The embodied carbon of building materials and the energy consumed during construction have a significant impact on the environmental credentials of buildings. The structural systems of a building present opportunities to reduce environmental emissions and energy. In this regard, mass timber materials have considerable potential as sustainable materials over other alternatives such as steel and concrete. The aim of this investigation was to compare the environment impact, energy consumption, and life cycle cost (LCC) of different wood-based materials in identical single-story residential buildings. The materials compared are laminated veneer lumber (LVL) and glued laminated timber (GLT). GLT has less global warming potential (GWP), ozone layer depletion (OLD), and land use (LU), respectively, by 29%, 37%, and 35% than LVL. Conversely, LVL generally has lower terrestrial acidification potential (TAP), human toxicity potential (HTP), and fossil depletion potential (FDP), respectively, by 30%, 17%, and 27%. The comparative outcomes revealed that using LVL reduces embodied energy by 41%. To identify which of these materials is the best alternative, various environmental categories, embodied energy, and cost criteria require further analysis. Therefore, the multi-criteria decision-making (MCDM) method has been applied to enable robust decision-making. The outcome showed that LVL manufacturing using softwood presents the most sustainable choice. These research findings contribute to the body of knowledge about the use of mass timber in construction.
Researchers are increasingly showing interest in the application of a Butler matrix for fifth-generation (5G) base station antennas. However, the design of the Butler matrix is challenging at millimeter wave because of the very small wavelength. The literature has reported issues of high insertion losses and incorrect output phases at the output ports of the Butler matrix, which affects the radiation characteristics. To overcome these issues, the circuit elements of the Butler matrix such as the crossover, the quadrature hybrid and the phase shifter must be designed using highly accurate dimensions. This paper presents a low-loss and compact single-layer 8 × 8 Butler matrix operating at 28 GHz. The optimum design of each circuit element is also demonstrated in detail. The designed Butler matrix was fabricated to validate the simulated results. The measured results showed return losses of less than -10 dB at 28 GHz. The proposed Butler matrix achieved a low insertion loss and a low phase error of ± 2 dB and ± 10°, respectively. In sum, this work obtained a good agreement between the simulated and measured results.
A comprehensive review on recent developments and applications of circularly polarized (CP) dielectric resonator antennas (DRAs) is proposed in this paper. DRAs have received more considerations in various applications due to their advantages such as wide bandwidth, high gain, high efficiency, low losses, and low profile. A broad justification for circular polarization and DRAs is stated at the beginning of the review. Various techniques such as single feed, dual, or multiple feeds used by different researchers for generating circular polarization in DRAs are briefly studied in this paper. Multiple-input-multiple-output (MIMO) CP DRAs, which can increase channel capacity, link reliability, and data rate, have also been analyzed. Additionally, innovative design solutions for broadening the circular polarization bandwidth and reducing mutual coupling are studied. Several applications of DRA are also discussed comprehensively. This paper finishes with concluding remarks.
Breast cancer is the most common cancer diagnosed in women and the leading cause of cancer-related deaths among women worldwide. The death rate is high because of the lack of early signs. Due to the absence of a cure, immediate treatment is necessary to remove the cancerous cells and prolong life. For early breast cancer detection, it is crucial to propose a robust intelligent classifier with statistical feature analysis that considers parameter existence, size, and location. This paper proposes a novel Multi-Stage Feature Selection with Binary Particle Swarm Optimization (MSFS-BPSO) using Ultra-Wideband (UWB). A collection of 39,000 data samples from non-tumor and with tumor sizes ranging from 2 to 7 mm was created using realistic tissue-like dielectric materials. Subsequently, the tumor models were inserted into the heterogeneous breast phantom. The breast phantom with tumors was imaged and represented in both time and frequency domains using the UWB signal. Consequently, the dataset was fed into the MSFS-BPSO framework and started with feature normalization before it was reduced using feature dimension reduction. Then, the feature selection (based on time/frequency domain) using seven different classifiers selected the frequency domain compared to the time domain and continued to perform feature extraction. Feature selection using Analysis of Variance (ANOVA) is able to distinguish between class-correlated data. Finally, the optimum feature subset was selected using a Probabilistic Neural Network (PNN) classifier with the Binary Particle Swarm Optimization (BPSO) method. The research findings found that the MSFS-BPSO method has increased classification accuracy up to 96.3% and given good dependability even when employing an enormous data sample.