Currently, research on gesture recognition systems has been on the rise due to the capabilities these systems provide to the field of human–machine interaction, however, gesture recognition in prosthesis and orthesis has been carried out through the use of an extensive amount of channels and electrodes to acquire the EMG (Electromyography) signals, increasing the cost and complexity of these systems. The scientific literature shows different approaches related to gesture recognition based on the analysis of EMG signals using deep learning models, highlighting the recurrent neural networks with deep learning structures. This paper presents the implementation of a Recurrent Neural Network (RNN) model using Long-short Term Memory (LSTM) units and dense layers to develop a gesture classifier for hand prosthesis control, aiming to decrease the number of EMG channels and the overall model complexity, in order to increase its scalability for embedded systems. The proposed model requires the use of only four EMG channels to recognize five hand gestures, greatly reducing the number of electrodes compared to other approaches found in the literature. The proposed model was trained using a dataset for each gesture EMG signals, which were recorded for 20 s using a custom EMG armband. The model reached an accuracy of to 99% for the training and validation stages, and an accuracy of 87 ± 7% during real-time testing. The results obtained by the proposed model establish a general methodology for the reduction of complexity in the recognition of gestures intended for human.machine interaction for different computational devices.

While the world population continues to grow, increasing the need to produce more and better-quality food, climate change, urban growth and unsustainable agricultural practices accelerate the loss of available arable land, compromising the sustainability of agricultural lands both in terms of productivity and environmental resilience, and causing serious problems for the production-consumption balance. This scenario highlights the urgent need for agricultural modernization as a crucial step to face forthcoming difficulties. Precision agriculture techniques appear as a feasible option to help solve these problems. However, their use needs to be reinvented and tested according to different parameters, in order to define both the environmental and the economic impact of these new technologies not only on agricultural production, but also on agricultural sustainability. This paper intends, therefore, to contribute to a better understanding of the impact of precision agriculture through the use of unmanned aerial vehicles (UAV)/remotely piloted aircraft systems (RPAS) and normalized difference vegetation index (NDVI) techniques in small Mediterranean farms. We present specific data obtained through the application of the aforementioned techniques in three farms located along the Portuguese-Spanish border, considering three parameters (seeding failure, differentiated irrigation and differentiated fertilization) in order to determine not only the ecological benefits of these methods, but also their economic and productivity aspects. The obtained results, based on these methods, highlight the fact that an efficient combination of UAV/RPAS and NDVI techniques allows for important economic savings in productivity factors, thus promoting a sustainable agriculture both in ecological and economic terms. Additionally, contrary to what is generally defended, even in small farms, as the ones assessed in this study (less than 50 ha), the costs associated with the application of the aforementioned precision agriculture processes are largely surpassed by the economic gains achieved with their application, regardless of the notorious environmental benefits introduced by the reduction of crucial production inputs as water and fertilizers.

Through an in-depth evaluation of the potential effectiveness of intellectual property protection on environmental technologies, the aim of the present research is to investigate the effect of patents on environmental innovation, energy use, GDP, and trade openness on environmental deregulation in Spain using nonlinear ARDL techniques. Specifically, the study findings indicate that patents on environmental technologies enhance environ-mental sustainability in Spain, as evidenced by the novel results from the nonlinear ARDL. Secondly, our study reveals that Spain’s growing economy degrades the quality of the environment. Based on the findings of the study, positive changes in trade openness could have positive effects on Spain’s environmental sustainability, suggesting that better productivity, more international trade, and increased economic openness could facilitate an improvement in Spain’s environmental impact. Lastly, this study provides evidence demonstrating that Spain’s environmental quality could suffer due to excessive energy consumption. In the light of this study’s policy recommendations, the policymakers and the Spanish government should encourage collaboration between private and public partnerships on environmental technologies to address global climate change or regional pollution. It is necessary for research and development to contribute to the development of technological progress in the Spanish energy sector; however, promoting patenting should be prioritized. By expanding patent protection, eco-friendly technologies that can combat carbon emissions can be developed swiftly in Spain, which will enable life to be more sustainable by lowering the use of energy and resources. A strong patent protection sys-tem will foster environmentally-friendly technologies and economic development while reducing CO₂ emissions in Spain.

Throughout history, times of crisis have always been preceded by periods of change. In fact, the current moment of health, economic, and social crisis that we are experiencing seems to be no exception. Tourism seems to be one of the most affected activities by the COVID-19 pandemic crisis—due not only the cancelled flights but also to the fear of being infected with the virus. In this sense, the present article intends to identify the Azores Archipelago residents’ tourism expectations during the COVID-19 crisis. Using the Azores Islands as a case study and merging it with the authors’ knowledge about this regional reality, it was possible to collect a substantial quantity of data. Thus, this study provides an in-depth perception of the main issues about how this pandemic crisis affects the tourism expectations of the Azores region residents, and consequently, how this situation will influence regional sustainable development. Through the study, it was possible to verify that the significant part of the Azores Archipelago residents (57.5%) will not be going to travel to spend vacations in 2020. Additionally, 84.2% will have holidays in the region; therefore, they stay in the Azores Archipelago. Besides, this study shows that majority of the respondents (61.6%) would not make a reservation for a 2020 vacation.

The relationships between territorial governance and the pursuit of sustainable development are evidenced to be critical. Exploratory tools, like Geographic Information Systems (GIS), enable us to comprehend the patterns, dynamics, and parameters of land-use changes over the years. The results from such studies could be used in the design of a sustainable territorial governance strategy. Contextually, a study has been conducted based on the changes that occurred in land uses in the Canary Archipelago in the years 1990, 2000, 2012, and 2018 using CORINE (Coordination of Information on the Environment) data. Even if most of the land uses have been stable over the analyzed period, the investigation shows a decrease in agricultural areas. By contrast, it is possible to verify an increase in semi-natural areas and urban agglomerations. Moreover, the authors believe that an assessment of the land-use changes on these ultra-peripheral areas will also enable us to disclose some obstacles and opportunities for sustained development.