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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 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.

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.

This paper aims to provide a conceptual understanding of the drivers and outcomes of actor opportunism in the context of the three key actors of the sharing economy – the service provider, the platform and the consumer.

The research uses a conceptual approach by drawing on literature from within and outside of marketing.

The current research introduces a conceptual framework of opportunism in the sharing economy with seven underlying propositions. The framework posits a U-shaped moderating effect of social capital for the relationship between opportunism and its drivers, actor vulnerability and asset specificity. Furthermore, a 2 × 2 matrix consisting of two types of opportunistic behaviors (active and passive) and two coping strategies by other actors (defensive and nondefensive) suggests that passive opportunism tends to lead to value codestruction independently of the coping strategies employed by other actors. Counterintuitively, the combination of active opportunism and defensive coping strategy presents an opportunity for value cocreation due to its potential to break up older structures and generate new ones.

While our research provides a higher-level understanding of opportunism pertaining to platform, consumers and service providers in the sharing economy, future research could situate our framework within specific regulatory environments, incorporate the role of competitors and examine individual interaction effects between type of opportunism and coping strategies.

The framework enables service providers, platforms and consumers to identify drivers of opportunistic behaviors of their partners and discern instances in which opportunistic behaviors lead to value codestruction for all actors.

This research transcends prior work on the bright and dark sides of the sharing economy by identifying its dynamic nature and examining the contributing role of opportunism.

We analyzed the effects of different combinations of organic soil amendments (OSAs) and chemical fertilizers on agrifood production, focusing on banana yields in China, the second-largest producer of bananas globally.

We computed these combinations by dividing the expenditures on OSAs by those on chemical fertilizers and called them OSA-CF ratios. First, we classified farmers based on quintiles of expenditures on chemical fertilizers. Then, we studied the association between OSA-CF ratios and banana yields for each quintile. We also considered an alternate specification in which farmers were grouped along the OSA-CF ratio continuum. The first group comprised farmers not using OSAs. Their OSA-CF ratio was zero. Farmers applying low, medium, and high OSA-CF ratios constituted groups two, three, and four; the groups were delineated based on the OSA-CF ratio tertiles, and the associations between tertiles of OSA-CF ratios and banana yields for each quintile were analyzed. The data used in this study were collected by surveying 616 households in three major banana-producing provinces (Guangdong, Hainan, and Yunnan) of China. Standard linear regressions and the two-stage predictor substitution method were employed to complete the analysis.

There were variations in the effects of OSA-CF ratios on banana yields obtained by farmers iifferent quintiles. For the first and second quintiles, low, medium, and high OSA-CF ratios improved banana yields relative to not using OSAs. For farmers in the first quintile using only chemical fertilizers, applying a low OSA-CF ratio was associated with an improvement of 792 kg/mu in banana yields. For their counterparts in the second quintile, the same transition was associated with a gain of 534 kg/mu. For the fifth quintile, comprising farmers spending 320 yuan/mu or more on chemical fertilizers, applying a high OSA-CF ratio instead of using only chemical fertilizers was associated with a 401 kg/mu decline in banana yields. Even so, for this group, no differences were observed between the yields of farmers not applying OSAs and those using low and medium OSA-CF ratios.

Banana farmers in southern China, using only chemical fertilizers, can improve yields by combining them with OSAs if their chemical fertilizer expenditures are less than 66.67 yuan/mu. Those using only chemical fertilizers and spending between 68 yuan/mu and 300 yuan/mu on them can maintain yields by applying OSAs in conjunction with chemical fertilizers. However, yields may decline for farmers using only chemical fertilizers and spending 320 yuan/mu or more on them if they incorporate OSAs such that the OSA-CF ratio reaches 0.78 or higher. Overall, combining OSAs with chemical fertilizers can improve yields while attenuating the adverse effects of chemical fertilizers on the environment. Policymakers should inform farmers of these benefits and accelerate the transition to sustainable agriculture through educational and awareness programs.

Farmers apply OSAs such as organic fertilizers and farmyard manure to adjust and remedy soil nutrition to improve farm productivity. However, little is known about how combining OSAs with chemical fertilizers affects banana yields. This study provided the first attempt to explore the associations between OSA-CF ratios and banana yields using cross-sectional data on farming households.

Recognising the as-built state of construction elements is crucial for construction progress monitoring. Construction scholars have used computer vision-based algorithms to automate this process. Robust object recognition from indoor site images has been inhibited by technical challenges related to indoor objects, lighting conditions and camera positioning. Compared with traditional machine learning algorithms, one-stage detector deep learning (DL) algorithms can prioritise the inference speed, enable real-time accurate object detection and classification. This study aims to present a DL-based approach to facilitate the as-built state recognition of indoor construction works.

The one-stage DL-based approach was built upon YOLO version 4 (YOLOv4) algorithm using transfer learning with few hyperparameters customised and trained in the Google Colab virtual machine. The process of framing, insulation and drywall installation of indoor partitions was selected as the as-built scenario. For training, images were captured from two indoor sites with publicly available online images.

The DL model reported a best-trained weight with a mean average precision of 92% and an average loss of 0.83. Compared to previous studies, the automation level of this study is high due to the use of fixed time-lapse cameras for data collection and zero manual intervention from the pre-processing algorithms to enhance visual quality of indoor images.

This study extends the application of DL models for recognising as-built state of indoor construction works upon providing training images. Presenting a workflow on training DL models in a virtual machine platform by reducing the computational complexities associated with DL models is also materialised.