The physical examination of the fruit of soursop fruit (Annona muricata) selected from different parent trees was investigated. Three-stage modified Soxhlet method was used which includes a percolator (boiler and reflux) which circulates the solvent, a thimble (usually made of thick filter paper) which retains the seed to be extracted, and a siphon mechanism, which periodically empties the condensed solvent from the thimble back into the percolator. The extraction of oil from the seed and the percentage yield was examined. The oil samples were characterized for physico-chemical properties. The maximum values of physical parameters found were fruit weight 3.7 ± 7.09, fruit length 12.2 ± 28.3 cm, with 15.2 ± 20.81 cm and 0.12 ± 18.91 g for pulp weight. The percentage oil yield of 48.5% was obtained due to the environmental factors such as the soil type, planting season and optimal temperature of the region of seed cultivation. The result of chemical properties showed maximum acid value 0.46 mg KOH, FFA of 0.33 mg, saponification of 189.4 mg KOH mg and peroxide value of 4.33 mg/g. The oil physical properties as discovered have a melting point of 32˚C, smoke point of 198˚C and flash point of 280˚C. The results obtained in this study further reveal the potential of oil from seed of soursop as a substitute for conventional vegetable oil due to its high flash point which is an indication of its low flammability and can be used as a good source of food, industrially can be used as an anti-microbial agent and for pest control.
In recent years,with the rapid development of deep learning technology,relational triplet extraction techniques have also achieved groundbreaking progress.Traditional pipeline models have certain limitations due to error propagation.To overcome the limitations of traditional pipeline models,recent research has focused on jointly modeling the two key subtasks-named entity recognition and relation extraction-within a unified framework.To support future research,this paper provides a comprehensive review of recently published studies in the field of relational triplet extraction.The review examines commonly used public datasets for relational triplet extraction techniques and systematically reviews current mainstream joint extraction methods,including joint decoding methods and parameter sharing methods,with joint decoding methods further divided into table filling,tagging,and sequence-to-sequence approaches.In addition,this paper also conducts small-scale replication experiments on models that have performed well in recent years for each method to verify the reproducibility of the code and to compare the performance of different models under uniform conditions.Each method has its own advantages in terms of model design,task handling,and application scenarios,but also faces challenges such as processing complex sentence structures,cross-sentence relation extraction,and adaptability in low-resource environments.Finally,this paper systematically summarizes each method and discusses the future development prospects of joint extraction of relational triples.
In the international shipping industry, digital intelligence transformation has become essential, with both governments and enterprises actively working to integrate diverse datasets. The domain of maritime and shipping is characterized by a vast array of document types, filled with complex, large-scale, and often chaotic knowledge and relationships. Effectively managing these documents is crucial for developing a Large Language Model (LLM) in the maritime domain, enabling practitioners to access and leverage valuable information. A Knowledge Graph (KG) offers a state-of-the-art solution for enhancing knowledge retrieval, providing more accurate responses and enabling context-aware reasoning. This paper presents a framework for utilizing maritime and shipping documents to construct a knowledge graph using GraphRAG, a hybrid tool combining graph-based retrieval and generation capabilities. The extraction of entities and relationships from these documents and the KG construction process are detailed. Furthermore, the KG is integrated with an LLM to develop a Q&A system, demonstrating that the system significantly improves answer accuracy compared to traditional LLMs. Additionally, the KG construction process is up to 50% faster than conventional LLM-based approaches, underscoring the efficiency of our method. This study provides a promising approach to digital intelligence in shipping, advancing knowledge accessibility and decision-making.
Simultaneous recovery of Ni and Co from Fe(Ⅲ)and Al is a critical challenge in hydrometallurgical processes.Recognized solvent extraction systems often struggle with selectivity and effective performance in mixed metal ion environments.Herein,a new synergistic solvent extraction(SSX)system comprised of a novel pyridine analog,N,N-bis(pyridin-2-ylmethyl)dodecan-1-amine(BPMDA),and dinonylnaphthalene sulfonic acid(DNNSA)with tributyl phosphate as phase modifier is introduced.The SSX system demonstrates high extraction performance achieving>90%for Ni and>97%for Co in a singlestage extraction process,with high selectivity.Under optimal conditions,the selectivity sequence is observed as Co^(2+)(>97%)>Ni^(2+)(>90%)>Mn^(2+)(<20%)>Fe^(3+)(<10%)>Mg^(2+)(<5%)>Al^(3+)(<2%)>Ca^(2+)(<1%).Spectroscopic analysis evidences the preferential binding of BPMDA with Ni and Co in the presence of DNNSA,concurrently achieving a significant reduction in the co-extraction of Fe(Ⅲ)and Al.The selective complexation of Ni and Co using the SSX system offers a highly efficient and selective approach for their extraction,with promising potential for applications in recovery-based processes.
Bharat Prasad SharmaTianzhang WangYufeng LiangJinping XiongLiangrong YangZheng Li
