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Women’s Religious Agency and the Positioning of the Mosque: a Case Study of State-Sponsored Female Preaching in Egypt
No full textThis paper captures women’s religious agency and their bonding with the mosque by taking a snapshot of the discourse and experiences of female preachers, appointed by the Egyptian Ministry of Endowments, who were confronted with the closure of mosques within the outbreak of the covid-19 pandemic. Though these female preachers have managed to perform their preaching roles while being detached from the mosque, their spiritual affinity to the mosque could not escape notice. This paper argues that the detachment of the female preachers from the mosque due to covid-19 offers a novel conceptualization of ‘religious’ agency that could be partially ascribed to their attachment to the mosque, not as a locale for their ‘official’ or ‘semi-official’ affiliation with the state, but as a ‘sacred’ extension of the private space of the home
Enhancing Sustainable Development Goals Through Future Vapor Pressure Deficit Analysis in the Nile River Basin
No full textVapor Pressure Deficit (VPD) is crucial in meteorology and agriculture for understanding plant-environment interactions. Its application as an indicator in agricultural practices notably advances Sustainable Development Goals such as Zero Hunger (SDG 2) and Climate Action (SDG 13). This research focuses on the impact of climate change on agricultural productivity and food security in the Nile River Basin (NRB), emphasizing the role of VPD, temperature, and precipitation. Utilizing Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets from NEX-GDDP-CMIP6, the study analyzes key climatic variables that influence agricultural conditions. The study applies the Mann-Kendall test to evaluate VPD trends from 2000 to 2060 under two Shared Socioeconomic Pathways (SSPs), SSP2-4.5 and SSP5-8.5. The study\u27s findings on the implications of rising VPD levels in the Nile River Basin (NRB), particularly under the SSP 5-8.5 scenario, highlight a critical challenge for the region\u27s agricultural productivity and food security. The increased VPD, indicative of drier conditions, leads to a moisture deficit for crops, potentially reducing agricultural yields. This scenario poses a significant threat to food security, as lower crop yields can result in food shortages and higher food prices, adversely affecting vulnerable populations. The study underscores the necessity of integrating VPD insights into agricultural and water resource management strategies to uphold food security against climatic variations in support of the SDGs
Exploring Polyaniline Nanofilaments for Enhanced Optical Recognition of Lead in Water: An Integrated Approach of Experimental and Theoretical Studies
No full textIn this study, the behavior of lead adsorbed onto polyaniline composite was examined theoretically with the DFT method and experimentally using spectrophotometry. The synthesis and characterization of polyaniline nanofilaments (PANI) were executed. Density functional theory and Becke\u27s three-parameter exchange functional approach were employed for quantum mechanical calculations of geometry and energy. The 6.311G** basis set and the Lee-Yang-Parr correlation functional method (B3LYP/DFT) were used in a water solution environment to complement the experimental data. Experimental results were visualized using 3D molecular electrostatic potential maps (MEP), which aided in the determination and explanation of various properties, including mean polarizability, total static dipole moment, anisotropy of polarizability, and mean first-order hyperpolarizability. The findings indicate that Pb-PANI-EB shows promise as a potential material for non-linear optical (NLO) applications. PANI demonstrate efficacy as a sensor capable of detecting Pb concentrations as low as 0.05 ppm. These results justify further exploration of the use of PANI in the development of a fast, economical, robust, and highly sensitive lead (Pb) sensor
Construction Lean Scoring and Benchmarking System
No full textThe construction industry is known to have several inadequacies leading to cost and schedule overruns. One of the popular methods that attempts to eliminate these inadequacies is lean construction, which is a set of principles and tools that aim to maximize value, eliminate waste and optimize efficiency. The success of lean construction depends on several factors. In other words, implementing lean construction tools does not guarantee reduction in cost and time overruns. There is a gap when it comes to identifying the factors that support the success/failure of implementing lean construction tools. In addition, the literature lacks a scoring system for measuring lean implementation. The goal of this research is to fill the abovementioned gap through developing and benchmarking a scoring system that utilizes lean principles to evaluate the “leanness” of construction projects. To this end, the authors: (1) identified the key factors that influence the leanness of construction projects; (2) determined the relative importance of the identified factors through an expert-based survey; (3) developed a scoring system called “the construction leanness score” for measuring lean implementation; and (4) benchmarked the leanness score representing the industry’s performance through collecting data from 30 construction projects. Results indicate that there are 27 key lean factors affecting the efficiency of lean implementation with the top two factors being early involvement of key stakeholders and trust between parties. The developed leanness score is considered the first of its kind to link leanness factors to project performance. Also, the developed benchmarking scale enables companies to compare their level of leanness to that of other companies in the industry. With this, companies are able to benchmark their performance, pinpoint the areas of weaknesses and take necessary actions to meet industry practices. Thus, improving the overall quality of construction projects, decreasing overruns
Visual Stress Grading Automation Using Image Processing and Segmentation Analysis
No full textThe variability in wood mechanical properties is one of the concerning factors when considering timber in structural applications. This variability is influenced by the presence of visible defects such as knots, grain deviations, and splits. Multiple models were developed to predict the mechanical performance of timber by means of visual stress grading. Stress modification factors are determined according to the frequency of knot sizes and slope of the grain within a certain stress grade of a wood species, to be applied on clear wood strength values for that species. The development of stress grades requires large surveys of knot properties and distribution within a timber species, where knot sizes on nominal dimension lumber faces are measured to develop knot data, and these data are used to determine the average sum of knot sizes in 1-foot lengths taken at 2-inch intervals on each timber board. According to the American standards, to develop a stress grade, physical mapping and measurements of knot data for at least 1000 linear foot of lumber should be done. Such exhaustive and time-consuming process can be automated by state-of-the-art computer vision and segmentation analysis techniques. Images are captured for pieces of lumber, and image adjustments are made to enhance contrast and emphasize features. Then, a first-order Gaussian derivative filter is applied on each picture to develop a binary contour image that contains the edge features of all knots. Those components formed by edge detection are then measured in pixels, where the nominal dimension of the lumber is used to set the scale for pixel dimensions to real-life dimensions conversion. This paper purposes a knot detection and segmentation algorithm for Casuarina glauca lumber, resulting in a fully automated knot data collection process
Review of strategic methods for encapsulating essential oils into chitosan nanosystems and their applications
No full textEssential oils (EOs) are hydrophobic, concentrated extracts of botanical origin containing diverse bioactive molecules that have been used for their biomedical properties. On the other hand, the volatility, toxicity, and hydrophobicity limited their use in their pure form. Therefore, nano-encapsulation of EOs in a biodegradable polymeric platform showed a solution. Chitosan (CS) is a biodegradable polymer that has been intensively used for EOs encapsulation. Various approaches such as homogenization, probe sonication, electrospinning, and 3D printing have been utilized to integrate EOs in CS polymer. Different CS-based platforms were investigated for EOs encapsulation such as nanoparticles (NPs), nanofibers, films, nanoemulsions, 3D printed composites, and hydrogels. Biological applications of encapsulating EOs in CS include antioxidant, antimicrobial, and anticancer functions. This review explores the principles for nanoencapsulation strategies, and the available technologies are also reviewed, in addition to an in-depth overview of the current research and application of nano-encapsulated EOs
Advanced Defect Detection in Wrap Film Products: A Hybrid Approach with Convolutional Neural Networks and One-Class Support Vector Machines with Variational Autoencoder-Derived Covariance Vectors
No full textThis study proposes a novel approach that utilizes Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs) to tackle a critical challenge: detecting defects in wrapped film products. With their delicate and reflective film wound around a core material, these products present formidable hurdles for conventional visual inspection systems. The complex task of identifying defects, such as unwound or protruding areas, remains a daunting endeavor. Despite the power of commercial image recognition systems, they struggle to capture anomalies within wrap film products. Our research methodology achieved a 90% defect detection accuracy, establishing its practical significance compared with existing methods. We introduce a pioneering methodology centered on covariance vectors extracted from latent variables, a product of a Variational Autoencoder (VAE). These covariance vectors serve as feature vectors for training a specialized One-Class SVM (OCSVM), a key component of our approach. Unlike conventional practices, our OCSVM does not require images containing defects for training; it uses defect-free images, thus circumventing the challenge of acquiring sufficient defect samples. We compare our methodology against feature vectors derived from the fully connected layers of established CNN models, AlexNet and VGG19, offering a comprehensive benchmarking perspective. Our research represents a significant advancement in defect detection technology. By harnessing the latent variable covariance vectors from a VAE encoder, our approach provides a unique solution to the challenges faced by commercial image recognition systems. These advancements in our study have the potential to revolutionize quality control mechanisms within manufacturing industries, offering a brighter future for product integrity and customer satisfaction
Anticipating Emerging Research Frontiers Related to Indoor Air Quality: What Did We Learn from the COVID-19 Pandemic?
No full textBackground: While the COVID-19 pandemic has officially ended, it remains a significant era that profoundly tests humanity’s ability to solve challenges across various domains related to health hazards’ crisis management, technological innovation, and requestioning the management of Indoor Air Quality (IAQ) in different building typologies. Methods: This study examines early publications related to IAQ during the early phase of the pandemic, from March 2020 to August 2021, to identify thematic research areas anticipated to shape the scientific community’s future interests for at least the following 10 years. This study proposes an analytical framework to further interpret the identified thematic areas of research related to IAQ based on intentionality and impact. Results: Topics included the spatial design of indoor environments, occupants’ health, thermal comfort, building performance and ventilation, technology use and energy efficiency, as well as health and social equity. The authors commented on key topics requiring immediate attention from architects, building operators, and researchers. Conclusions: This review foresees the need for (1) building codes that balance spatial design and health aspects to reduce the rate of viral transmission, (2) carbon footprint reduction plans in response to IAQ ventilation requirements, and (3) ventilation systems that consider the thermal comfort of occupants, minimize energy losses, and safeguard air quality from external pollutants. Finally, (4) find a balance between the identified parameters to enhance the IAQ system control
Enhanced Indoor Air Quality Dashboard Framework and Index for Higher Educational Institutions
No full textThis research proposes a 10-step methodology for developing an enhanced IAQ dashboard and classroom index (CI) in higher educational facilities located in arid environments. The identified parameters of the enhanced IAQ dashboard–inspired by the pandemic experience, result from the literature review and the outcome of two electronic surveys of (52) respondents, including health professionals and facility management experts. On the other hand, the indicators included in the CI are based on (80) occupant survey responses, including parameters related to IAQ, Indoor Environmental Quality (IEQ), and thermal comfort, amongst other classroom operative considerations. The CI is further tested in four learning spaces at the American University in Cairo, Egypt. The main contribution of this research is to suggest a conceptual visualization of the dashboard and a practical classroom index that integrates a representative number of contextual indicators to recommend optimal IAQ scenarios for a given educational facility. This study concludes by highlighting several key findings: (1) both qualitative and quantitative metrics are necessary to capture indoor air quality-related parameters accurately; (2) tailoring the dashboard as well as the CI to specific contexts enhances its applicability across diverse locations; and finally, (3) the IAQ dashboard and CI offer flexibility for ad-hoc applications