Achievable tolerances in robotic feature machining operations using a low-cost hexapod

Portable robotic machine tools potentially allow feature machining processes to be brought to large parts in various industries, creating an opportunity for capital expenditure and operating cost reduction. However, robots lack the machining capability of conventional equipment, which ultimately results in dimensional errors in parts. This work showcases a low-cost hexapod-based robotic machine tool and presents experimental research conducted to investigate how the widely researched robotic machining challenges, e.g. structural dynamics and kinematics, translate to achievable tolerance ranges in real-world production to highlight currently feasible applications and provide a context for considering technology improvements. Machining trials assess the total dimensional errors in the final part over multiple geometries. A key finding is error variation which is in the sub-millimetre range, although, in some cases, upper tolerance limits < 100 μm are achieved. Practical challenges are also noted. Most significantly, it is demonstrated that dimensional machining error is mainly systematic in nature and therefore that the total error can be dramatically reduced with in situ measurement and compensation. Potential is therefore found to achieve a flexible, high-performance robotic machining capability despite complex and diverse underlying scientific challenges. Overall, the work presented highlights achievable tolerances in low-cost robotic machining and opportunities for improvement, also providing a practical benchmark useful for process selection

A basin-wide approach for water allocation and dams location-allocation

Construction of new dams in undeveloped transboundary basins causes two serious disputes between the stakeholders: conflicts over more water interest and over the new dams locations. Hence, water development planning of these basins needs to be done in conjunction with the examination of stakeholders new water shares. This study extends the model presented in Roozbahani et al. (Water Resour Manag 31:45394556, 2017) to be multi-objective and applies the methodology outlined in Roozbahni et al. (Ann Oper Res 229:657676, 2015a) to solve the model. The proposed three steps approach determines the equitable allocation of the surface water of an undeveloped transboundary basin while determining optimal number, locations and capacities of new dams. The first step utilizes a mixed-integer-multi-objective model to outline the water shares of stakeholders, as well as optimal dam locations for a given number of dams. Using a sensitivity analysis, the second step pinpoints the required number of dams. The role of third step is the exploration of the dams lowest possible capacities. Environmentally, our approach takes the entire watersheds water requirements into account. We have applied the proposed approach to the Sefidrud Basin, a transboundary basin located in Iran. The results of the approach show that, to significantly improve the security of the Sefidrud Basins water supply, three new dams would be optimal

A systematic review of the prevalence of anxiety among the general population during the COVID-19 pandemic

Background: The COVID-19 pandemic has had an adverse effect on the mental health of population worldwide. This study was conducted to systematically review the existing literature to identify the individuals at higher risk of anxiety with a view to provide targeted mental health services during this outbreak. Methods: In this study, the studies focusing on anxiety prevalence among the general population during the COVID-19 pandemic were searched in the PubMed, EMBASE, Scopus, Web of Science (WoS) and Google Scholar from the beginning of Covid-19 pandemic to February 2021. Results: 103 studies constituting 140732 people included in the review. The findings showed that anxiety prevalence was 27.3 (95 CI, 23.7; 31.2) among general population while the prevalence in COVID-19 patients was 39.6 (95 CI, 30.1; 50.1). Anxiety was significantly higher among females and older adults (p�0.05). In addition Europe revealed the highest prevalence of anxiety 54.6 (95 CI, 42.5; 66.2) followed by America 31.5 (95 CI, 19; 47.5) and Asia 28.3 (95 CI, 20.3; 38). In the general population the highest prevalence of anxiety was in Africa 61.8 (95 CI, 57-66.4) followed by America 34.9 (95 CI, 27.7-42.9), Europe 30.7 (95 CI, 22.8-40) and Asia 24.5 (95 CI, 20.7-28.9). Conclusion: During the COVID-19 crisis, through identifying those who are more likely to be suffered from mental disorders at different layers of populations, it would be possible to apply appropriate supportive interventions with a view to provide targeted mental health services during the outbreak. © 2021 Elsevier Lt

Feature Selection and Classification of Electroencephalographic Signals: An Artificial Neural Network and Genetic Algorithm Based Approach

Feature selection is an important step in many pattern recognition systems aiming to overcome the so-called curse of dimensionality. In this study, an optimized classification method was tested in 147 patients with major depressive disorder (MDD) treated with repetitive transcranial magnetic stimulation (rTMS). The performance of the combination of a genetic algorithm (GA) and a back-propagation (BP) neural network (BPNN) was evaluated using 6-channel pre-rTMS electroencephalographic (EEG) patterns of theta and delta frequency bands. The GA was first used to eliminate the redundant and less discriminant features to maximize classification performance. The BPNN was then applied to test the performance of the feature subset. Finally, classification performance using the subset was evaluated using 6-fold cross-validation. Although the slow bands of the frontal electrodes are widely used to collect EEG data for patients with MDD and provide quite satisfactory classification results, the outcomes of the proposed approach indicate noticeably increased overall accuracy of 89.12% and an area under the receiver operating characteristic (ROC) curve (AUC) of 0.904 using the reduced feature set

Deep Learning of EEG Data in the NeuCube Brain-Inspired Spiking Neural Network Architecture for a Better Understanding of Depression

In the recent years, machine learning and deep learning techniques are being applied on brain data to study mental health. The activation of neurons in these models is static and continuous-valued. However, a biological neuron processes the information in the form of discrete spikes based on the spike time and the firing rate. Understanding brain activities is vital to understand the mechanisms underlying mental health. Spiking Neural Networks are offering a computational modelling solution to understand complex dynamic brain processes related to mental disorders, including depression. The objective of this research is modeling and visualizing brain activity of people experiencing symptoms of depression using the SNN NeuCube architecture. Resting EEG data was collected from 22 participants and further divided into groups as healthy and mild-depressed. NeuCube models have been developed along with the connections across different brain regions using Synaptic Time Dependent plasticity (STDP) learning rule for healthy and depressed individuals. This unsupervised learning revealed some distinguishable patterns in the models related to the frontal, central and parietal areas of the depressed versus the control subjects that suggests potential markers for early depression prediction. Traditional machine learning techniques, including MLP methods have been also employed for classification and prediction tasks on the same data, but with lower accuracy and fewer new information gained