Deterrents to the IoT for smart buildings and infrastructure development: A partial least square modeling approach.

Implementing Internet of things (IoT) technology in the context of intelligent buildings and infrastructure development has garnered significant attention within the construction sector. Nonetheless, the implementation of IoT could be improved by assessing various barriers. The purpose of this study was to examine the obstacles related to the adaptation of IoT techniques within the construction sector, as well as the effects on the advancement of intelligent building and infrastructure systems. The study employed a mixed-method approach involving exploratory factor analysis (EFA) and structural equation modeling (SEM) to determine six types of barriers: knowledge, technical, standardization, creativity, complexity, and economics. The study revealed that the implementation of IoT for developing smart construction and infrastructure in the construction sector was significantly influenced by all six constructs. The results of this study offer significant ramifications for the field. The study underscores the necessity for heightened consciousness and instruction regarding the advantages of implementing IoT. The study posits that the technical barriers, including interoperability, modernization of legacy infrastructure, and coordination and collaboration difficulties, require attention from the industry. The study highlights the significance of establishing industry-wide standards and protocols for implementing IoT and regulatory and legal frameworks. Finally, the study underscores the necessity for augmented funding and financing options for IoT endeavors. Subsequent study endeavors may expand upon the present findings by delving into the barriers encountered by alternative sectors and nations and assessing the efficacy of the suggested measures in this investigation.

Decoding seasonal variability of air pollutants with climate factors: A geostatistical approach using multimodal regression models for informed climate change mitigation.

In response to changes in climatic patterns, a profound comprehension of air pollutants (AP) variability is vital for enhancing climate models and facilitating informed decision-making in nations susceptible to climate change. Earlier research primarily depended on limited models, potentially neglecting intricate relationships and not fully encapsulating associations. This study, in contrast, probed the spatiotemporal variability of airborne particles (CO, CH4, SO2, and NO2) under varying climatic conditions within a climate-sensitive nation, utilizing multiple regression models. Spatial and seasonal AP data were acquired via the Google Earth Engine platform, which indicated elevated AP concentrations in primarily urban areas. Remarkably, the average airborne particle levels were lower in 2020 than in 2019, though they escalated during winter. The study employed linear regression, Pearson's correlation (PC), Spearman rank correlation models, and Geographically Weighted Regression (GWR) models to probe the relationship between pollutant variability and climatic elements such as rainfall, temperature, and humidity. Across all seasons, APs showed a negative correlation with rainfall while displaying positive correlations with temperature and humidity. The GWR and PC models produced the most reliable results from all the models employed, with the GWR model superseding the rest. Moreover, heightened aerosol levels were detected within a rainfall range of 600 mm/season, a temperature range of 25-30 °C, and humidity levels of 75 %-85 %. Overall, this study emphasizes the growing levels of APs in correlation with meteorological changes. By adopting a comprehensive approach and considering multiple factors, this research provides a more sophisticated understanding of the relationship between AP variability and climatic shifts.

A study on the strength and durability characteristics of fiber-reinforced recycled aggregate concrete modified with supplementary cementitious material.

Recycled aggregate (RA) made from waste concrete is an environmentally friendly alternative to natural aggregate (NA) for concrete manufacturing. However, compared to NA concrete, concrete produced with recycled aggregates has poor characteristics. Supplementary cementitious materials (SCMs) can be used to enhance the poor properties of recycled aggregate concrete (RAC). Silica fume and fly ash are commonly used SCMs in the World, but their high usage led to a shortage of silica fume and fly ash. Still, the deficiency of these materials in large parts of the world is a challenge that requires exploring alternative feedstock materials for the construction industry in the coming years. Wheat straw ash (WSA) is an agricultural waste product that could be used as an alternative SCM due to its pozzolanic behavior to enhance the properties of RAC. In addition, concrete is brittle and needs reinforcement, for which polypropylene fibers (PPFs) can be used. The current research examines the mechanical characteristics of fiber-reinforced RAC, including compressive strength, splitting tensile strength, and ductility performance. Durability indicators, such as chloride diffusion, chloride penetration, acid resistance, and water absorption test, were also assessed. The results showed that concrete samples with 10% WSA, 50% RA and 1.5% PPFs had the highest compressive and splitting tensile strength, 60.2 MPa and 7.25 MPa, respectively, representing increases of 24.75% and 30.65%, as compared to plain samples at 56 days. In these samples, water absorption was reduced by 13% due to the finer WSA particles resulting in the lowest reduction in strength and mass recorded when exposing concrete samples to acidic media. The statistical analysis also validated that irrespective of WSA and PPFs, the concrete with 0% RA had the highest performance in strength and durability behavior. The study showed that WSA and PPFs might be employed in tandem to offset the poor behavior of RA, enhance the bond between fibers and concrete, and improve the mechanical strength and durability performance of RAC, thus demonstrating its suitability as a sustainable and economical construction material.

Antecedents for online food delivery platform leading to continuance usage intention via e-word-of-mouth review adoption.

The focus of hospitality initially was on ambience and novelty to attract customers. With the rise of the digital revolution, the hospitality industry has also undergone significant change. Long-distance travel at the workplace, odd working hours, and a variety of food options have driven people staying in Indian metropolises towards online food delivery (OFD) services. The popularity of OFD services has risen because of their practicality, simplicity, and a rise in consumer confidence in digital payments. Specifically, for the food industry, digitalization has opened new horizons to capture customers. The competition is not among the big brands, but big brands are competing with homemakers who run tiffin services, and street food hawkers who claim to provide traditional Dhaba-style food and fast food. The customers are loaded with unlimited options to choose the food in terms of price, cuisine, quality, etc. The present research examines the associations between service quality of OFD services, perceived ease of use, and word-of-mouth review adoption, leading to expectation confirmation modeling. The path analysis was carried out using data from 500 Indian respondents residing in Tier-I cities who have been using OFD services regularly. The research outcome shows that servqual has a positive influence on perceived ease of use and confirmation. Additionally, it encourages continued usage intentions because of its favorable impact on the adoption of e-word-of-mouth reviews.

Determining engineering properties of ultra-high-performance fiber-reinforced geopolymer concrete modified with different waste materials.

Reprocessing solid waste materials is a low-cost method of preserving the environment, conserving natural resources, and reducing raw material consumption. Developing ultra-high-performance concrete materials requires an immense quantity of natural raw materials. The current study seeks to tackle this issue by evaluating the effect of various discarded materials, waste glass (GW), marble waste (MW), and waste rubber powder (WRP) as a partial replacement of fine aggregates on the engineering properties of sustainable ultra-high-performance fiber-reinforced geopolymer concrete (UHPGPC). Ten different mixtures were developed as a partial substitute for fine aggregate, each containing 2% double-hooked end steel fibers, 5%, 10%, and 15% GW, MW, and WRP. The present study assessed the fresh, mechanical, and durability properties of UHPGPC. In addition, to evaluate concrete development at the microscopic level due to the addition of GW, MW, and WRP. Spectra of X-ray diffraction (XRD), thermogravimetric analysis (TGA), and mercury intrusion (MIP) tests were conducted. The test results were compared to current trends and procedures identified in the literature. According to the study, adding 15% marble waste and 15% waste rubber powder reduced ultra-high-performance geopolymer concrete's strength, durability, and microstructure properties. Even so, adding glass waste improved the properties, as the sample with 15% GW had the highest compressive strength of 179 MPa after 90 days. Furthermore, incorporating glass waste into the UHPGPC resulted in a good reaction between the geopolymerization gel and the waste glass particles, enhancing strength properties and a packed microstructure. The inclusion of glass waste in the mix resulted in the control of crystal-shaped humps of quartz and calcite, according to XRD spectra. During the TGA analysis, the UHPGPC with 15% glass waste had the minimum weight loss (5.64%) compared to other modified samples.

Experimental study on the properties of ultra-high-strength geopolymer concrete with polypropylene fibers and nano-silica.

Because of the recent progress in materials properties, specifically high-strength concrete, further research is needed to evaluate its suitability, understanding, and performance in the modern-day world. This research aims to enhance the performance of ultra-high-strength geopolymer concrete (UHS-GPC) by adding nano-silica (NS) and polypropylene fibers (PPFs). Three 1%, 2%, and 3% different amounts of PPFs and three NS 5%, 10%, and 15% were utilized in the samples. Various performance parameters of UHS-GPC were evaluated, such as fresh property, compressive strength, modulus of elasticity split tensile, flexural and bonding strength, drying shrinkage, load-displacement test, fracture performance, and elevated temperature. The test outcomes showed that by raising the percentage of PPFs and NS to the allowable limit, the performance of UHS-GPC can be improved significantly. The most improved performance of UHS-GPC was obtained at 2% polypropylene fibers and 10% nano-silica, as the compressive, splitting tensile, flexural. Bond strength was improved by 17.07%, 47.1%, 36.52, and 37.58%, and the modulus of elasticity increased by 31.4% at 56 days. The study showed that the sample with 2% PPFs and 10% NS had excellent performance in the load-displacement test, drying shrinkage, fracture behavior, and elevated temperature. At 750°C elevated temperature, the samples' strength was reduced drastically, but at 250°C, the modified samples showed good resistance to heat by retaining their compressive strength to some degree. The present work showed the suitability of PPFs and NS to develop ultra-high-strength geopolymer concrete, which can be used as a possible alternate material for Portland cement-based concrete.

Prediction of implementing ISO 14031 guidelines using a multilayer perceptron neural network approach.

The purpose of this study was to model the link between the implementation of ISO 14031 and ISO 14001. This study connects ISO 14031's guidelines as independent variables to a dependent variable expressed by the ISO 14001 certification situation of industrial organizations based on the judgments of environmental managers in Saudi Arabia. Applying the quantitative approach using a survey with 596 responses from organizations functioning in 30 economic activities, a multi-layered neural network was trained to examine the relationship between standards and predict whether the organization is ISO 14001 certified in addition to testing the developed network on a group of collected cases. The results demonstrated the ability of the network to classify the organization's certification status by 94.00% according to the training sample and its ability to predict 91.00% of the test sample, with an overall prediction efficiency of 91.30%. This work provides insights and adds to the environmental performance evaluation literature providing a neural network model based on ISO 14031 guidelines that can be extended to include other international standards such as ISO 9001. This study supports the merging of ISO 14001 with ISO 14031 into a binding standard.