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Images taken from construction site interiors often suffer from low illumination and poor natural colors, which restrict their application for high-level site management purposes. The state-of-the-art low-light image enhancement method provides promising image enhancement results. However, they generally require a longer execution time to complete the enhancement. This study aims to develop a refined image enhancement approach to improve execution efficiency and performance accuracy.

To develop the refined illumination enhancement algorithm named enhanced illumination quality (EIQ), a quadratic expression was first added to the initial illumination map. Subsequently, an adjusted weight matrix was added to improve the smoothness of the illumination map. A coordinated descent optimization algorithm was then applied to minimize the processing time. Gamma correction was also applied to further enhance the illumination map. Finally, a frame comparing and averaging method was used to identify interior site progress.

The proposed refined approach took around 4.36–4.52 s to achieve the expected results while outperforming the current low-light image enhancement method. EIQ demonstrated a lower lightness-order error and provided higher object resolution in enhanced images. EIQ also has a higher structural similarity index and peak-signal-to-noise ratio, which indicated better image reconstruction performance.

The proposed approach provides an alternative to shorten the execution time, improve equalization of the illumination map and provide a better image reconstruction. The approach could be applied to low-light video enhancement tasks and other dark or poor jobsite images for object detection processes.

Construction progress monitoring (CPM) is considered a difficult and tedious task in construction projects, which focuses on identifying discrepancies between the as-built product and the as-planned design. Computer vision (CV) technology is applied to automate the CPM process. However, the synergy between the CV and CPM in literature and industry practice is lacking. This study aims to fulfil this research gap.

A Delphi qualitative approach was used in this study by conducting two interview rounds. The collected data was analysed using manual content analysis.

This study identified seven stages of CPM; data acquisition, information retrieval, verification, progress estimation and comparison, visualisation of the results and schedule updating. Factors such as higher accuracy in data, less labourious process, efficiency and near real-time access are some of the significant enablers in instigating CV for CPM. Major challenges identified were occlusions and lighting issues in the site images and lack of support from the management. The challenges can be easily overcome by implementing suitable strategies such as familiarisation of the workforce with CV technology and application of CV research for the construction industry to grow with the technology in line with other industries.

This study addresses the gap pertaining to the synergy between the CV in CPM literature and the industry practice. This research contributes by enabling the construction personnel to identify the shortcomings and the opportunities to apply automated technologies concerning each stage in the progress monitoring process.

The purpose of this study is to develop a deep learning framework for additive manufacturing (AM), that can detect different defect types without being trained on specific defect data sets and can be applied for real-time process control.

This study develops an explainable artificial intelligence (AI) framework, a zero-bias deep neural network (DNN) model for real-time defect detection during the AM process. In this method, the last dense layer of the DNN is replaced by two consecutive parts, a regular dense layer denoted (L1) for dimensional reduction, and a similarity matching layer (L2) for equal weight and non-biased cosine similarity matching. Grayscale images of 3D printed samples acquired during printing were used as the input to the zero-bias DNN.

This study demonstrates that the approach is capable of successfully detecting multiple types of defects such as cracks, stringing and warping with high accuracy without any prior training on defective data sets, with an accuracy of 99.5%.

Once the model is set up, the computational time for anomaly detection is lower than the speed of image acquisition indicating the potential for real-time process control. It can also be used to minimize manual processing in AI-enabled AM.

To the best of the authors’ knowledge, this is the first study to use zero-bias DNN, an explainable AI approach for defect detection in AM.

This paper aims to produce lightweight concrete by combining aerated concrete with expanded polystyrene beads concrete to create structural aerated-polystyrene lightweight concrete that satisfies the criteria of sustainability for thermal and sound insulation properties and the structural criteria of having satisfactory compressive strength for structural elements.

The experimental study was carried out to reach the largest compressive strength while maintaining the lowest possible density by preparing nine mixes of concrete, involving different ratios of aluminum waste powder and polystyrene beads as 0%, 0.2% and 0.3% and 0%, 0.1% and 0.2%, respectively, by weight of cement to produce the lightweight concrete with different densities. The performance of mechanical properties, thermal conductivity, ultrasonic pulse velocity, density, modulus of elasticity, acoustic impedance and scanning electron microscopy were studied and discussed.

Results showed that aerated-expended polystyrene beads concrete had the most suitable properties when the proportions of aluminum waste powder and expanded polystyrene beads were 0.2% and 0.1%, respectively. The compressive strength, density, thermal conductivity and acoustic impedance were 38.5 MPa, 1,768 Kg/m³, 0.358 W/(m.k) and 4.91 Kg/m² s, respectively.

The experimental work was done using aluminum scrap waste powder as an expanding agent to produce aerated concrete and combining it with expanded polystyrene bead concrete to produce structural aerated-polystyrene concrete, which contains fine materials (silica fume and local natural raw limestone) and superplasticizers.

The purpose of this study is to investigate environmental factors impacting Ajanta mural deterioration, assessing global tourism effects and visitor conduct on cave environment and murals. This study recognizes stakeholder roles in conservation, providing data-driven insights to guide institutions like the Archaeological Survey of India. The objective is sustainable tourism practices to balance public access with mural preservation for future generations.

Over 25 years, Ajanta Caves' visitors doubled, impacting microclimatic conditions for ancient murals. This study assesses visitor impact to establish a regime and determine carrying capacity, considering temperature, humidity and pollution. Challenges arise from quantifying capacity because of variables. This research informs global tourism and heritage conservation, offering methodologies applicable to cultural sites worldwide.

This study examines environmental impacts on mural preservation in Ajanta Caves, including humidity, microbial growth, sunlight exposure, air quality and tourist presence. Tourist influx escalates CO₂ levels, directly endangering murals. Concerns about particulate matter, especially during visits, emphasize the need for data-driven decision-making and modern technology use to protect Ajanta Caves' artwork, crucial because of its global significance and tourism-related vulnerabilities.

This study carries substantial social implications with a global resonance. The active engagement of the local community and tourism stakeholders in conserving and promoting the Ajanta Caves fosters empowerment, igniting a sense of pride, ownership and responsibility among residents and ensuring sustainable enjoyment of cultural heritage while safeguarding it for future generations. In addition, there will be socioeconomic benefits to local residents such as employment opportunities as tour operators, tour guides, hospitality staff, artisans and souvenir shopkeepers.

This study integrates art conservation, environmental science, cultural heritage preservation and social aspects to address global tourism challenges. Focusing on a site of worldwide significance, this study offers practical strategies for artwork preservation, tourism management and environmental concerns. These recommendations provide real-world solutions applicable to heritage sites globally, bridging scientific analysis with social and cultural insights.

本研究调查了影响阿詹塔壁画退化的环境因素, 评估了全球旅游业和游客行为对洞穴环境和壁画的影响。它承认利益相关者在保护中的作用, 为印度考古调查等机构提供数据支撑的见解。目的是实践可持续旅游, 平衡公众进入和供子孙后代使用的壁画保护。

25年来, 阿詹塔洞穴的游客增加了一倍, 影响了古壁画的小气候条件。该研究评估了游客的影响, 考虑温度、湿度和污染, 建立了一个制度并确定承载能力。量化承载能力的指标面临着挑战。这项研究为全球旅游业和遗产保护提供了信息, 提供了适用于世界各地文化遗址的方法。

这项研究考察了环境对阿詹塔洞穴壁画保护的影响, 包括湿度、微生物生长、阳光照射、空气质量和游客的存在。游客的涌入使二氧化碳含量上升, 直接危及壁画。对颗粒物的考虑, 尤其是在参观期间, 强调了数据驱动决策和现代技术应用对保护阿詹塔洞穴的艺术品的必要性。这一点至关重要, 因为它具有全球意义和旅游相关的脆弱性。

这项研究具有重大的社会影响并引起全球共鸣。当地社区和旅游利益相关者积极参与阿旃陀石窟的保护和推广, 可以增强居民的赋权, 激发居民的自豪感、主人翁意识和责任感, 确保可持续享受文化遗产, 同时为子孙后代保护文化遗产。此外, 还将为当地居民带来社会经济效益, 例如旅游经营者、导游、接待人员、工匠、纪念品店主等的就业机会。

这项研究综合了艺术保护、环境科学、文化遗产保护和社会方面以应对全球旅游业的挑战。它关注一个具有世界意义的遗址, 为艺术品保护、旅游管理和环境问题提供了实用的策略。这些建议提供了适用于全球遗产地的现实世界解决方案, 将科学分析与社会和文化见解联系起来。

El estudio investiga los factores medioambientales que influyen en el deterioro de los murales de Ajanta, evaluando los efectos globales del turismo y el comportamiento de los visitantes sobre el entorno de las cuevas y los murales. Se examina el papel que desempeñan las partes interesadas en la conservación y aporta datos para orientar a instituciones como la encuesta arqueológica de India. El objetivo son las prácticas turísticas sostenibles para equilibrar el acceso del público con la conservación de los murales para las generaciones futuras.

A lo largo de 25 años, los visitantes de las cuevas de Ajanta se han duplicado, con un impacto en las condiciones microclimáticas de los murales antiguos. El estudio evalúa el impacto de los visitantes para establecer una regulación y determinar la capacidad de carga, teniendo en cuenta la temperatura, la humedad y la contaminación. La cuantificación de la capacidad plantea problemas debido a las variables. La investigación aporta información al turismo mundial y a la conservación del patrimonio, ofreciendo metodologías aplicables a sitios culturales de todo el mundo.

Este estudio examina los impactos ambientales en la conservación de los murales de las cuevas de Ajanta, incluyendo la humedad, el crecimiento microbiano, la exposición a la luz solar, la calidad del aire y la presencia de turistas. La afluencia de turistas aumenta los niveles de CO2, poniendo directamente en peligro los murales. La preocupación por las partículas, especialmente durante las visitas, pone de relieve la necesidad de tomar decisiones basadas en datos y de utilizar tecnología actual para proteger las obras de arte de las cuevas de Ajanta, algo crucial debido a su importancia mundial y a las vulnerabilidades relacionadas con el turismo.

Este estudio conlleva importantes implicaciones sociales con una resonancia global. La participación activa de la comunidad local y las partes interesadas del turismo en la conservación y promoción de las Cuevas de Ajanta fomenta el empoderamiento, generando un sentido de orgullo, propiedad y responsabilidad entre los residentes y garantiza el disfrute sostenible del patrimonio cultural al mismo tiempo que lo salvaguarda para las generaciones futuras. Además, habrá beneficios socioeconómicos para los residentes locales, como oportunidades de empleo como operadores turísticos, guías turísticos, personal de hostelería, artesanos, comerciantes de souvenirs, etc.

Este estudio integra la conservación del arte, las ciencias medioambientales, la preservación del patrimonio cultural y los aspectos sociales para abordar los retos del turismo mundial. Centrándose en un sitio de importancia mundial, ofrece estrategias prácticas para la conservación de las obras de arte, la gestión del turismo y los problemas medioambientales. Estas recomendaciones aportan soluciones reales aplicables a lugares patrimoniales de todo el mundo, tendiendo puentes entre el análisis científico y las percepciones sociales y culturales.

The soft stabilized slab and pile-supported (SSPS) embankment is an improvement technique to increase the efficiency of resources in road construction. To capture the effects of stabilized slabs on the stress transfer mechanism, the differential settlements and the lateral displacement of the embankment completely. A theoretical model of SSPS is proposed by considering the effect of soil arching and the interaction between the embankment fill, stabilized soil, pile, foundation soil and bearing stratum.

In the theoretical model, the stress and strain coordination relationship of the system was analyzed in view of the minimum potential energy theory and equal settlement plane theory. Subsequently, the theoretical method was applied to field tests for comparison. Finally, the influence of the elastic modulus and the thickness of the stabilized slab on the stress concentration ratio and foundation settlement were examined.

In addition to the experimental findings, the method has been revealed to be reasonable and feasible, considering its ability to effectively exploit the stabilized slab effect and improve the bearing capacity of soil and piles. An economical and reasonable arrangement scheme for the thickness and strength of stabilized slabs was obtained. The results reveal that the optimum elastic modulus was chosen as 28 MPa–60 MPa, and the optimum thickness of the stabilized slab was selected as 1.5 m–2.1 m using the parameters of field tests, which can provide guidance to engineering design.

An optimization calculation method is established to analyze the load transfer mechanics of the SSPS embankment based on a double-equal settlement plane. The model’s rationality was analyzed by comparing the settlement and stress concentration ratios in the field tests. Subsequently, the influence of the elastic modulus and the thickness of the stabilized slab on the stress concentration ratio and settlement were examined. An economical and reasonable arrangement scheme for the thickness and elastic modulus of stabilized slabs was obtained, which can provide a novel approach for engineering design.

Low-carbon concrete represents a new direction in mitigating the global warming effects caused by clinker manufacturing. Utilizing Saudi agro-industrial by-products as an alternative to cement is a key support in reducing clinker production and promoting innovation in infrastructure and circular economy concepts, toward decarbonization in the construction industry. The use of fly ash (FA) as a cement alternative has been researched and proven effective in enhancing the durability of FA-based concrete, especially at lower replacement levels. However, at higher replacement levels, a noticeable impediment in mechanical strength indicators limits the use of this material.

In this study, low-carbon concrete mixes were designed by replacing 50% of the cement with FA. Varying ratios of nano-sized glass powder (4 and 6% of cement weight) were used as nanomaterial additives to enhance the mechanical properties and durability of the designed concrete. In addition, a 10% of the mixing water was replaced with EMs dosage.

The results obtained showed a significant positive impact on resistance and durability properties when replacing 10% of the mixing water with effective microorganisms (EMs) broth and incorporating nanomaterial additives. The optimal mix ratios were those designed with 10% EMs and 4–6% nano-sized glass powder additives. However, it can be concluded that advancements in eco-friendly concrete additive technologies have made significant contributions to the development of sophisticated concrete varieties.

This study focused at developing nanomaterial additives from Saudi industrial wastes and at presenting a cost-effective and feasible solution for enhancing the properties of FA-based concrete. It has also been found that the inclusion of EMs contributes effectively to enhancing the concrete's resistance properties.

Teaching wine tasting online is challenging, even given the curated digital tools of the Wine and Spirit Education Trust, a highly renowned online wine certification system is used. This paper aims to initially explore wine experts’ opinions about online wine education and subsequently examine the feasibility of customizing wine appreciation lexicons for Chinese learners.

A two-study multimethod approach was adopted. Study 1, a two-stage Delphi study, was conducted with 17 wine experts representing a number of countries, using a mix of closed/open-ended questions in an online survey. Data was collected in a market study in Study 2, conducted at agricultural markets in Thailand (pilot test) and China. Dialogues with market sellers were undertaken, evoking mental imagery of wine descriptors to explore the relevance of traditional versus local aromas and flavors in describing wine.

Findings concentrate on three main areas: general advantages/disadvantages of online wine education, reactions toward asynchronous/synchronous methods of wine tasting and, finally, the feasibility of customizing a wine appreciation lexicon for Chinese learners.

The study presents novel insights into the role of online wine education in China.

In today’s intricate and dynamic construction sector, traditional project management techniques, which view projects in isolation, are no longer sufficient. Project Portfolio Management (PPM) has proven to be an efficient alternative solution for handling multiple construction projects. As such, based on a Machine Learning (ML) approach, this study aims to explore the Critical Success Factors (CSFs) influencing the adoption of PPM, aiming to enhance PPM implementation in construction projects.

A questionnaire based on CSFs gathered from prior studies was developed and validated by experts. Afterward, exploratory data analysis is conducted to understand CSF–PPM relationships. Preprocessing techniques ensure uniformity in variable magnitudes. Lastly, ML techniques, namely Linear Discriminant Analysis (LDA), Logistic Regression (LR) and Extra Trees Classifier (ETC) are developed to model and investigate CSFs' impact on PPM adoption.

The findings pointed out that the ETC model marginally outperforms other ML models with a classification accuracy of 93%. Also, the project size, utilized PPM tool and resource allocation-related factors are the most significant CSFs that influenced the PPM performance by about 48.5%.

This study contributes to the existing body of knowledge by raising awareness among construction companies and other project stakeholders about the pivotal CSFs that must be considered when prioritizing projects and designing an optimal PPM approach.

Additive manufacturing (AM), also known as three-dimensional (3D) printing technology, has been used in the health-care industry for over two decades. It is in high demand in the health-care industry due to its strength to manufacture custom-designed and personalized 3D constructs. Recently, AM technologies are being explored to develop personalized drug delivery systems, such as personalized oral dosages, implants and others due to their potential to design and develop systems with complex geometry and programmed controlled release profile. Furthermore, in 2015, the US Food and Drug Administration approved the first AM medication, Spritam® (Apprecia Pharmaceuticals) which has led to tremendous interest in exploring this technology as a bespoke solution for patient-specific drug delivery systems. The purpose of this study is to provide a comprehensive overview of AM technologies applied to the development of personalized drug delivery systems, including an analysis of the commercial status of AM based drugs and delivery devices.

This review paper provides a detailed understanding of how AM technologies are used to develop personalized drug delivery systems. Different AM technologies and how these technologies can be chosen for a specific drug delivery system are discussed. Different types of materials used to manufacture personalized drug delivery systems are also discussed here. Furthermore, recent preclinical and clinical trials are discussed. The challenges and future perceptions of personalized medicine and the clinical use of these systems are also discussed.

Substantial works are ongoing to develop personalized medicine using AM technologies. Understanding the regulatory requirements is needed to establish this area as a point-of-care solution for patients. Furthermore, scientists, engineers and regulatory agencies need to work closely to successfully translate the research efforts to clinics.

This review paper highlights the recent efforts of AM-based technologies in the field of personalized drug delivery systems with an insight into the possible future direction.