The most practical way of storing hydrogen gas for fuel cell vehicles is to use a composite overwrapped pressure vessel. Depending on the driving distance range and power requirement of the vehicles, there can be various operational pressure and volume capacity of the tanks, ranging from passenger vehicles to heavy-duty trucks. The current commercial hydrogen storage method for vehicles involves storing compressed hydrogen gas in high-pressure tanks at pressures of 700 bar for passenger vehicles and 350 bar to 700 bar for heavy-duty trucks. In particular, hydrogen is stored in rapidly refillable onboard tanks, meeting the driving range needs of heavy-duty applications, such as regional and line-haul trucking. One of the most important factors for fuel cell vehicles to be successful is their cost-effectiveness. So, in this review, the cost analysis including the process analysis, raw materials, and manufacturing processes is reviewed. It aims to contribute to the optimization of both the cost and performance of compressed hydrogen storage tanks for various applications.

There has been an increase in the deterioration of buildings and infrastructure in dense urban regions, and several defects in the structures are being exposed. To ensure the effective diagnosis of building conditions, vision-based automatic damage recognition techniques have been developed. However, conventional image processing techniques have some limitations in real-world situations owing to their manual feature extraction approach. To overcome these limitations, a convolutional neural network-based image recognition technique was adopted in this study, and a convolution-based concrete multi-damage recognition neural network (CMDnet) was developed. The image datasets consisted of 1981 types of concrete surface damages, including surface cracks, rebar exposure and delamination, as well as intact. Furthermore, it was experimentally demonstrated that the proposed model could accurately classify the damage types. The results obtained in this study reveal that the proposed model can recognize the different damage types from digital images of the surfaces of concrete structures. The trained CMDnet demonstrated a damage-detection accuracy of 98.9%. Moreover, the proposed model could be applied in automatic damage detection networks to achieve superior performance with regard to concrete surface damage detection and recognition, as well as accelerating efficient damage identification during the diagnosis of deteriorating structures used in civil engineering applications.

the world's energy. [1,2] Considering the energy efficiency impact of an indoor set-point temperature, 1.5% of the total energy consumption for HVAC per 1° of Fahrenheit change, [3,4] a significant energy conservation opportunity exists. Designing and operating HVAC systems in an energy-efficient manner to meet low-energy targets are therefore essential. [5] Several studies have suggested that significant energy savings can be achieved by using feedback from sensorbased occupancy detection when operating HVAC systems. [6][7][8][9] These studies demonstrate a significant theoretical energy-saving potential, i.e., when perfect occupancy detection and predictions are assumed. However, the accuracy of occupancy detection and predictions significantly affects the theoretical energysaving potential. [10] This issue calls for the development of reliable yet simple and inexpensive real-time occupancy detection approaches to include occupancy information when optimizing real-time HVAC operation. In recent years, various types of occupancy detection methods have been developed using tools such as passive infrared sensors, [11] cameras, [12] wireless sensor networks, [13] radio frequency identification sensors, [14,15] and CO 2 sensors, [16][17][18] among Heating, ventilation, and air conditioning systems in building operations have extensively gained significant interest in recent years for providing a comfortable environment and energy savings by utilizing the occupancy information of buildings. Herein, a flexible heterogeneous temperature and humidity sensor-based occupancy detection device with an integrated wireless communication system is introduced. The multichannel (threechannel) Pt-based temperature and single-channel humidity sensors afford excellent electrical conductivity, exceptional linearity, and high mechanical flexibility. The presented occupancy detection device design contains a multichannel temperature sensor, an interdigitated humidity sensor, a customized signal processing integrated circuit, a flexible battery, and a wireless communication system, which can be used to perform occupancy trials in offices. The proposed occupancy detection device is mounted on an office chair to analyze the environmental changes according to the user's activities and to address the challenge of long-term continuous monitoring of temperature and humidity for managing the energy consumption in the building operations. The presented occupancy detection device provides a new platform for introducing a flexible device system to collect the actual occupancy information and is a new step toward "smart office" applications.

Compressive strength of concrete is a significant factor to assess building structure health and safety. Therefore, various methods have been developed to evaluate the compressive strength of concrete structures. However, previous methods have several challenges in costly, time-consuming, and unsafety. To address these drawbacks, this paper proposed a digital vision based concrete compressive strength evaluating model using deep convolutional neural network (DCNN). The proposed model presented an alternative approach to evaluating the concrete strength and contributed to improving efficiency and accuracy. The model was developed with 4,000 digital images and 61,996 images extracted from video recordings collected from concrete samples. The experimental results indicated a root mean square error (RMSE) value of 3.56 (MPa), demonstrating a strong feasibility that the proposed model can be utilized to predict the concrete strength with digital images of their surfaces and advantages to overcome the previous limitations. This experiment contributed to provide the basis that could be extended to future research with image analysis technique and artificial neural network in the diagnosis of concrete building structures.