Search Results (23)

Effective asset management in the architecture, engineering, and construction/facilities management (AEC/FM) industry is crucial for improving asset performance and lifespan, as well as reducing downtimes and maintenance costs. Current asset management practices mostly rely on outdated paper-based approaches that are prone to data loss, security attacks, and missing information. Emerging technologies, such as digital twins, are being proposed to solve existing asset management problems in the AEC industry. However, the industry perspective is often missing in the evaluation of such technology-led approaches regarding actual applications and implementation challenges. This study seeks to understand the potential of digital twins in solving current asset management issues and challenges within the United Arab Emirates (UAE) context. To achieve this aim, structured interviews were conducted with 14 industry experts to capture their understanding of current digital technologies and existing issues in asset management. The findings of this study underscore the transformative potential of digital twins as a tool for optimizing asset performance and decision-making throughout the asset lifecycle. Full article

The effects of buildings on the environment can be reduced with research-based alternative building designs. This study focuses on reducing the building space to lower the overall size of a building as a strategy to reduce the building’s embodied energy. The aim of this study was to investigate the initial embodied energy (IEE) of a residential building that was systematically reduced in size. Using input–output-based hybrid analysis, the IEE for three architecturally distinct four-bedroom residential prototypes (P1, P2, and P3) was calculated. The IEE for P1 (525 m²), P2 (266 m²), and P3 (109 m²) were 3555, 2008, and 1000 GJ, respectively. This indicates a 72% reduction in embodied energy consumption when the largest prototype (P1) was transitioned to the smallest (P3). When analyzing IEE/m² and IEE/m²/occupant, it becomes apparent that larger spaces tend to have a lower IEE/m². However, when the occupancy increases, the IEE/m²/occupant decreases by 25–33%. Therefore, considering occupant-centered design for residential buildings, the benefits of a large house are not justifiable. These findings can help inform decisions regarding the optimization of residential spaces to minimize environmental impacts. Full article

Ensuring global food security amid mounting challenges, such as population growth, disease infestations, resource limitations, and climate change, is a pressing concern. Anticipated increases in food demand add further complexity to this critical issue. Plant pathogens, responsible for substantial crop losses (up to 41%) in major crops like wheat, rice, maize, soybean, and potato, exacerbate the situation. Timely disease detection is crucial, yet current practices often identify diseases at advanced stages, leading to severe infestations. To address this, remote sensing and Hyperspectral imaging (HSI) have emerged as robust and nondestructive techniques, exhibiting promising results in early disease identification. Integrating machine learning algorithms with image data sets enables precise spatial–temporal disease identification, facilitating timely detection, predictive modeling, and effective disease management without compromising fitness or climate adaptability. By harnessing these cutting-edge technologies and data-driven decision-making, growers can optimize input costs while achieving enhanced yields, making significant strides toward global food security in the face of climate change risks. This review will discuss some of the foundational concepts of remote sensing, several platforms used for remote sensing data collection, successful application of the approach, and its future perspective. Full article

Traditional teaching methods in construction management education often face challenges in providing students with practical, real-world experiences crucial for skill development. To address these limitations, this study explores the potential of integrating building information modeling (BIM) and virtual reality (VR) as educational tools for construction management students. Our aim is to assess the effectiveness of a 4D-BIM-based VR simulation in enhancing student’s learning experiences and performance in construction project management. This research employs a mixed-method approach, combining quantitative data and qualitative insights from a comparative experiment involving undergraduate students. Quantitative data were collected through objective error detection measures in construction sequences and processes, while qualitative insights were gathered from participant feedback. The findings highlight that students using VR-based simulations detected more errors in construction sequences and processes than in traditional 2D drawings, showcasing the utility of BIM and VR-enabled approaches in teaching construction management. This study contributes to the ongoing discourse on integrating advanced technologies into educational practices, particularly in construction management, where practical hands-on experiences are crucial for skill development and real-world application. Full article

The present study was designed to investigate and compare the effects of plant-derived smoke (PDS) and auxin (IAA and IBA) on maize growth under the application of 2,3,5-triiodo benzoic acid (TIBA). For this purpose, indole-3-acetic acid (IAA) and indole-3-butyric acid (IBA), each at a concentration of 10 ppm, along with PDS at a ratio of 1:500 (v/v) were used alone and in combination with 10 ppm of TIBA. The results indicate that the germination percentage (%) of maize seeds was enhanced under IAA, IBA and PDS treatment. However, IAA and IBA resulted in reduced germination when applied in combination with TIBA. Importantly, the germination percentage (%) was improved by PDS under TIBA treatment. The analysis of seedling height, length of leaves, and number of primary, seminal and secondary/lateral roots showed improvement under individual treatments of IAA and IBA, PDS and PDS + TIBA treatment, while these values were reduced under IAA + TIBA and IBA + TIBA application. Chlorophyll content, total soluble sugars and antioxidative enzymatic activity including POD and SOD increased in seedlings treated with PDS alone or both PDS and TIBA, while in seedlings treated with IAA and TIBA or IBA and TIBA, their levels were decreased. APX and CAT responded in the opposite way—under IAA, IBA and PDS treatment, their levels were found to be lower than the control (simple water treatment), while TIBA treatment with either IAA, IBA or PDS enhanced their levels as compared to the control. These results reveal that PDS has the potential to alleviate the inhibitory effects of TIBA. This study highlights the role of PDS in preventing TIBA from blocking the auxin entry sites. Full article

The present study was conducted to fabricate and characterize mucilage-based polymeric networks of Aloe vera for controlled drug release. Aloe vera mucilage was used to develop a polymeric network via the free-radical polymerization method using potassium persulphate as the initiator, N′ N′-Methylene bisacrylamide as the crosslinker, and acrylamide as the monomer. Using varying concentrations of Aloe vera mucilage, crosslinker, and monomer, we developed different formulations. Swelling studies were conducted at pH 1.2 and 7.4. Concentrations of polymer, monomer, and crosslinker were optimized as a function of swelling. Porosity and gel content were calculated for all samples. FTIR, SEM, XRD, TGA, and DSC studies were conducted for the characterization of polymeric networks. Thiocolchicoside was used as a model drug to study the in vitro release in acidic and alkaline pH. Various kinetics models were applied by using a DD solver. Increasing content of monomer and crosslinker swelling, porosity, and drug release decreased while gel content increased. An increase in Aloe vera mucilage concentration promotes swelling, porosity, and drug release of the polymeric network but decreases gel content. The FTIR study confirmed the formation of crosslinked networks. SEM indicated that the polymeric network had a porous structure. DSC and XRD studies indicated the entrapment of drugs inside the polymeric networks in amorphous form. The analytical method was validated according to ICH guidelines in terms of linearity, range, LOD, LOQ, accuracy, precision, and robustness. Analysis of drug release mechanism revealed Fickian behavior of all formulations. All these results indicated that the M1 formulation was considered to be the best polymeric network formulation in terms of sustaining drug release patterns. Full article

The emergence of immersive technologies, such as virtual reality (VR) headsets, has revolutionized the way we experience the physical world by creating a virtual, interactive environment. In the field of education, this technology has immense potential to provide students with a safe and controlled environment in which to experience real-world scenarios that may be otherwise unfeasible or unsafe. However, limited research exists on the effectiveness of integrating immersive technologies into technical education delivery. This research investigated the potential use of immersive virtual reality (IVR) in university-level construction management courses, with a focus on integrating IVR technology into traditional education for construction project planning and control. The experiment involved comparing the students’ learning and understanding of the subject matter using a set of two-dimensional construction drawings and a critical path method (CPM)-based construction schedule, with and without the use of an immersive environment. The findings suggested that the use of immersive technology significantly improved the students’ ability to understand technical concepts and identify any errors in the construction sequence when compared to traditional teaching methods. This paper presents the details of the experiment and a comparative analysis of both approaches in terms of students’ learning and understanding of project planning, sequencing, and scheduling. Full article

Zero-valent iron nanoparticles (ZVI-NPs) are utilized for the indemnification of a wide range of environmental pollutants. Among the pollutants, heavy metal contamination is the major environmental concern due to their increasing prevalence and durability. In this study, heavy metal remediation capabilities are determined by the green synthesis of ZVI-NPs using aqueous seed extract of Nigella sativa which is a convenient, environmentally friendly, efficient, and cost-effective technique. The seed extract of Nigella sativa was utilized as a capping and reducing agent for the generation of ZVI-NPs. UV-visible spectrophotometry (UV-vis), scanning electron microscopy coupled with energy-dispersive X-ray spectroscopy (SEM-EDX), and Fourier transform infrared spectroscopy (FTIR) was used to investigate the ZVI-NP composition, shape, elemental constitution, and perspective functional groups, respectively. The biosynthesized ZVI-NPs displayed a peak of plasmon resonance spectra at 340 nm. The synthesized NPs were cylindrical in shape, with a size of 2 nm and (-OH) hydroxyl, (C-H) alkanes and alkynes N-C, N=C, C-O, =CH functional groups attached to the surface of ZVI-NPs. Heavy metals were successfully remediated from industrial wastewater collected from the various tanneries of Kasur. During the reaction duration of 24 h, different concentrations of ZVI-NPs (10 μg, 20 μg and 30 μg) per 100 mL were utilized for the removal of heavy metals from industrial wastewater. The 30 μg/100 mL of ZVI-NPs proved the pre-eminent concentration of NPs as it removed >90% of heavy metals. The synthesized ZVI-NPs were analyzed for compatibility with the biological system resulting in 87.7% free radical scavenging, 96.16% inhibition of protein denaturation, 60.29% and 46.13% anti-cancerism against U87-MG and HEK 293 cell lines, respectively. The physiochemical and exposure mathematical models of ZVI-NPs represented them as stable and ecofriendly NPs. It proved that biologically synthesized NPs from a seed tincture of Nigella sativa have a strong potential to indemnify heavy metals found in industrial effluent samples. Full article

Alzheimer’s disease (AD) is a global health issue that predominantly affects older people. It affects one’s daily activities by modifying neural networks in the brain. AD is categorized by the death of neurons, the creation of amyloid plaques, and the development of neurofibrillary tangles. In clinical settings, an early diagnosis of AD is critical to limit the problems associated with it and can be accomplished using neuroimaging modalities, such as magnetic resonance imaging (MRI) and positron emission tomography (PET). Deep learning (DL) techniques are widely used in computer vision and related disciplines for various tasks such as classification, segmentation, detection, etc. CNN is a sort of DL architecture, which is normally useful to categorize and extract data in the spatial and frequency domains for image-based applications. Batch normalization and dropout are commonly deployed elements of modern CNN architectures. Due to the internal covariance shift between batch normalization and dropout, the models perform sub-optimally under diverse scenarios. This study looks at the influence of disharmony between batch normalization and dropout techniques on the early diagnosis of AD. We looked at three different scenarios: (1) no dropout but batch normalization, (2) a single dropout layer in the network right before the softmax layer, and (3) a convolutional layer between a dropout layer and a batch normalization layer. We investigated three binaries: mild cognitive impairment (MCI) vs. normal control (NC), AD vs. NC, AD vs. MCI, one multiclass AD vs. NC vs. MCI classification problem using PET modality, as well as one binary AD vs. NC classification problem using MRI modality. In comparison to using a large value of dropout, our findings suggest that using little or none at all leads to better-performing designs. Full article

Faults in the power system affect the reliability, safety, and stability. Power-distribution systems are familiar with the different faults that can damage the overall performance of the entire system, from which they need to be effectively cleared. Underground power systems are more complex and require extra accuracy in fault detection and location for optimum fault management. Slow processing and the unavailability of a protection zone for relay coordination are concerns in fault detection and location, as these reduce the performance of power-protection systems. In this regard, this article proposes an optimized solution for a fault detection and location framework for underground cables based on a discrete wavelet transform (DWT). The proposed model supports area detection, the identification of faulty sections, and fault location. To overcome the abovementioned facts, we optimize the relay coordination for the overcurrent and timing relays. The proposed protection zone has two sequential stages for the current and time at which it optimizes the current and time settings of the connected relays through Newton–Raphson analysis (NRA). Moreover, the traveling times for the DWT are modeled, which relate to the protection zone provided by the relay coordination, and the faulty line that is identified as the relay protection is not overlapped. The model was tested for $132 \mathrm{kV}/$11 $\mathrm{kV}$ and 16-node networks for underground cables, and the obtained results show that the proposed model can detect and locate the cable’s faults speedily, as it detects the fault in 0.01 s, and at the accurate location. MATLAB/Simulink (DigSILENT Toolbox) is used to establish the underground network for fault location and detection. Full article

Bionanotechnology is the combination of biotechnology and nanotechnology for the development of biosynthetic and environmentally friendly nanomaterial synthesis technology. The presented research work adopted a reliable and environmentally sustainable approach to manufacturing silver nanoparticles from Brachychiton populneus (BP-AgNPs) leaf extract in aqueous medium. The Brachychiton populneus-derived silver nanoparticles were characterized by UV–Vis spectroscopy, Fourier-transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), and energy dispersive X-ray analysis (EDX). In addition, the antioxidant, anti-inflammatory, antidiabetic, and cytotoxic activities of AgNPs were brought to light. The synthesis of BP-AgNPs was verified at 453 nm wavelength by UV–Vis spectrum. FTIR analysis revealed that synthesis, stability, and capping of AgNPs depend on functional groups such as alkane, alkene, nitro, flouro, phenol, alcoholic, and flavones, present in plant extract. The SEM analysis revealed evenly distributed cubical-shaped nanoparticles. The average diameter of AgNPs was 12 nm calculated from SEM image through ImageJ software. EDX spectrum confirmed the presence of Ag at 3 keV and other trace elements such as oxygen and chlorine. The biosynthesized silver nanoparticles exhibited proven antioxidant (DPPH assay), antidiabetic (alpha amylase assay), anti-inflammatory (albumin denaturation assay), and cytotoxic (MTT assay) potential against U87 and HEK293 cell lines in comparison to standard drugs. In these assays, BP-AgNPs exhibited inhibition in a concentration-dependent manner and had lower IC₅₀ values compared to standards. All these outcomes suggest that silver nanoparticles work as a beneficial biological agent. The salient features of biosynthesized silver nanoparticles propose their effective applications in the biomedical domain in the future. Full article

The progress monitoring (PM) of construction projects is an essential aspect of project control that enables the stakeholders to make timely decisions to ensure successful project delivery, but ongoing practices are largely manual and document-centric. However, the integration of technologically advanced tools into construction practices has shown the potential to automate construction PM (CPM) using real-time data collection, analysis, and visualization for effective and timely decision making. In this study, we assess the level of automation achieved through various methods that enable automated computer vision (CV)-based CPM. A detailed literature review is presented, discussing the complete process of CV-based CPM based on the research conducted between 2011 and 2021. The CV-based CPM process comprises four sub-processes: data acquisition, information retrieval, progress estimation, and output visualization. Most techniques encompassing these sub-processes require human intervention to perform the desired tasks, and the inter-connectivity among them is absent. We conclude that CV-based CPM research is centric on resolving technical feasibility studies using image-based processing of site data, which are still experimental and lack connectivity to its applications for construction management. This review highlighted the most efficient techniques involved in the CV-based CPM and accentuated the need for the inter-connectivity between sub-processes for an effective alternative to traditional practices. Full article

Tribulus terrestris L. belongs to the family Zygophyllaceae and integral part of various ancient medicinal systems including Chinese, Indian, and European to combat various health ailments. The aim of the present study was to assess the phytochemical constituents, in vitro antioxidant activity using DPPH, FRAP, and H₂O₂ assays, in vitro anticancer activity using MTT assay, and in vitro and in vivo anti-inflammatory properties of T. terrestris. The acute and sub-acute toxicity of extracts exhibiting most biological potential was examined using murine models. Liquid–liquid partitioning followed by RP–HPLC sub-fraction of crude extract was performed. After that, ESI-MS/MS analysis was done for the timid identification of bioactive metabolites responsible for bioactivities of sub-fractions and HPLC analysis to quantify the compounds using external standards. Among all extracts, T. terrestris methanol extract was noted to hold maximum phenolic (341.3 mg GAE/g) and flavonoid (209 mg QE/g) contents, antioxidant activity in DPPH (IC₅₀ 71.4 µg/mL), FRAP (35.3 mmol/g), and H₂O₂ (65.3% inhibition) assays, anti-inflammatory activities in vitro at 400 µg/mL (heat-induced hemolysis, % inhibition 68.5; egg albumin denaturation, % inhibition 75.6%; serum albumin denaturation, % inhibition 80.2), and in vivo at 200 mg/kg (carrageenan-induced paw edema, % inhibition 69.3%; formaldehyde-induced paw edema, % inhibition 71.3%) and anticancer activity against breast cancer cell (MCF-7) proliferation (IC₅₀ 74.1 µg/mL). Acute and sub-acute toxicity studies recorded with no change in body weight, behavior, hematological, serum, and histopathological parameters in treated rats with T. terrestris methanol extracts when compared to control group. Fraction B obtained through liquid–liquid partitioning resulted in more bioactive potential as compared to the parent methanol extract. RP–HPLC analysis of fraction B resulted with four sub-fractions (TBTMF1-TBTMF4), wherein TBTMF3 delineated notable bioactive capabilities as compared to other fractions and parent methanol extract. ESI-MS/MS analysis of TBTMF3 resulted with tentative identification of myricetin, rutin, liquitrigenin, physcion, and protodioscin. It can be stated that T. terrestris is a potential bearing herb and findings of current study further verify the claims made in ancient medicinal systems. However, after investigation of each identified compound, it must be considered for drug discovery. Full article

This study aimed to realize Sustainable Development Goals (SDGs), i.e., no poverty, zero hunger, and sustainable cities and communities through the implementation of an intelligent cattle-monitoring system to enhance dairy production. Livestock industries in developing countries lack the technology that can directly impact meat and dairy products, where human resources are a major factor. This study proposed a novel, cost-effective, smart dairy-monitoring system by implementing intelligent wireless sensor nodes, the Internet of Things (IoT), and a Node-Micro controller Unit (Node-MCU). The proposed system comprises three modules, including an intelligent environmental parameter regularization system, a cow collar (equipped with a temperature sensor, a GPS module to locate the animal, and a stethoscope to update the heart rate), and an automatic water-filling unit for drinking water. Furthermore, a novel IoT-based front end has been developed to take data from prescribed modules and maintain a separate database for further analysis. The presented Wireless Sensor Nodes (WSNs) can intelligently determine the case of any instability in environmental parameters. Moreover, the cow collar is designed to obtain precise values of the temperature, heart rate, and accurate location of the animal. Additionally, auto-notification to the concerned party is a valuable addition developed in the cow collar design. It employed a plug-and-play design to provide ease in implementation. Moreover, automation reduces human intervention, hence labor costs are decreased when a farm has hundreds of animals. The proposed system also increases the production of dairy and meat products by improving animal health via the regularization of the environment and automated food and watering. The current study represents a comprehensive comparative analysis of the proposed implementation with the existing systems that validate the novelty of this work. This implementation can be further stretched for other applications, i.e., smart monitoring of zoo animals and poultry. Full article