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The traditional vision system cannot automatically adjust the feature point extraction method according to the type of welding seam. In addition, the robot cannot self-correct the laying position error or machining error. To solve this problem, this paper aims to propose a hierarchical visual model to achieve automatic arc welding guidance.

The hierarchical visual model proposed in this paper is divided into two layers: welding seam classification layer and feature point extraction layer. In the welding seam classification layer, the SegNet network model is trained to identify the welding seam type, and the prediction mask is obtained to segment the corresponding point clouds. In the feature point extraction layer, the scanning path is determined by the point cloud obtained from the upper layer to correct laying position error. The feature points extraction method is automatically determined to correct machining error based on the type of welding seam. Furthermore, the corresponding specific method to extract the feature points for each type of welding seam is proposed. The proposed visual model is experimentally validated, and the feature points extraction results as well as seam tracking error are finally analyzed.

The experimental results show that the algorithm can well accomplish welding seam classification, feature points extraction and seam tracking with high precision. The prediction mask accuracy is above 90% for three types of welding seam. The proposed feature points extraction method for each type of welding seam can achieve sub-pixel feature extraction. For the three types of welding seam, the maximum seam tracking error is 0.33–0.41 mm, and the average seam tracking error is 0.11–0.22 mm.

The main innovation of this paper is that a hierarchical visual model for robotic arc welding is proposed, which is suitable for various types of welding seam. The proposed visual model well achieves welding seam classification, feature point extraction and error correction, which improves the automation level of robot welding.

Terminology is the set of technical words or expressions used in specific contexts, which denotes the core concept in a formal discipline and is usually applied in the fields of machine translation, information retrieval, information extraction and text categorization, etc. Bilingual terminology extraction plays an important role in the application of bilingual dictionary compilation, bilingual ontology construction, machine translation and cross‐language information retrieval etc. This paper aims to address the issues of monolingual terminology extraction and bilingual term alignment based on multi‐level termhood.

A method based on multi‐level termhood is proposed. The new method computes the termhood of the terminology candidate as well as the sentence that includes the terminology by the comparison of the corpus. Since terminologies and general words usually have different distribution in the corpus, termhood can also be used to constrain and enhance the performance of term alignment when aligning bilingual terms on the parallel corpus. In this paper, bilingual term alignment based on termhood constraints is presented.

Experimental results show multi‐level termhood can get better performance than the existing method for terminology extraction. If termhood is used as a constraining factor, the performance of bilingual term alignment can be improved.

The termhood of the candidate terminology and the sentence that includes the terminology is used for terminology extraction, which is called multi‐level termhood. Multi‐level termhood is computed by the comparison of the corpus. Bilingual term alignment method based on termhood constraint is put forward and termhood is used in the task of bilingual terminology extraction. Experimental results show that termhood constraints can improve the performance of terminology alignment to some extent.

Owing to the complex space environment and limited computing resources, traditional and deep learning-based methods cannot complete the task of satellite component contour extraction effectively. To this end, this paper aims to propose a high-quality real-time contour extraction method based on lightweight space mobile platforms.

A contour extraction method that combines two edge clues is proposed. First, Canny algorithm is improved to extract preliminary contours without inner edges from the depth images. Subsequently, a new type of edge pixel feature is designed based on surface normal. Finally, surface normal edges are extracted to supplement the integrity of the preliminary contours for contour extraction.

Extensive experiments show that this method can achieve a performance comparable to that of deep learning-based methods and can achieve a 36.5 FPS running rate on mobile processors. In addition, it exhibits better robustness under complex scenes.

The proposed method is expected to promote the deployment process of satellite component contour extraction tasks on lightweight space mobile platforms.

A pixel feature for edge detection is designed and combined with the improved Canny algorithm to achieve satellite component contour extraction. This study provides a new research idea for contour extraction and instance segmentation research.

This paper aims to perform an exhaustive revision of relevant and recent related studies, which reveals that both extraction methods are currently used to analyze credibility on Twitter. Thus, there is clear evidence of the need of having different options to extract different data for this purpose. Nevertheless, none of these studies perform a comparative evaluation of both extraction techniques. Moreover, the authors extend a previous comparison, which uses a recent developed framework that offers both alternates of data extraction and implements a previously proposed credibility model, by adding a qualitative evaluation and a Twitter-Application Programming Interface (API) performance analysis from different locations.

As one of the most popular social platforms, Twitter has been the focus of recent research aimed at analyzing the credibility of the shared information. To do so, several proposals use either Twitter API or Web scraping to extract the data to perform the analysis. Qualitative and quantitative evaluations are performed to discover the advantages and disadvantages of both extraction methods.

The study demonstrates the differences in terms of accuracy and efficiency of both extraction methods and gives relevance to much more problems related to this area to pursue true transparency and legitimacy of information on the Web.

Results report that some Twitter attributes cannot be retrieved by Web scraping. Both methods produce identical credibility values when a robust normalization process is applied to the text (i.e. tweet). Moreover, concerning the time performance, Web scraping is faster than Twitter API and it is more flexible in terms of obtaining data; however, Web scraping is very sensitive to website changes. Additionally, the response time of the Twitter API is proportional to the distance from the central server at San Francisco.

The output of academic literature has increased significantly due to digital technology, presenting researchers with a challenge across every discipline, including materials science, as it is impossible to manually read and extract knowledge from millions of published literature. The purpose of this study is to address this challenge by exploring knowledge extraction in materials science, as applied to digital scholarship. An overriding goal is to help inform readers about the status knowledge extraction in materials science.

The authors conducted a two-part analysis, comparing knowledge extraction methods applied materials science scholarship, across a sample of 22 articles; followed by a comparison of HIVE-4-MAT, an ontology-based knowledge extraction and MatScholar, a named entity recognition (NER) application. This paper covers contextual background, and a review of three tiers of knowledge extraction (ontology-based, NER and relation extraction), followed by the research goals and approach.

The results indicate three key needs for researchers to consider for advancing knowledge extraction: the need for materials science focused corpora; the need for researchers to define the scope of the research being pursued, and the need to understand the tradeoffs among different knowledge extraction methods. This paper also points to future material science research potential with relation extraction and increased availability of ontologies.

To the best of the authors’ knowledge, there are very few studies examining knowledge extraction in materials science. This work makes an important contribution to this underexplored research area.

The purpose of this paper is to evaluate the antioxidant activity and toxicity of Clinacanthus nutans leaves from three conventional extraction methods, i.e. maceration, Soxhlet and magnetic stirring.

Total flavonoid content (TFC) and phenolic content (TPC) were determined using colorimetric method of aluminum chloride and Folin-Ciocalteu (FC) assay, respectively. Antioxidant property of C. nutans was evaluated using 2,2'-diphenyl-1-pierylhydrazyl (DPPH) free radical scavenging assay. Cytotoxic activity of C. nutans against brine shrimp was evaluated based on LC50 (lethality concentration) after 24 h exposure to the plant extract.

The highest TPC of C. nutans was observed with Soxhlet extraction method (98.87 ± 10.43 mg of gallic acid equivalents (GAE/g) followed by maceration (68.77 ± 2.45 mg of GAE/g) and magnetic stirring (46.75 ± 2.45 mg of GAE/g). Interestingly, remarkable highest TFC was observed with magnetic stirring (568.90 ± 4.85 mg of rutin equivalent (RE)/g) followed by maceration (249.60 ± 2.79 mg of RE/g) and Soxhlet (174.8 ± 1.74 mg of RE/g). On the other hands, the extract obtained using maceration method showed the highest antioxidant activity (IC50: 14.18 mg/mL compared to ascorbic acid 144.36 µg/mL). Cytotoxicity of C. nutans from all extraction methods showed similar LC50 values with maceration (3.81 mg/mL), Soxhlet (2.61 mg/mL) and magnetic stirring (4.56 mg/mL), respectively.

Both phenolic and flavonoids are responsible for the antioxidant activity, of C. nutans extracts. Based on Meyer's toxicity index, all extracts were nontoxic (LC50>1 mg/mL).

This paper aims to review the chemical composition and industrial benefits of oil extracted from dikanut kernels.

Several literatures on chemical composition of dikanut kernels, methods of oil extraction from dikanut kernels and chemical composition of oil extracted from dikanut kernels were critically reviewed.

The review showed that proximate composition of dikanut kernels ranged from 2.10 to 11.90 per cent, 7.70 to 9.24 per cent, 51.32 to 70.80 per cent, 0.86 to 10.23 per cent, 2.26 to 6.80 per cent and 10.72 to 26.02 per cent for moisture, crude protein, crude fat, crude fibre, ash and carbohydrate contents, respectively. The methods of oil extraction from dikanut kernels include soxhlet extraction method, novel extraction method, enzymatic extraction method and pressing method. The quality attributes of dikanut kernel oil ranged from 1.59 to 4.70 g/100g, 0.50 to 2.67 meq/Kg, 4.30 to 13.40 g/100g, 187.90 to 256.50 mg KOH/g and 3.18 to 12.94 mg KOH/g for free fatty acid, peroxide value, iodine value, saponification value and acid value, respectively. Also, the percentage compositions of oleic, myristic, stearic, linolenic, palmitic, lauric, saturated fatty acids, monosaturated fatty acids and polyunsaturated fatty acids ranging from 0.00 to 6.90, 20.50 to 61.68, 0.80 to 11.40, 0.27 to 6.40, 5.06 to 10.30, 27.63 to 40.70, 97.45 to 98.73, 1.82 to 2.12 and 0.27 to 0.49 respectively. The results showed that dikanut kernels has appreciable amount of protein, carbohydrate and high level of fat content while oil extracted from dikanut kernels have high saponification value, high myristic acid and high lauric acid.

There are scanty information/published works on industrial products made from oil extracted from dikanut kernels.

The review helps in identifying different methods of extraction of oil from dikanut kernels apart from popular soxhlet extraction method (uses of organic solvent). Also, it helps to identify the domestic and industrial benefits of oil extracted from dikanut kernels.

The review showed that oil extracted from dikanut kernels could be useful as food additive, flavour ingredient, coating fresh citrus fruits and in the manufacture of margarine, oil creams, cooking oil, defoaming agent, cosmetics and pharmaceutical products.

Modern thermal and non-thermal pretreatment techniques, namely, enzymatic treatment, gas phase plasma treatment and ohmic heating have become more pronounced over conventional techniques for enhanced coloured phytochemicals (pigments) extraction. Presently, numbers of pretreatment techniques are available with some unique feature. It is difficult to choose best pretreatment method to be employed for phytochemicals extraction from different sources. Therefore, this paper aims to discuss different modern pretreatment techniques for extraction with their potential results over conventional techniques.

Research and review articles targeting to the thermal and non-thermal pretreatment techniques were collected from Google Scholar. The required information has been tabulated and discussed which included qualities of modern pretreatment techniques over conventional techniques, phytochemical extraction and best pretreatment methods for optimized results.

Every pre-treatment has its own advantages and disadvantages for a particular phytochemical and its extraction from various sources. Enzymes can be used in combinations to enhance final yield like extraction of carotenoids (pectinase, cellulase and hemicellulase) from chillies and lycopene (pectinase and cellulase) from tomato. Utilization of each method depends upon many factors such as source of pigment, cost and energy consumption. CO₂ pretreatment gives good results for carotenoid extraction from algae sources. Ohmic heating can yield high anthocyanin content. Modifications in conventional blanching has reduced final waste and improvised the properties of pigment.

This study comprises collective information regarding modern pre-treatment for extraction over conventional pre-treatments. The study also covers future trends and certain new hybrid approaches which are still less flourished.

The amount of food wasted every year is 1.3 billion metric tonne (MT), out of which 0.5 billion MT is contributed by the fruits processing industries. The waste includes by-products such as peels, pomace and seeds and is a good source of bioactive compounds like phenolic compounds, flavonoids, pectin lipids and dietary fibres. Hence, the purpose of the present study is to review the novel extraction techniques used for the extraction of the bio active compounds from food waste for the selection of suitable extraction method.

Novel extraction techniques such as ultrasound-assisted extraction, microwave-assisted extraction, enzyme-assisted extraction, supercritical fluid extraction, pulsed electric field extraction and pressurized liquid extraction have emerged to overcome the drawbacks and constraints of conventional extraction techniques. Hence, this study is focussed on novel extraction techniques, their limitations and optimization for the extraction of bioactive compounds from fruit and vegetable waste.

This study presents a comprehensive review on the novel extraction processes that have been adopted for the extraction of bioactive compounds from food waste. This paper also summarizes bioactive compounds' optimum extraction condition from various food waste using novel extraction techniques.

Food waste is rich in bioactive compounds, and its efficient extraction may add value to the food processing industries. Hence, compressive analysis is needed to overcome the problem associated with the extraction and selection of suitable extraction techniques.

Selection of a suitable extraction method will not only add value to food waste but also reduce waste dumping and the cost of bioactive compounds.

This paper presents the research progress on the extraction of bioactive active compounds from food waste using novel extraction techniques.

Strategic alliances among organizations are some of the central drivers of innovation and economic growth. However, the discovery of alliances has relied on pure manual search and has limited scope. This paper proposes a text-mining framework, ACRank, that automatically extracts alliances from news articles. ACRank aims to provide human analysts with a higher coverage of strategic alliances compared to existing databases, yet maintain a reasonable extraction precision. It has the potential to discover alliances involving less well-known companies, a situation often neglected by commercial databases.

The proposed framework is a systematic process of alliance extraction and validation using natural language processing techniques and alliance domain knowledge. The process integrates news article search, entity extraction, and syntactic and semantic linguistic parsing techniques. In particular, Alliance Discovery Template (ADT) identifies a number of linguistic templates expanded from expert domain knowledge and extract potential alliances at sentence-level. Alliance Confidence Ranking (ACRank)further validates each unique alliance based on multiple features at document-level. The framework is designed to deal with extremely skewed, noisy data from news articles.

In evaluating the performance of ACRank on a gold standard data set of IBM alliances (2006–2008) showed that: Sentence-level ADT-based extraction achieved 78.1% recall and 44.7% precision and eliminated over 99% of the noise in news articles. ACRank further improved precision to 97% with the top20% of extracted alliance instances. Further comparison with Thomson Reuters SDC database showed that SDC covered less than 20% of total alliances, while ACRank covered 67%. When applying ACRank to Dow 30 company news articles, ACRank is estimated to achieve a recall between 0.48 and 0.95, and only 15% of the alliances appeared in SDC.

The research framework proposed in this paper indicates a promising direction of building a comprehensive alliance database using automatic approaches. It adds value to academic studies and business analyses that require in-depth knowledge of strategic alliances. It also encourages other innovative studies that use text mining and data analytics to study business relations.