Removal of graffiti paint from construction materials coated with TiO2-based photocatalysts.

Graffiti on construction materials has significant social and economic impacts, especially on artistic and historical artefacts. Anti-graffiti protective coatings are used to generate low surface energies that limit graffiti adhesion to the surface, thereby reducing surface damage and facilitating removal. The anti-graffiti properties of three commercial TiO2-based coatings were tested under outdoor exposure conditions using four colours of graffiti paint (red, blue, black, and white). Chemical removers were used to clean the stained surfaces to understand the impact of the photocatalytic coatings during the conventional cleaning procedure. The effectiveness of cleaning was assessed by visual observations, colour measurements, and the percentage of residual stain. The anti-graffiti efficacy was strongly dependent on the colour of the graffiti and characteristics of the TiO2 coating. The cleaning performance of TiO2-treated samples was likely related to the photocatalytic redox reactions that decompose the graffiti. Additionally, their hydrophilicity may also prevent the adhesion and/or penetration of graffiti paint on the surface and/or pore matrix.

A method to assess bullying and harassment as an upstream determinant of construction worker mental health.

BACKGROUND: Bullying and harassment in the workplace are increasingly recognized as hazardous exposures associated with poor mental health and suicidality. The construction sector has one of the highest rates of suicide among all occupations and is actively engaged in efforts to destigmatize mental health support. However, there has been less focus on reducing factors that may be contributing to poor well-being among construction workers. METHODS: As a step toward addressing work-related determinants of mental health in construction, we collaborated with a large construction union to survey members about their experiences of abusive conduct. We adapted standardized questionnaires to better suit the sector, such as by assessing "hazing that went too far" and apprenticeship status. Additionally, we included questions on reporting of abuse, concern about the impacts of abuse, and an open-response to allow participants to share their perspectives. RESULTS: We developed and tested a carefully-tailored survey of abusive conduct. The responses to the survey (over 3300, including 500 narrative responses) will facilitate data-driven interventions with the potential to prevent and address abuse. This paper describes the survey development process in collaboration with the union, domains of abuse that are relevant to the construction context, and the survey protocol. CONCLUSION: Through a collaborative effort, we developed an instrument to understand abusive conduct in construction and benchmark success in reducing adverse experiences of bullying and harassment. We recommend its use throughout the sector to reduce exposure to this well-being hazard.

Effects of training methods on performance of a scaffolding task for workers with different spatial ability.

With software developments and advances in display technologies substantially improved, augmented reality (AR) application has gained popularity. In this study, we discuss using classic PowerPoint and AR for two kinds of scaffolding tasks (task-lifeline assembly and hedge assembly) for users with different spatial ability. We considered both objective and subjective measures of performance, i.e., correct rate and system usability and the ITC-sense of presence inventory (ITC-SOPI) scale. The results of the study show that participants using AR achieved higher operating performance than those using PowerPoint. Furthermore, the users' learning effect was influenced by spatial ability when using PowerPoint. Participants with high spatial ability achieved higher performance than participants with low spatial ability in PowerPoint. However, participants who used AR as a training method did not show significantly different operating performance at different levels of spatial ability. Consequently, AR was believed to be a potential method for enhancing training performance.

Using olfactory cues in text materials benefits delayed retention and schemata construction.

Olfactory cues are considered a new sensory medium that can enhance learning, but the lack of empirical data has hampered their widespread use in educational practice. This requires empirical research to explore the effects of olfactory cues on learning. To address this research need, an experimental research study was conducted among 87 fourth graders from a Chinese elementary school. It explored the innovative design of adding olfactory cues to text materials by examining their effects on retention and schemata construction as learning outcomes, as well as their influence on learners' cognitive load and learning experience. In this between-subjects design experiment, the experimental group (n = 44) learned text materials with the introduction of olfactory cues, while the control group (n = 43) only learned text materials. After the learning activity, participants were asked to complete the questionnaires, immediate test, and delayed test. The results revealed that the usage of olfactory cues synchronized with text materials can enhance delayed retention, facilitate schemata construction, and improve learner experience without increasing cognitive load. This study confirms the potential of well-designed olfactory cues in educational practice and provides insights for designing and presenting multimedia learning resources.

An eco-friendly solution for construction and demolition waste: Recycled coarse aggregate with CO2 utilization.

Carbonation of recycled coarse aggregate (RCA) is an eco-friendly solution for the recycling of construction and demolition waste. This paper provides a comprehensive understanding of utilizing CO2 in RCA. The carbonation mechanism associated with CO2 treatment of RCA has been systematically summarized. The methods for CO2 treatment of RCA and the calculation of CO2 sequestration were discussed. Meanwhile, the efficiency of physical properties enhancement of carbonized RCA was analyzed. The microstructure, mechanical properties and durability improvement of recycled concrete containing carbonized RCA were reviewed. Additionally, the environmental benefits of carbonized RCA were provided through carbon footprint, carbon accounting and carbon intensity. Furthermore, the future perspectives of RCA with CO2 utilization were prospected.

The self-in-the-world map emerged in the primate brain as a basis for Homo sapiens abilities.

The brain in the genus Homo expanded rapidly during evolution, accelerated by a reciprocated interaction between neural, cognitive, and ecological niches (triadic niche construction, or TNC). This biologically costly expansion incubated latent cognitive capabilities that, with a quick and inexpensive rewiring of brain areas in a second phase of TNC, provided the basis for Homo sapiens specific abilities. The neural demands for perception of the human body in interaction with tools and the environment required highly integrated sensorimotor domains, inducing the parietal lobe expansion seen in humans. These newly expanded brain areas allowed connecting the sensations felt in the body to the actions in the world through the cognitive function of "projection". In this opinion article, we suggest that as a relationship of equivalence between body parts, tools and their external effects was established, mental mechanisms of self-objectification might have emerged as described previously, grounding notions of spatial organization, idealized objects, and their transformations, as well as socio-emotional states in the sensing agent through a self-in-the-world map. Therefore, human intelligence and its features such as symbolic thought, language, mentalizing, and complex technical and social behaviors could have stemmed from the explicit awareness of the causal relationship between the self and intentional modifications to the environment.

Evaluation of safety culture factors in the construction industry: a cross-country study of sites.

In the construction industry, most safety culture studies are limited to a single country, with minimal attention to cross-country studies. This limits creating a foundation for a robust framework and reliable safety culture scale. This study addresses this gap by studying safety culture in 10 countries, including those without previous studies. The survey instrument, completed by 311 construction employees, identified seven key factors measuring safety culture, with content and construct validity ensuring the reliability and validity of survey findings. Results indicated that work experience, education level and employment status have significant impacts on employees' safety culture. Additionally, similarities and differences in these factors across countries were investigated, and the fatalism and optimism factor and the work pressure and priority factor are the most significant contributors to the weakening of safety culture in the construction industry. This research allows industry practitioners to systematically assess on-site safety culture, oversee practices and improve.

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.

Generalized Linear Mixed Effects Modeling (GLMM) of Functional Analysis Graphical Construction Elements on Visual Analysis.

Multielement designs are the quintessential design tactic to evaluate outcomes of a functional analysis in applied behavior analysis. Protecting the credibility of the data collection, graphing, and visual analysis processes from a functional analysis increases the likelihood that optimal intervention decisions are made for individuals. Time-series graphs and visual analysis are the most prevalent method used to interpret functional analysis data. The current project included two principal aims. First, we tested whether the graphical construction manipulation of the x-to-y axes ratio (i.e., data points per x- axis to y-axis ratio [DPPXYR]) influenced visual analyst's detection of a function on 32 multielement design graphs displaying functional analyses. Second, we investigated the alignment between board certified behavior analysts (BCBAs; N = 59) visual analysis with the modified visual inspection criteria (Roane et al., Journal of Applied Behavior Analysis, 46, 130-146, 2013). We found that the crossed GLMM that included random slopes, random intercepts, and did not include an interaction effect (AIC = 1406.1, BIC = 1478.2) performed optimally. Second, alignment between BCBAs decisions and the MVI appeared to be low across data sets. We also leveraged current best practices in Open Science for raw data and analysis transparency.

Scene construction processes in the anterior hippocampus during temporal episodic memory retrieval.

Although the hippocampus has been implicated in both the temporal organization of memories and association of scene elements, some theoretical accounts posit that the role of the hippocampus in episodic memory is largely atemporal. In this study, we set out to explore this discrepancy by identifying hippocampal activity patterns related to scene construction while participants performed a temporal order memory task. Participants in the fMRI scanner were shown a sequence of photographs, each consisting of a central object and a contextual background scene. On each retrieval trial, participants were shown a pair of the original photographs (FULL), objects from the scenes without the background (OBJ), or background contexts without the main foreground object (BACK). In the temporal order judgment (TOJ) task, participants judged the temporal order of the pair of scenes; in the Viewing trials, two identical scenes were shown without any task. First, we found that the anterior hippocampus-particularly the CA1 and subiculum-showed similar patterns of activation between the BACK and OBJ conditions, suggesting that scene construction occurred spontaneously during both TOJ and Viewing. Furthermore, neural markers of scene construction in the anterior hippocampus did not apply to incorrect trials, showing that successful temporal memory retrieval was functionally linked to scene construction. In the cortex, time-processing areas, such as the supplementary motor area and the precuneus, and scene-processing areas, such as the parahippocampal cortex, were activated and functionally connected with the hippocampus. Together, these results support the view that the hippocampus is concurrently involved in scene construction and temporal organization of memory and propose a model of hippocampal episodic memory that takes both processes into account.

Exploring prognostic DNA methylation genes in bladder cancer: a comprehensive analysis.

The current study aimed to investigate the status of genes with prognostic DNA methylation sites in bladder cancer (BLCA). We obtained bulk transcriptome sequencing data, methylation data, and single-cell sequencing data of BLCA from public databases. Initially, Cox survival analysis was conducted for each methylation site, and genes with more than 10 methylation sites demonstrating prognostic significance were identified to form the BLCA prognostic methylation gene set. Subsequently, the intersection of marker genes associated with epithelial cells in single-cell sequencing analysis was obtained to acquire epithelial cell prognostic methylation genes. Utilizing ten machine learning algorithms for multiple combinations, we selected key genes (METRNL, SYT8, COL18A1, TAP1, MEST, AHNAK, RPP21, AKAP13, RNH1) based on the C-index from multiple validation sets. Single-factor and multi-factor Cox analyses were conducted incorporating clinical characteristics and model genes to identify independent prognostic factors (AHNAK, RNH1, TAP1, Age, and Stage) for constructing a Nomogram model, which was validated for its good diagnostic efficacy, prognostic prediction ability, and clinical decision-making benefits. Expression patterns of model genes varied among different clinical features. Seven immune cell infiltration prediction algorithms were used to assess the correlation between immune cell scores and Nomogram scores. Finally, drug sensitivity analysis of Nomogram model genes was conducted based on the CMap database, followed by molecular docking experiments. Our research offers a reference and theoretical basis for prognostic evaluation, drug selection, and understanding the impact of DNA methylation changes on the prognosis of BLCA.

A promoter-RBS library for fine-tuning gene expression in Methanosarcina acetivorans.

Methanogens are the main biological producers of methane on Earth. Methanosarcina acetivorans is one of the best characterized methanogens that has powerful genetic tools for genome editing. To study the physiology of this methanogen in further detail as well as to effectively balance the flux of their engineered metabolic pathways in expansive project undertakings, there is the need for controlled gene expression, which then requires the availability of well-characterized promoters and ribosome-binding sites (RBS). In this study, we constructed a library of 33 promoter-RBS combinations that includes 13 wild-type and 14 hybrid combinations, as well as six combination variants in which the 5'-untranslated region (5'UTR) was rationally engineered. The expression strength for each combination was calculated by inducing the expression of the ß-glucuronidase reporter gene in M. acetivorans cells in the presence of the two most used growth substrates, either methanol (MeOH) or trimethyl amine (TMA). In this study, the constructed library covers a relatively wide range (140-fold) between the weakest and strongest promoter-RBS combination as well as shows a steady increase and allows different levels of gene expression. Effects on the gene expression strength were also assessed by making measurements at three distinct growth phases for all 33 promoter-RBS combinations. Our promoter-RBS library is effective in enabling the fine-tuning of gene expression in M. acetivorans for physiological studies and the design of metabolic engineering projects that, e.g., aim for the biotechnological valorization of one-carbon compounds. IMPORTANCE: Methanogenic archaea are potent producers of the greenhouse gas methane and thus contribute substantially to global warming. Under controlled conditions, these microbes can catalyze the production of biogas, which is a renewable fuel, and might help counter global warming and its effects. Engineering the primary metabolism of Methanosarcina acetivorans to render it better and more useful requires controllable gene expression, yet only a few well-characterized promoters and RBSs are presently available. Our study rectifies this situation by providing a library of 33 different promoter-RBS combinations with a 140-fold dynamic range in expression strength. Future metabolic engineering projects can take advantage of this library by using these promoter-RBS combinations as an efficient and tunable gene expression system for M. acetivorans. Furthermore, the methodologies we developed in this study could also be utilized to construct promoter libraries for other types of methanogens.

Airborne particulate matter and diesel engine exhaust on infrastructure construction sites in the Copenhagen metropolitan area.

Diesel engine exhaust (DEE) is carcinogenic and potentially hazardous for those working in close proximity to diesel-powered machines. This study characterizes workplace exposure to DEE and its associated particulate matter (PM) during outdoor construction activities. We sampled at 4 construction sites in the Copenhagen metropolitan area. We used portable constant-flow pumps and quartz-fiber filters to quantify personal exposure to elemental carbon (EC), and used real-time instruments to collect activity-based information about particle number and size distribution, as well as black carbon (BC) concentration. Full-shift measurements of EC concentration ranged from < 0.3 to 6.4 µg/m3. Geometric mean (GM) EC exposure was highest for ground workers (3.4 µg/m3 EC; geometric standard deviation, GSD = 1.3), followed by drilling rig operators (2.6 µg/m3 EC; GSD = 1.4). Exposure for non-drilling-rig machine operators (1.2 µg/m3 EC; GSD = 2.9) did not differ significantly from background (0.9 µg/m3 EC; GSD = 1.7). The maximum 15-min moving average concentration of BC was 17 µg/m3, and the highest recorded peak concentration was 44 µg/m3. In numbers, the particle size distributions were dominated by ultrafine particles ascribed to DEE and occasional welding activities at the sites. The average total particle number concentrations (PNCs) measured in near-field and far-field positions across all worksites were 10,600 (GSD = 3.0) and 6,000 (GSD = 2.8)/cm3, respectively. Sites with active drilling rigs saw significantly higher average total PNCs at their near-field stations (13,600, 32,000, and 9,700/cm3; GSD = 2.4, 3.4, and 2.4) than sites without (4,700/cm3; GSD = 1.6). Overall, the DEE exposures at these outdoor construction sites were below current occupational exposure limits for EC (10 µg/m3 in Denmark; 50 µg/m3 in the European Union), but extended durations of exposure to the observed DEE levels may still be a health risk.

Circular Economy of Construction and Demolition Waste for Nanocomposite Cement: XRD, NMR, Vickers, Voltammetric and EIS Characterization.

In this paper, we present the structural, mechanical and electrical properties of composite cement materials that can be widely used as substituent for cement. We start with the characterization of a composite cement sample using an analysis of X-ray diffraction (XRD) and nuclear magnetic resonance (NMR) spectra. The measurements of the Vickers hardness, cyclic and sweep linear voltammetry and electrochemical impedance spectroscopy (EIS) of composite cement materials were also recorded. This study compared the effect of the different nanocomposites added to cement on the mitigation of the alkali-silica reaction, which is responsible for the swelling, cracking and deleterious behavior of the material. The enhancement in Vickers hardness was more pronounced for composite cement materials. In contrast, the values of Vickers hardness decreased for the composite cement containing mortar and the control sample, suggesting that the long-term performance of cement was compromised. In order to obtain information about the bulk resistance of the composite cement material, electrochemical impedance spectroscopy (EIS) data were employed. The results suggest that for composite cement materials, there is an improvement in bulk electrical resistance, which can be attributed to the lower amounts of cracks and swelling due to lower expansion. In the control sample, a reduction in the bulk resistance suggests the formation of microcracks, which cause the aging and degradation of the material. The intersection of arcs in the EIS spectrum of the mixed composite cement sample gradually increased by an alkaline exposure of up to 21 days and finally shifted towards a low value of high frequency with an increase in alkaline exposure of up to 28 days.

Robust construction of CeNi quantum dots/Ni-MOL nanosheets for superior photocatalytic CO2 reduction.

Since the intensification of global environmental pollution and energy shortages, photocatalytic CO2 reduction reaction (CO2RR) has emerged as a promising strategy to convert solar energy into clean chemical energy. Herein, we construct a robust and efficient heterojunction construction photocatalyst for CO2RR, composed of the highly reactive CeNi quantum dots (CeNi QDs) and nickel metal-organic layer (Ni-MOL) ultrathin nanosheets. This design facilitates the rapid separation of photogenerated charge carriers, as confirmed by X-ray photoelectron spectroscopy (XPS), photoluminescence spectroscopy (PL) and other characterizations. Mechanistic studies with in situ diffuse reflectance Fourier transform infrared spectroscopy (in situ DRIFTS) and the d-band center calculation indicate that the propensity of photocatalyst for CO2 absorption and CO desorption, leading to high performance and selectivity. The optimized loading amount of CeNi quantum dots and modified structure result in a CO yield of 30.53 mmol·g-1 within 6 h under irradiation. This work not only paves a new and convenient way for developing high-activity quantum dot materials for CO2RR but also exploits novel avenues to fabricate more heterojunction composites for solar energy conversion.

Pre-task planning for construction worker safety and health: Implementation and assessment.

BACKGROUND: Compared to other industry sectors, construction workers experience a disproportionately high rate of occupational injuries and fatalities. As research findings suggest, most of these incidents could be prevented if hazards were proactively recognized and properly addressed. In the construction industry, pre-task planning (PTP) is a preventive process intended to describe each step of work, identify associated safety and health hazards, and recommend controls to eliminate or mitigate the hazards before work begins. Despite its importance, the construction industry lacks comprehensive guidelines to design and implement PTP in a consistent and effective manner. To fill this gap, this study pursued two objectives: (1) identify shortcomings in current PTP practices and explore recommended solutions from practitioners' perspectives and (2) translate research findings into an applied tool to help practitioners assess and improve the quality of their PTP process. METHODS: To fill the gap, 28 construction safety and health professionals and 104 workers were interviewed, and seven onsite PTP sessions were directly observed. RESULTS: Shortcomings of current PTP practices as well as recommended solutions were categorized as (1) planning and implementation, (2) all-trades coordination, (3) engagement and buy-in, (4) training and logistics, (5) workforce diversity and the language barrier, and (6) PTP content accessibility. DISCUSSION/CONCLUSIONS: An effective PTP process should be based on workers' direct involvement and input on task requirements and hazards. It needs to be regularly updated to reflect the changing work conditions. In addition to task-related information, to increase workers' awareness, PTP should paint a holistic view of the project and other trades' scopes.

Optimizing compressive strength prediction using adversarial learning and hybrid regularization.

The infrastructure industry consumes natural resources and produces construction waste, which has a detrimental impact on the environment. To mitigate these adverse effects and reduce raw material consumption, waste materials can be repurposed to achieve sustainability. However, recycled materials deteriorate the intrinsic properties of concrete. A suitable ratio of natural resources and recycled aggregates can produce the desired compressive strength. Compiling sufficient data in civil engineering laboratories to make reliable conclusions is time-consuming and costly. Therefore, this research proposes a novel approach for predicting compressive strengths using limited data. The generative adversarial network was employed to generate synthetic data. Hybrid training, utilizing either conventional loss or heuristic loss, prevents the model from overfitting by adaptively adjusting the regularization term. Random noise from a multivariate normal distribution is embedded heuristically into the training samples to capture intricate data variations. Sensitivity analysis indicated that the size of recycled coarse aggregate and water are the most significant features, aligning with their correlations. Interestingly, superplasticizer, density of recycled coarse aggregate, and water absorption ratio of recycled coarse aggregate contributed significantly to predictions despite their low correlations. The propounded method outperforms random forest, support vector regression, artificial neural network, and adaptive boosting by scoring a mean squared error of 7.97, a root mean squared error of 2.82, a mean absolute error of 2.13, and a coefficient of determination of 0.96. These results suggest that the proposed technique can effectively contribute to sustainable construction practices by accurately predicting compressive strengths.

Lane Attribute Classification Based on Fine-Grained Description.

As an indispensable part of the vehicle environment perception task, road traffic marking detection plays a vital role in correctly understanding the current traffic situation. However, the existing traffic marking detection algorithms still have some limitations. Taking lane detection as an example, the current detection methods mainly focus on the location information detection of lane lines, and they only judge the overall attribute of each detected lane line instance, thus lacking more fine-grained dynamic detection of lane line attributes. In order to meet the needs of intelligent vehicles for the dynamic attribute detection of lane lines and more perfect road environment information in urban road environment, this paper constructs a fine-grained attribute detection method for lane lines, which uses pixel-level attribute sequence points to describe the complete attribute distribution of lane lines and then matches the detection results of the lane lines. Realizing the attribute judgment of different segment positions of lane instances is called the fine-grained attribute detection of lane lines (Lane-FGA). In addition, in view of the lack of annotation information in the current open-source lane data set, this paper constructs a lane data set with both lane instance information and fine-grained attribute information by combining manual annotation and intelligent annotation. At the same time, a cyclic iterative attribute inference algorithm is designed to solve the difficult problem of lane attribute labeling in areas without visual cues such as occlusion and damage. In the end, the average accuracy of the proposed algorithm reaches 97% on various types of lane attribute detection.

A Multi-Sensing IoT System for MiC Module Monitoring during Logistics and Operation Phases.

Modular integrated construction (MiC) is now widely adopted by industry and governments. However, its fragile and delicate logistics are still a concern for impeding project performance. MiC logistic operations involve rigorous multimode transportation, loading-unloading, and stacking during storage. Such processes may induce latent and intrinsic damage to the module. This damage causes safety hazards during assembly and deteriorates the module's structural health during the building use phase. Also, additional inspection and repairs before assembly cause uncertainties and can delay the whole supply chain. Therefore, continuous monitoring of the module's structural response during MiC logistics and the building use phase is vital. An IoT-based multi-sensing system is developed, integrating an accelerometer, gyroscope, and strain sensors to measure the module's structural response. The compact, portable, wireless sensing devices are designed to be easily installed on modules during the logistics and building use phases. The system is tested and calibrated to ensure its accuracy and efficiency. Then, a detailed field experiment is demonstrated to assess the damage, safety, and structural health during MiC logistic operations. The demonstrated damage assessment methods highlight the application for decision-makers to identify the module's structural condition before it arrives on site and proactively avoid any supply chain disruption. The developed sensing system is directly helpful for the industry in monitoring MiC logistics and module structural health during the use phase. The system enables the researchers to investigate and improve logistic strategies and module design by accessing detailed insights into the dynamics of MiC logistic operations.

Non-Destructive Testing for Documenting Properties of Structural Concrete for Reuse in New Buildings: A Review.

Reuse in new buildings of structural concrete components from demolitions holds the potential for avoiding the use of raw materials to produce new components, including cement for new castings. Reuse rates are high in the circular economy; however, reusing structural components requires documentation of the properties to equate the safety of using reused and new components. Yet, there is no structured or recognized way to perform the documentation. This paper discusses a framework for the documentation requirements for structural concrete, stating the need for documenting the mechanical properties, concrete heterogeneity, and corrosion status of the reinforcement. The possibility is explored for documenting the required properties while the components are in the donor building by use of non-destructive test (NDT) methods. Such use of NDT methods is new. A comprehensive literature survey on the indirect literature, where NDT methods are used to demonstrate similar concrete properties though related to other purposes, is conducted. The overall conclusion is that the use of NDT methods has the potential to document the requested properties before reuse. The next steps towards implementation of NDT for documenting the properties of structural concrete components for reuse involve research in combined NDT methods and the development of AI systems for data interpretation.