Search Results (42)

Automation in textile manufacturing plays a pivotal role in enhancing production efficiency, precision, and innovation. This study investigates the integration of intelligent technologies in the knitting sector, focusing on industrial flat knitting machines from a leading manufacturer and the use of the advanced software platform M1plus V7.5. The software’s capabilities for the digital design and simulation of complex patterned and structural knits are explored through the development and production of five experimental knitted designs. Each sample is evaluated in terms of its structural characteristics and dimensional behaviour after washing. The results highlight the potential of software-driven optimisation to improve product accuracy, reduce shrinkage variability, and support smart manufacturing practices in the textile industry. Full article

Accurate photovoltaic (PV) power forecasting is crucial for effective smart grid management, given the intermittent nature of PV generation. To address these challenges, this paper proposes the Temporal Bottleneck-enhanced Bidirectional Temporal Convolutional Network with Multi-Head Attention and Autoregressive (TB-BTCGA) model. It introduces a temporal bottleneck structure and Deep Residual Shrinkage Network (DRSN) into the Temporal Convolutional Network (TCN), improving feature extraction and reducing redundancy. Additionally, the model transforms the traditional TCN into a bidirectional TCN (BiTCN), allowing it to capture both past and future dependencies while expanding the receptive field with fewer layers. The integration of an autoregressive (AR) model optimizes the linear extraction of features, while the inclusion of multi-head attention and the Bidirectional Gated Recurrent Unit (BiGRU) further strengthens the model’s ability to capture both short-term and long-term dependencies in the data. Experiments on complex datasets, including weather forecast data, station meteorological data, and power data, demonstrate that the proposed TB-BTCGA model outperforms several state-of-the-art deep learning models in prediction accuracy. Specifically, in single-step forecasting using data from three PV stations in Hebei, China, the model reduces Mean Absolute Error (MAE) by 38.53% and Root Mean Square Error (RMSE) by 33.12% and increases the coefficient of determination ($R^2$) by 7.01% compared to the baseline TCN model. Additionally, in multi-step forecasting, the model achieves a reduction of 54.26% in the best MAE and 52.64% in the best RMSE across various time horizons. These results underscore the TB-BTCGA model’s effectiveness and its strong potential for real-time photovoltaic power forecasting in smart grids. Full article

Smart concrete is a structural element that can combine both sensing and structural capabilities. In addition, smart concrete can monitor the curing of concrete, positively impacting design and construction approaches. In concrete, if the curing process is not well developed, the structural element may develop cracks in this early stage due to shrinkage, decreasing structural mechanical strength. In this paper, a system of measurement using fiber Bragg grating (FBG) sensors for monitoring the curing of concrete was developed to evaluate autogenous shrinkage strain, temperature, and relative humidity (RH) in a single system. Furthermore, K-type thermocouples were used as reference temperature sensors. The results presented maximum autogenous shrinkage strains of 213.64 $\mu \epsilon$, 125.44 $\mu \epsilon$, and 173.33 $\mu \epsilon$ for FBG4, FBG5, and FBG6, respectively. Regarding humidity, the measured maximum relative humidity was 98.20 %RH, which was reached before 10 h. In this case, the recorded maximum temperature was 63.65 °C and 61.85 °C by FBG2 and the thermocouple, respectively. Subsequently, the concrete specimen with the FBG strain sensor embedded underwent a bend test simulating beam behavior. The measurement system can transform a simple structure like a beam into a smart concrete structure, in which the FBG sensors’ signal was maintained by the entire applied load cycles and compared with FBG strain sensors superficially positioned. In this test, the maximum strain measurements were 85.65 $\mu \epsilon$, 123.71 $\mu \epsilon$, and 56.38 $\mu \epsilon$ on FBG7, FBG8, and FBG3, respectively, with FBG3 also monitoring autogenous shrinkage strain. Therefore, the results confirm that the proposed system of measurement can monitor the cited parameters throughout the entire process of curing concrete. Full article

In the context of ongoing global urbanization, the disparity in urban development, marked by the dual phenomena of urban sprawl and urban shrinkage at the regional level, has become increasingly evident. In this vein, two land-related governance strategies—smart growth (SG) and smart shrinkage (SS)—emerge as potential remedies to these challenges, targeting urban expansion and shrinkage, respectively. This study bridges the gap in the fragmented discourse surrounding SG and SS by conducting a comprehensive comparative review on the respective literatures. Utilizing the Scopus database, our research employs trend analysis, text and topic mining, time node analysis, and regional analysis, augmented by qualitative reviews of seminal papers. The findings reveal a notable shift in research focus, with interest in SS surging around 2010 (the number of SS-related papers published after 2010 accounts for 92.3% of the total number of the entire study period) as attention to SG waned, suggesting an impending paradigm shift in urban sustainability. The analysis indicates that SS research lacks the disciplinary diversity, thematic breadth, and empirical depth of SG studies, underscoring a need for a more robust theoretical foundation to support urban sustainability. Furthermore, while both SG and SS derive from environmental science foundations, SG predominantly addresses the physical and landscape attributes of urban areas, whereas SS focuses more on socio-economic dimensions. Our findings point to an intrinsic link between SG and SS, which could lay the groundwork for their integration into a unified theoretical framework to better advance urban sustainability. Full article

Estimating and monitoring chlorophyll content is a critical step in crop spectral image analysis. The quick, non-destructive assessment of chlorophyll content in rice leaves can optimize nitrogen fertilization, benefit the environment and economy, and improve rice production management and quality. In this research, spectral analysis of rice leaves is performed using hyperspectral and fluorescence spectroscopy for the detection of chlorophyll content in rice leaves. This study generated ninety experimental spectral datasets by collecting rice leaf samples from a farm in Sichuan Province, China. By implementing a feature extraction algorithm, this study compresses redundant spectral bands and subsequently constructs machine learning models to reveal latent correlations among the extracted features. The prediction capabilities of six feature extraction methods and four machine learning algorithms in two types of spectral data are examined, and an accurate method of predicting chlorophyll concentration in rice leaves was devised. The IVSO-IVISSA (Iteratively Variable Subset Optimization–Interval Variable Iterative Space Shrinkage Approach) quadratic feature combination approach, based on fluorescence spectrum data, has the best prediction performance among the CNN+LSTM (Convolutional Neural Network Long Short-Term Memory) algorithms, with corresponding RMSE-Train (Root Mean Squared Error), RMSE-Test, and RPD (Ratio of standard deviation of the validation set to standard error of prediction) indexes of 0.26, 0.29, and 2.64, respectively. We demonstrated in this study that hyperspectral and fluorescence spectroscopy, when analyzed with feature extraction and machine learning methods, provide a new avenue for rapid and non-destructive crop health monitoring, which is critical to the advancement of smart and precision agriculture. Full article

Considerable attention has been paid to (1E)-1,2-diaryldiazenes (azo dyes) possessing liquid–crystalline (LC) and optical properties because they can switch color through thermal phase transitions and photoisomerizations. Although multifunctional molecules with both LC and fluorescent properties based on a donor–π-acceptor (D-π-A)-type tolane skeleton have been developed, functional molecules possessing LC and dye properties have not yet been developed. Therefore, this study proposes to develop LC dyes consisting of (1E)-1,2-diaryldiazenes with a D–π-A-type tolane skeleton as the aryl moiety. The (1E)-1,2-diaryldiazene derivatives exhibited a smectic phase, regardless of the flexible-chain structure, whereas the melting temperature was significantly increased by introducing fluoroalkyl moieties into the flexible chain. Evaluation of the optical properties revealed that compounds with decyloxy chains exhibited an orange color, whereas compounds with semifluoroalkoxy chains absorbed at a slightly blue-shifted wavelength, which resulted in a pale orange color. The thermal phase transition caused a slight color change accompanied by a change in the absorption properties, photoisomerization-induced shrinkage, and partial disappearance of the LC domain. These results indicate that (1E)-1,2-diaryldiazenes with a D–π-A-type tolane skeleton can function as thermo- or photoresponsive dyes and are applicable to smart windows and in photolithography. Full article

The coexistence of urban expansion and shrinkage in China has become increasingly apparent; therefore, the current strategic model of growth-oriented urban planning as the top-level design needs to be adjusted. This paper focuses on the city of Baishan, which is a typical shrinking city in China, and explores the feasibility of implementing the concept of smart shrinkage planning in shrinking cities in China by constructing a coupled PLUS-SD model. The results demonstrate the following conclusions: (1) The overall simulation of the coupled PLUS-SD model is superior to that of the PLUS model. In Baishan, the areas with the most changes in construction land will be located at the edges of the landforms by 2030. (2) Using the traditional planning scenario would only exacerbate the rate of construction land expansion in Baishan, deepening the incongruity between the city’s population and construction land. (3) The smart shrinkage scenario will require strict control of the scale of construction land and optimization of the structure of the urban construction land, which would push the city in the direction of healthy and sustainable development. (4) The concept of smart shrinkage planning is a scientific and feasible plan for realizing the efficient and sustainable use of construction land in shrinking cities. Full article

Vulnerabilities in smart contracts may trigger serious security events, and the detection of smart contract vulnerabilities has become a significant problem. In this paper, to solve the limitations of current deep learning-based vulnerability detection methods in extracting various code critical features, using the multi-scale cascade encoder architecture as the backbone, we propose a novel Multi-Scale Encoder Vulnerability Detection (MEVD) approach to hit well-known high-risk vulnerabilities in smart contracts. Firstly, we use the gating mechanism to design a unique Surface Feature Encoder (SFE) to enrich the semantic information of code features. Then, by combining a Base Transformer Encoder (BTE) and a Detail CNN Encoder (DCE), we introduce a dual-branch encoder to capture the global structure and local detail features of the smart contract code, respectively. Finally, to focus the model’s attention on vulnerability-related characteristics, we employ the Deep Residual Shrinkage Network (DRSN). Experimental results on three types of high-risk vulnerability datasets demonstrate performance compared to state-of-the-art methods, and our method achieves an average detection accuracy of 90%. Full article

The recent advances in smart beekeeping focus on remote solutions for bee colony monitoring and applying machine learning techniques for automatic decision making. One of the main applications is a swarming alarm, allowing beekeepers to prevent the bee colony from leaving their hive. Swarming is a naturally occurring phenomenon, mainly during late spring and early summer, but it is extremely hard to predict its exact time since it is highly dependent on many factors, including weather. Prevention from swarming is the most effective way to keep bee colonies; however, it requires constant monitoring by the beekeeper. Drone bees do not survive the winter and they occur in colonies seasonally with a peak in late spring, which is associated with the creation of drone congregation areas, where mating with young queens takes place. The paper presents a method of early swarming mood detection based on the observation of drone bee activity near the entrance to a hive. Audio recordings are represented by Mel Frequency Cepstral Coefficients and their first and second derivatives. The study investigates which MFCC coefficients, selected by the Least Absolute Shrinkage and Selection Operator, are significant for the worker bee and drone bee classification task. The classification results, obtained by an autoencoder neural network, allow to improve the detection performance, achieving accuracy slightly above 95% for the chosen set of signal features, selected by the proposed method, compared to the standard set of MFCC coefficients with only up to 90% accuracy. Full article

Plastic shrinkage cracking is a complex and multifaceted process that occurs in the period between placement and the final setting. During this period, the mixture is viscoplastic in nature and therefore possesses rheological properties. The investigation of the relationship between rheological behavior and its propensity to undergo cracking during the plastic phase presents an intriguing subject of study. However, many factors influence plastic cracking, and the corresponding interaction of its effects is complex in nature. This study aimed to evaluate the impact of rheological and physicomechanical properties on the occurrence of plastic cracking in high-performance shotcrete containing various supplementary cementitious materials. To achieve this, plastic cracking was evaluated employing the ASTM C 1579 standard and a smart crack viewer FCV-30, and the rheological parameters were controlled using an ICAR rheometer. In addition, a study was conducted to assess the strength development and fresh properties. Further, a relationship was established via statistical evaluation, and the best predicting models were selected. According to the study results, it can be concluded that high-yield stress and low plastic viscosity for colloidal silica mixtures are indicators of plastic cracking resistance owing to improved fresh microstructure and accelerated hydration reaction. However, earlier strength development and the presence of a water-reducing admixture allowed mixtures containing silica fume to achieve crack reduction. A higher indicator of yield stress is an indicator of the capillary pressure development of these mixtures. In addition, a series containing ultrafine fly ash (having high flow resistance and torque viscosity) exhibited a risk of early capillary pressure build-up and a decrease in strength characteristics, which could be stabilized with the addition of colloidal silica. Consequently, the mixture containing both silica fume and colloidal silica exhibited the best performance. Thus, the results indicated that rheological characteristics, compressive strength, and water-reducer content can be used to control the plastic shrinkage cracking of shotcrete. Full article

Unmanned aerial vehicles (UAVs) are an important part of smart farms and have been widely used in granular fertilizer spreading. The multichannel pneumatic granular fertilizer spreader (MPGFS) has the advantages of light weight and precision spreading, and has been applied to UAV variable rate fertilization. Based on the problem that the airflow field disorder of the existing MPGFS reduces the uniformity of spreading, the aim of this study was to further improve the performance of the MPGFS through rectification. The computational fluid dynamics and discrete element method (CFD-DEM) and coupling simulation method were used to study the characteristics of the airflow field and fertilizer particle motion, and a honeycomb rectifier and grid rectifier were developed. The aperture of the honeycomb rectifier and the grid size of the grid rectifier were optimized. Then, the test bench was built to test the consistency of the discharge rate of each channel and the spreading uniformity of the MPGFS. The simulation results of the existing MPGFS showed that the airflow provided by the axial flow fan was rotational, and this caused the particles’ motion to be skewed in the shrinkage section, so the discharge rate of each channel was inconsistent. The airflow field analysis results of the shrinkage section showed that the airflow rotation was reduced after the rectification of the honeycomb rectifier and the grid rectifier. The bench test results showed that the coefficient of variation (CV) of each channel discharge rate of the existing MPGFS was 20.16%, the optimal honeycomb rectifier was 13.07%, and the optimal grid rectifier was 5.27%. The bench test results of spreading uniformity show that the CV of spreading uniformity of the existing MPGFS was 15.32%, the optimal honeycomb rectifier was 15.81%, and the optimal grid rectifier was 8.02%. The grid rectifier spread pattern was more reasonable and the CV of uniformity was better. This study demonstrated that the use of a grid rectifier to rectify the airflow field of MPGFS can effectively improve its spreading uniformity, which was of guiding significance for the design and research of MPGFS. Full article

The measurement of respiratory volume based on upper body movements by means of a smart shirt is increasingly requested in medical applications. This research used upper body surface motions obtained by a motion capture system, and two regression methods to determine the optimal selection and placement of sensors on a smart shirt to recover respiratory parameters from benchmark spirometry values. The results of the two regression methods (Ridge regression and the least absolute shrinkage and selection operator (Lasso)) were compared. This work shows that the Lasso method offers advantages compared to the Ridge regression, as it provides sparse solutions and is more robust to outliers. However, both methods can be used in this application since they lead to a similar sensor subset with lower computational demand (from exponential effort for full exhaustive search down to the order of O (n²)). A smart shirt for respiratory volume estimation could replace spirometry in some cases and would allow for a more convenient measurement of respiratory parameters in home care or hospital settings. Full article

Currently, Chinese cities are experiencing both overall growth and localized shrinkage. Therefore, it becomes crucial to quantify urban shrinkage and explore the transformation and sustainable development of shrinking cities from the perspective of urban resilience. This study focuses on the three provinces of Northeast China, which are representative areas of urban shrinkage, as its research subjects. Employing the analytic hierarchy process, a comprehensive evaluation system for urban shrinkage is constructed based on three dimensions: population, economy, and space. Furthermore, urban resilience is scientifically measured from four aspects: economy, society, ecology, and infrastructure. The study analyzes the spatiotemporal evolution characteristics of urban shrinkage and urban resilience in the three northeastern provinces from 2012 to 2018. It also examines the impact of urban shrinkage on urban resilience through regression analysis and mediation models. The results indicate the following: (1) Half of the cities in the three northeastern provinces experienced shrinkage, although the extent of shrinkage decreased with the implementation of the Northeast China revitalization strategy. Population-related shrinkage was the most extensive and continued to expand, while economy-related shrinkage was the most severe, and space-related shrinkage was the least severe. (2) The resilience of shrinking cities was lower than the average level. Population-shrinking cities and economy-shrinking cities exhibited low levels of economic resilience, and the gap between them continued to widen. Space-shrinking cities generally had low infrastructure resilience. (3) The urban shrinkage index had a significant positive impact on the urban resilience index, mediated through intermediary variables, such as innovation capability and cultural development. Notably, both the direct and indirect effects of innovation capability were the greatest. Population-related shrinkage had the largest impact on urban resilience, while more intermediary variables of economy-related shrinkage passed the significance test. Full article

Rural industrialization is one of the core drivers of urban and rural spatial evolution and economic transformation in China. Given the background of stock and reduction planning, the development of rural industrial land, which has long relied on land inputs to increase production and inefficient expansion, is facing severe constraints and challenges. How to improve the spatial performance of rural industrial land and promote industrial upgrading and intensive land use have become vital issues for the healthy development of rural areas. This paper draws upon smart shrinkage theory to provide an analytical framework for the intensive-use-oriented performance evaluation of rural industrial land, unlike the evaluation method of efficiency orientation for industrial land, which emphasizes the core goal of the input and output of production factors per unit area. Based on the analysis framework, this study explored the parcel-microscale performance evaluation methods for rural industrial land, and the evaluation index system construction covers the four dimensions of economic performance, social performance, ecological performance, and land use structure performance. Wujiang District of Suzhou City was used as a case study to carry out a comprehensive performance evaluation and analyze the differences in RILP in space and industry. Based on the evaluation results, the key problems of rural industrial land were identified, and corresponding optimization strategies for rural industrial land are proposed from the aspects of land use control, spatial agglomeration, and industrial upgrading. This study intended to address the current major national strategic needs and solve the real dilemmas faced in the process of rural industrial land development. It is hoped that the study will provide a theoretical reference for the transformation of rural industrial land and policy-making for rural revitalization. Full article

Recently, silsesquioxanes (SSQ) and polyhedral oligomeric silsesquioxanes (POSS) have gained much interest in the area of biomaterials, mainly due to their intrinsic properties such as biocompatibility, complete non-toxicity, the ability to self-assemble and to form a porous structure, facilitating cell proliferation, creating a superhydrophobic surface, osteoinductivity, and ability to bind hydroxyapatite. All the above has resulted in new developments in medicine. However, the application of POSS-containing materials in dentistry is still at initial stage and deserves a systematic description to ensure future development. Significant problems, such as reduction of polymerization shrinkage, water absorption, hydrolysis rate, poor adhesion and strength, unsatisfactory biocompatibility, and corrosion resistance of dental alloys, can be addressed by the design of multifunctional POSS-containing materials. Because of the presence of silsesquioxanes, it is possible to obtain smart materials that allow the stimulation of phosphates deposition and repairing of micro-cracks in dental fillings. Hybrid composites result in materials exhibiting shape memory, as well as antibacterial, self-cleaning, and self-healing properties. Moreover, introducing POSS into polymer matrix allows for materials for bone reconstruction, and wound healing. This review covers the recent developments in the field of POSS application in dental materials and gives the future perspectives within a promising field of biomedical material science and chemical engineering. Full article