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This paper aims to carry out tribological experiments to explore the applications of femtosecond laser surface texturing technology on rock bit sliding bearing to enhance the lifetime and working performance of rock bit sliding bearing under high temperature and heavy load conditions.

Surface textures on beryllium bronze specimen were fabricated by femtosecond laser ablation (800 nm wavelength, 40 fs pulse duration, 1 kHz pulse repetition frequency), and then the tribological behaviors of pin-on-disc configuration of rock bit bearing were performed with 20CrNiMo/beryllium bronze tribo-pairs under non-Newtonian lubrication of rock bit grease.

The results showed that the surface texture on beryllium bronze specimens with specific geometrical features can be achieved by optimizing femtosecond laser processing via adjusting laser peak power and exposure time; more than 52 per cent of friction reduction was obtained from surface texture with a depth-to-diameter ratio of 0.165 and area ratio of 5 per cent at a shear rate of 1301 s−1 under the heavy load of 20 MPa and high temperature of 120°C, and the lubrication regime of rock bit bearing unit tribo-pairs was improved from boundary to mixed lubrication, which indicated that femtosecond laser ablation technique showed great potential in promoting service life and working performance of rock bit bearing.

Femtosecond laser-irradiated surface texture has the potential possibility for application in rock bit sliding bearing to improve the lubrication performance. Because proper micro dimples showed good lubrication and wear resistance performance for unit tribo-pairs of rock bit sliding bearing under high temperature, heavy load and non-Newtonian lubrication conditions, which is very important to improve the efficiency of breaking rock and accelerate the development of deep-water oil and gas resources.

This work presents a system based on an ensemble of Convolutional Neural Networks (CNNs) and descriptors for bioimage classification that has been validated on different datasets of color images. The proposed system represents a very simple yet effective way of boosting the performance of trained CNNs by composing multiple CNNs into an ensemble and combining scores by sum rule. Several types of ensembles are considered, with different CNN topologies along with different learning parameter sets. The proposed system not only exhibits strong discriminative power but also generalizes well over multiple datasets thanks to the combination of multiple descriptors based on different feature types, both learned and handcrafted. Separate classifiers are trained for each descriptor, and the entire set of classifiers is combined by sum rule. Results show that the proposed system obtains state-of-the-art performance across four different bioimage and medical datasets. The MATLAB code of the descriptors will be available at https://github.com/LorisNanni.

The purpose of this paper is to investigate the effect of round pits arrangement patterns on tribological properties of journal bearing. In this paper, the tribological behaviors of journal bearing with different arrangement patterns under lubrication condition were studied based on M-2000 friction and wear tester.

The friction and wear of journal bearing contact surface were simulated by ANSYS. The wear mechanism of bearing contact surfaces was investigated by the means of energy dispersive spectrum analysis on the surface morphology and friction and wear status of the journal bearing specimens by Scanning Electron Microscopy (SEM) and Energy Dispersive Spectrometer (EDS). Besides, the wearing capacity of the textured bearing was predicted by using the GM (1,1) and Grey–Markov model.

As the loads increase, the friction coefficient of journal bearing specimens decrease first and then increase slowly. The higher rotation speed, the lower friction coefficient and the faster temperature build-up. The main friction method of the bearing sample is three-body friction. The existence of texture can effectively reduce friction and wear. In many arrangement patterns, the best is 4# bearing with round pits cross-arrangement pattern. Its texturing diameters are 60 µm and 125 µm, and the spacing and depth are 200 µm and 25 µm, respectively. In addition, the Grey–Markov model prediction result is more accurate and fit the experimental value better.

The friction and wear mechanism is helpful for scientific research and engineers to understand the tribological behaviors and engineering applications of textured bearing. The wear capacity of textured bearing is predicted by using the Grey–Markov model, which provides technical help and theoretical guidance for the service life and reliability of textured bearing.

This paper aims to investigate the feasibility of adding macro-textures to triangle meshes for additive manufacturing (AM) focusing on possible time and quality issues in both software processing and part fabrication.

A demonstrative software tool was developed to apply user-selected textures to existing meshes. The computational procedure is a three-dimensional extension of the solid texturing method used in computer graphics. The tool was tested for speed and quality of results, considering also the pre- and post-processing operations required. Some textured meshes were printed by different processes to test build speed and quality.

The tool can handle models with realistic complexity in acceptable computation times. Parts are built without difficulties or extra-costs achieving a good aesthetic yield of the texture.

The tool cannot reproduce sample patterns but requires the development of a generation algorithm for different type of textures. Mesh processing operations may take a long time when very fine textures are added to large parts.

Direct texturing can help obtain parts with aesthetic or functional textures without the need for surface post-treatments, which can be especially difficult and expensive for plastic parts.

The proposed method improves the uniformity and consistency of textures compared to existing approaches, and can support future systematic studies on the detail resolution of AM processes.

An accurate detection of overhead ground wire under open surroundings with varying illumination is the premise of reliable line grasping with the off-line arm when the inspection robot cross obstacle automatically. This paper aims to propose an improved approach which is called adaptive homomorphic filter and supervised learning (AHSL) for overhead ground wire detection.

First, to decrease the influence of the varying illumination caused by the open work environment of the inspection robot, the adaptive homomorphic filter is introduced to compensation the changing illumination. Second, to represent ground wire more effectively and to extract more powerful and discriminative information for building a binary classifier, the global and local features fusion method followed by supervised learning method support vector machine is proposed.

Experiment results on two self-built testing data sets A and B which contain relative older ground wires and relative newer ground wire and on the field ground wires show that the use of the adaptive homomorphic filter and global and local feature fusion method can improve the detection accuracy of the ground wire effectively. The result of the proposed method lays a solid foundation for inspection robot grasping the ground wire by visual servo.

This method AHSL has achieved 80.8 per cent detection accuracy on data set A which contains relative older ground wires and 85.3 per cent detection accuracy on data set B which contains relative newer ground wires, and the field experiment shows that the robot can detect the ground wire accurately. The performance achieved by proposed method is the state of the art under open environment with varying illumination.

The purpose of this paper is to analyze geographic heterogeneity of consumer preferences for intrinsic quality attributes of rice in South and Southeast Asia and the drivers of demand for these attributes, with a particular focus on rice fragrance and the role of gender.

Stated-preference surveys were conducted with 4,231 urban and rural consumers in 37 cities across seven countries (Bangladesh, India, Cambodia, Indonesia, the Philippines, Thailand, and Vietnam) during 2013–2014 and analyzed through a rank-ordered logistic regression with incomplete ranking choice data.

Preferences for rice attributes are found to be significantly heterogeneous among consumers in South and Southeast Asia. Urban Thai consumers tend to prioritize appearance and cooking characteristics over taste and nutritional benefits, relative to all other surveyed consumers. In contrast with South Asian consumers, Southeast Asian consumers have largely adopted Thai preferences for rice texture and fragrance, a trend that was earlier coined “Jasminization.” We find that demand for rice fragrance is mainly driven by women, educated consumers, large families, families spending a lower share of their food expenditures on rice, and consumers in Southeast Asia (particularly the Philippines and Cambodia).

Little is known about geographic heterogeneity, drivers, and the role of gender in demand for rice fragrance. This paper fills these knowledge gaps. Our findings suggest that the more women are empowered in grocery decision-making, the more demand for aromatic rice is expected to rise. These insights can assist market-driven and gender-responsive rice breeding programs in simultaneously enhancing rice farmers' livelihoods and gender equity.

Micronutrient malnutrition is a public health problem in many developing countries, especially in the poorest population segments. Fortification and other food-based approaches, such as using more nutritious ingredients in processing, could help to address this problem, but little is known about poor consumers’ attitudes toward nutritionally enhanced foods. The purpose of this paper is to analyze whether poor consumers in Africa would purchase foods with more nutritious ingredients and the related willingness and ability to pay.

A survey and choice experiment were conducted with 600 randomly selected households in the poorest neighborhoods of Nairobi (Kenya) and Kampala (Uganda). Participants were asked to choose between various alternatives of porridge flour with different types of nutritional attributes. The data were analyzed with mixed logit models. Porridge flour is widely consumed among the urban poor, so that the example can also provide interesting broader lessons.

Poor consumers welcome foods that are micronutrient-fortified or include new types of nutritious ingredients. However, willingness to pay for nutritional attributes is small. New ingredients that are perceived to have little effect on taste and appearance are seen more positively than ingredients that may change food products more notably.

New nutritionally enhanced foods have good potential in markets for the poor, if they build on local consumption habits and are not associated with significant price increases.

This is among the first studies to explicitly analyze poor consumers’ preferences for nutritionally enhanced foods.

Improving the competitiveness of East Africa's rice industries necessitates increased and viable production of rice of the quality desired by consumers. This paper aims to understand consumer preferences for rice quality attributes in Uganda and Kenya to inform the countries' rice breeding programs and value chain development interventions.

Rice samples are obtained from retail markets in various districts/counties across the two countries. The samples are analyzed in a grain quality laboratory for the rice's physicochemical characteristics and the resulting data are used to non-parametrically estimate hedonic price functions. District/county dummies are included to account for potential heterogeneity in consumer preferences.

Ugandan consumers are willing to pay a price premium for rice with a relatively high proportion of intact grains, but the consumers discount chalkiness. Kenyan consumers discount high amylose content and impurities. There is evidence of heterogeneity in consumer preferences for rice in Mbale, Butaleja and Arua districts of Uganda and in Kericho and Busia counties of Kenya.

The study makes a novel contribution to the literature on consumer preferences for rice in East Africa by applying a hedonic pricing model to the data generated from a laboratory analysis of the physicochemical characteristics of rice samples obtained from the market. Rather than base our analysis on consumers' subjective sensory assessment of the quality characteristics of rice, standard laboratory methods are used to generate the data, which enables a more objective assessment of the relationship between market prices and the quantities of attributes present in the rice samples.

Unconventional machining processes, particularly ultrasonic vibration cutting (UVC), can overcome such technical bottlenecks. However, the precise mechanism through which UVC affects the in-service functional performance of advanced aerospace materials remains obscure. This limits their industrial application and requires a deeper understanding.

The surface integrity and in-service functional performance of advanced aerospace materials are important guarantees for safety and stability in the aerospace industry. For advanced aerospace materials, which are difficult-to-machine, conventional machining processes cannot meet the requirements of high in-service functional performance owing to rapid tool wear, low processing efficiency and high cutting forces and temperatures in the cutting area during machining.

To address this literature gap, this study is focused on the quantitative evaluation of the in-service functional performance (fatigue performance, wear resistance and corrosion resistance) of advanced aerospace materials. First, the characteristics and usage background of advanced aerospace materials are elaborated in detail. Second, the improved effect of UVC on in-service functional performance is summarized. We have also explored the unique advantages of UVC during the processing of advanced aerospace materials. Finally, in response to some of the limitations of UVC, future development directions are proposed, including improvements in ultrasound systems, upgrades in ultrasound processing objects and theoretical breakthroughs in in-service functional performance.

This study provides insights into the optimization of machining processes to improve the in-service functional performance of advanced aviation materials, particularly the use of UVC and its unique process advantages.

Surveillance is the emerging concept in the current technology, as it plays a vital role in monitoring keen activities at the nooks and corner of the world. Among which moving object identifying and tracking by means of computer vision techniques is the major part in surveillance. If we consider moving object detection in video analysis is the initial step among the various computer applications. The main drawbacks of the existing object tracking method is a time-consuming approach if the video contains a high volume of information. There arise certain issues in choosing the optimum tracking technique for this huge volume of data. Further, the situation becomes worse when the tracked object varies orientation over time and also it is difficult to predict multiple objects at the same time. In order to overcome these issues here, we have intended to propose an effective method for object detection and movement tracking. In this paper, we proposed robust video object detection and tracking technique. The proposed technique is divided into three phases namely detection phase, tracking phase and evaluation phase in which detection phase contains Foreground segmentation and Noise reduction. Mixture of Adaptive Gaussian (MoAG) model is proposed to achieve the efficient foreground segmentation. In addition to it the fuzzy morphological filter model is implemented for removing the noise present in the foreground segmented frames. Moving object tracking is achieved by the blob detection which comes under tracking phase. Finally, the evaluation phase has feature extraction and classification. Texture based and quality based features are extracted from the processed frames which is given for classification. For classification we are using J48 ie, decision tree based classifier. The performance of the proposed technique is analyzed with existing techniques k-NN and MLP in terms of precision, recall, f-measure and ROC.