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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.

This paper aims to propose a method for better discovering topic evolution path and semantic relationship from the perspective of patent entity extraction and semantic representation. On the one hand, this paper identifies entities that have the same semantics but different expressions for accurate topic evolution path discovery. On the other hand, this paper reveals semantic relationships of topic evolution for better understanding what leads to topic evolution.

Firstly, a Bi-LSTM-CRF (bidirectional long short-term memory with conditional random field) model is designed for patent entity extraction and a representation learning method is constructed for patent entity representation. Secondly, a method based on knowledge outflow and inflow is proposed for discovering topic evolution path, by identifying and computing semantic common entities among topics. Finally, multiple semantic relationships among patent entities are pre-designed according to a specific domain, and then the semantic relationship among topics is identified through the proportion of different types of semantic relationships belonging to each topic.

In the field of UAV (unmanned aerial vehicle), this method identifies semantic common entities which have the same semantics but different expressions. In addition, this method better discovers topic evolution paths by comparison with a traditional method. Finally, this method identifies different semantic relationships among topics, which gives a detailed description for understanding and interpretation of topic evolution. These results prove that the proposed method is effective and useful. Simultaneously, this method is a preliminary study and still needs to be further investigated on other datasets using multiple emerging deep learning methods.

This work provides a new perspective for topic evolution analysis by considering semantic representation of patent entities. The authors design a method for discovering topic evolution paths by considering knowledge flow computed by semantic common entities, which can be easily extended to other patent mining-related tasks. This work is the first attempt to reveal semantic relationships among topics for a precise and detailed description of topic evolution.

Identifying the flow regime is a prerequisite for accurately modeling two-phase flow. This paper aims to introduce a comprehensive data-driven workflow for flow regime identification.

A numerical two-phase flow model was validated against experimental data and was used to generate dynamic pressure signals for three different flow regimes. First, four distinct methods were used for feature extraction: discrete wavelet transform (DWT), empirical mode decomposition, power spectral density and the time series analysis method. Kernel Fisher discriminant analysis (KFDA) was used to simultaneously perform dimensionality reduction and machine learning (ML) classification for each set of features. Finally, the Shapley additive explanations (SHAP) method was applied to make the workflow explainable.

The results highlighted that the DWT + KFDA method exhibited the highest testing and training accuracy at 95.2% and 88.8%, respectively. Results also include a virtual flow regime map to facilitate the visualization of features in two dimension. Finally, SHAP analysis showed that minimum and maximum values extracted at the fourth and second signal decomposition levels of DWT are the best flow-distinguishing features.

This workflow can be applied to opaque pipes fitted with pressure sensors to achieve flow assurance and automatic monitoring of two-phase flow occurring in many process industries.

This paper presents a novel flow regime identification method by fusing dynamic pressure measurements with ML techniques. The authors’ novel DWT + KFDA method demonstrates superior performance for flow regime identification with explainability.

The purpose of this paper is to extract, characterise and quantify curcumin from selected Zingiberaceae of “kunyit” or turmeric (Curcuma longa), “temu lawak” or Javanese turmeric (Curcuma xanthorrhiza), “temu pauh” (Curcuma mangga), “lempoyang” (Zingiber zerumbet) and “bonglai” (Zingiber cassumunar) using alkaline and chemical-based extraction method for antimicrobial and antioxidant activities.

Through the alkaline-based extraction method, all parts of rhizome samples were freeze-dried for 72 h before grounded into a fine powder and kept at −20°C. The powdered sample (0.1 g) was weighed and placed in a 50 mL tube. About 20 mL of 2 M NaOH solution was added into the tube. The solution was allowed to stand for 30 min. Then, 20 mL of ethyl acetate was added into the tube. The solution was mixed well then centrifuged at 13,500 rpm for 3 min. The upper layer was collected using a pipette. The process was repeated until the upper layer became almost colourless. The collected ethyl acetate solution was concentrated using a rotary evaporator to remove the ethyl acetate from the extracted compound. The concentrated curcumin was placed in a universal bottle, which was then dried from the remaining ethyl acetate using nitrogen drying process. The dried curcumin was then stored inside the freezer at −20ºC. The antimicrobial activities were using agar diffusion method against bacterial and fungi, while the antioxidant activity was evaluated using 2,2-diphenyl-1-picrylhydrazyl (DPPH) scavenging assay.

All the samples successfully showed a single peak (curcumin) that gained from the high-performance liquid chromatography (HPLC) chromatogram analysis (at 425 nm) using the alkaline-based extraction method and the highest curcumin content was in turmeric (12.95 ± 1.07mg/g DW). At 10.0 mg/mL curcumin concentration, the best antibacterial activity was against on methicillin-resistant staphylococcus aureus (MRSA) with 7.50 ± 0.71 mm inhibition zone, while the best antifungal activity was against on Aspergillus niger with 8.00 ± 0.41 mm inhibition zone. The DPPH antioxidant test resulted in the highest inhibition (110.41 per cent) was at 0.25 mg/mL curcumin concentration.

Through HPLC analysis, all samples successfully showed a single peak of curcumin at 425 nm. The total carotenoid determination from turmeric revealed that the samples content was substantially higher using alkaline-based extraction (18.40 ± 0.07 mg/g DW) compared to chemical-based extraction (9.42 ± 0.20 mg/g ± SD).

Ontology of a domain mainly consists of a set of concepts and their semantic relations. It is typically constructed and maintained by using ontology editors with substantial human intervention. It is desirable to perform the task automatically, which has led to the development of ontology learning techniques. One of the main challenges of ontology learning from the text is to identify key concepts from the documents. A wide range of techniques for key concept extraction have been proposed but are having the limitations of low accuracy, poor performance, not so flexible and applicability to a specific domain. The propose of this study is to explore a new method to extract key concepts and to apply them to literature in the nuclear domain.

In this article, a novel method for key concept extraction is proposed and applied to the documents from the nuclear domain. A hybrid approach was used, which includes a combination of domain, syntactic name entity knowledge and statistical based methods. The performance of the developed method has been evaluated from the data obtained using two out of three voting logic from three domain experts by using 120 documents retrieved from SCOPUS database.

The work reported pertains to extracting concepts from the set of selected documents and aids the search for documents relating to given concepts. The results of a case study indicated that the method developed has demonstrated better metrics than Text2Onto and CFinder. The method described has the capability of extracting valid key concepts from a set of candidates with long phrases.

The present study is restricted to literature coming out in the English language and applied to the documents from nuclear domain. It has the potential to extend to other domains also.

The work carried out in the current study has the potential of leading to updating International Nuclear Information System thesaurus for ontology in the nuclear domain. This can lead to efficient search methods.

This work is the first attempt to automatically extract key concepts from the nuclear documents. The proposed approach will address and fix the most of the problems that are existed in the current methods and thereby increase the performance.

This paper aims to propose a robotic automation system for processing special-shaped thin-walled workpieces, which includes a measurement part and a processing part.

In the measurement part, to efficiently and accurately realize the three-dimensional camera hand-eye calibration based on a large amount of measurement data, this paper improves the traditional probabilistic method. To solve the problem of time-consuming in the extraction of point cloud features, this paper proposes a point cloud feature extraction method based on seed points. In the processing part, the authors design a new type of chamfering tool. During the process, the robot adopts admittance control to perform compensation according to the feedback of four sensors mounted on the tool.

Experiments show that the proposed system can make the tool smoothly fit the chamfered edge during processing and the machined chamfer meets the processing requirements of 0.5 × 0.5 to 0.9 × 0.9 mm².

The proposed design and approach can be applied on many types of special-shaped thin-walled parts. This will give a new solution for the automation integration problem in aerospace manufacturing.

A novel robotic automation system for processing special-shaped thin-walled workpieces is proposed and a new type of chamfering tool is designed. Furthermore, a more accurate probabilistic hand-eye calibration method and a more efficient point cloud extraction method are proposed, which are suitable for this system when comparing with the traditional methods.

Internal fluffy portion along with fibrous strands of ripe pumpkin is considered as waste in processing industries though it contains sufficient amount of ß-carotene pigment. The purpose of this paper is to use the leftover fluffy portion of ripe pumpkin (Cucurbita maxima) after the use of its flesh for the purpose of processing.

The data were analyzed statistically by following a complete randomized design (CRD). All analysis were performed using the software OPSTAT.

One hour pre-enzymatic treatment before solvent extraction showed significant improvement in extraction yield in comparison to the isolation of ß-carotene pigment through solvent only. Temperature time combination was optimized as 40°C for 2 h during solvent extraction to obtain maximum yield irrespective of the type of extraction method used.

Extracted carotene pigment can further be used as a natural food colorant in processed food products not only to enhance the color appeal but also it improves the nutritional value of the product as ß-carotene acts as a precursor of vitamin A.

Coloring agents of natural origin are becoming famous among society due to their health benefits. Consumers are becoming reluctant to use synthetic colors because of the undesirable allergic reactions caused by them, so carotene bio-pigment produced is a natural coloring compound with wide application in the food sector.

Even though few researchers have worked on the extraction of carotene pigment from pumpkin, but no researcher has reported the use of a waste fluffy portion of C. maxima for extraction of ß-carotene pigment.

This study analyzed the interdisciplinary characteristics of Chinese research studies in library and information science (LIS) measured by knowledge elements extracted through the Lexicon-LSTM model.

Eight research themes were selected for experiment, with a large-scale (N = 11,625) dataset of research papers from the China National Knowledge Infrastructure (CNKI) database constructed. And it is complemented with multiple corpora. Knowledge elements were extracted through a Lexicon-LSTM model. A subject knowledge graph is constructed to support the searching and classification of knowledge elements. An interdisciplinary-weighted average citation index space was constructed for measuring the interdisciplinary characteristics and contributions based on knowledge elements.

The empirical research shows that the Lexicon-LSTM model has superiority in the accuracy of extracting knowledge elements. In the field of LIS, the interdisciplinary diversity indicator showed an upward trend from 2011 to 2021, while the disciplinary balance and difference indicators showed a downward trend. The knowledge elements of theory and methodology could be used to detect and measure the interdisciplinary characteristics and contributions.

The extraction of knowledge elements facilitates the discovery of semantic information embedded in academic papers. The knowledge elements were proved feasible for measuring the interdisciplinary characteristics and exploring the changes in the time sequence, which helps for overview the state of the arts and future development trend of the interdisciplinary of research theme in LIS.