Search Results (15)

This paper is an attempt to investigate the rock indentation behaviors of a conical pick under different loading rates (1, 2, 3, and 4 mm/min), indenter types (sharp and blunt indenters), and types of rock (concrete, limestone, granite). Serial indentation tests by indenters were first performed by an automatic universal testing machine and monitored by an i-SPEED high-speed camera to record the peak pick force, indentation depth, rock fracture area, and rock failure process. Accordingly, the effect of loading rates, rock brittleness, and pick type on rock indentation behaviors was subsequently analyzed for a sound understanding of rock fragmentation mechanisms with indenters. It was found that higher loading rates necessitate a higher pick force and indentation depth to achieve rock fragmentation, resulting in a larger fractured area. Notably, a positive linear relationship exists between loading rates, rock-breaking forces, and fracture areas. A sharp indenter induces multiple cycles of repeated crushing and chipping phases, resulting in an arcuate-shaped fracture pattern with a smaller fractured area. Conversely, the rounded blunt indenter leads to a single stage of compression, with cracks propagating directly through the rock specimen, producing a larger fractured area. In addition, rock brittleness is another key factor to control rock failure efficiency, with tensile strength serving as a significant component. Full article

Automatic first-break(FB) picking is a key task in seismic data processing, with numerous applications in the field. Over the past few years, both unsupervised and supervised learning algorithms have been applied to 2D seismic arrival time picking and obtained good picking results. In this paper, we introduce a strategy of optimizing certain geometric properties of the target curve for first-break picking which can be implemented in both unsupervised and supervised learning modes. Specifically, in the case of unsupervised learning, we design an effective curve evolving algorithm according to the active contour(AC) image segmentation model, in which the length of the target curve and the fitting region energy are minimized together. It is interpretable, and its effectiveness and robustness are demonstrated by the experiments on real world seismic data. We further investigate three schemes of combining it with human interaction, which is shown to be highly useful in assisting data annotation or correcting picking errors. In the case of supervised learning especially for deep learning(DL) models, we add a curve loss term based on the target curve geometry of first-break picking to the typical loss function. It is demonstrated by various experiments that this curve regularized loss function can greatly enhance the picking quality. Full article

The Internet of Things (IoT) is a system that enables wirelessly linked devices to be tracked and managed remotely. It uses Ethernet protocols and the principles behind wireless sensor networks. Soil moisture monitoring, hydraulic pressure monitoring, soil testing, preventing trespassing through motion detection, and conserving energy are only some of the agricultural and irrigational operations that are the subject of this research. The implementation shown in this work breaks down larger systems into several smaller ones. A subsystem incorporates a vibration warning sensor, pump, and the ability to monitor soil moisture and hydraulic pressure to detect movement in and around the associated field. The second method will be utilized to deter intruders by picking up on their presence when they move within range of the necessary field barrier. Sensors for measuring current and voltage will be included for energy management regulation. It will be utilized for controlling the system. The main system will receive data through ZigBee from the first and second subsystems, monitor them, and then transfer them to the network router via ZigBee, where the necessary data will be shown on a website home page alongside the appropriate Ethernet protocols and current operating data. Full article

This paper proposes a method and an algorithm called Q-sorting for reinforcement learning (RL) problems with multiple cumulative constraints. The primary contribution is a mechanism for dynamically determining the focus of optimization among multiple cumulative constraints and the objective. Executed actions are picked through a procedure with two steps: first filter out actions potentially breaking the constraints, and second sort the remaining ones according to the Q values of the focus in descending order. The algorithm was originally developed upon the classic tabular value representation and episodic setting of RL, but the idea can be extended and applied to other methods with function approximation and discounted setting. Numerical experiments are carried out on the adapted Gridworld and the motor speed synchronization problem, both with one and two cumulative constraints. Simulation results validate the effectiveness of the proposed Q-sorting in that cumulative constraints are honored both during and after the learning process. The advantages of Q-sorting are further emphasized through comparison with the method of lumped performances (LP), which takes constraints into account through weighting parameters. Q-sorting outperforms LP in both ease of use (unnecessity of trial and error to determine values of the weighting parameters) and performance consistency (6.1920 vs. 54.2635 rad/s for the standard deviation of the cumulative performance index over 10 repeated simulation runs). It has great potential for practical engineering use. Full article

With advancements in both the quality and collection speed of magnetic data captured by uncrewed aerial vehicle (UAV)-based systems, there is a growing need for robust and efficient systems to automatically interpret such data. Many existing conventional methods require manual inspection of the survey data to pick out candidate areas for further analysis. We automate this initial process by implementing unsupervised machine learning techniques to identify small, well-defined regions. When further analysis is conducted with magnetic inversion algorithms, then our approach also reduces the nonlinear computation and time costs by breaking one huge inversion problem into several smaller ones. We also demonstrate robustness to noise and sidestep the requirement for large quantities of labeled training data: two pitfalls of current automation approaches. We propose first to use hierarchical clustering on filtered magnetic gradient data and then to fit ellipses to the resulting clusters to identify subregions for further analysis. In synthetic data experiments and on real-world datasets, our model successfully captures all true targets while simultaneously proposing fewer computationally costly false positives. With this approach, we take an important step towards fully automating the detection of high-risk subregions, but we wish to emphasize the importance of prudent skepticism until it has been tested and proven on more diverse data. Full article

Traditional strategies for the metabolic profiling of doping are limited by the unpredictable metabolic pathways and the numerous proportions of background and chemical noise that lead to inadequate metabolism knowledge, thereby affecting the selection of optimal detection targets. Thus, a stable isotope labeling-based nontargeted strategy combined with ultra-high-performance liquid chromatography–high-resolution mass spectrometry (UHPLC-HRMS) was first proposed for the effective and rapid metabolism analysis of small-molecule doping agents and demonstrated via its application to a novel doping BPC-157. Using ¹³C/¹⁵N-labeled BPC-157, a complete workflow including automatic ¹³C₀,¹⁵N₀-¹³C₆,¹⁵N₂ m/z pair picking based on the characteristic behaviors of isotope pairs was constructed, and one metabolite produced by a novel metabolic pathway plus eight metabolites produced by the conventional amide-bond breaking metabolic pathway were successfully discovered from two incubation models. Furthermore, a specific method for the detection of BPC-157 and the five main metabolites in human urine was developed and validated with satisfactory detection limits (0.01~0.11 ng/mL) and excellent quantitative ability (linearity: 0.02~50 ng/mL with R² > 0.999; relative error (RE)% < 10% and relative standard deviation (RSD)% < 5%; recovery > 90%). The novel metabolic pathway and the in vitro metabolic profile could provide new insights into the biotransformation of BPC-157 and improved targets for doping control. Full article

In this paper, we propose an ultrasonically coupled mechanical rock-breaking technology, creatively design an ultrasonically coupled mechanical rock-breaking drum, concurrently develop an ultrasonic cracking simulation method based on test coordination, and study the cracking mechanism and characteristics of ultrasonically pre-broken rock in order to increase the rock-breaking efficiency of shearer drums and lengthen pickaxe service life. To further understand the theory behind ultrasonic-coupled mechanical rock breaking, the operation of a fusion drum and the implications of ultrasonic field theory in a solid medium are first examined. Second, the impact and mechanism of the ultrasonic pre-crushing of the target red sandstone are investigated in conjunction with conducting a rock uniaxial compression test and RFPA^2D modeling. Furthermore, an ultrasonic pre-crushing fracturing mechanism test of the target red sandstone further reveals the effect and mechanism of ultrasonic fracturing. The efficacy of ultrasonic-coupled mechanical single-cutter cutting is then investigated using the discrete element cutting model (PFC^2D) of red sandstone. The results show that under the action of ultrasonic waves with an excitation frequency of 41 kHz, cracks can effectively be produced inside the rock mass of the target red sandstone, and the cumulative amount of acoustic emission is as high as 513, which reduces the strength of the rock mass and disintegrates its internal structure; the average cut-off force of the purely mechanical rock-breaking mode is 6374 N, and that of ultrasonically coupled rock breaking is 4185 N, which is a reduction of 34.34%, and can be attributed to the fact that ultrasonic waves can loosen the structure of the rock mass. This is explained by the ability of ultrasonic vibrations to weaken the structure of rock. The coupled rock-breaking technology not only simplifies mechanical cutting and rock breaking but the lower force can also reduce a pick-shaped trunnion’s wear failure cycle. This improves the environment for subsequent pick-shaped trunnion cutting and rock breaking and prevents the pick-shaped trunnion from being subjected to high-stress loads for an extended period of time so as to prolong its working life. Full article

Energy efficiency is an increasingly important issue in the wine industry worldwide. The focus on quality in wine production has led to increased attention being paid to the product at all stages of processing. The interaction with mechanical components is considered one of the possible critical points in the vinification process, and it becomes fundamental to optimize specific points in the wine production line using the best extraction technique. Therefore, in this work, experimental monitoring of two types of product circulation systems in fermentation was carried out in a winery in Puglia (Italy). In particular, the functional performance and energy consumption of two identical vinification lines were monitored, in which the only variables were two types of circulating systems for the fermenting must: pump-over and pneumatic cap breaking. During the trials, a homogeneous batch of Primitivo grapes was processed, hand-picked and taken to the winery within 1 h of harvesting, where a “ready-to-drink” wine production line was set up. A net quantity of 1000 hL of destemmed grapes was placed in two identical vertical steel tanks. Both wine tanks were monitored and equipped with an automated assembly system and a pneumatic marc breaker. Once both tanks were filled, a first break of the cap was carried out using a pneumatic system in one tank and an automatic pump-over in the other. For the grapes and type of wine studied, the pneumatic system showed better functional performance in terms of vinification speed and energy consumption; on the other hand, the pump-over system performed better in analytical terms. Finally, the results obtained highlight the need for further studies on equipment design to obtain significant benefits in terms of wine production costs while maintaining the quality standards required for “ready-to-drink” wines. Full article

The presence of bruises on fruits often indicates cell damage, which can lead to a decrease in the ability of the peel to keep oxygen away from the fruits, and as a result, oxygen breaks down cell walls and membranes damaging fruit content. When chemicals in the fruit are oxidized by enzymes such as polyphenol oxidase, the chemical reaction produces an undesirable and apparent brown color effect, among others. Early detection of bruising prevents low-quality fruit from entering the consumer market. Hereupon, the present paper aims at early identification of bruised lemon fruits using 3D-convolutional neural networks (3D-CNN) via a local spectral-spatial hyperspectral imaging technique, which takes into account adjacent image pixel information in both the frequency (wavelength) and spatial domains of a 3D-tensor hyperspectral image of input lemon fruits. A total of 70 sound lemons were picked up from orchards. First, all fruits were labeled and the hyperspectral images (wavelength range 400–1100 nm) were captured as belonging to the healthy (unbruised) class (class label 0). Next, bruising was applied to each lemon by freefall. Then, the hyperspectral images of all bruised samples were captured in a time gap of 8 (class label 1) and 16 h (class label 2) after bruising was induced, thus resulting in a 3-class ternary classification problem. Four well-known 3D-CNN model namely ResNet, ShuffleNet, DenseNet, and MobileNet were used to classify bruised lemons in Python. Results revealed that the highest classification accuracy (90.47%) was obtained by the ResNet model, followed by DenseNet (85.71%), ShuffleNet (80.95%) and MobileNet (73.80%); all over the test set. ResNet model had larger parameter sizes, but it was proven to be trained faster than other models with fewer number of free parameters. ShuffleNet and MobileNet were easier to train and they needed less storage, but they could not achieve a classification error as low as the other two counterparts. Full article

Deep reflection seismic data are usually accompanied by large-offset data, and the accurate and rapid identification of the first arrivals of seismic records plays an important role in eliminating the effects of topography and other factors that increase with the increasing offsets. In this paper, we propose a method based on convolutional neural networks (CNNs) that can accurately identify the first arrivals of large-offset seismic data. A time window for linear dynamic correction was established to convert the raw seismic data into rectangular images so as to reduce the amount of invalid sample data and improve the training efficiency. In order to enhance the prediction effect of the far-offset first arrivals, we propose the strategy of adjusting the weight of the far-offset data to increase the weight of the far-offset data in the training dataset and, thus, to improve the first arrival accuracy. The manually picked first arrivals are used as labels and the input to the CNNs for training, and the full-offset first arrivals are the output. The travel time tomography velocity is modeled and compared based on the first arrivals obtained through manual picking, industrial software automatic picking, and CNN prediction. The results show that the application of CNNs to large-offset seismic datasets can help researchers to obtain the first arrivals at different offsets, while the inclusion of far-offset weights can effectively improve the modeling depth of the tomography inversion, and the accuracy of the results is high. Full article

The process and efficiency of sizing aramid yarns before the weaving process was studied. The sizing was carried out under different conditions, with and without the pre-wetting of the threads before the actual sizing process. Two groups of yarns were tested. The first group consisted of five yarn samples that were blended with 95% meta-aramid and 5% para-aramid in counts of 20 × 2, 17 × 2, 14 × 2 and 12.5 × 2 tex. The second group of yarns consisted of three yarn samples that were blended with 93% meta-aramid, 5% para-aramid and 2% carbon in counts of 20, 20 × 2 and 17 × 2 tex. The inlet moisture of the yarn before sizing was 40% (with pre-wetting) and 4% (without pre-wetting), and the outlet moisture after drying was 4%. In order to carry out such tests to reproduce them, the sizing was carried out on a laboratory-sizing machine with the possibility of adapting to industrial conditions. According to the obtained results related to the properties of yarn before and after sizing, it can be concluded that sizing of aramid yarns is justified. When sizing the yarn without pre-wetting, the mechanical properties improved, especially breaking force, strength and abrasion resistance. Irregularity and hairiness were also reduced, especially when sizing with pre-wetting. Yarn hairiness or the frequency of protruding fibres also decreased with sizing in almost all samples and sizing conditions. The second group of yarns with a carbon fibre content mostly showed better mechanical properties before sizing, which continued after sizing. In general, the aramid yarn sized with pre-wetting showed certain deformations caused by stretching in the wet state and thus reduced the size pick-up, which caused less breaking forces and strength. Sizing with pre-wetting resulted in a slightly better smoothness of the thread and its higher evenness. It can be concluded that the aramid yarn should be sized with a lower size percentage (up to 4.5%), i.e., without pre-wetting in order to minimise the deformation of the yarn during sizing and thus improve the mechanical properties in the weaving process. Full article

A novel bud-forcing technique aimed at obtaining two crops (primary and forced) within the same season was tested on potted Pinot noir grapevines. Removing young, vegetative organs from primary shoots trimmed to six nodes in early summer allows dormant buds to break para-dormancy, leading to a delayed, second crop. Meanwhile, the primary crop is left untouched. In our study, bud-forcing was applied at three different timings (full flowering, fruit-set, groat-sized berries) and compared with an unforced control (UC). Vegetative growth, yield components, shoot and vine balance as leaf area-to-yield ratios, leaf gas exchange, and grape composition were determined. Regardless of the timing of application, forcing was effective at unlocking either apical or sub-apical dormant buds on the trimmed shoot, whereas the more basal nodes stayed dormant. The additional crop present on forced shoots was 40%–50% of primary crop, which equated to approximately 1 kg/vine for all treatments. Fruitfulness on newly formed forced shoots varied from 0.8 to 1.1 clusters/shoot. Primary clusters in vines subjected to forced treatments reached target maturity with a delay of 7–12 days compared to UC, whereas forced-crop, picked at the latest available date (October 7) showed higher total soluble solids, anthocyanins and phenolics than the primary crop while retaining higher acidity. This ripening behavior was reflected in the higher A rates measured in late season on the basal leaves of forced shoots versus those of primary shoots. Forcing did not compromise fruitfulness of the basal primary nodes, which set at about 1.2 inflorescence primordia/shoot. This is the first report supporting the feasibility of double cropping in Vitis vinifera L. in warm viticulture regions. Full article

The discussion of whether or not humans are able to act freely is ongoing, even though, and precisely because, technical methods for detecting the physical state of the brain are constantly improving. The brain as a physical–chemical object seems to be pre-determined by its physical and chemical states, while at the same time human consciousness gives the impression of being able to decide subjectively and freely on its own. Determinists claim that this free decision is just a form of misinterpretation of an epiphenomenon and that the alleged “free decision” has actually been determined by the physical state of the brain before the human subject gives the impression of being able to decide freely. The basis for this is a set of experiments, the first of which was specified by Benjamin Libet. Determinism, as the philosophical position that all events are entirely determined by previously existing causes, in principle enables the existence of a perfect predictor. In this paper, a thought-experiment is introduced which demonstrates that a subjective consciousness can break any forecast about its physical state, independently of the method of its detection, and, consequentially, to refute claims about its purely deterministic role. The thought-experiment picks up on an idea of the philosopher Alvin I. Goldman. Logically, the proof follows the path of a ‘reductio ad absurdum’. Full article

Coastal evolution occurs on a wide range of time-scales, from storms, seasonal and inter-annual time-scales to longer-term adaptation to changing environmental conditions. Measuring campaigns typically either measure morphological evolution on a short-time scale (days) with high frequency (hourly) or long-time scales (years) but intermittently (monthly). This leaves an important observational gap that limits morphological variability assessments. Traditional echo sounding measurements on this long time-scale and high-frequency sampling require a significant financial injection. Shore-based video systems with high spatiotemporal resolution can bridge this gap. For the first time, hourly Kalman filtered video-derived bathymetries covering 1.5 years of morphological evolution with an hourly resolution obtained at Porhtowan, UK are presented. Here, the long-term hourly dataset is used and aims to show its added value for, and provide an in-depth, morphological analyses with unprecedented temporal resolution. The time-frame includes calm and extreme (storm) wave conditions in a macro-tidal environment. The video-derived bathymetries allow hourly beach state classification while before this was not possible due to the dependence on foam patterns of wave breaking (e.g., saturation during storms). The study period covers extreme storm erosion during the most energetic winter season in 60 years (2013–2014). Recovery of the beach takes place on several time-scales: (1) an immediate initial recovery after the storm season (first 2 months), (2) limited recovery during low energetic summer conditions and (3) accelerated recovery as the wave conditions picked up in the subsequent fall—under wave conditions that are typically erosive. The video-derived bathymetries are shown to be effective in determining bar-positions, outer-bar three-dimensionality and volume analyses with an unprecedented hourly temporal resolution. Full article

A new first break picking for three-component (3C) vertical seismic profiling (VSP) data is proposed to improve the estimation accuracy of first arrivals, which adopts gesture detection calibration and polarization analysis based on the eigenvalue of the covariance matrix. This study aims at addressing the problem that calibration is required for VSP data using the azimuth and dip angle of geophones, due to the direction of geophones being random when applied in a borehole, which will further lead to the first break picking possibly being unreliable. Initially, a gesture-measuring module is integrated in the seismometer to rapidly obtain high-precision gesture data (including azimuth and dip angle information). Using re-rotating and re-projecting using earlier gesture data, the seismic dataset of each component will be calibrated to the direction that is consistent with the vibrator shot orientation. It will promote the reliability of the original data when making each component waveform calibrated to the same virtual reference component, and the corresponding first break will also be properly adjusted. After achieving 3C data calibration, an automatic first break picking algorithm based on the autoregressive-Akaike information criterion (AR-AIC) is adopted to evaluate the first break. Furthermore, in order to enhance the accuracy of the first break picking, the polarization attributes of 3C VSP recordings is applied to constrain the scanning segment of AR-AIC picker, which uses the maximum eigenvalue calculation of the covariance matrix. The contrast results between pre-calibration and post-calibration using field data show that it can further improve the quality of the 3C VSP waveform, which is favorable to subsequent picking. Compared to the obtained short-term average to long-term average (STA/LTA) and the AR-AIC algorithm, the proposed method, combined with polarization analysis, can significantly reduce the picking error. Applications of actual field experiments have also confirmed that the proposed method may be more suitable for the first break picking of 3C VSP. Test using synthesized 3C seismic data with low SNR indicates that the first break is picked with an error between 0.75 ms and 1.5 ms. Accordingly, the proposed method can reduce the picking error for 3C VSP data. Full article