Axial-Flexural Behaviour of Reinforced Concrete Masonry Columns Confined by FRP Jackets

Confining existing concrete and masonry columns by Fibre Reinforced Polymers (FRP) is a beneficial method for enhancing the column capacity and ductility. The popularity of using FRP for strengthening and upgrading columns is mainly attributed to the high strength and lightweight characteristics of the FRP materials. Using FRP composites reduces additional dead load associated with traditional strengthening solutions and simplify the application in areas with limited access. The goal of this research is to experimentally quantify the enhancement in strength and strain capacity of Carbon FRP (CFRP) confined concrete masonry columns under concentric and eccentric loading. Research on FRP-strengthened concrete masonry columns under eccentric loads is essential to understand the effect of this retrofitting technique on the performance of columns. The experimental data was then used to propose a simplified methodology that predicts the axial force-moment interaction diagram of fully grouted reinforced concrete masonry column strengthened with FRP jackets. The methodology considers short prismatic reinforced concrete masonry columns failing in a compression controlled manner and complies with equilibrium and strain compatibility principles. To achieve the research goals, 47 scaled fully grouted concrete block masonry columns were tested under concentric, eccentric, and bending loading up to failure. Parameters investigated in this research include the thickness of CFRP jacket, corner radius of cross section and the magnitude of eccentricity. The proposed analytical methodology showed a good correlation with the experimental data. Parametric study was carried out to determine the effect of design variables on the axial-flexural interaction of fully grouted reinforced concrete masonry column strengthened by FRP jackets

Edge Deep Learning and Computer Vision-Based Physical Distance and Face Mask Detection System Using Jetson Xavior NX

This paper proposes a fully automated vision-based system for real-time COVID-19 personal protective equipment detection and monitoring. Through this paper, we aim to enhance the capability of on-edge real-time face mask detection as well as improve social distancing monitoring from real-live digital videos. Using deep neural networks, researchers have developed a state-of-the-art object detector called "You Only Look Once Version Five" (YOLO5). On real images of people wearing COVID19 masks collected from Google Dataset Search, YOLOv5s, the smallest variant of the object detection model, is trained and implemented. It was found that the Yolov5s model is capable of extracting rich features from images and detecting the face mask with a high precision of better than 0.88 mAP_0.5. This model is combined with the Density-Based Spatial Clustering of Applications with Noise method in order to detect patterns in the data to monitor social distances between people. The system is programmed in Python and implemented on the NVIDIA Jetson Xavier board. It achieved a speed of more than 12 frames per second. Doi: 10.28991/ESJ-2023-SPER-05 Full Text: PD

Exchange Interactions and High-Energy Spin States in Mn_12-acetate

We perform inelastic neutron scattering measurements on the molecular nanomagnet Mn_12-acetate to measure the excitation spectrum up to 45meV (500K). We isolate magnetic excitations in two groups at 5-6.5meV (60-75K) and 8-10.5meV (95-120K), with higher levels appearing only at 27meV (310K) and 31meV (360K). From a detailed characterization of the transition peaks we show that all of the low-energy modes appear to be separate S = 9 excitations above the S = 10 ground state, with the peak at 27meV (310K) corresponding to the first S = 11 excitation. We consider a general model for the four exchange interaction parameters of the molecule. The static susceptibility is computed by high-temperature series expansion and the energy spectrum, matrix elements and ground-state spin configuration by exact diagonalization. The theoretical results are matched with experimental observation by inclusion of cluster anisotropy parameters, revealing strong constraints on possible parameter sets. We conclude that only a model with dominant exchange couplings J_1 ~ J_2 ~ 5.5meV (65K) and small couplings J_3 ~ J_4 ~ 0.6meV (7K) is consistent with the experimental data.Comment: 17 pages, 12 figure

Vibroacoustic Efficiency Evaluation of Anti-Vibration Piping Support for Engineering Systems of Multi-Storey Buildings

The causes of low-frequency noise in high-rise building pipelines are studied. Sources of increased vibration of pipeline systems are identified. The analysis of methods of damping increased vibrations of pipelines is conducted. A description of the pipeline support structures used in the field of building construction and special anti-vibration supports is given. The advantages and disadvantages of the designs of anti-vibration supports of pipeline systems are identified. A method for improving the existing anti-vibration support consisting of all-metal elastic-damping elements pressed from spirals of wire is proposed. An anti-vibration support, consisting of a body and sealed capsules filled with a damping fluid, was developed. The design of the anti-vibration support, which allows adapting to the vibration mode due to the displacement of the pipeline relative to the support body and the redistribution of the damping fluid pressure in the capsules, is presented. The effectiveness of the proposed design support was confirmed experimentally. © Published under licence by IOP Publishing Ltd

Variations in Litterfall Dynamics, C:N:P Stoichiometry and Associated Nutrient Return in Pure and Mixed Stands of Camphor Tree and Masson Pine Forests

Litterfall, directly and indirectly, affects the soil physicochemical properties, microbial activity, and diversity of soil fauna and flora by adding organic matter and nutrients to the soil. This study explores litterfall dynamics such as litterfall production, litter decomposition rate, and associated nutrient return in three forest types, that is, camphor tree forest (CTF), Masson pine forest (MPF), and camphor tree and Masson pine mixed forest (CMF), in subtropical China. Results showed that CMF had the highest mean annual litterfall production (4.30 ± 0.22 t ha−1), which was significantly higher than that of MPF (3.41 ± 0.25 t ha−1) and CTF (3.26 ± 0.17 t ha−1). Leaf represented the major fraction of litterfall, constituting over 71% of the total litterfall mass in the three forest types. The contribution of branch litter was 16.3, 8.9, and 16.9%, and miscellaneous litter was 12.6, 18.9, and 11.1% in CTF, MPF, and CMF, respectively. The concentration of macronutrients ranked as N > Ca > K > Mg > P in all litter fractions. The total annual macronutrient return to the soil from the litterfall was in order as CTF (74.2 kg ha−1‧yr−1) > CMF (70.7 kg ha−1‧yr−1) > MPF (33.6 kg ha−1‧yr−1). The decomposition rate was higher in leaf litter than in branch litter throughout the three forests. Among the forest types, the leaf and branch decomposition rates were in a pattern: CTF > CMF > MPF. The ratio of C/N in both leaf and branch litters was significantly higher in MPF than in CTF and CMF, while no significant differences in N/P ratio were found in these litters among the three forests. The high N:P ratios in leaf litter (23/30) and the branch (24/32) litter indicated the high N returning and low nutrient returning to the soil. Our results suggested that the broadleaved forests have faster litter decomposition and higher macronutrient returns than conifer forests. Moreover, the litter decomposition rate was mainly associated with litterfall quality and chemical composition. The introduction of broadleaved trees into monoculture coniferous stands could increase litter production nutrients return, and thus, it had advantages in soil nutrients restoration and sustainable forest management

Harnessing Artificial Intelligence for Effective Leadership: Opportunities and Challenges

Abstract: The integration of Artificial Intelligence (AI) into leadership practices is transforming organizational dynamics and This decision-making processes. paper explores how AI can enhance leadership effectiveness by providing data-driven insights, optimizing decision-making, and automating routine tasks. It also examines the challenges leaders face in adopting AI, including ethical considerations, potential biases in AI systems, and the need for upskilling. By analyzing current applications of AI in leadership and discussing future trends, this study aims to provide a comprehensive overview of the opportunities AI presents for effective leadership and the strategies required to address its associated challenges

Ten antenna array using a small footprint capacitive-coupled-shorted loop antenna for 3.5 GHz 5G smartphone applications

ABSTRACT: A self-isolated 10-element antenna array operating in the long-term evolution 42 (LTE42) frequency band is proposed for 5G massive MIMO smartphone applications. The proposed antenna elements are placed in a 2D array configuration; they are placed symmetrically along the two long edges of the mobile chassis. The proposed antenna structure is a shorted loop antenna resonating at half-wavelength mode, which is rarely deployed by researchers due to its large size compared to other quarter wavelength antenna structures. It is a printed, shorted, and compact loop antenna of a total footprint area of 6 × 6.5 mm2 (λ/14.3 ×λ/13.2, where λ is the free space wavelength at 3.5 GHz). A small capacitive coupling flag-shaped strip is used to excite the proposed loop antenna. The compactness is achieved using an inward meandering that forms an internal loop in the element. The position and the dimensions of this loop are used to tune the resonant frequency and matching level at 3.5 GHz. The results (theoretical, simulated, and measured) show that the 3.5 GHz band (3.4-3.6 GHz) is achieved with impedance matching better than −10 dB, and total efficiency higher than 65%. A 10 × 10 MIMO system is formed and it has an excellent MIMO and diversity performance in-terms of the envelope correlation coefficient (below 0.055), and apparently it has the highest channel capacity (about 54.3 bps/Hz) among other MIMO systems of the same order. Simulation results of the specific absorption rate (SAR) demonstrates that the proposed antenna solution satisfied SAR criterion. Thus, the proposed ten-element MIMO antenna represent an excellent candidate for sub-6 GHz 5G smartphone applications

A Real-Time Olive Fruit Detection for Harvesting Robot Based on Yolo Algorithms

Deep neural network models have become powerful tools of machine learning and artificial intelligence. They can approximate functions and dynamics by learning from examples. This paper reviews the state-of-art of deep learning-based object detection frameworks that are used for fruit detection in general and for olive fruit in particular. A dataset of olive fruit on the tree is built to train and evaluate deep models. The ultimate goal of this work is the capability of on-edge real-time olive fruit detection on the tree from digital videos. Recent work in deep neural networks has led to the development of a state-of-the-art object detector termed You Only Look Once version five (YOLOv5). This paper builds a dataset of 1.2 K source images of olive fruit on the tree and evaluates the latest object detection algorithms focusing on variants of YOLOv5 and YOLOR. The results of the YOLOv5 models show that the YOLOv5 new network models are able to extract rich olive features from images and detect the olive fruit with a high precision of higher than 0.75 mAP_0.5. YOLOv5s performs better for real-time olive fruit detection on the tree over other YOLOv5 variants and YOLOR