Search Results (2,414)

The implementation of lean construction is very important in the construction industry to reduce waste and increase productivity. To ensure its effective implementation, clear and measurable Key Performance Indicators (KPIs) are necessary. Therefore, this research aimed to develop SMART-based KPIs (Specific, Measurable, Attainable, Realistic, and Time-bound) for lean construction implementation, which measure indicators throughout the project life cycle. In this context, both qualitative and quantitative methods were used to collect data. Quantitative data were collected through surveys, assessing the perceptions of respondents concerning KPIs that had been developed. Meanwhile, qualitative data were collected through interviews and expert Focus Group Discussions (FGDs), which included in-depth analysis and conclusions regarding lean construction KPIs. The results produced were KPIs that could be used to measure effectiveness in implementing lean construction, particularly for building projects in Indonesia. Consequently, this research provided new views concerning effective lean construction, which could be explored in more depth and implemented for stakeholders in the construction industry. This development could eventually improve project performance by reducing waste and increasing productivity in construction projects. Full article

Colleges and universities are large consumers of energy, with a huge potential for building energy efficiency, and need to reduce energy consumption to build a low-carbon, energy-saving campus. Predicting the energy consumption of campus buildings can help to accurately manage the electricity consumption of buildings and reduce the energy consumption of buildings. However, the electricity consumption of a building’s operation is affected by many factors, and it is difficult to establish a model for analysis and prediction. Therefore, in this study, the training building of the BIM education center on campus was selected as the research object, and a digital twin O&M platform was established by integrating IoT, digital twin technology (DDT), smart meter monitoring devices, and indoor environment monitoring devices. The O&M management platform can monitor real-time changes in indoor power consumption data and environmental parameters, and organize data on multiple influencing factors and power consumption. Following training, validation, and testing, the machine learning models (back propagation neural network, support vector model, and multiple linear regression model) were assessed and compared for accuracy. Following the multiple linear regression and support vector models, the backpropagation neural network model exhibited the highest accuracy. Consistent with the actual power consumption detection results in the BIM education center, the backpropagation neural network model produced results. Consequently, the BP model created in this study demonstrated its dependability and ability to forecast campus building power usage, assisting the university in organizing its energy supply and creating a campus that prioritizes conservation. Full article

Thermochromic smart windows realize an intelligent response to changes in environmental temperature through reversible physical phase transitions. They complete a real-time adjustment of solar transmittance, create a livable indoor temperature for humans, and reduce the energy consumption of buildings. Nevertheless, conventional materials that are used to prepare thermochromic smart windows face challenges, including fixed transition temperatures, limited solar modulation capabilities, and inadequate mechanical properties. In this study, a novel thermochromic hydrogel was synthesized from 2-hydroxy-3-butoxypropyl hydroxyethyl celluloses (HBPEC) and poly(N-isopropylacrylamide) (PNIPAM) by using a simple one-step low-temperature polymerization method. The HBPEC/PNIPAM hydrogel demonstrates a wide response temperature (24.1–33.2 °C), high light transmittance (T_lum = 87.5%), excellent solar modulation (ΔT_sol = 71.2%), and robust mechanical properties. HBPEC is a functional material that can be used to adjust the lower critical solution temperature (LCST) of the smart window over a wide range by changing the degree of substitution (DS) of the butoxy group in its structure. In addition, the use of HBPEC effectively improves the light transmittance and mechanical properties of the hydrogels. After 100 heating and cooling cycles, the hydrogel still has excellent stability. Furthermore, indoor simulation experiments show that HBPEC/PNIPAM hydrogel smart windows have better indoor temperature regulation capabilities than traditional windows, making these smart windows potential candidates for energy-saving building materials. Full article

The development of smart cities will require the construction of smart buildings. Smart buildings will demand the incorporation of elements for efficient monitoring and control of electrical consumption. The development of efficient AI algorithms is needed to generate more accurate electricity consumption predictions; therefore; anomaly detection in electricity consumption predictions has become an important research topic. This work focuses on the study of the detection of anomalies in domestic electrical consumption in Mexico. A predictive machine learning model of future electricity consumption was generated to evaluate various anomaly-detection techniques. Their effectiveness in identifying outliers was determined, and their performance was documented. A 30-day forecast of electrical consumption and an anomaly-detection model have been developed using isolation forest. Isolation forest successfully captured up to 75% of the anomalies. Finally, the Shapley values have been used to generate an explanation of the results of a model capable of detecting anomalous data for the Mexican context. Full article

Designing smart building IoT applications is a complex task. It requires efficiently integrating a broad number of heterogeneous, low-resource devices that adopt lightweight strategies. IoT frameworks, especially if they are standard-based, may help designers to scaffold the applications. OpenFog, established as IEEE 1934 standard, promotes the use of free open source (FOS) technologies and has been identified for use in smart buildings. However, smart building systems may present vulnerabilities, which can put their integrity at risk. Adopting state-of-the-art security mechanisms in this domain is critical but not trivial. It complicates the design and operation of the applications, increasing the cost of the deployed systems. In addition, difficulties may arise in finding qualified cybersecurity personnel. OpenFog identifies the security requirements of the applications, although it does not describe clearly how to implement them. This article presents a scalable architecture, based on the OpenFog reference architecture, to provide security by design in buildings of different sizes. It adopts FOS technologies over low-cost IoT devices. Moreover, it presents guidelines to help developers create secure applications, even if they are not security experts. It also proposes a selection of technologies in different layers to achieve the security dimensions defined in the X.805 ITU-T recommendation. A proof-of-concept Indoor Environment Quality (IEQ) system, based on low-cost smart nodes, was deployed in the Faculty of Engineering of Vitoria-Gasteiz to illustrate the implementation of the presented approach. The operation of the IEQ system was analyzed using software tools frequently used to find vulnerabilities in IoT applications. The use of state-of-the-art security mechanisms such as encryption, certificates, protocol selection and network partitioning/configuration in the OpenFog-based architecture improves smart building security. Full article

NZCCs aim to minimise urban carbon emissions for healthier cities in line with national and international low-carbon targets and Sustainable Development Goals (SDGs). Many countries have recently adopted Net-Zero Carbon City (NZCC) policies and strategies. While there are many studies available on NZCC cities’ definitions and policymaking, currently, research is rare on understanding the role of urban data-driven technologies such as Building Information Modelling (BIM) and Geographic Information Systems (GIS), as well as AI, for achieving the goals of NZCCs in relation to sustainable development goals (SDGs), e.g., SDGs 3, 7,11, 13, and 17. This paper aims to fill this gap by establishing a systematic review and ascertaining the opportunities and barriers of data-driven approaches, analytics, digital technologies, and AI for supporting decision-making and monitoring progress toward achieving NZCC development and policy/strategy development. Two scholarly databases, i.e., Web of Science and Scopus databases, were used to find papers based on our selected relevant keywords. We also conducted a desktop review to explore policies, strategies, and visualisation technologies that are already being used. Our inclusion/exclusion criteria refined our selection to 55 papers, focusing on conceptual and theoretical research. While digital technologies and data analytics are improving and can help in the move from net-zero carbon concepts and theories to practical analysis and the evaluation of cities’ emission levels and in monitoring progress toward reducing carbon, our research shows that these capabilities of digital technologies are not used thoroughly yet to bridge theory and practice. These studies ignore advanced tools like city digital twins and GIS-based spatial analyses. No data, technologies, or platforms are available to track progress towards a NZCC. Artificial Intelligence, big data collection, and analytics are required to predict and monitor the time it takes for each city to achieve net-zero carbon emissions. GIS and BIM can be used to estimate embodied carbon and predict urban development emissions. We found that smart city initiatives and data-driven decision-making approaches are crucial for achieving NZCCs. Full article

Much of today’s power grid was designed and built using technologies and organizational principles developed decades ago. The lack of energy resources and classic power networks are the main causes of the development of the smart grid to efficiently use energy resources, with stable and safe operation. In such a network, one of the fundamental priorities is provided by non-intrusive appliance load monitoring (NIALM) in order to analyze, recognize and determine the electricity consumption of each consumer. In this paper, we propose a new smart system approach for the characterization of the appliance load signature based on a data-driven method, namely the phase diagram. Our aim is to use the non-intrusive load monitoring of appliances in order to recognize different types of consumers that can exist within a smart building. Full article

Polymer self-assembly can prepare various shapes and sizes of pores, making it widely used. The complexity and diversity of biomolecules make them a unique class of building blocks for precise assembly. They are particularly suitable for the new generation of biomaterials integrated with life systems as they possess inherent characteristics such as accurate identification, self-organization, and adaptability. Therefore, many excellent methods developed have led to various practical results. At the same time, the development of advanced science and technology has also expanded the application scope of self-assembly of synthetic polymers. By utilizing this technology, materials with unique shapes and properties can be prepared and applied in the field of tissue engineering. Nanomaterials with transparent and conductive properties can be prepared and applied in fields such as electronic displays and smart glass. Multi-dimensional, controllable, and multi-level self-assembly between nanostructures has been achieved through quantitative control of polymer dosage and combination, chemical modification, and composite methods. Here, we list the classic applications of natural- and artificially synthesized polymer self-assembly in the fields of biomedicine and materials, introduce the cutting-edge technologies involved in these applications, and discuss in-depth the advantages, disadvantages, and future development directions of each type of polymer self-assembly. Full article

The timely and accurate recognition of multi-type structural surface damage (e.g., cracks, spalling, corrosion, etc.) is vital for ensuring the structural safety and service performance of civil infrastructure and for accomplishing the intelligent maintenance of smart cities. Deep learning and computer vision have made profound impacts on automatic structural damage recognition using nondestructive test techniques, especially non-contact vision-based algorithms. However, the recognition accuracy highly depends on the training data volume and damage completeness in the conventional supervised learning pipeline, which significantly limits the model performance under actual application scenarios; the model performance and stability for multi-type structural damage categories are still challenging. To address the above issues, this study proposes a dual-stage optimization-based few-shot learning segmentation method using only a few images with supervised information for multi-type structural damage recognition. A dual-stage optimization paradigm is established encompassing an internal network optimization based on meta-task and an external meta-learning machine optimization based on meta-batch. The underlying image features pertinent to various structural damage types are learned as prior knowledge to expedite adaptability across diverse damage categories via only a few samples. Furthermore, a mathematical framework of optimization-based few-shot learning is formulated to intuitively express the perception mechanism. Comparative experiments are conducted to verify the effectiveness and necessity of the proposed method on a small-scale multi-type structural damage image set. The results show that the proposed method could achieve higher segmentation accuracies for various types of structural damage than directly training the original image segmentation network. In addition, the generalization ability for the unseen structural damage category is also validated. The proposed method provides an effective solution to achieve image-based structural damage recognition with high accuracy and robustness for bridges and buildings, which assists the unmanned intelligent inspection of civil infrastructure using drones and robotics in smart cities. Full article

Hemodialysis is achieved by implanting a smart arteriovenous graft (AVG) to build a vascular pathway, but reliability and stability in data transmission cannot be guaranteed. To address this issue, a miniaturized dual-band circularly polarized implantable antenna operating at 1.4 GHz (for energy transmission) and 2.45 GHz (for wireless telemetry), implanted in a wireless arteriovenous graft monitoring device (WAGMD), has been designed. The antenna design incorporates a rectangular serpentine structure on the radiation surface to reduce its volume to 9.144 mm³. Furthermore, matching rectangular slots on the radiation surface and the ground plane enhance the antenna’s circular polarization performance. The simulated effective 3 dB axial ratio (AR) bandwidths are 11.43% (1.4 GHz) and 12.65% (2.45 GHz). The simulated peak gains of the antenna are −19.55 dBi and −22.85 dBi at 1.4 GHz and 2.45 GHz, respectively. The designed antenna is implanted in a WAGMD both in the simulation and the experiment. The performance of the system is simulated in homogeneous human tissue models of skin, fat, and muscle layers, as well as a realistic adult male forearm model. The measurement results in a minced pork environment align closely with the simulation results. Full article

Various smart home companies are adding displays to smart home control devices and are also releasing smart home control functions for devices with displays. Since smart home management devices with displays are multifunctional, they can store more digital evidence than traditional management devices. Therefore, we propose a smart home environment forensic methodology focused on wallpads, which are smart home management devices with displays. And we validate the proposed methodology by building a smart home environment centered around wallpads and conducting tests with three vendors (Samsung, Kocom, and Commax). Following the proposed methodology, we identified the software and hardware specifications of devices within the testbed, particularly the wallpads. Based on this, we were able to extract network packets, disk images, and individual files stored internally using methods such as packet capture, vulnerability exploits, serial ports, and chip-off. Through analysis, we confirmed that significant user-related information and videos are stored in these control devices. The digital evidence obtained through the proposed methodology can be used as critical legal evidence, and this study contributes to efficiently analyzing important security issues and evidential data in various smart home IoT environments. Full article

Stimuli-responsive microgels have attracted great interest in recent years as building blocks for fabricating smart surfaces with many technological applications. In particular, PNIPAM microgels are promising candidates for creating thermo-responsive scaffolds to control cell growth and detachment via temperature stimuli. In this framework, understanding the influence of the solid substrate is critical for tailoring microgel coatings to specific applications. The surface modification of the substrate is a winning strategy used to manage microgel–substrate interactions. To control the spreading of microgel particles on a solid surface, glass substrates are coated with a PEI or an APTES layer to improve surface hydrophobicity and add positive charges on the interface. A systematic investigation of PNIPAM microgels spin-coated through a double-step deposition protocol on pristine glass and on functionalised glasses was performed by combining wettability measurements and Atomic Force Microscopy. The greater flattening of microgel particles on less hydrophilic substrates can be explained as a consequence of the reduced shielding of the water–substrate interactions that favors electrostatic interactions between microgels and the substrate. This approach allows the yielding of effective control on microgel coatings that will help to unlock new possibilities for their application in biomedical devices, sensors, or responsive surfaces. Full article

With the advancement of urbanization and the continuous deepening of reforms in urban–rural systems, China’s urbanization process has entered a new era of integrated urban–rural integration. Currently, as a global “new green revolution” gains momentum, numerous countries are deeply integrating the concept of sustainable development into new urban planning. Against this backdrop, urban planners worldwide are committed to building green, livable, and smart cities that can meet the needs of the present generation without compromising the ability of future generations to meet their needs, thus achieving the vision of harmonious coexistence between humanity and nature. Characteristic towns, leveraging their resource advantages, play a significant role in achieving sustainable regional economic development. They serve as valuable references for China’s urban transformation and upgrading, as well as for promoting rural urbanization, and are crucial avenues for advancing China’s urban–rural integration development strategy. The evaluation of the development level of characteristic towns is a necessary step in their progress and a strong guarantee for promoting their construction and development. Therefore, effectively evaluating the social benefits of characteristic towns is paramount. This study constructs an evaluation model based on the grey rough set theory and Technique for Order Preference by Similarity to Ideal Solution of TOPSIS. Firstly, an evaluation index system for the development level of characteristic towns is established. Then, the grey relational analysis method and rough set theory are used to reduce the index attributes, while the conditional information entropy theory is introduced to determine the weights of the reduced indicators. Finally, the TOPSIS model is applied to evaluate the development level of characteristic towns. Through empirical research, eight characteristic towns in Zhejiang Province, China, were assessed and ranked, verifying the effectiveness and feasibility of the proposed model. Full article

The logistics landscape in e-commerce is undergoing a profound transformation toward sustainability and autonomy. This paper explores the implementation of autonomous maritime and last-mile transportation solutions to optimize the entire logistics chain from factory to customer. Building on the lessons learned from the maritime industry’s digital transformation, the study identifies key features and proposes a forward-looking autonomous maritime and last-mile transportation system. Emphasizing the role of geospatial technologies, the proposed system employs GIS-based electronic route optimization for efficient goods delivery, integrating onboard and ashore GIS-based sensors for enhanced location precision. A case study was built to analyze the implementation of autonomous means of transport along the route of a product from factory to customer. The integration of autonomous systems shows substantial improvements in logistics performance. Synchromodal logistics and smart steaming techniques can be utilized to optimize transportation routes, resulting in reduced fuel consumption and emissions. The findings reveal that autonomous maritime and last-mile transport systems can significantly enhance the efficiency, flexibility and sustainability of e-commerce logistics. The study emphasizes the need for advanced technological integration and provides a comprehensive framework for future research and practical applications in the logistics industry. Full article

The distributed energy power system needs to provide sufficient and flexible computing power on demand to meet the increasing digitization and intelligence requirements of the smart grid. However, the current distribution of the computing power and loads in the energy system is unbalanced, with data center loads continuously increasing, while there is a large amount of idle computing power at the edge. Meanwhile, there are a large number of real-time computing tasks in the distributed energy power system, which have strict requirements on execution deadlines and require reasonable scheduling of multi-level heterogeneous computing power to meet real-time computing demands. Based on the aforementioned background and issues, this paper studies the real-time service scheduling problem in a multi-level heterogeneous computing network of distributed energy power systems. Specifically, we consider the divisibility of tasks in the model. This paper presents a hierarchical real-time task-scheduling framework specifically designed for distributed energy power systems. The framework utilizes an orchestrating agent (OA) as the execution environment for the scheduling module. Building on this, we propose a hierarchical selection algorithm for choosing the appropriate network layer for real-time tasks. Further, we develop two scheduling algorithms based on greedy strategy and genetic algorithm, respectively, to effectively schedule tasks. Experiments show that the proposed algorithms have a superior success rate in scheduling compared to other current algorithms. Full article