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

Postdisaster Volatility of Blood Donations in an Unsteady Blood Supply Chain*

The stochastic behavior of both transfusion (demand) and blood donations (collection) is a challenge for the blood supply chain. Although donations are not fully within the control of blood supply chain, the blood service can marginally moderate it by postponing appointments in the case of having an overstock, or by triggering a call for additional blood when faced with shortages. Such shortages are often observed as a consequence of catastrophic events. Past studies show that the response to a call for blood after a disaster is substantive. Yet the consequential impact on the supply chain is not well understood. This is due to the perishability of blood and the fact that donors are not eligible to give blood for a certain period after a donation has been made. In this study, the donation process is modeled with a Markov chain and the impact of a call for blood resulting from a disaster is investigated. This article highlights new actionable insights that aid planners to mitigate the negative impacts of a substantial response to a call for blood

Finite time horizon fill rate analysis for multiple customer cases

The item fill rate –defined as the fraction of demand that is immediately satisfied from on-hand stock –is commonly used as a performance measure in service level agreements between customers and sup- pliers. Under such agreements, the fill rate is measured over a finite horizon (the performance review period) and the supplier faces a financial penalty if an agreed target is not met. The distribution of the item fill rate (fill rate) determines the probability of exceeding the agreed target, it is therefore a point of interest in SLA coordination. The average finite horizon fill rate decreases with an increase in performance review period length. However, the impact of performance review period length on the shape of the fill rate distribution is not well understood. Past studies of finite horizon fill rate only consider a single cus- tomer in the supply chain. In this study, we analyze fill rate distributions for a supplier that has multiple customers each with their own service level agreement. We examine the effects of performance review period length, choice of demand fulfillment (service) policy and correlation between customers’ demands on both the average fill rate and the probability of achieving the target fill rate. This study provides new insights into service level agreement coordination between suppliers and customers. For instance, the results show that a supplier with multiple customers must take care with choosing a service policy, as rationing will affect the fill rate distribution and hence the realized service level

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

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