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Intra-row weed control in organic or low-input cropping systems is more difficult than in conventional agriculture. The various mechanical and thermal devices available for intra-row weed control are reported in this review. Low-tech mechanical devices such as cultivators, finger-weeders, brush weeders, and torsionweeders tend to be used in low density crops, while spring-tine harrows are mainly applied in narrow-row high-density crops. Flame weeding can be used for both narrow and wide-row sown crops, provided that the crop is heat-tolerant. Robotic weeders are the most recent addition to agricultural engineering, and only a few are available on the market. Nowadays, robotic weeders are not yet used in small and medium sized farms. In Europe, highincome niche crops are often cultivated in small farms and farmers cannot invest in high-tech solutions. Irrespectively of the choice of low- or high-tech machines, there are several weeders that can be used to reduce the use of herbicides, making of them a judicious use, or decide to avoid them.

In this paper, we focus on a novel simple chaotic circuit with a memristor, a memcapacitor and a linear inductor in parallel. Then we establish the circuit’s dimensionless mathematical model. Nineteen types of different chaotic attractors are found in the circuit. The chaotic system’s equilibrium point and stability are analyzed by using the traditional dynamic analysis methods, and the dynamical behaviors with three varying parameters of this circuit are analyzed in detail. Furthermore, some special phenomena such as state transition, chaos degradation and the multiple coexisting attractors are discovered. Finally, we implement this circuit through the DSP platform and the results illustrate the validity of the theoretical analysis. Theoretical analysis and simulation results indicate that the simple chaotic circuit has very rich dynamical characteristics.

The physical properties of distinct raw materials, such as hardness, homogeneity, and grain size, have been recurrently suggested as some of the key reasons for human decision-making, namely the selection, production, and use of stone implements in the past. However, little is known, concerning the relationship between stone tools and human behaviour and how this is reflected in the variability seen in the archaeological record. Therefore, investigating stone tools’ properties and performance brings fundamental insights into identifying and understanding the origins of some of the major human technological behavioural traits. In this study, we aim to address this topic by measuring the variability of the properties of lithic raw materials from the perspective of tool use. A controlled experiment was designed to test the mechanical performance with a focus on the efficiency (ratio between effectiveness and durability) of four distinct raw materials (quartzite, dacite, flint, and obsidian). Our study addresses the null hypothesis: “Edge efficiency does not vary according to the different lithic raw materials.” Efficiency is assessedby the combination of penetration depth (proxy to measure effectiveness) and edge wear (proxy to measure durability). These two variables were measured, and the results correlated with the physical properties of various raw materials, including hardness and grain size. Our results show significant differences in the efficiency between the different types of raw materials. The outcome demonstrates that the variables by which we test the edge efficiency of lithic raw materials are highly relevant for raw material selection and, consequently, may have been of utmost importance in influencing the decision-aking process of past hunter-gatherers. A decrease in tool efficiency during use may have constrained daily activities, necessitating technological adaptations. This strongly suggests that each raw material used in archaeological contexts to produce blanks should be evaluated for its efficiency. In addition, it may be pertinent to extend this approach to other blunt artefactssuch as scrapers, burins, anvils, and hammerstones when investigating aspects of interconnected behaviours such as artefact variability, resource economy, group mobility, and site function. Such choices and decisions are coded in the archaeological record and represent cultural factors that were transmitted through learning and likely triggered the human decision-making process of past hunter-gatherers.

Metrology has been successfully used in the last decade to quantify use-wear on stone tools. Such techniques have been mostly applied to fine-grained rocks (chert), while studies on coarse-grained raw materials have been relatively infrequent. In this study, confocal microscopy was employed to investigate polished surfaces on a coarse-grained lithology, quartzite. Wear originating from contact with five different worked materials were classified in a data-driven approach using machine learning. Two different classifiers, a decision tree and a support-vector machine, were used to assign the different textures to a worked material based on a selected number of parameters ( Mean density of furrows , Mean depth of furrows , Core material volume-Vmc ). The method proved successful, presenting high scores for bone and hide (100%). The obtained classification rates are satisfactory for the other worked materials, with the only exception of cane, which shows overlaps with other materials. Although the results presented here are preliminary, they can be used to develop future studies on quartzite including enlarged sample sizes.

The identification of ancient worked materials is one of the fundamental goals of lithic use wear analysis and one of the most important parts of understanding how stone tools were used in the past. Given the documented overlaps in wear patterns generated by different materials, it is imperative to understand how individual materials’ mechanical properties might influence wear formation. Because isolating physical parameters and measuring their change is necessary for such an endeavor, controlled (rather than replicative) experiments combined with objective measurements of surface topography are necessary to better grasp how surface modifications formed on stone tools. Therefore, we used a tribometer to wear natural flint surfaces against five materials (bone, antler, beech wood, spruce wood, and ivory) under the same force, and speed, over one, three, and five hours. The study aimed to test if there is a correlation between surface modifications and the hardness of the worked material. We measured each raw material’s hardness using a nano-indentation test, and we compared the surface texture of the flint bits using a 3D optical profilometer. The interfacial detritus powder was analyzed with a scanning electron microscope to look for abraded flint particles. We demonstrate that, contrary to expectation, softer materials, such as wood, create a smoother surface than hard ones, such as ivory.

Broken arrows and worn stone gear speak to the plight of the ancient alpine hunter.

The Conceive Design Implement Operate (CDIO) initiative is an innovative engineering education framework aiming to produce industry-ready graduates. Over the past two decades, the approach has been increasingly popular, particularly in the mechanical engineering field, thanks to its practical approach and outcome-based assessments. However, the CDIO approach remains absent from the pedagogical tools employed in marine engineering education curricula. This paper argues that, although unrecognized as such, modern marine engineering courses have been employing an approach akin to that of the CDIO initiative. Four international case studies, in both undergraduate and postgraduate higher education, are employed to demonstrate that the marine engineering courses under consideration indeed utilize the CDIO approach to engineering education. Furthermore, this paper identifies the CDIO initiative as a relevant pedagogy for the development of novel marine engineering courses and activities. It is anticipated that this first recognition of the use of the CDIO initiative in marine engineering education will contribute to formalizing the implementation of the CDIO initiative in this field, as well as enable greater synergies between the various disciplines of engineering education.

Multi-sensor imaging systems have a very important role and wide applications in surveillance and security systems. In many applications, it is necessary to use an optical protective window as an optical interface connecting the imaging sensor and object of interest’s space; meanwhile an imaging sensor is mounted in a protective enclosure, providing separation from environmental conditions. Optical windows are often used in various optical and electro-optical systems, fulfilling different sometimes very unusual tasks. There are lots of examples in the literature that define optical window design for targeted applications. Through analysis of the various effects that follow optical window application in connection with imaging systems, we have suggested a simplified methodology and practical recommendation for how to define optical protective window specifications in multi-sensor imaging systems, using a system engineering approach. In addition, we have provided initial set of data and simplified calculation tools that can be used in initial analysis to provide proper window material selection and definition of the specifications of optical protective windows in multi-sensor systems. It is shown that although the optical window design seems as a simple task, it requires serious multidisciplinary approach.

As the world’s deepest hydraulic tunnels, the Jinping ultra-deep tunnels provide world-class conditions for research on deep rock mechanics under extreme conditions. This study analyzed the time-dependent behavior of different tunneling sections in the Jinping tunnels using the Nishihara creep model implemented in Abaqus. Validated numerical simulations of representative cross-sections at 1400 m and 2400 m depths in the diversion tunnel reveal that long-term creep deformations (over a 20-year period) substantially exceed instantaneous excavation-induced displacements. The stress concentrations and strain magnitudes exhibit significant depth dependence. The maximum principal stress at a 2400 m depth reaches 1.71 times that at 1400 m, while the vertical strain increases 1.46-fold. Based on this, the long-term mechanical behavior of the surrounding rock during the expansion of the Jinping auxiliary tunnel was further calculated and predicted. It was found that the stress concentration at the top and bottom of the left sidewall increases from 135 MPa to 203 MPa after expansion, identifying these as critical areas requiring focused monitoring and early warnings. The total deformation of the rock mass increases by approximately 5 mm after expansion, with the cumulative deformation reaching 14 mm. Post-expansion deformation converges within 180 days, with creep deformation of 2.5 mm–3.5 mm observed in both sidewalls, accounts for 51.0% of the total deformation during expansion. The surrounding rock reaches overall stability three years after the completion of expansion. These findings establish quantitative relationships between the excavation depth, time-dependent deformation, and stress redistribution and support the stability design, risk management, and infrastructure for ultra-deep tunnels in a stress state at a 2400 m depth. These insights are critical to ensuring the long-term stability of ultra-deep tunnels and operational safety assessments.

Agricultural tractors are connected with various implements such as plow, baler, rotovator, and loader for performing agricultural work. In particular, the rotovator is used to crush and uniformly spread the soil after plowing operations. However, achieving uniformly spread soil and flattened fields using rotovator can be extremely challenging, because the soil is often pushed to a particular side or remains on a slope owing to the variations in soil composition, plowing depth, and the skill level of workers, which consequently affects the transplantation work. This study aims to analyze the prediction accuracy of the implement and tractor attitudes as a reference standard by (a) developing an algorithm to predict the implement attitude based on the hitch height, support points of the lower link and lift rod, and distance between the lower links, through four-section link mathematical modeling for a three-point hitch system, and (b) using an observer to predict the tractor attitude using a Kalman filter with gyroscopic and acceleration sensors. We developed a control algorithm using the gyroscopic and acceleration information from the sensor to improve the precision and adjustment speed of the conventional tractor–implement leveling-control system. In addition, the performance improvement was verified by comparing the conventional and proposed systems. The results revealed that the error rates in the proposed system were up to 72% less than those of the conventional system, implying that the control performance of the stated system could be improved by reducing the implement attitude estimation error and tractor attitude measurement delay.