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If we look at the historical evolution of architecture - from the massive pyramids of Egypt to the framed structures of Greek and Roman construction, to the lighter Gothic vaulting and eventually modern architecture of the twentieth century - we see a continuous, almost linear progression from solid mass construction to diaphanous skins of glass and steel.
Book
Lightweight Concrete—From Basics to Innovations
Lightweight concrete has a history of more than two-thousand years and its technical development is still proceeding. This review starts with a retrospective that gives an idea of the wide range of applications covered by lightweight concrete during the last century. Although lightweight concrete is well known and has proven its technical potential in a wide range of applications over the past decades, there are still hesitations and uncertainties in practice. For that reason, lightweight aggregate properties and the various types of lightweight concrete are discussed in detail with a special focus on current standards. The review is based on a background of 25 years of practical and theoretical experience in this field. One of the main challenges in designing lightweight concrete is to adapt most of design, production and execution rules since they often deviate from normal weight concrete. Therefore, aspects are highlighted that often are the cause of misunderstandings, such as nomenclature or the informational value of certain tests. Frequently occurring problems regarding the mix design and production of lightweight concrete are addressed and the unintended consequences are described. A critical view is provided on some information given in existing European concrete standards regarding the mechanical properties of structural lightweight concrete. Finally, the latest stage of development of very light lightweight concretes is presented. Infra-lightweight concrete is introduced as an innovative approach for further extending the range of applications of lightweight concrete by providing background knowledge and experiences from case records.
Journal Article
A Survey on Lightweight Cryptographic Algorithms in IoT
The Internet of Things (IoT) will soon penetrate every aspect of human life. Several threats and vulnerabilities are present due to the different devices and protocols used in an IoT system. Conventional cryptographic primitives or algorithms cannot run efficiently and are unsuitable for resource-constrained devices in IoT. Hence, a recently developed area of cryptography, known as lightweight cryptography, has been introduced, and over the years, numerous lightweight algorithms have been suggested. This paper gives a comprehensive overview of the lightweight cryptography field and considers various popular lightweight cryptographic algorithms proposed and evaluated over the past years for analysis. Different taxonomies of the algorithms and other associated concepts were also provided, which helps new researchers gain a quick overview of the field. Finally, a set of 11 selected ultra-lightweight algorithms are analyzed based on the software implementations, and their evaluation is carried out using different metrics.
Journal Article
Lightweight Cryptography: A Solution to Secure IoT
In Internet of Things (IoT), the massive connectivity of devices and enormous data on the air have made information susceptible to different type of attacks. Cryptographic algorithms are used to provide confidentiality and maintain the integrity of the information. But small size, limited computational capability, limited memory, and power resources of the devices make it difficult to use the resource intensive traditional cryptographic algorithms for information security. In this scenario it becomes impertinent to develop lightweight security schemes for IoT. A thorough study on the lightweight cryptography as a solution to the security problem of resource-constrained devices in IoT has been presented in this work. This paper is a comprehensive attempt to provide an in-depth and state of the art survey of available lightweight cryptographic primitives till 2019. In this paper 21 lightweight block ciphers, 19 lightweight stream ciphers, 9 lightweight hash functions and 5 variants of elliptic curve cryptography (ECC) has been discussed i.e. in total 54 LWC primitives are compared in their respective classes. The comparison of the ciphers has been carried out in terms of chip area, energy and power, hardware and software efficiency, throughput, latency and figure of merit (FoM). Based on the findings it can be observed that AES and ECC are the most suitable for used lightweight cryptographic primitives. Several open research problems in the field of lightweight cryptography have also been identified.
Journal Article
Current Trends in Automotive Lightweighting Strategies and Materials
The automotive lightweighting trends, being driven by sustainability, cost, and performance, that create the enormous demand for lightweight materials and design concepts, are assessed as a part of the circular economy solutions in modern mobility and transportation. The current strategies that aim beyond the basic weight reduction and cover also the structural efficiency as well as the economic and environmental impact are explained with an essence of guidelines for materials selection with an eco-friendly approach, substitution rules, and a paradigm of the multi-material design. Particular attention is paid to the metallic alloys sector and progress in global R&D activities that cover the “lightweight steel”, conventional aluminum, and magnesium alloys, together with well-established technologies of components manufacturing and future-oriented solutions, and with both adjusting to a transition from internal combustion engines to electric vehicles. Moreover, opportunities and challenges that the lightweighting creates are discussed with strategies of achieving its goals through structural engineering, including the metal-matrix composites, laminates, sandwich structures, and bionic-inspired archetypes. The profound role of the aerospace and car-racing industries is emphasized as the key drivers of lightweighting in mainstream automotive vehicles.
Journal Article
An efficient lightweight convolutional neural network for industrial surface defect detection
Since surface defect detection is significant to ensure the utility, integrality, and security of productions, and it has become a key issue to control the quality of industrial products, which arouses interests of researchers. However, deploying deep convolutional neural networks (DCNNs) on embedded devices is very difficult due to limited storage space and computational resources. In this paper, an efficient lightweight convolutional neural network (CNN) model is designed for surface defect detection of industrial productions in the perspective of image processing via deep learning. By combining the inverse residual architecture with coordinate attention (CA) mechanism, a coordinate attention mobile (CAM) backbone network is constructed for feature extraction. Then, in order to solve the small object detection problem, the multi-scale strategy is developed by introducing the CA into the cross-layer information flow to improve the quality of feature extraction and augment the representation ability on multi-scale features. Hereafter, the multi-scale feature is integrated to design a novel bidirectional weighted feature pyramid network (BWFPN) to improve the model detection accuracy without increasing much computational burden. From the comparative experimental results on open source datasets, the effectiveness of the developed lightweight CNN is evaluated, and the detection accuracy attains on par with the state-of-the-art (SOTA) model with less parameters and calculation.
Journal Article
A comprehensive survey of deep learning-based lightweight object detection models for edge devices
This study concentrates on deep learning-based lightweight object detection models on edge devices. Designing such lightweight object recognition models is more difficult than ever due to the growing demand for accurate, quick, and low-latency models for various edge devices. The most recent deep learning-based lightweight object detection methods are comprehensively described in this work. Information on the lightweight backbone architectures used by these object detectors has been listed. The training and inference processes concerning to deep learning applications on edge devices is being discussed. To raise readers’ awareness of this developing domain, a variety of applications for deep learning-based lightweight object detectors and related utilities have been offered. Designing potent, lightweight object detectors based on deep learning has been suggested as a counter to such problems. On well-known datasets such as MS-COCO and PASCAL-VOC, we thoroughly examine the performance of certain conventional deep learning-based lightweight object detectors.
Journal Article
Estimation of compressive strength of ultra-high performance lightweight concrete
High strength and lightweight are key trends in concrete development. Achieving a balance between these properties to produce high structural efficiency (strength-to-weight ratio) concrete is challenging due to the complex relationship between compressive strength and material components. In this study, two artificial neural network (ANN) models-the BP and Elman networks were used to predict the compressive strength of ultra-high-performance lightweight concrete (UHPLC), based on a robust database of 115 test datasets from previous studies. The investigated parameters included the cement grade (Grade 42.5 and Grade 52.5), cement content (352 kg/m.sup.3 -938 kg/m.sup.3 ), silica fume content (0 kg/m.sup.3 -350 kg/m.sup.3 ), fly ash content (0 kg/m.sup.3 -220 kg/m.sup.3 ), microsphere content (0 kg/m.sup.3 -624 kg/m.sup.3 ), lightweight sand types (pottery sand, expanded perlite sand, and expanded shale lightweight sand), lightweight sand content (0 kg/m.sup.3 -769 kg/m.sup.3 ), sand type (quartz sand, river sand), sand content (0 kg/m.sup.3 -1314 kg/m.sup.3 ), water (90 kg/m.sup.3 -395 kg/m.sup.3 ), water reduce (0 kg/m.sup.3 -42.8 kg/m.sup.3 ), steel fiber content (0 kg/m.sup.3 -234 kg/m.sup.3). Correlation analysis and sensitive analysis indicated that lightweight sand content and sand content had the most significant effects on UHPLC compressive strength, followed by water content. Conversely, fly ash content and lightweight sand type had minimal impact. The developed ANN models for UHPLC compressive strength demonstrated high predictive accuracy for both training and testing datasets, which the RMSE of BP network and Elman network were 0.226 and 0.160, respectively, while R.sup.2 of both two developed models were more than 0.98. Additionally, UHPLC exhibited a higher compressive strength-to-density ratio than high-strength concrete, ultra-high-performance concrete, and even Q235 steel. Three strategies were proposed for creating ultra-high-performance lightweight composites: optimizing packing density and lowering the water-binder ratio, along with careful selection of lightweight aggregates.
Journal Article
Effect of alkali activator and granulated blast furnace slag on the properties of lithium slag-based high-strength lightweight aggregates
This study investigated the effects of NaOH molarity (6–14 M) and ground granulated blast furnace slag (GBFS) content (0–45%) on the properties of lithium slag (LS)-based cold-bonded lightweight aggregates. Bulk density, water absorption, porosity, and cylinder compressive strength were evaluated, and microstructural characterization was conducted using SEM, XRD, FTIR, MIP, and TG/DTG. Results showed that increasing NaOH molarity and GBFS content reduced water absorption (from 15.87 to 5.88%) and porosity (from 34.79 to 13.39%), while enhancing bulk density (731–1074 kg/m³) and compressive strength. At 30% GBFS, the 28-day strength increased by 224.48%, from 3.35 MPa (M6-30) to 10.87 MPa (M14-30). At 12 M NaOH, raising GBFS content from 0 to 45% increased strength by 435.62%, from 2.33 MPa to 12.48 MPa. LS without GBFS achieved 2.33 MPa, indicating inherent pozzolanic activity. Microstructural analysis revealed that performance improvement was due to enhanced geopolymerization and reduced harmful pores (> 200 nm). The M8-30 mix (915.68 kg/m³, 5.98 MPa) showed potential for meeting high-strength lightweight aggregate criteria with mix optimization. These findings demonstrate the feasibility of valorizing LS into high-performance lightweight aggregates, contributing to waste utilization and low-carbon construction.
Journal Article
Water surface garbage detection based on lightweight YOLOv5
With the development of deep learning technology, researchers are increasingly paying attention to how to efficiently salvage surface garbage. Since the 1980s, the development of plastic products and economic growth has led to the accumulation of a large amount of garbage in rivers. Due to the large amount of garbage and the high risk of surface operations, the efficiency of manual garbage retrieval will be greatly reduced. Among existing methods, using YOLO algorithm to detect target objects is the most popular. Compared to traditional detection algorithms, YOLO algorithm not only has higher accuracy, but also is more lightweight. This article presents a lightweight YOLOv5 water surface garbage detection algorithm suitable for deployment on unmanned ships. This article has been validated on the Orca dataset, experimental results showed that the detection speed of the improved YOLOv5 increased by 4.3%, mAP value reached 84.9%, precision reached 88.7%, the parameter quantity only accounts for 12% of the original data. Compared with the original algorithm, the improved algorithm not only has higher accuracy, but also can be applied to more hardware devices due to its lighter weight.
Journal Article