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The purpose of this paper is to present a distance accuracy-based industrial robot kinematic calibration model. Nowadays, the repeatability of the industrial robot is high, while the absolute positioning accuracy and distance accuracy are low. Many factors affect the absolute positioning accuracy and distance accuracy, and the calibration method of the industrial robot is an important factor. When the traditional calibration methods are applied on the industrial robot, the accumulative error will be involved according to the transformation between the measurement coordinate and the robot base coordinate.

In this manuscript, a distance accuracy-based industrial robot kinematic calibration model is proposed. First, a simplified kinematic model of the robot by using the modified Denavit–Hartenberg (MDH) method is introduced, then the proposed distance error-based calibration model is presented; the experiment is set up in the next section.

The experimental results show that the proposed calibration model based on MDH and distance error can improve the distance accuracy and absolute position accuracy dramatically.

The proposed calibration model based on MDH and distance error can improve the distance accuracy and absolute position accuracy dramatically.

Detecting objects in images and videos is a difficult task that has challenged the field of computer vision. Most of the algorithms for object detection are sensitive to background clutter and occlusion, and cannot localize the edge of the object. An object's shape is typically the most discriminative cue for its recognition by humans. The purpose of this paper is to introduce a model‐based object detection method which uses only shape‐fragment features.

The object shape model is learned from a small set of training images and all object models are composed of shape fragments. The model of the object is in multi‐scales.

The major contributions of this paper are the application of learned shape fragments‐based model for object detection in complex environment and a novel two‐stage object detection framework.

The results presented in this paper are competitive with other state‐of‐the‐art object detection methods.

Human health is strongly affected by diet. By the increased use of food industries products, public knowledge about health factors and side effects of chemical additives, the concepts of human health founded an important aspect during past years, and application of natural-based ingredients such as coloring, flavoring, texturizing and anti-oxidative agents was increased.

The aim of the present paper is to review the published scientific research studies about lycopene health benefits in different human disease or disorders and bold the necessity of study the health effects of lycopene after its formulation in food industrial products. About 190 papers were searched in Google Scholar, PubMed, Web of Science databases and 72 relevant papers were used. It was found that in medical studies, the lycopene oleoresin or powder is used directly to the subjects. However, it is necessary to study the effectiveness of lycopene in diet food products.

According to the literature, it has beneficial effects on cancers, glands, reproductive system, bone, gastric system, liver and fat reduction in the body. Also, it was concluded from the literature that lycopene oxidative cleavages make also its chemo protective effects which is a lost key element to study different food processing on lycopene products or its isomers in final food products and on human health.

Many ingredients in food formulations are substituted by natural products. Lycopene is a colorant but also, according to the literature, has a strong antioxidative and anti-inflammatory effect to reduce the risk of most important human disease and disorders. Future research in food science can emphasize the effect of different unit operations or formulations on lycopene effects on human health.

With the growing climate problem, it has become a consensus to develop low-carbon technologies to reduce emissions. Electric industry is a major carbon-emitting industry, accounting for 35% of global carbon emissions. Universities, as an important patent application sector in China, promote their patent application and transformation to enhance Chinese technological innovation capability. This study aims to analyze low-carbon electricity technology transformation in Chinese universities.

This paper uses IncoPat to collect patent data. The trend of low-carbon electricity technology patent applications in Chinese universities, the status, patent technology distribution, patent transformation status and patent transformation path of valid patent is analyzed.

Low-carbon electricity technology in Chinese universities has been promoted, and the number of patents has shown rapid growth. Invention patents proportion is increasing, and the transformation has become increasingly active. Low-carbon electricity technology in Chinese universities is mainly concentrated in individual cooperative patent classification (CPC) classification numbers, and innovative technologies will be an important development for electric reduction.

This paper innovatively uses valid patents to study the development of low-carbon electricity technology in Chinese universities, and defines low-carbon technology patents by CPC patent classification system. A new attempt focuses on the development status and direction in low-carbon electricity technology in Chinese universities, and highlights the contribution of valid patents to patent value.