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Three years ago, a drill bit struck immense oil deposits deep off the coast of Brazil. Brazilian President Luiz Inácio Lula da Silva promised to use the oil revenue for education and public health. But Brazil's R&D sector has been first to benefit. Three years ago, a drill bit struck immense oil deposits deep off the coast of Brazil. Petrobras, the national oil company, tapped undersea fields now estimated to hold about 80 billion barrels of oil and natural gas—about three times the size of the reservoir under Prudhoe Bay, Alaska. It brought the promise of new wealth and expectations that Brazil will climb to the world's top rung of achievement in science and technology. Brazilian President Luiz Inácio Lula da Silva once termed the oil strike \"a second independence for Brazil\" and promised to use the oil revenue for education and public health. But Brazil's R&D sector has been first to benefit.

All-natural rubber is harvested from rubber trees (Hevea brasiliensis Muell. Arg.) by traditional tapping knives, so rubber tapping still heavily relies on labor. Therefore, this study explored a novel, hand-held mechanical rubber tapping machine for rubber tree harvesting. In this study, a mechanical tapping cutter with a vertical blade and adjustable guide was first described. The response surface method was applied to evaluate factors affecting the tapping effect. The experimental values were in close agreement with the predicted value. Machine-tapped latex was comparable in quality to hand-tapped latex. Based on the single-factor results, the response surface method (RSM) and the center combined rotation design (CCRD) optimization method were adopted to explore the influence of three factors influencing vertical blade height (A), cutting force (B), and spiral angle (C) on the tapping effect. Regarding the cutting rate of the old rubber line (Y1), cutting time (Y2), latex flow rate (Y3), and average cutting current (Y4) as evaluation indexes of the tapping effect, an optimization scheme was determined. The quadratic model fits for all the responses. The test results showed that the main factors affecting Y1, Y2, Y3, and Y4 were A and B, B, A and C, and B, respectively. Under optimal conditions, the influencing factors of A, B, and C were 10.24 mm, 51.67 N, and 24.77°, respectively, when the evaluation index values of Y1, Y2, Y3, and Y4 were 98%, 8.65 mL/5 min, 9.00 s, and 1.16 A. The range of the relative error between the experimental and predicted results was from −11.11% to 11.11%. According to the optimized treatment scheme, a comparison test was designed between mechanical and manual rubber tapping tools. To verify the availability and effect of the mechanical tapping method preliminarily, the important rubber tapping evaluation indexes included bark thickness, bark excision, latex flow time, cutting time, ash content, and cutting depth, which were selected to serve as a comparison test. There was no significant difference between hand and mechanical methods, except ash content (p < 0.05) and cutting time (p < 0.01). The mechanical tapping machine proposed in this study is meaningful to improve cutting efficiency, practicality, and operability. Furthermore, it provides crucial theoretical references for the development of intelligent tapping machines.

This study assesses the economic and technical feasibility of two resin extraction methods, Bore Hole Tapping (BH) and Bark Streak Tapping (BS), applied to Pinus brutia in the Korudağ Forest, Türkiye. Resin production from 758 trees was analyzed over six months to compare setup costs, operational expenses, labor costs, and yields. Additionally, a feasibility study involving 2500 trees was conducted to evaluate market, technical, and financial aspects. Results show that both methods incur high production costs, making resin production economically unviable under current market conditions. The BH method is particularly unfeasible due to significant initial investments and high operating costs. In contrast, the BS method, with lower costs, approaches viability at historically higher resin prices. Sensitivity analyses suggest that the BS method could become feasible with a significant rise in market prices. However, resin production from Pinus brutia remains economically unfeasible at present.

Fibre-reinforced composites are widely applied in structural parts due to their high specific mechanical properties. The use of self-tapping screws in aerospace applications has been considered a controversial joining technique for years, owing to the risk of distortion and cracking of the composite material during fastening. The typical mechanical component used to assembly aeronautic parts is the rivet. Thus, the study of alternative components to produce joints and both their application and performance is necessary. This work investigates the threading processes by the forming and machining in carbon fibre-reinforced composites. Three taps were used in this work, the first one based on form tapping and the other two related to machine tapping. The results indicated that in the two tapping processes, the mechanisms involved consisted of a combination of plastic deformation, shearing, and bending failure. In general, the speed of 28 m/min provides minimum thrust force, 166.5% lower than the 10 m/min speed, in machining process, while the temperature is reduced by 53.9%. The speed/tool of 28 m/min and MH-tap (tapping process) reveals the most efficiently interaction. Filling rates of the thread profiles were acceptable for practical engineering applications, mainly when high speeds were applied. The results demonstrated that threaded workpieces could be applied to aeronautics components with limitations; nevertheless, additional studies should be carried out to determine the application range.

Background Assessment and rating of Parkinson’s Disease (PD) are commonly based on the medical observation of several clinical manifestations, including the analysis of motor activities. In particular, medical specialists refer to the MDS-UPDRS (Movement Disorder Society – sponsored revision of Unified Parkinson’s Disease Rating Scale) that is the most widely used clinical scale for PD rating. However, clinical scales rely on the observation of some subtle motor phenomena that are either difficult to capture with human eyes or could be misclassified. This limitation motivated several researchers to develop intelligent systems based on machine learning algorithms able to automatically recognize the PD. Nevertheless, most of the previous studies investigated the classification between healthy subjects and PD patients without considering the automatic rating of different levels of severity. Methods In this context, we implemented a simple and low-cost clinical tool that can extract postural and kinematic features with the Microsoft Kinect v2 sensor in order to classify and rate PD. Thirty participants were enrolled for the purpose of the present study: sixteen PD patients rated according to MDS-UPDRS and fourteen healthy paired subjects. In order to investigate the motor abilities of the upper and lower body, we acquired and analyzed three main motor tasks: (1) gait, (2) finger tapping, and (3) foot tapping. After preliminary feature selection, different classifiers based on Support Vector Machine (SVM) and Artificial Neural Networks (ANN) were trained and evaluated for the best solution. Results Concerning the gait analysis, results showed that the ANN classifier performed the best by reaching 89.4% of accuracy with only nine features in diagnosis PD and 95.0% of accuracy with only six features in rating PD severity. Regarding the finger and foot tapping analysis, results showed that an SVM using the extracted features was able to classify healthy subjects versus PD patients with great performances by reaching 87.1% of accuracy. The results of the classification between mild and moderate PD patients indicated that the foot tapping features were the most representative ones to discriminate (81.0% of accuracy). Conclusions The results of this study have shown how a low-cost vision-based system can automatically detect subtle phenomena featuring the PD. Our findings suggest that the proposed tool can support medical specialists in the assessment and rating of PD patients in a real clinical scenario.

Finger-tapping tasks are one of the most common paradigms used to study the human motor system in functional neuroimaging studies. These tasks can vary both in the presence or absence of a pacing stimulus as well as in the complexity of the tapping task. A voxel-wise, coordinate-based meta-analysis was performed on 685 sets of activation foci in Talairach space gathered from 38 published studies employing finger-tapping tasks. Clusters of concordance were identified within the primary sensorimotor cortices, supplementary motor area, premotor cortex, inferior parietal cortices, basal ganglia, and anterior cerebellum. Subsequent analyses performed on subsets of the primary set of foci demonstrated that the use of a pacing stimulus resulted in a larger, more diverse network of concordance clusters, in comparison to varying the complexity of the tapping task. The majority of the additional concordance clusters occurred in regions involved in the temporal aspects of the tapping task, rather than its execution. Tapping tasks employing a visual pacing stimulus recruited a set of nodes distinct from the results observed in those tasks employing either an auditory or no pacing stimulus, suggesting differing cognitive networks when integrating visual or auditory pacing stimuli into simple motor tasks. The relatively uniform network of concordance clusters observed across the more complex finger-tapping tasks suggests that further complexity, beyond the use of multi-finger sequences or bimanual tasks, may be required to fully reveal those brain regions necessary to execute truly complex movements.

The yield of latex is strongly influenced by the tapping time and age of the plant. This study was conducted to evaluate the yield of latex obtained from different times of tapping and the age of the plants. The observation was conducted on the 15 and 11-year-old PB260 rubber clones, which were tapped at three different times: 5 am, 6 am, and 7 am with ten plant samples for each plant age and tapping time, respectively. In total, 60 plant samples were collected. The 15-year rubber clone produced a higher latex yield than the 11-year-old rubber clone. The 15-year-old plant produced 47 ml, 25 ml, and 21 ml when tapped at 5 am, 6 am, and 7 am, respectively. The 11-year-old rubber clone produced 29 ml, 23 ml, and 15 ml when tapped at 5 am, 6 am, and 7 am, respectively. 5 am was the best tapping time to produce a higher latex yield in the PB260 rubber clone.

Movements result from a complex interplay of multiple brain regions. These regions are assembled into distinct functional networks depending on the specific properties of the action. However, the nature and details of the dynamics of this complex assembly process are unknown. In this study, we sought to identify key markers of the neural processes underlying the preparation and execution of motor actions that always occur irrespective of differences in movement initiation, hence the specific neural processes and functional networks involved. To this end, EEG activity was continuously recorded from 18 right-handed healthy participants while they performed a simple motor task consisting of button presses with the left or right index finger. The movement was performed either in response to a visual cue or at a self-chosen, i.e., non-cued point in time. Despite these substantial differences in movement initiation, dynamic properties of the EEG signals common to both conditions could be identified using time–frequency and phase locking analysis of the EEG data. In both conditions, a significant phase locking effect was observed that started prior to the movement onset in the δ–θ frequency band (2–7Hz), and that was strongest at the electrodes nearest to the contralateral motor region (M1). This phase locking effect did not have a counterpart in the corresponding power spectra (i.e., amplitudes), or in the event-related potentials. Our finding suggests that phase locking in the δ–θ frequency band is a ubiquitous movement-related signal independent of how the actual movement has been initiated. We therefore suggest that phase-locked neural oscillations in the motor cortex are a prerequisite for the preparation and execution of motor actions. •We found phase locking in the delta–theta frequency band in motor areas prior to movement execution.•Phase locking occurred irrespective of how the action was initiated.•Our results suggest that phase locking constitutes a prerequisite to trigger movement execution.

Aiming at the problem that lightweight algorithm models are difficult to accurately detect and locate tapping surfaces and tapping key points in complex rubber forest environments, this paper proposes an improved YOLOv8n-IRP model based on the YOLOv8n-Pose. First, the receptive field attention mechanism is introduced into the backbone network to enhance the feature extraction ability of the tapping surface. Secondly, the AFPN structure is used to reduce the loss and degradation of the low-level and high-level feature information. Finally, this paper designs a dual-branch key point detection head to improve the screening ability of key point features in the tapping surface. In the detection performance comparison experiment, the YOLOv8n-IRP improves the D_mAP50 and P_mAP50 by 1.4% and 2.3%, respectively, over the original model while achieving an average detection success rate of 87% in the variable illumination test, which demonstrates enhanced robustness. In the positioning performance comparison experiment, the YOLOv8n-IRP achieves an overall better localization performance than YOLOv8n-Pose and YOLOv5n-Pose, realizing an average Euclidean distance error of less than 40 pixels. In summary, YOLOv8n-IRP shows excellent detection and positioning performance, which not only provides a new method for the key point localization of the rubber-tapping robot but also provides technical support for the unmanned rubber-tapping operation of the intelligent rubber-tapping robot.