The Industrial 4.0 Revolution: Can it Positively Step into Sustainable Hospitality?

Technological advancements recently affected production, social and sustainable development. Few publications have addressed the industry 4.0 contribution to the sustainable hospitality industry. In this study, we review the ways and effectiveness of industry 4.0 in achieving sustainable development goals in the hospitality sector. Due to the modernity of the issue, resources used in this paper included articles from databases like SCOPUS, Sage, Elsevier, and Google scholar using keywords such as big data analytics, simulation, Artificial intelligence, Industry 4.0 in hospitality, sustainable hotels, industry 4.0 adaption in hospitality and smart hospitality system. This literature paper outline has five main sections—section one introduces industry 4.0. Section two is a literature review that includes industry 4.0 connotation, Industry 4.0 elements, features and drawbacks. Regarding material and methods, this literature review was conducted using articles from online databases from 2016 to 2021. The primary output of this paper is Table.1, which summarizes the most critical components of advanced technology that can aid in achieving sustainable development goals in the industry, followed by the conclusion

Advanced Three-dimensional Nonlinear Analysis of Reinforced Concrete Structures Subjected to Fire and Extreme Loads

With the rise in hazards that structures are potentially subjected to these days, ranging from pre-contemplated terror attacks to accidental and natural disasters, safeguarding structures against such hazards has increasingly become a common design requirement. The extreme loading conditions associated with these hazards renders the concept of imposing generalized codes and standards guidelines for structural design unfeasible. Therefore, a general shift towards performance-based design is starting to dominate the structural design field. This study introduces a powerful structural analysis tool for reinforced concrete structures, possessing a high level of reliability in handling a wide range of typical and extreme loading conditions in a sophisticated structural framework. VecTor3, a finite element computer program previously developed at the University of Toronto for nonlinear analysis of three-dimensional reinforced concrete structures employing the well-established Modified Compression Field Theory (MCFT), has been further developed to serve as the desired tool. VecTor3 is extended to include analysis capabilities for extreme loading conditions, advanced reinforced concrete mechanisms, and new material types. For extreme loading conditions, an advanced coupled heat and moisture transfer algorithm is implemented in VecTor3 for the analysis of reinforced concrete structures subjected to fire. This algorithm not only calculates the transient temperature through the depth of concrete members, but also calculates the elevated pore pressure in concrete, which enables the prediction of the occurrence of localized thermally-induced spalling. Dynamic loading conditions are also extended to include seismic loading, in addition to blast and impact loading. Advancing the mechanisms considered, VecTor3 is developed to include the Disturbed Stress Field Model (DSFM), dowel action and buckling of steel reinforcement bars, geometric nonlinearity effects, strain rate effects for dynamic loading conditions, and the deterioration of mechanical properties at elevated temperatures for fire loading conditions. Finally, the newly-developed Simplified Diverse Embedment Model (SDEM) is implemented in VecTor3 to add analysis capability for steel fibre-reinforced concrete (SFRC). Various analyses covering a wide range of different structural members and loading conditions are carried out using VecTor3, showing good agreement with experimental results available in the literature. These analyses verify the reliability of the models, mechanisms, and algorithms incorporated in VecTor3.Ph

The Industrial 4.0 Revolution: Can it Positively Step into Sustainable Hospitality?

Technological advancements recently affected production, social and sustainable development. Few publications have addressed the industry 4.0 contribution to the sustainable hospitality industry. In this study, we review the ways and effectiveness of industry 4.0 in achieving sustainable development goals in the hospitality sector. Due to the modernity of the issue, resources used in this paper included articles from databases like SCOPUS, Sage, Elsevier, and Google scholar using keywords such as big data analytics, simulation, Artificial intelligence, Industry 4.0 in hospitality, sustainable hotels, industry 4.0 adaption in hospitality and smart hospitality system. This literature paper outline has five main sections—section one introduces industry 4.0. Section two is a literature review that includes industry 4.0 connotation, Industry 4.0 elements, features and drawbacks. Regarding material and methods, this literature review was conducted using articles from online databases from 2016 to 2021. The primary output of this paper is Table.1, which summarizes the most critical components of advanced technology that can aid in achieving sustainable development goals in the industry, followed by the conclusion

Machine Learning for Precision Agriculture Using Imagery from Unmanned Aerial Vehicles (UAVs): A Survey

Unmanned aerial vehicles (UAVs) are increasingly being integrated into the domain of precision agriculture, revolutionizing the agricultural landscape. Specifically, UAVs are being used in conjunction with machine learning techniques to solve a variety of complex agricultural problems. This paper provides a careful survey of more than 70 studies that have applied machine learning techniques utilizing UAV imagery to solve agricultural problems. The survey examines the models employed, their applications, and their performance, spanning a wide range of agricultural tasks, including crop classification, crop and weed detection, cropland mapping, and field segmentation. Comparisons are made among supervised, semi-supervised, and unsupervised machine learning approaches, including traditional machine learning classifiers, convolutional neural networks (CNNs), single-stage detectors, two-stage detectors, and transformers. Lastly, future advancements and prospects for UAV utilization in precision agriculture are highlighted and discussed. The general findings of the paper demonstrate that, for simple classification problems, traditional machine learning techniques, CNNs, and transformers can be used, with CNNs being the optimal choice. For segmentation tasks, UNETs are by far the preferred approach. For detection tasks, two-stage detectors delivered the best performance. On the other hand, for dataset augmentation and enhancement, generative adversarial networks (GANs) were the most popular choice

A multi-Kalman filter-based approach for decoding arm kinematics from EMG recordings

BACKGROUND: Remarkable work has been recently introduced to enhance the usage of Electromyography (EMG) signals in operating prosthetic arms. Despite the rapid advancements in this field, providing a reliable, naturalistic myoelectric prosthesis remains a significant challenge. Other challenges include the limited number of allowed movements, lack of simultaneous, continuous control and the high computational power that could be needed for accurate decoding. In this study, we propose an EMG-based multi-Kalman filter approach to decode arm kinematics; specifically, the elbow angle (θ), wrist joint horizontal (X) and vertical (Y) positions in a continuous and simultaneous manner. RESULTS: Ten subjects were examined from which we recorded arm kinematics and EMG signals of the biceps, triceps, lateral and anterior deltoid muscles corresponding to a randomized set of movements. The performance of the proposed decoder is assessed using the correlation coefficient (CC) and the normalized root-mean-square error (NRMSE) computed between the actual and the decoded kinematic. Results demonstrate that when training and testing the decoder using same-subject data, an average CC of 0.68 ± 0.1, 0.67 ± 0.12 and 0.64 ± 0.11, and average NRMSE of 0.21 ± 0.06, 0.18 ± 0.03 and 0.24 ± 0.07 were achieved for θ, X, and Y, respectively. When training the decoder using the data of one subject and decoding the data of other subjects, an average CC of 0.61 ± 0.19, 0.61 ± 0.16 and 0.48 ± 0.17, and an average NRMSE of 0.23 ± 0.07, 0.2 ± 0.05 and 0.38 ± 0.15 were achieved for θ, X, and Y, respectively. CONCLUSIONS: These results suggest the efficacy of the proposed approach and indicates the possibility of obtaining a subject-independent decoder. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12938-022-01030-6