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Purpose – The purpose of this paper is to describe the overall equipment cost loss (OECL) methodology and an implementation of this methodology, to compare the outcomes of OECL with those of overall equipment effectiveness (OEE), and finally to identify the benefits offered by this new methodology. Design/methodology/approach – The proposed methodology, OECL, combines six large loss models and a financial model in the performance evaluation of equipment. The six large losses are converted into monetary units. OECL is a new way of evaluating equipment performance that differs from the original OEE methodology and overcomes some of the limitations of OEE. This new methodology can be used to rank problematic machines by accounting for production elements together with finance elements. Findings – The OECL and OEE methodologies rank problematic machines differently. Research limitations/implications – Efforts were made in this research to identify factors affecting OECL outcomes, but it was found that it was not possible to apply OECL to all scenarios. Practical implications – The OECL model can be implemented in a real manufacturing company to help decision-makers better determine the magnitudes of equipment problems and rank problematic pieces of equipment appropriately. Originality/value – This OECL method is able to overcome some of OEE’s weaknesses. It can properly prioritise problematic machines by considering both cost and losses.

Three-dimensional printed concrete (3DPC) has received growing attention as digital fabrication technologies continue to influence construction workflows. This study presents a systematic review of research published between 2015 and 2026, following PRISMA guidelines. Based on the reviewed literature, six opportunity categories and six challenge categories were identified. The opportunities include design optimization, construction speed, materials innovation, labor reduction, remote area construction, and circular economy. The findings show that material innovation is the most extensively researched category; a substantial proportion of the literature focuses on materials, rheology, printability, and structural behavior, reflecting an emphasis on improving fresh-state properties, interlayer bonding, and mechanical performance. The second most researched opportunity is design optimization, followed by the circular economy. Research in the remaining three categories of construction speed, labor reduction, and remote area construction appears to be limited. The main challenges identified include reinforcement integration, structural performance, mixing and curing, high equipment costs, durability, and the lack of standards. High equipment costs and the lack of standards appear to be the least researched challenges, while the four remaining challenge categories are actively addressed by the research community.

Equipment costs play a critical role in decision making during design and construction, which requires up-to-date information and data. The design of this study incorporates the inputs from the literature review on the influencing factors of equipment costs and major targeted equipment types to enhance decision support for equipment selection, project construction, and maintenance issues. Two traditional cost estimation methods and five machine-learning methods were compared in this study to identify significant attributes related to the predictions of the costs and residual values of each targeted equipment type. The novelty of this study is that the developed method improves prediction accuracy by establishing a comprehensive and well-structured database framework. A comparison of this method with the existing prediction models reveals that the results and the accuracy of multiple regression analysis are improved in the range of (3% to 33.97%) with the use of a modified decision-tree model combined with support vector machines. The major contribution of this research is the design, implementation, and validation of a machine-learning-based modified decision tree with a support vector machine model for improved accuracy and decision support in construction management. Future research should consider the relationship between geographical variations and value changes.

This research aims to provide knowledge about how to create and the need for air circulation in negative pressure isolation rooms. If practiced directly, it will require large costs because it requires costs for procuring space and equipment. By using virtual reality (VR) technology, this problem can be solved because VR can provide an immersive environment like being in the real world. VR users will be able to experience how the requirements and how to create a negative pressure isolation room are like how it feels in the real world. So this can provide information and training on how to make a negative pressure isolation room before making an actual isolation room. Activities using VR will reduce the risk of errors and training costs on how to make an isolation room even without the cost of equipment. The purpose of this research is to provide convenience for health educators and the government on how to make negative pressure isolation rooms and the need for air circulation. So, it is enough to use VR technology, so the information on how to use VR is enough to make the isolation room and its needs work without having to use the right tools. This research method is descriptive and quantitative which begins with a literature review followed by an analysis of the need for isolation rooms and VR immersive environments. The implementation and testing process results in VR technology being able to help understand negative pressure isolation rooms for health workers.

Hyperspectral imaging (HSI) has recently emerged as a promising tool for various agricultural applications. However, high equipment cost, instrumentation complexity, and data-intensive nature have limited its widespread adoption. To overcome these challenges, reconstructing hyperspectral data from simple, cost-effective color or RGB (red-green-blue) images using advanced deep learning algorithms offers a practically attractive solution for a wide range of applications in food quality control and assurance. Through advanced deep learning algorithms, it is possible to capture and reconstruct spectral information from simple, cost-effective RGB imaging to create a reliable, efficient, and scalable system with accuracy comparable to dedicated, expensive HSI systems. This review provides a comprehensive overview of recent advances in deep learning techniques for HSI reconstruction and highlights the transformative impact of deep learning-based hyperspectral image reconstruction on agricultural and food products and anticipates a future where these innovations will lead to more advanced and widespread applications in the agri-food industry.

Prognostics and health management (PHM) is critical for enhancing equipment reliability and reducing maintenance costs, and research on intelligent PHM has made significant progress driven by big data and deep learning techniques in recent years. However, complex working conditions and high-cost data collection inherent in real-world scenarios pose small-data challenges for the application of these methods. Given the urgent need for data-efficient PHM techniques in academia and industry, this paper aims to explore the fundamental concepts, ongoing research, and future trajectories of small data challenges in the PHM domain. This survey first elucidates the definition, causes, and impacts of small data on PHM tasks, and then analyzes the current mainstream approaches to solving small data problems, including data augmentation, transfer learning, and few-shot learning techniques, each of which has its advantages and disadvantages. In addition, this survey summarizes benchmark datasets and experimental paradigms to facilitate fair evaluations of diverse methodologies under small data conditions. Finally, some promising directions are pointed out to inspire future research.

Measurement-device-independent quantum key distribution (MDI QKD) removes all detector side channels and enables secure QKD with an untrusted relay. It is suitable for building a star-type quantum access network, where the complicated and expensive measurement devices are placed in the central untrusted relay and each user requires only a low-cost transmitter, such as an integrated photonic chip. Here, we experimentally demonstrate a 1.25-GHz silicon photonic chip-based MDI QKD system using polarization encoding. The photonic chip transmitters integrate the necessary encoding components for a standard QKD source. We implement random modulations of polarization states and decoy intensities, and demonstrate a finite-key secret rate of31bit/sover 36-dB channel loss (or 180-km standard fiber). This key rate is higher than state-of-the-art MDI QKD experiments. The results show that silicon photonic chip-based MDI QKD, benefiting from miniaturization, low-cost manufacture, and compatibility with CMOS microelectronics, is a promising solution for future quantum secure networks.

A rapidly increasing selection of laboratory equipment can be fabricated with open-source three-dimensional printers at low cost. Most experimental research projects are executed with a combination of purchased hardware equipment, which may be modified in the laboratory and custom single-built equipment fabricated inhouse. However, the computer software that helps design and execute experiments and analyze data has an additional source: It can also be free and open-source software (FOSS) ( 1 ). FOSS has the advantage that the code is openly available for modification and is also often free of charge. In the past, customizing software has been much easier than custom-building equipment, which often can be quite costly because fabrication requires the skills of machinists, glassblowers, technicians, or outside suppliers. However, the open-source paradigm is now enabling creation of open-source scientific hardware by combining three-dimensional (3D) printing with open-source microcontrollers running on FOSS. These developments are illustrated below by several examples of equipment fabrication that can better meet particular specifications at substantially lower overall costs.

Based on the local climate in Lianyungang, this paper analyzes the local heating demand, a comprehensive comparison of mainstream heating equipment and terminal forms, and analysis and discussion, respectively. By simulating the heating situation in Lianyungang in winter, the heating cost of different equipment in Lianyungang in winter is calculated and combined with the operation of existing projects, and the differences in cost, use, installation, and maintenance of different heating equipment are compared. Finally, the most suitable heating form for Lianyungang users is summarized by integrating theoretical data and actual situations.

With rapidly decreasing purchase prices of electric vehicles, charging costs are becoming ever more important for the diffusion of electric vehicles as required to decarbonize transport. However, the costs of charging electric vehicles in Europe are largely unknown. Here we develop a systematic classification of charging options, gather extensive market data on equipment cost, and employ a levelized cost approach to model charging costs in 30 European countries (European Union 27, Great Britain, Norway, Switzerland) and for 13 different charging options for private passenger transport. The findings demonstrate a large variance of charging costs across countries and charging options, suggesting different policy options to reduce charging costs. A specific analysis on the impacts and relevance of publicly accessible charging station utilization is performed. The results reveal charging costs at these stations to be competitive with fuel costs at typical utilization rates exhibited already today. Charging costs are important for the diffusion of electric vehicles as required to decarbonize transport. Here, the authors show large variance of electrical vehicle charging costs across 30 European countries and charging options, suggesting different policy options to reduce charging costs.