Matthew J. Catenacci, Jeffery R. Owens, Glenn R. Johnson
et al.
The commercialization of 3D concrete printing (3DCP) technology is rapidly advancing. However, building-scale field demonstrations and trials reveal that there are shortcomings that hamper its implementation in the construction industry. One critical challenge is the development of cementitious formulations compatible with the wide range of printer platforms and their use in relevant environments. Academic and start-up research and development (R&D) teams’ work is limited by the excessive upfront costs of large-scale 3DCP printers, as well as high costs of consumables, and replacement parts when experimental results do not go as expected. This work introduces how cementitious formulations can be tested using an inexpensive piston press to quickly evaluate the printability of experimental cementitious formulations. The results demonstrate the use of a piston press to screen materials for extrusion and layer-by-layer builds of cementitious formulations. This low-cost approach streamlines the validation of new formulations compatible with 3DCP. The approach can facilitate the transition of 3DCP materials from bench-scale R&D to practical civil engineering applications.
With the new applications of neo-liberalism in Turkey, the characteristics expected from employees have begun to change. Young people who are part of this change have to adapt their career planning to the new market and conditions. In this process, the founding forces of flexibility are developing through personal development, lifelong learning and the appeal of building a career. Entrepreneurship, career planning and lifelong learning discourses are reinforced not only through the market but also by education, schools, universities, media, consultancy companies serving the business world and many other platforms. When therapeutic discourse is added to entrepreneurship discourse, the spirit of Neo-liberalism emerges, which constantly deals with the self, increases the self-responsible, entrepreneurial, flexible and self-centered subjectivity and tries to manage the transitions of young people with entrepreneurial and therapeutic discourses. Especially in recent years, entrepreneurship and career discourses in Turkey have been used in relation to branches of therapy, which include psychological ideas and diagnoses. In this article, it will be attempted to show and how entrepreneurship, career management, lifelong learning and therapeutic approaches have transformed young people into entrepreneurial self-personalities with neoliberalism, and how career construction has been individualized.
Curing plays a fundamental role in determining the mechanical performance, durability, and sustainability of concrete structures. Traditional curing practices, such as water and air curing, are widely used but often limited by long durations, high water demand, and reduced effectiveness under extreme climatic conditions. In response, advanced curing methods such as steam, microwave, electric, autoclave, and accelerated carbonation have been developed to accelerate hydration, refine pore structures, and enhance durability. This review critically examines the performance of both conventional and advanced curing strategies across a range of concrete systems. Findings show that microwave curing achieves up to 85–95% of 28-day wet-cured strength within 24 h, whilst autoclave curing enhances early strength by 40–60%. Electric curing reduces energy demand by approximately 40% compared to steam curing, and carbonation curing lowers carbon dioxide emissions by 30–50% through carbon sequestration. While steam and autoclave curing provide rapid early strength, they may compromise long-term durability through microcracking and increased porosity. No single method was identified as universally optimal; the effectiveness depends on the mix design, application, and environmental conditions. The review highlights future opportunities in smart curing systems, integrating Internet of Things (IoT), sensor technologies, and AI-driven predictive control to enable real-time optimisation of curing conditions. Such innovations represent a critical pathway for improving concrete performance while addressing sustainability targets in the building and construction industry.
Incorporating green and low-carbon building targets into the rural planning management system requires scientific and quantitative methods for assessing carbon emissions from rural land use. At present, the research in this field mainly focuses on urban areas, but there are fewer theoretical and practical studies on the assessment of carbon emissions from rural land use. This paper proposes a new carbon emission assessment method based on land use modes, and the model can not only assess carbon emissions but also directly reflect the carbon emission intensity of different land use spaces in rural areas and guide the carbon emission control of construction land in village planning. In this paper, we take suburban rural areas in Hunan Province as an example and establish a land use carbon emission assessment model with 13 indicators in five dimensions: total carbon emission, carbon emission efficiency, carbon emission intensity per unit of land use, carbon emission structure of land use, and carbon emission intensity of buildings, based on the bottom-up field research data. We practised our method in Jinlong Town, Hunan Province, and gave examples of model applications. It was found that the carbon emission calculation method based on the carbon emission intensity of land use can be used to calculate the current status of carbon emissions in different villages in Jinlong Town. At the same time, the carbon emission assessment results can be used as a scientific basis for carbon emission control in detailed village planning in Jinlong Town. In general, the carbon emission assessment model can complete the assessment of land carbon emissions in rural areas and provide a low-carbon land use management tool for the government.
Speech recognition technology has many applications in our daily life. However, for many low-resource languages without written forms, acquiring sufficient training data remains a significant challenge for building accurate ASR models. The Tu language, spoken by an ethnic minority group in Qinghai Province in China, is one such example. Due to the lack of written records and the great diversity in regional pronunciations, there has been little previous research on Tu-language speech recognition. This work seeks to address this research gap by creating the first speech dataset for the Tu language spoken in Huzhu County, Qinghai. We first formulated the relevant pronunciation rules for the Tu language based on linguistic analysis. Then, we constructed a new speech corpus, named HZ-TuDs, through targeted data collection and annotation. Based on the HZ-TuDs dataset, we designed several baseline sequence-to-sequence deep neural models for end-to-end Tu-language speech recognition. Additionally, we proposed a novel SA-conformer model, which combines convolutional and channel attention modules to better extract speech features. Experiments showed that our proposed SA-conformer model can significantly reduce the character error rate from 23% to 12%, effectively improving the accuracy of Tu language recognition compared to previous approaches. This demonstrates the effectiveness of our dataset construction and model design efforts in advancing speech recognition technology for this low-resource minority language.
Michael Mullin, James McClory, Winston Haynes
et al.
The development of bispecific antibodies that bind at least two different targets relies on bringing together multiple binding domains with different binding properties and biophysical characteristics to produce a drug-like therapeutic. These building blocks play an important role in the overall quality of the molecule and can influence many important aspects from potency and specificity to stability and half-life. Single-domain antibodies, particularly camelid-derived variable heavy domain of heavy chain (VHH) antibodies, are becoming an increasingly popular choice for bispecific construction due to their single-domain modularity, favorable biophysical properties, and potential to work in multiple antibody formats. Here, we review the use of VHH domains as building blocks in the construction of multispecific antibodies and the challenges in creating optimized molecules. In addition to exploring traditional approaches to VHH development, we review the integration of machine learning techniques at various stages of the process. Specifically, the utilization of machine learning for structural prediction, lead identification, lead optimization, and humanization of VHH antibodies.
Abdul Ghani Olabi, Rania M. Ghoniem, Abdul Hai Alami
et al.
DC power may be more efficient than AC power in certain applications, especially when it comes to local generation and storage. This is because AC power requires extra equipment to convert it to DC power, which can lead to energy losses. Using DC power, on the other hand, makes it easier for devices to use it directly, resulting in higher energy efficiency. Additionally, using DC power can reduce equipment capital costs as it eliminates the need for additional AC–DC conversion equipment. Finally, DC power systems can offer new communication capabilities, including plug-and-play for generation and storage devices, making it simpler to integrate these systems into existing infrastructure. This paper analyzes the optimal size of a photovoltaic/PEM fuel cell system to supply a certain DC commercial load in NEOM city. To identify the best size of the PV/PEMFC, minimizing the cost of energy (COE) and minimizing the net present cost (NPC) are considered. The paper studies three sizes of PEMFCs: 15 kW, 20 kW, and 25 kW. In addition, five different PV modules are selected: Axitec 450 Watt, Jinko 415 Watt, REC Solar 410 Watt, Seraphim 310 Watt, and Tongwei 415 Watt. The results of the study confirmed that the best size of the hybrid system comprises a 15 kW PEMFC, a 267 kW Tongwei PV array, a 60 kg electrolyzer, and a 20 kg hydrogen tank. Under these conditions, the COE and NPC are 0.293 USD/kWh and 498,984 USD, respectively.
Hosam Elhegazy, Debaditya Chakraborty, Hazem Elzarka
et al.
In a decision-making study, design alternatives are compared with respect to cost, performance, and reliability, and the best is selected. Often, due to the time constraints imposed by the design schedule and budget restrictions, the number of design alternatives considered is limited. This research focuses on composite flooring systems for multistory buildings and the application of value techniques to the construction industry. The study uses data that obtained from the RSMeans Assemblies Books for the period 1997–2019. The data obtained from RSMeans consists of assembly cost ($/sf of floor) as the dependent variable; and the structural span (ft.), superimposed load (p.s.f), unit cost of sheet metal ($/LF of sheet), unit cost of concrete ($/CY), unit cost of steel structural ($/ton), and total load (p.s.f) as the independent variables. A simple computer model is designed for recommending the optimal composite flooring system of a multistory building, during the preliminary design stage. The value engineering (VE) team to achieve the VE goals mentioned above can use the model.