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This study investigates the enhancement of hydraulic lime mortar (HLM) using varying contents of metakaolin (MK) to improve its application in the restoration of historic buildings. Samples from historic structures were analyzed, and the effects of different MK contents on the physical and mechanical properties of HLM were examined. The reaction mechanism and microstructural changes were evaluated using XRD and SEM analysis. The results indicated that increasing MK levels in HLM led to a decrease in fluidity, with fluidity reducing by 4.8% at 12% MK. The addition of MK increased water consumption for standard consistency by 5.4% and shortened the final setting time by 10.2%. MK consumption promoted secondary hydration, enhancing compressive strength by up to 98.1% and flexural strength by up to 55.1%, and increasing bonding strength by 26.9%. The density of HLM improved with MK addition, slightly reducing moisture content by 4.5% and water absorption by 4.6%, while the water vapor transmission properties decreased by 50.9%, indicating reduced porosity. The elastic modulus of the mortar increased significantly from 2.19 GPa to 7.88 GPa with the addition of MK, enhancing rigidity and crack resistance. The optimal blend for restoration materials was found to be 9.0% MK and 25.0% heavy calcium carbonate and was characterized by moderate mechanical strength, enhanced early strength, commendable permeability, minimal risk of cracking, and ease of application. This blend is highly suitable for the rehabilitation of historic structures. [ABSTRACT FROM AUTHOR]

Sensor-related indoor localization has attracted considerable attention in recent years. The accuracy of conventional fingerprint solutions based on a single sensor, such as a Wi-Fi sensor, is affected by multipath interferences from other electronic devices that are produced as a result of complex indoor environments. Light sensors and magnetic (i.e., geomagnetic) field sensors can be used to enhance the accuracy of a system since they are less vulnerable to disturbances. In this paper, we propose a deep feedforward (DFF)-neural-network-based method, termed DFF-WGL, which integrates the data from the embedded Wi-Fi sensor, geomagnetic field sensor, and light sensor (WGL) in a smart device to localize the device in an indoor environment. DFF-WGL does not require complex and expensive auxiliary equipment, except for basic fluorescent lamps and low-density Wi-Fi signal coverage, conditions that are easily satisfied in modern offices or educational buildings. The proposed system was implemented on a commercial off-the-shelf android device, and performance was evaluated through an experimental analysis conducted in two different indoor testbeds, one measuring 60.5 m2 and the other measuring 38 m2, with 242 and 60 reference points, respectively. The results indicate that the model prediction with an input consisting of the combination of light, a magnetic field sensor, and two Wi-Fi RSS signals achieved mean localization errors of 0.01 m and 0.04 m in the two testbeds, respectively, compared with any subset of combination of sensors, verifying the effectiveness of the proposed DFF-WGL method. [ABSTRACT FROM AUTHOR]

The technology for human activity recognition has diverse applications within the Internet of Things spectrum, including medical sensing, security measures, smart home systems, and more. Predominantly, human activity recognition methods have relied on contact sensors, and some research uses inertial sensors embedded in smartphones or other devices, which present several limitations. Additionally, most research has concentrated on recognizing discrete activities, even though activities in real-life scenarios tend to be continuous. In this paper, we introduce a method to classify continuous human activities, such as walking, running, squatting, standing, and jumping. Our approach hinges on the micro-Doppler (MD) features derived from continuous-wave radar signals. We first process the radar echo signals generated from human activities to produce MD spectrograms. Subsequently, a bidirectional gate recurrent unit (Bi-GRU) network is employed to train and test these extracted features. Preliminary results highlight the efficacy of our approach, with an average recognition accuracy exceeding 90%. [ABSTRACT FROM AUTHOR]

Device-free localization (DFL) is a technology designed to determine the positions of targets without the need for them to carry electronic devices. It achieves this by analyzing the shadowing effects of radio links within wireless sensor networks (WSNs). However, obtaining high precision in DFL often results in increased energy consumption, severe electromagnetic interference, and other challenges that impact positioning accuracy. Most DFL schemes for accurate tracking require substantial memory and computing resources, which make them unsuitable for resource-constrained applications. To address these challenges, we propose an intelligent mesh cluster (IMC) algorithm that achieves accurate tracking by adaptively activating a subset of wireless links. This approach not only reduces electromagnetic interference but also saves energy. The IMC algorithm leverages geometric objects, such as meshes and mesh clusters formed by wireless links, to achieve low computational complexity. By scanning a subset of mesh cluster-related wireless links near the DFL target, the algorithm significantly reduces the computational requirements. The target's location estimate is determined based on the connection information among the mesh clusters. We conducted numerous simulations to evaluate the performance of the IMC algorithm. The results demonstrate that the IMC algorithm outperforms grid-based and particle filter-based DFL methods, confirming its effectiveness in achieving accurate and efficient localization. [ABSTRACT FROM AUTHOR]

Polyurethane acrylate prepolymers with different contents of HIPIH and HIH were synthesized via reacting excessive isophorone diisocyanate (IPDI) with poly(propylene glycol) (PPG) and then end-capping with 2-hydroxypropyl methacrylate (HPMA) in isobornyl methacrylate (IBOMA). After the addition of the photoinitiator PI 1173, the resulting prepolymer resins were irradiated by UV light to form cured materials. The structures of the prepolymers were confirmed by 1H NMR, FT-IR, and GPC. SEM analyses proved that no obvious phase separation was observed within the cured sample. As the content of HIH increased, the viscosity of the prepolymers increased slightly. In addition, the gel content, solvent resistance, Shore hardness, Young's modulus, and the tensile strength of the cured films increased, whereas the elongation at break decreased gradually. The volume shrinkage of the cured samples ranged between 4.5% and 4.8%. DMA analyses showed that the Tgs of the cured samples increased as more HIH structures existed. TGA analyses revealed that the cured samples had high thermal stability. This solvent-free fabrication process was simple, convenient, and controllable. By simply regulating the contents of HIPIH and HIH in the prepolymers, the performances of the cured materials could be adjusted to a wide range. [ABSTRACT FROM AUTHOR]

In the tracking scheme in which global navigation satellite system (GNSS) measurement is temporally lost or the sampling frequency is insufficient, dead reckoning based on the inertial measurement unit (IMU) and other location-related information can be fused as a supplement for real-time trajectory recovery. The tracking scheme based on interval analysis outputs interval results containing the ground truth, which gives it the advantage of convenience in multi-source information fusion. In this paper, a trajectory-recovery algorithm based on interval analysis is proposed, which can conveniently fuse GNSS measurement, IMU data, and map constraints and then output an interval result containing the actual trajectory. In essence, the location-related information such as satellite measurement, inertial data, and map constraints is collected by practical experiments and then converted into interval form. Thereby, the interval-overlapping calculation is performed through forward and backward propagation to accomplish the trajectory recovery. The practical experimental results show that the trajectory recovery accuracy based on the proposed algorithm performs better than the traditional Kalman filter algorithm, and the estimated interval results deterministically contain the actual trajectory. More importantly, the proposed interval algorithm is approved to be convenient to fuse additional location-related information. [ABSTRACT FROM AUTHOR]

Although reducing environmental inequities is widely recognized as an essential step towards sustainable cities, decision-makers frequently lack the tools to identify inequity distribution patterns and designing effective intervention policies. This study seeks to present a framework that can help decision-making processes by classifying environmental inequity districts in multiple perspectives, especially in absolute and relative terms. This framework includes four steps: (A) variable selection, (B) data normalization, (C) ranking indicators, (D) summarizing inequity classification, which then assign results to selected areas. The framework aims to classify and compare environmental inequities in multiple perspectives, and can be applied in various environmental problems, with advantages such as high acceptability and clear comprehensibility. To show the potential use of this framework, a case application in Beijing, China, was conducted to evaluate the environmental inequity of air pollution. The results suggest that decision-makers should focus on the central urban area and some southern regions of Beijing to implement various improvement policies. Based on the results from Beijing, how the framework can be used to help decision-makers, the future roles of this framework with the government and the public, as well as the framework's limitations are further discussed. [ABSTRACT FROM AUTHOR]