Abstract-Recommender systems use the past experiences and preferences of the target users as a basis to provide personalized recommendations for them and as the same time, solve the information overloading problem. Context as the dynamic information describing the situation of items and users and affecting the user's decision process is essential to be used by recommender systems. Multidimensional approach to recommender systems that can provide recommendations based on additional contextual information besides the typical information on users and items used in most of the current recommender systems. This approach supports multiple dimensions, profiling information, and hierarchical aggregation of recommendations. the recommender system could simultaneously possess the advantages of content-based recommendation, knowledge-based recommendation, collaborative filtering recommendation and OLAP in segmenting the information. Following the improvement of the recommendation structure, it doesn't have to limit its analysis on the user and product to compute for the recommendation result and it could also handle and determine more complex contextual information as recommendation computation foundation. It could develop better results if applied in different domains.

Bentonite has been modified using H2SO4 at various concentrations to alter Si/Al ratio that is responsible for catalytic pyrolysis of single-use waste polyethylene (SUWP). The XRF analysis confirmed a gradual decrease of Al2O3 content with an increasing concentration of H2SO4. The highest liquid hydrocarbon yield (87.48%) was obtained at Si/Al ratio of 34.24. To further activate acid-modified bentonite, bimetals (Cu/Ni, Fe/Ni, and Co/Ni) impregnation was conducted. The catalytic performance was compared with monometals (Ru, Co, Ni, Cu, and Fe) impregnated bentonite counterparts. The Co, Ni, or Cu impregnated bentonite catalysts (with 86, 83, and 86% yield) outperformed Fe and Ru impregnated bentonite catalysts (with 68% and 79% yield). However, bimetallic (Cu/Ni, Fe/Ni and Co/Ni) catalysts enhanced severe cracking leading to the formation of more gaseous hydrocarbons and thus less amount of liquid (70–80%) is obtained. Incorporation of iron into bentonite or Ni/bentonite increased the amount of 2-Octene, 3,7-dimethyl-, (Z)- compared to acid-treated bentonite and Ni/ bentonite catalysts. Moreover, Fe/Ni/bentonite has also increased the amount of 2-Octene, 2,6-dimethyl- compounds. The BET analysis shows that both surface area and pore diameter increased due to acid treatment resulting in an increase in the percent yield of liquid compared to raw bentonite. The FTIR and GC-MS analysis confirmed that the liquid hydrocarbon consists of linear and branched alkanes and alkenes with some cyclic hydrocarbons. The NMR analysis showed the liquid hydrocarbon is free from the aromatic compounds and polycyclic aromatic hydrocarbons (PAH).

Qur’an is Allah (SWT)’s greatest miracle and is the source of all knowledge and information. As a Muslim, obligation is not only to recite the Qur’an but also it is important to gain knowledge from the Qur’an. Ontology is the best way to retrieve Quranic knowledge in a technical way. With the help of internet many search engines are found to discover information from Qur’an. But most of the search engines or information retrieval systems are keyword-based which can often lead to irrelevant results. To overcome this problem, semantic search is most useful in this case. The aim of research is to develop an ontological semantic based method to retrieve the food related verses and concepts from holy Qur’an by using natural language query. In this work, triplet extraction algorithm has been used for generating triple, the protege OWL editor 4.3 version used to create food ontology and the SPARQL Apache Jena fuseki server was used for querying. Quranic data are collected from English translation of the holy Qur’an. IIUC Studies Vol.18, December 2021: 101-122

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