Design Construction Research Articles

Stress-dependent instantaneous cohesion and friction angle for the Mohr–Coulomb criterion

The strength criterion of rock is essential for stability control and safety design of geotechnical engineering constructions. Due to its widespread adoption, the Mohr–Coulomb (M-C) criterion is prominent among strength criteria. However, the M-C criterion is constrained by three significant limitations: it fails to capture the nonlinear strength response, overlooks the critical state, and disregards σ2. This study introduces a novel Stress-dependent Instantaneous Friction angle and Cohesion (SIFC) model for the M-C criterion to represent the convex strength envelope of intact rock, covering the spectrum from non-critical to critical states. In pursuit of this objective, an innovative approach for calculating these instantaneous shear parameters at each corresponding σ3 is initially introduced. By examining the confining pressure dependency of the instantaneous friction angle and cohesion, the SIFC model is derived and introduced to the M-C criterion. The SIFC-enhanced M-C criterion, utilizing parameters obtained from triaxial tests under lower σ3, delineates the complete non-linear strength envelope in (σ1, σ3) space, covering brittle to ductile behavior. This criterion is then extended to polyaxial stress conditions. Validation through triaxial test data confirms that the SIFC-enhanced M-C criterion accurately reflects the strength characteristics of the tested rocks.

Intermediate-point bracing for columns: New stiffness–buckling load formulae

According to the stiffness equation provided in the American Institute of Steel Construction (AISC) design specifications for intermediate-point braces, the required point brace stiffness increases with the number of point braces. This research aims to formulate a new stiffness design equation for intermediate-point braces that accounts for the decline in required stiffness with an increasing number of braces. The brace stiffness (β)–buckling load (Pcr) relationships for columns with point braces were investigated by performing a buckling analysis using the energy method with the Fourier series. A formula was derived, defining the β−Pcr relationships for each buckling mode of the columns, regardless of the number of point braces and whether the buckling is elastic or inelastic. The AISC stiffness equations for point braces were significantly conservative under most buckling loads. The proposed stiffness design equation requires lower brace stiffness with an increasing number of point braces, resulting in smaller brace members than the AISC design equations. Additionally, the proposed strength design equation can set relatively high strength requirements for the brace, reflecting that stiffness governs the design of the point brace. These new equations offer a more precise and efficient method for calculating required brace stiffness, providing valuable insights to improve structural design capabilities.

Geometry prediction and design for energy storage salt caverns using artificial neural network

Salt cavern is one of the best storage for hydrogen, compressed air, and natural gas. However, the current physical/numerical simulation-based construction design cannot yield optimal solutions due to the complex correlation of multiple construction parameters. This paper proposes a novel machine-learning-enabled method for the geometry prediction and design optimization during salt cavern construction. A dataset of 1197 simulations of salt cavern construction is collected using our previously developed program. Construction parameters are included as inputs, and the simulated cavern geometries as outputs. A Gated Recurrent Unit model is selected and well-trained from 600 artificial neural network models. The model achieves a mean absolute error of 1.6m in the test dataset and of 2.83m for the geometric prediction of cavern JT52 in Jintan, meeting the field design requirements. Furthermore, an optimized construction design method is proposed by looping generating construction parameters, shape prediction, and deviation calculation until the deviation meets requirements. It succeed in designing an ideal ellipsoid cavern in approximately 51 min, with a capacity ratio fc 31 % larger than those of the field caverns. This approach demonstrates the potential for machine learning methods to serve as the third generation of construction design methods after physical and numerical modeling.

Development of novel near-infrared photothermal material for efficient breast cancer therapy

Photothermal therapy (PTT) has become a candidate treatment for breast cancer due to its non-invasive, specific and controllable properties. However, there are few strategies to develop photothermal agents with high photothermal conversion efficiency, adequate tumor accumulation, and the molecular design theory is not perfect. Thus, to obtain the photothermal agent with better comprehensive performance, a novel conjugated polymer PAPQ-Th, comprising quinoxaline derivative 10,11-bis(4-(octyloxy) phenyl)acenaphtho[1,2-b]pyrazino[2,3-g]quinoxaline (PAPQ) as the acceptor and thiophene (Th) as the donor, was designed and synthesized. The PAPQ-Th exhibited the high non-radiative rate of 7.3 × 1011 s−1 and faint fluorescence emission. And PAPQ-Th nanoparticles exhibited strong NIR absorption from 650 nm to 900 nm, negligible ROS generation, and an impressively high photothermal conversion efficiency of 61 % under an 808 nm laser irradiation. Furthermore, PAPQ-Th nanoparticles can be successfully used for both in vitro and in vivo experiments, and exhibited remarkable photothermal antitumor efficacy. After intravenous administration and 808 nm laser irradiation, 4T1 tumor-bearing mice achieve complete tumor remission without recurrence. The results demonstrate that the design of new quinoxaline derivative and construction of novel donor-acceptor conjugated polymer are the effective strategy for the synthesis of efficient photothermal agent.

Impact of the traffic on a population of European pond turtles (Emys orbicularis) inhabiting heavily urbanized area at the city of Burgas

We report on the impact of vehicle traffic on a local population of European pond turtles (Emys orbicularis) inhabiting the city of Burgas (Bulgaria). We performed a six-year monitoring program to collect data on the presence of turtles on road E 87 in the south region of the city. The road section splits a Natura 2000 site and crosses through an artificial water basin with fresh to brackish water. We visited the road section in irregular intervals from 3 to over 20 days in the period March to October from 2016 to 2021. For the period of the monitoring, we were able to detect a total of over 300 specimens from all ontogenetic stages (33 juveniles, 74 subadults and 224 adults). During our field surveys in 2016, we detected a total of 47 live and 23 dead adults. In the next years, we found more dead adult turtles than live ones. Similar results were recorded for subadults. In the juveniles, the dead specimens were almost equal to those which were detected alive. Our results indicate an impact of the traffic on the local population of E. orbicularis. We discuss the design and position of constructions, which can mitigate the conflict and reduce the potential for Wildlife-vehicle collisions (WVC).

Flexural Behavior of Galvanized Iron Based Cold-Formed Steel Back-to-Back Built-Up Beams at Elevated Temperatures

Cold-formed steel (CFS) sections have become popular in construction due to several advantages over structural steel. However, research on the performance of galvanized iron (GI)-based CFS under high temperatures, especially regarding its flexural behavior, has been limited. This study extensively investigates how GI-based CFS beams with varying spans behave under elevated temperatures and subsequent cooling using air and water. This study examines the impact of temperature loading and compares the effectiveness of air- and water-cooling methods. Experimental results are validated and analyzed alongside findings from finite element modeling (FEM) using ABAQUS (2019_09_13-23.19.31) and the Direct Strength Method (DSM). Additionally, this study conducts a parametric investigation to assess how beam span influences flexural capacity. Among beams heated to the same temperature, those cooled with water show slightly lower load capacities compared to those cooled with air. The highest load capacity observed is 64.3 kN for the reference specimen, while the lowest is 26.2 kN for the specimen heated for 90 min and cooled with water, a 59.27% difference between them. Stiffness decreases as heating duration increases, with the reference section exhibiting significantly higher stiffness compared to the section heated for 90 min and cooled with water, with a 92.76% difference in stiffness. As heating duration increases, ductility also increases. Various failure modes are observed based on different heating and cooling conditions across different beam spans. This study provides insights into how GI-based CFS beams perform under temperature stress and different cooling scenarios, contributing valuable data for structural design and safety considerations in construction.

Bilateral Unsymmetrical Disulfurating Reagent Design for Polysulfide Construction.

Polysulfides are significant compounds in life science, pharmaceutical science, and materials science. Therefore, polysulfide construction is in great demand. The controllable sequential installation of groups on both ends of a S-S motif faces an enormous challenge owing to the reversible nature of the covalent S-S bond. A library was established with two divergent mask groups for bilateral unsymmetrical disulfurating reagents (R1O-SS-SO2R2). Sequential coupling with preferential activation of the S-SO2 bond (37.6 kcal/mol) and controllable activation of the S-O bond (54.8 kcal/mol) in the presence of the S-S bond (62.0 kcal/mol) enabled successive reactions at each end of the S-S motif to afford unsymmetrical disulfides and trisulfides, even for the cross-linkage of natural products, pharmaceuticals, peptides, and a protein (bovine serum albumin).

Bilateral Unsymmetrical Disulfurating Reagent Design for Polysulfide Construction

AbstractPolysulfides are significant compounds in life science, pharmaceutical science, and materials science. Therefore, polysulfide construction is in great demand. The controllable sequential installation of groups on both ends of a S−S motif faces an enormous challenge owing to the reversible nature of the covalent S−S bond. A library was established with two divergent mask groups for bilateral unsymmetrical disulfurating reagents (R1O−SS−SO2R2). Sequential coupling with preferential activation of the S−SO2 bond (37.6 kcal/mol) and controllable activation of the S−O bond (54.8 kcal/mol) in the presence of the S−S bond (62.0 kcal/mol) enabled successive reactions at each end of the S−S motif to afford unsymmetrical disulfides and trisulfides, even for the cross‐linkage of natural products, pharmaceuticals, peptides, and a protein (bovine serum albumin).

Forecasting the architecture billings index (ABI) using machine learning predictive models

PurposeThis research aims to forecast the ABI as a leading indicator of U.S. construction activities, applying multivariate machine learning predictive models over different horizons and utilizing the nonlinear and long-term dependencies between the ABI and macroeconomic and construction market variables. To assess the applicability of the machine learning models, six multivariate machine learning predictive models were developed considering the relationships between the ABI and other construction market and macroeconomic variables. The forecasting performances of the developed predictive models were evaluated in different forecasting scenarios, such as short-term, medium-term, and long-term horizons comparable to the actual timelines of construction projects.Design/methodology/approachThe architecture billings index (ABI) as a macroeconomic indicator is published monthly by the American Institute of Architects (AIA) to evaluate business conditions and track construction market movements. The current research developed multivariate machine learning models to forecast ABI data for different time horizons. Different macroeconomic and construction market variables, including Gross Domestic Product (GDP), Total Nonresidential Construction Spending, Project Inquiries, and Design Contracts data were considered for predicting future ABI values. The forecasting accuracies of the machine learning models were validated and compared using the short-term (one-year-ahead), medium-term (three-year-ahead), and long-term (five-year-ahead) ABI testing datasets.FindingsThe experimental results show that Long Short Term Memory (LSTM) provides the highest accuracy among the machine learning and traditional time-series forecasting models such as Vector Error Correction Model (VECM) or seasonal ARIMA in forecasting the ABIs over all the forecasting horizons. This is because of the strengths of LSTM for forecasting temporal time series by solving vanishing or exploding gradient problems and learning long-term dependencies in sequential ABI time series. The findings of this research highlight the applicability of machine learning predictive models for forecasting the ABI as a leading indicator of construction activities, business conditions, and market movements.Practical implicationsThe architecture, engineering, and construction (AEC) industry practitioners, investment groups, media outlets, and business leaders refer to ABI as a macroeconomic indicator to evaluate business conditions and track construction market movements. It is crucial to forecast the ABI accurately for strategic planning and preemptive risk management in fluctuating AEC business cycles. For example, cost estimators and engineers who forecast the ABI to predict future demand for architectural services and construction activities can prepare and price their bids more strategically to avoid a bid loss or profit loss.Originality/valueThe ABI data have been forecasted and modeled using linear time series models. However, linear time series models often fail to capture nonlinear patterns, interactions, and dependencies among variables, which can be handled by machine learning models in a more flexible manner. Despite the strength of machine learning models to capture nonlinear patterns and relationships between variables, the applicability and forecasting performance of multivariate machine learning models have not been investigated for ABI forecasting problems. This research first attempted to forecast ABI data for different time horizons using multivariate machine learning predictive models using different macroeconomic and construction market variables.

Knit-Mycelium Hybrids: Textile Design Strategies for Biofabrication Systems Based on Mycocrete Injection in 3D Knitted Tubular Formworks

This paper investigates the design potential of monolithically grown Mycelium-based Composites (MBCs), focusing on integrating 3D knitted formworks within the fabrication process. Mycelium has gained attention as a sustainable, biodegradable construction material with insulating and self-healing capabilities. While there have been advancements in MBC, challenges remain regarding geometrical complexity and design potential at scale through in situ practices. Recent interest has emerged in digital fabrication methods and mould creation using textile logic and CNC knitting. However, fabricating mycelium within tubular soft moulds present a lack of stability and uneven growth distribution. The novel technique using injection filling of Mycocrete, a viscous mycelium paste, overcomes this structural disintegration. This method enables the creation of complex designs within a two-staged fabrication process while providing high structural performance. The paper presents three case studies demonstrating the impact of hanging, draping, and internal shape integration as fabrication steps as design tools for geometrical possibilities and diversity of expressions. It examines the relationship between knitted soft preform design and biofabrication parameters in relation to design expression, scale and mycelium growth. The paper concludes by emphasising the significance of 3D knitted formworks in scaling up MBCs in the built environment and its potential for implementing textile design parameters in MBC expressions and geometries. The fabrication steps provide newfound design opportunities, opening possibilities for aesthetical, material-saving and functionally graded design approaches. Further research in this area holds promise for advancing sustainable construction and textile design practices.

Building a Speech Dataset and Recognition Model for the Minority Tu Language

Speech recognition technology has many applications in our daily life. However, for many low-resource languages without written forms, acquiring sufficient training data remains a significant challenge for building accurate ASR models. The Tu language, spoken by an ethnic minority group in Qinghai Province in China, is one such example. Due to the lack of written records and the great diversity in regional pronunciations, there has been little previous research on Tu-language speech recognition. This work seeks to address this research gap by creating the first speech dataset for the Tu language spoken in Huzhu County, Qinghai. We first formulated the relevant pronunciation rules for the Tu language based on linguistic analysis. Then, we constructed a new speech corpus, named HZ-TuDs, through targeted data collection and annotation. Based on the HZ-TuDs dataset, we designed several baseline sequence-to-sequence deep neural models for end-to-end Tu-language speech recognition. Additionally, we proposed a novel SA-conformer model, which combines convolutional and channel attention modules to better extract speech features. Experiments showed that our proposed SA-conformer model can significantly reduce the character error rate from 23% to 12%, effectively improving the accuracy of Tu language recognition compared to previous approaches. This demonstrates the effectiveness of our dataset construction and model design efforts in advancing speech recognition technology for this low-resource minority language.

Axiomatic design for safe construction considering lean and ergonomic principles: an application in Turkey

This study proposes a systematic approach based on axiomatic design incorporating lean and ergonomic principles within the proposed design. The contribution to the body of knowledge is to keep construction workers safe by fostering a secure construction environment. Axiomatic design and lean approaches are combined to address human factors and eliminate wastes that hinder a safe construction environment. The application of the proposed system is set in the context of the construction industry. The methodology provides the construction sector with a road map toward reducing the occurrence of accidents and serves as a complementary approach between lean and ergonomic principles. The design solution has been validated and partially applied in a metro construction firm. Results show that the design solution can improve the efficiency of the construction phases by adopting a value-maximization strategy and has the potential to improve the safety and ergonomics of construction projects.

Influence of team emotional intelligence on the performance of construction design teams

Emotional Intelligence (EI) is an important competence in teamwork settings. Predominantly, previous EI studies on teams have focused on individual-level EI, neglecting the synergy of team members in a team. Therefore, this study aimed to assess the influence of team EI on the performance of construction design teams. A mixed method approach was used to collect data by means of a questionnaire survey and focus group interviews. The questionnaire was used to collect data on team EI and team performance, while the focus group interview was conducted to give insights on team activities based on the results from the questionnaire survey. A total of 50 projects were selected, constituting eight (8) teams through convenience and purposive sampling techniques. The 8 teams consisted of 38 individuals. Thus, the questionnaire was administered to 38 respondents, of whom 17 valid responses were received, while the focus group interview was conducted with members of one team. Means, percentages and correlational analysis were utilised in the analysis of the quantitative data, while content analysis was used to analyse the qualitative data. The results obtained for team EI and performance were used for correlational analysis, which revealed that team EI has a significant positive association with the team’s self-direction and average (overall) performance, as indicated by the correlation results (τb= .432, p=.046) and (τb= .401, p=.042) respectively. The content analysis revealed that team norms improved with participation in multiple projects. This suggests that TEI contributes significantly to the performance of the construction design teams. This study holds significant practical implications for the training of construction professionals, with the potential to foster the development of high emotional intelligence (EI). Furthermore, it extends its relevance to client organizations seeking to hire design team consultants who possess emotional intelligence. To achieve improved team performance, it is suggested that client organisations consider incorporating a team-based EI assessment alongside the evaluation of technical expertise during the hiring process.

Machine learning and 3D bioprinting.

48With the growing number of biomaterials and printing technologies, bioprinting has brought about tremendous potential to fabricate biomimetic architectures or living tissue constructs. To make bioprinting and bioprinted constructs more powerful, machine learning (ML) is introduced to optimize the relevant processes, applied materials, and mechanical/biological performances. The objectives of this work were to collate, analyze, categorize, and summarize published articles and papers pertaining to ML applications in bioprinting and their impact on bioprinted constructs, as well as the directions of potential development. From the available references, both traditional ML and deep learning (DL) have been applied to optimize the printing process, structural parameters, material properties, and biological/mechanical performance of bioprinted constructs. The former uses features extracted from image or numerical data as inputs in prediction model building, and the latter uses the image directly for segmentation or classification model building. All of these studies present advanced bioprinting with a stable and reliable printing process, desirable fiber/droplet diameter, and precise layer stacking, and also enhance the bioprinted constructs with better design and cell performance. The current challenges and outlooks in developing process-material-performance models are highlighted, which may pave the way for revolutionizing bioprinting technologies and bioprinted construct design.

Development of Three-Dimensional Light Wave Test Based on Three-Dimensional Learning Framework

The three-dimensional learning framework as a current science learning standard to facilitate the development of students' knowledge that is coherent and interconnected so that it can be used in various situations of daily life, is only possible if the assessment is oriented towards the science learning standards in question. The focus of this research is to develop a Three-Dimensional light wave test based on the Three-Dimensional Learning Framework. Development was carried out through quantitative research methods with construction design and validation, involving physics lecturers, physics teachers and high school students. Construction and validation were carried out using document analysis sheets, cognitive questionnaires, content validation sheets and student responses. Analysis was carried out through descriptive analysis, V-Aiken content validation, and Item Response Theory (IRT) using eIRT software. The construction and validation results show that the Three-Dimentional light wave test based on the Three-Dimentional Learning Framework has differentiating power in the good category, five items are very high, three items are high, eight items are medium and five items are low; the level of difficulty is in the medium category, two questions are very difficult, seven questions are difficult, eight questions are medium and four questions are easy; the pseudo guessing factor overall functions well; and reliable for measuring the level of ability of students with low to very high abilities. The Three-Dimensional test of light waves based on the Three-Dimentional Learning Framework can be used by educational practitioners as an assessment tool to explore students' coherent understanding of disciplinary core ideas (DCI), scientific practices (SP), and crosscutting concepts (CC) in wave material light.

DEVELOPMENT OF CONSTRUCTIVE DESIGN OF PICKLING UNITS FOR OPERATION IN HIGHLY AGGRESSIVE HOT SULPHURIC ACID SOLUTIONS

Problem statement. Technological process of metallurgical, pipe-rolling, chemical and other enterprises includes chemical treatment of metal products by pickling in hot (60–80 °C) sulphuric acid solutions with a concentration of 20–22 %. This treatment method is conducted in the pickling departments of enterprises in special containers (pickling units). Pickling solution of the specified concentration and temperature is a highly aggressive environment for all building structures made of traditional materials. Existing pickling units for chemical treatment of metal products do not ensure reliability and durability and are destroyed after 1-2 years of operation. Therefore, ferrous metallurgy enterprises that have pickling departments for chemical treatment of metal structures are classified as enterprises with a highly aggressive environment and their design should be developed with these conditions under consideration. The purpose of the work: Searching for and implementing mechanisms to improve the resistance of polymer concrete in aggressive environments in production. The object of research – is furan resins modified polymer concrete. The subject of the research – investigation of the particularities and improvement of constructive design for pickling baths made of materials based on polymer concrete in an aggressive environment and its implementation in production. Main part. Development of constructive design for technological pickling units made of structural corrosion-resistant material (furan resins modified polymer concrete). Conclusions. Pickling of metal products at ferrous metallurgy enterprises is conducted in highly aggressive hot sulphuric acid solutions of 20–25 % concentration at a temperature of 60–80 °C. Under the conditions of constant exposure to highly aggressive hot sulphuric acid solutions, even protection with chemically resistant materials does not ensure the required reliability and durability. To manufacture such structures, a new structural material is needed that combines chemical resistance with strength and durability. This material is a structurally chemical-resistant polymer concrete based on thermoset furan resins, which have high strength and universal chemical resistance, allowing us to solve the problem of reliability and durability of technological units for chemical treatment of metal structures at ferrous and non-ferrous metallurgy enterprises in a fundamentally new way. New constructive design for pickling units made of reinforced polymer concrete was developed in conjunction with shock-absorbing bars and an improved ventilation system, consisting of individual elements of full factory readiness weighing no more than 5 tonnes, which allows them to be transported and installed in operating pickling workshops using existing mechanisms.

Assessing the quality of conceptual knowledge through dynamic constructions

In this contribution, we address the gap that has appeared in mathematics education research and practice with the emergence of dynamic geometry environments and build on the opportunities these environments offer to school geometry. In our qualitative empirical study, we investigate how to elaborate on the general model of conceptual knowledge to make it applicable to dynamic geometry tasks, specifically to tasks including dynamic geometric constructions. We present a design of dynamic constructions of quadrilaterals that comply with Euclidean constructions, derive an assessment instrument based on them, and study what information the instrument can provide about the quality of students’ conceptual knowledge. We present the results in the form of an assessment framework consisting of an example of the assessment instrument and an ordered system of qualitative categories serving as an assessment codebook for interpreting students’ responses in terms of the quality of conceptual knowledge. To clarify the relations between the assessment framework and the general model of conceptual knowledge, we establish a system of subdimensions of conceptual knowledge that indicates how conceptual knowledge can be understood in the context of dynamic geometric constructions and identifies the conceptual knowledge needed to achieve individual categories of the assessment framework.

In silico design of multi-epitope vaccines against the hantaviruses by integrated structural vaccinology and molecular modeling approaches.

Hantaviruses are single-stranded RNA viruses belonging to the family Bunyaviridae that causes hantavirus cardiopulmonary syndrome (HCPS) and hemorrhagic fever with renal syndrome (HFRS) worldwide. Currently, there is no effective vaccination or therapy available for the treatment of hantavirus, hence there is a dire need for research to formulate therapeutics for the disease. Computational vaccine designing is currently a highly accurate, time and cost-effective approach for designing effective vaccines against different diseases. In the current study, we shortlisted highly antigenic proteins i.e., envelope, and nucleoprotein from the proteome of hantavirus and subjected to the selection of highly antigenic epitopes to design of next-generation multi-epitope vaccine constructs. A highly antigenic and stable adjuvant was attached to the immune epitopes (T-cell, B-cell, and HTL) to design Env-Vac, NP-Vac, and Com-Vac constructs, which exhibit stronger antigenic, non-allergenic, and favorable physiochemical properties. Moreover, the 3D structures were predicted and docking analysis revealed robust interactions with the human Toll-like receptor 3 (TLR3) to initiate the immune cascade. The total free energy calculated for Env-Vac, NP-Vac, and Com-Vac was -50.02 kcal/mol, -24.13 kcal/mol, and -62.30 kcal/mol, respectively. In silico cloning, results demonstrated a CAI value for the Env-Vac, NP-Vac, and Com-Vac of 0.957, 0.954, and 0.956, respectively, while their corresponding GC contents were 65.1%, 64.0%, and 63.6%. In addition, the immune simulation results from three doses of shots released significant levels of IgG, IgM, interleukins, and cytokines, as well as antigen clearance over time, after receiving the vaccine and two booster doses. Our vaccines against Hantavirus were found to be highly immunogenic, inducing a robust immune response that demands experimental validation for clinical usage.

Material compatibility of epoxies exposed to repeated low temperature vaporized hydrogen peroxide sterilization

ABSTRACT Through technological advancements, medical devices have evolved in their designs and have become more complex in both their design and materials of construction. Medical grade biocompatible epoxies are widely used in reusable medical devices. Choosing an epoxy that maintains its performance characteristics when subjected to repeated sterilization throughout the reusable medical device’s lifespan is a known challenge for medical device manufacturers. This study evaluated the material compatibility of seven cured 2-part and 1-part epoxies used in medical devices following exposure to 100 cycles in a low temperature vaporized hydrogen peroxide sterilizer. Six of the seven epoxies tested were found to be compatible with vaporized hydrogen peroxide sterilization based on qualitative, hardness and weight measurements conducted post exposure to 100 VHP cycles. The epoxies deemed to be compatible displayed no visual signs of physical defects, minimal reduction in hardness (≤2%) and total weight gain (≤2.9%). One of the epoxy samples did not maintain its texture and exhibited 17% loss in hardness post exposure to 100 VHP sterilization cycles and was found to be incompatible with the low temperature vaporized hydrogen peroxide process. This study verifies the need for material compatibility evaluations of epoxies prior to designing and developing a medical device while keeping in mind the application, lifecycle and intended use of medical devices.

Real-Time Spatial Mapping in Architectural Visualization: A Comparison among Mixed Reality Devices.

Recent advancements in communication technology have catalyzed the widespread adoption of realistic content, with augmented reality (AR) emerging as a pivotal tool for seamlessly integrating virtual elements into real-world environments. In construction, architecture, and urban design, the integration of mixed reality (MR) technology enables rapid interior spatial mapping, providing clients with immersive experiences to envision their desires. The rapid advancement of MR devices, or devices that integrate MR capabilities, offers users numerous opportunities for enhanced entertainment experiences. However, to support designers at a high level of expertise, it is crucial to ensure the accuracy and reliability of the data provided by these devices. This study explored the potential of utilizing spatial mapping within various methodologies for surveying architectural interiors. The objective was to identify optimized spatial mapping procedures and determine the most effective applications for their use. Experiments were conducted to evaluate the interior survey performance, using HoloLens 2, an iPhone 13 Pro for spatial mapping, and photogrammetry. The findings indicate that HoloLens 2 is most suited for the tasks examined in the scope of these experiments. Nonetheless, based on the acquired parameters, the author also proposes approaches to apply the other technologies in specific real-world scenarios.