In addition, solving the proposed nonconvex and nonsmooth NP-hard problem is challenging, and we present an efficient optimization algorithm to address it and acquire a closed-form solution of the transformation matrix. Extensive experiments demonstrate the effectiveness and efficiency of the proposed UFS by comparison with several state-of-the-art approaches to clustering and image segmentation tasks.

Triple Radical Sorting: Aryl-Alkylation of Alkenes.

The cross-coupling of aryl bromides with alkenes can provide access to diverse combinatorial chemical space. Two-component couplings between these partners are well-known, but three-component aryl-functionalizations of unactivated alkenes remain underdeveloped. In particular, the aryl-alkylation of unactivated alkenes would allow for rapid construction of molecular complexity and the expedient exploration of a pharmaceutically relevant and C(sp3)-rich structural landscape. Herein, we report a general approach toward the aryl-alkylation of alkenes through a triple radical sorting mechanism. Over the course of the reaction, a high energy aryl radical, a primary radical, and a hindered alkyl radical are simultaneously formed. Through mediation by a nickel-based catalyst, the three radicals are sorted into productive bond-forming pathways toward the efficient aryl-alkylation of alkenes. A wide range of electronically and sterically differentiated alkenes and aryl radical precursors can be used to access complex scaffolds. This method was further applied to the synthesis of highly substituted semisaturated fused heterocycles.

GENESIS OF RECREATIONAL SETTLEMENTS IN THE UKRAINIAN CARPATHIANS IN THE 21ST CENTURY

The article examines the phenomena of the emergence and spontaneous evolution of a new type of recreational facilities for the Ukrainian Carpathians region – multi-format recreational settlements that combine private rental residential units with their partial functioning, in the form of rental recreational housing, in the structure of a management company. The prerequisite for the emergence of this type of recreational facilities was the construction of a large ski complex in Bukovel. Accommodation in this complex offered both traditional hotels and comfortable single-family cottages. And these cottages turned out to be the most popular among vacationers and it were they that became a clear example for residents of surrounding villages and towns, who quickly adapted their own surroundings “for tourists.” After russia's occupation of Crimea in 2014, a significant number of the Ukrainian population redirected their holidays to the Carpathian region. The summer holiday season in the mountains has become as widespread and crowded as the winter season. Consequently, recreational facilities in the Ukrainian Carpathians over the course of 1- 2 years were transformed from single-season to year-round establishments, and the summer season surpassed the traditional winter season in terms of profitability. The global Covid 19 pandemic with strict long-term quarantine, psychological stress, and total remote work became the next strong impetus for popularizing the idea of owning a single-family cottage in the environmentally friendly, safe Carpathians. At that time, quarantined Europe and open Ukraine, a visa-free regime and the opening of a number of low-cost airlines to the Gulf countries also resulted in the appearance of a huge number of tourists from the Arab world in the Carpathians.The desire to own a single-family cottage in the Carpathians grew stronger after the large-scale russian invasion in February 2022, as all the delights of the Carpathians added to the feeling of safety from bombings and missile attacks. The response to this demand was the offer of private estates, apart-hotels, small cottage communities, and entire recreational settlements with developed internal infrastructure. The owner of such a building in a recreational settlement can live in it all the time. However, during absence, the management company of a recreational settlement has the right to rent out its building to third parties under the terms of a preliminary agreement with its owner. Thus, this property passively generates income for the owner during the period when he and his family are in another place. Technical issues of maintaining or repairing a building, providing energy resources, developing the territory, landscaping, etc. provided by the management company. Taking this into account, investing capital in exactly this type of real estate in the Ukrainian Carpathians is quite promising and relevant in our time.

LAND VALUE ASSESSMENT METHODS AND THEIR INFLUENCE ON THE MARKET PRICE OF LAND PLOTS

The paper analyzes the land market, which is part of the wider property market sector, focusing on the many factors that determine its dynamics and complexity. It was determined that the main drivers of land value are geographical location, economic conditions, legislative regulation, political stability, infrastructure and development prospects of the territory. The location of land plots in urbanized or strategically important locations significantly increases their value due to high demand, while plots in remote or less developed areas have a lower value. The article examines the impact of the economic climate, where during periods of economic growth the demand for land increases, leading to higher prices, while economic downturns can lead to lower prices. Land laws, tax policies, and land use regulations are key regulatory factors that have a significant impact on market value. Further prospects for the development of the area, such as plans for the construction of new residential complexes or commercial centers, can also significantly increase the value of land plots. It is noted that according to the International Valuation Standards, the market value is considered as the amount of money for which the property could be transferred as a result of a commercial agreement between knowledgeable and independent parties. The article emphasizes the importance of an integrated approach to the analysis of the land market, which includes the assessment of all potential factors affecting pricing, including the size of the plot and its specific purpose, which determine its market attractiveness and value. The use of combined evaluation methods, such as comparative, profitable and cost, allows to obtain a more accurate assessment of the value of land plots, which is critical for investors, developers and policy decisions. The results of the study can be used by governments to evaluate and revise land legislation, which will contribute to more effective management of land resources. The importance of accurate land valuation for the implementation of successful land reforms that can help ensure the equitable use of land resources.

Enhancement of OCT en face images by unsupervised deep learning

Objective. The quality of optical coherence tomography (OCT) en face images is crucial for clinical visualization of early disease. As a three dimensional and coherent imaging, defocus and speckle noise are inevitable, which seriously affect evaluation of microstructure of bio-samples in OCT images. The deep learning has demonstrated great potential in OCT refocusing and denoising, but it is limited by the difficulty of sufficient paired training data. This work aims to develop an unsupervised method to enhance the quality of OCT en face images. Approach. We proposed an unsupervised deep learning-based pipeline. The unregistered defocused conventional OCT images and focused speckle-free OCT images were collected by a home-made speckle modulating OCT system to construct the dataset. The image enhancement model was trained with the cycle training strategy. Finally, the speckle noise and defocus were both effectively improved. Main results. The experimental results on complex bio-samples indicated that the proposed method is effective and generalized in enhancing the quality of OCT en face images. Significance. The proposed unsupervised deep learning method helps to reduce the complexity of data construction, which is conducive to practical applications in OCT bio-sample imaging.

Shadows of wisdom: Classifying meta-cognitive and morally grounded narrative content via large language models.

We investigated large language models' (LLMs) efficacy in classifying complex psychological constructs like intellectual humility, perspective-taking, open-mindedness, and search for a compromise in narratives of 347 Canadian and American adults reflecting on a workplace conflict. Using state-of-the-art models like GPT-4 across few-shot and zero-shot paradigms and RoB-ELoC (RoBERTa -fine-tuned-on-Emotion-with-Logistic-Regression-Classifier), we compared their performance with expert human coders. Results showed robust classification by LLMs, with over 80% agreement and F1 scores above 0.85, and high human-model reliability (Cohen's κ Md across top models = .80). RoB-ELoC and few-shot GPT-4 were standout classifiers, although somewhat less effective in categorizing intellectual humility. We offer example workflows for easy integration into research. Our proof-of-concept findings indicate the viability of both open-source and commercial LLMs in automating the coding of complex constructs, potentially transforming social science research.

Revealing electrostatic-manipulated self-assembly mechanism helps to readily design enhanced MOF-derived microwave absorbers

The controllable self-assembly of Metal Organic Frameworks (MOFs) crystals plays a vital role in modulating the performance for microwave absorbers. However, most current strategies require colloidal or intricate chemical modifications of nanoparticles, thereby increasing the complexity of structural construction and impeding the broader application of microwave absorbers. To resolve this tough issue, we herein innovatively propose a more easily achievable electrostatic-manipulated self-assembly strategy. By readily manipulating the electrostatic adsorption and repulsion between particles, the aggregation and growth sites of initial MOF crystals are anchored, hence facilitating to the construction of solid-core/shell and hollow porous microstructures for absorbers. The substantial improvement depending on electrostatic effects is clearly observed in experimental results. It demonstrates that the Co@hollow N-doped carbon spheres (Co@HNCSs) absorber, induced by electrostatic repulsion, shows the best overall absorption performance, with a strong absorption of −65.4 dB at a thickness of 1.9 mm and an effective absorption bandwidth (EAB) of 5.4 GHz at a thickness of 1.6 mm, covering almost the entire Ku band. The influence of these strategies on the electromagnetic response mechanisms is further comprehensively discussed. Accordingly, we believe this strategy offers new insights for the MOFs’ structural absorbers via electrostatic-manipulated self-assembly strategy.

Techno-Economic Evaluation of a Residential Photovoltaic System: A Case Study From the Residential Complex Kass Suwailem in Hilla, Iraq

Recently, the construction of residential complexes has spread significantly in all cities of Iraq. Hence, incorporating renewable energy into residential buildings provides multiple advantages as an essential component of the global sustainability initiative. In this study, a techno-economic analysis of a photovoltaic (PV) system has been evaluated in residential buildings of Kass Suwailem I and II, Iraq-Hilla, as a case study. The evaluation involved the technical and economic feasibility of implementing a solar power generation system in this residential setting. Furthermore, it assessed the costs, benefits, and financial viability of installing and operating a PV system for electricity generation. The optimal size of the PV system based on the energy consumption, local solar irradiation levels, and electricity demand patterns for each apartment unit has been evaluated. For each apartment unit, the total PV system cost is assumed to be $12,470 consisting of a 14.7 kW array, 8-battery bank of 230 Ah each, controller, and 10 kW inverter. Life-Cycle Cost analysis has been employed to determine the comprehensive cost of the PV system, encompassing all expenditures accrued throughout its operational lifespan. The calculation of the installed PV system involves annual energy rationalization, along with determining the amount of cost reduction and comparing the system with a generator or any other source that uses fossil fuels.

Low viscosity and highly flexible stereolithographic 3D printing resins for flexible sensors

Flexible sensors show the wide range of applications in electronic products. However, challenges remain in developing sensors with internal complex construction, excellent flexibility, elasticity and strength. In this paper, a prepolymer (PU-PDMS-OH-HEMA) contain polyurethane (PU) segments and polysiloxane (PDMS) segments was synthesized, and the photosensitive resins with low viscosity for 3D printing were prepared by mixing prepolymers with diluents and photoinitiators. The resultant cured photosensitive resins showed the improved mechanical properties. The PDMS-OH-30 sample exhibit the excellent mechanical properties, with tensile strength and elongation at break of 10.03 MPa and 1046.15 %, respectively, and the resin viscosity of 62 cP. Conductive ionic liquids were added to the above photosensitive resins to prepare flexible sensors, which show the electrical sensitivity and stability. The findings in the work propose a contribution to the development of highly flexible 3D printing resins for fabricating flexible sensors.

Modeling Cost-Estimation Factors for Public Building Projects with Hybrid Approach in Addis Ababa

Assessing the most important cost-influencing factors is essential for enhancing the predictive ability of cost estimation for building construction projects. The goal of this study is to examine and design a valid cost prediction model for assessing factors that impact the cost estimation of public buildings in Addis Ababa. This research solves these issues that typically arise in predictive cost estimation models in two major processes. First, the insights of 133 professionals gathered on the 38 cost-impacting elements, and 15 top factors design, time or cost, and parties’ experience were determined. The suggested hybrid approach is based on the Akaike information criterion (AIC) and principal component regression (PCR) employed, coupling a stepwise linear regression model. According to the findings of the study, principal component analysis reduced important factors to 14 and efficiently solved the problem of multicollinearity with a variance inflation factor of less than 2, while stepwise cross-validation solved the overfitting problem at the lowest AIC. The cost prediction model sorted out five factors: design completion by the public body when bids are invited; completion of the project scope definition when bids are invited; level of construction complexity; importance of project completion within budget; and subcontractor experience and capability have all been identified as the main cost-determining factors. The study’s contribution is the first approach (PCR–AIC) utilized in this work to explore numerous cost-estimating components, eliminate those that were related to one another, and identify the most crucial ones that consisted of the majority of the original variables’ attributes.