Depositional Environment and Lithofacies Analyses of Eocene Lacustrine Shale in the Bohai Bay Basin:Insights from Mineralogy and Elemental Geochemistry

The effect of various depositional parameters including paleoclimate,paleosalinity and provenance,on the depositional mechanism of lacustrine shale is very important in reconstructing the depositional environment.The classification of shale lithofacies and the interpretation of shale depositional environment are key features used in shale oil and gas exploration and development activity.The lower 3rd member of the Eocene Shahejie Formation(Es_(3)^(x)shale)was selected for this study,as one of the main prospective intervals for shale oil exploration and development in the intracratonic Bohai Bay Basin.Mineralogically,it is composed of quartz(avg.9.6%),calcite(avg.58.5%),dolomite(avg.7%),pyrite(avg.3.3%)and clay minerals(avg.20%).An advanced methodology(thin-section petrography,total organic carbon and total organic sulfur contents analysis,X-ray diffraction(XRD),X-ray fluorescence(XRF),field-emission scanning electron microscopy(FE-SEM))was adopted to establish shale lithofacies and to interpret the depositional environment in the lacustrine basin.Six different types of lithofacies were recognized,based on mineral composition,total organic carbon(TOC)content and sedimentary structures.Various inorganic geochemical proxies(Rb/Sr,Ca/(Ca+Fe),Ti/Al,Al/Ca,Al/Ti,Zr/Rb)have been used to interpret and screen variations in depositional environmental parameters during the deposition of the Es_(3)^(x)shale.The experimental results indicate that the environment during the deposition of the Es_(3)^(x)shale was warm and humid with heightened salinities,moderate to limited detrital input,higher paleohydrodynamic settings and strong oxygen deficient(reducing)conditions.A comprehensive depositional model of the lacustrine shale was developed.The interpretations deduced from this research work are expected to not only expand the knowledge of shale lithofacies classification for lacustrine fine-grained rocks,but can also offer a theoretical foundation for lacustrine shale oil exploration and development.

Aspect-Based Sentiment Analysis for Social Multimedia:A Hybrid Computational Framework

People utilize microblogs and other social media platforms to express their thoughts and feelings regarding current events,public products and the latest affairs.People share their thoughts and feelings about various topics,including products,news,blogs,etc.In user reviews and tweets,sentiment analysis is used to discover opinions and feelings.Sentiment polarity is a term used to describe how sentiment is represented.Positive,neutral and negative are all examples of it.This area is still in its infancy and needs several critical upgrades.Slang and hidden emotions can detract from the accuracy of traditional techniques.Existing methods only evaluate the polarity strength of the sentiment words when dividing them into positive and negative categories.Some existing strategies are domain-specific.The proposed model incorporates aspect extraction,association rule mining and the deep learning technique Bidirectional EncoderRepresentations from Transformers(BERT).Aspects are extracted using Part of Speech Tagger and association rulemining is used to associate aspects with opinion words.Later,classification was performed using BER.The proposed approach attained an average of 89.45%accuracy,88.45%precision and 85.98%recall on different datasets of products and Twitter.The results showed that the proposed technique achieved better than state-of-the-art sentiment analysis techniques.

Sedimentology of Marl and Marly Limestone Sequence of Upper Cretaceous Kawagarh Formation from Northern Kalachitta Range, Attock Hazara Fold and Thrust Belt, Pakistan

Upper Cretaceous Kawagarh Formation is well exposed in the Attock Hazara Fold and Thrust Belt (AHFTB) and shows significant lateral and vertical variations in lithology. The present work deals with the sedimentological studies of marl and marly limestone sequence of Kawagarh Formation exposed at the Bagh Neelab, Ghariala north and Sojhanda villages in Northern Kalachitta Range. Detailed petrographic studies of marly limestone and hard marl substrate show that planktons and oysters are the main skeletal constituents of studied samples and clay and detrital quartz mainly composed the non skeletal fraction. X-Ray diffraction analyses of selected marl samples confirm the petrographic data. On the basis of skeletal and non skeletal content, two microfacies—marl microfacies and Planktonic microfacies are constructed. The faunal content, their paleoecology and detrital content of microfacies suggest that marl and marly limestone sequence of Kawagarh Formation was deposited over the mid and outer ramp settings.

Synergic Effect of Reworking for Imperfect Quality Items with the Integration of Multi-Period Delay-in-Payment and Partial Backordering in Global Supply Chains

In intercontinental trade and economics goods are bought from a global supplier.On occasion,the expected lot may include a fraction of defective items.These imperfect items still have worth and can be sold to customers after repair.It is cost-effective and sustainable to rework such items in nearby repair workshops rather than return them.The reworked items can be returned from the workshop to the buyer when shortages are equal to the quantity of imperfect items.In the meantime,the supplier correspondingly deals a multi-period delay-in-payments strategy with purchaser.The entire profit has been maximized with paybacks for interim financing.This study aims to develop a synergic inventory model to get the most profit by making an allowance for reworking,multi-period delay-in-payments policy,and shortages.The findings of the proposed model augment inventory management performance by monitoring cycle time as well as fraction of phase with optimistic inventory for a supply chain.The results demonstrate that profit is smaller if the permitted period given by supplier to buyer is equal to or greater than the cycle time,and profit is greater if the permitted period is smaller than the cycle time.The algebraic method is engaged to make a closed system optimum solution.The mathematical experiment of this study is constructed to provide management insights and tangible practices.

Interactive Evolutionary Multi-Objective Optimization Algorithm Using Cone Dominance

As the number of objectives increases,the performance of the Pareto dominance-based Evolutionary Multi-objective Optimization( EMO) algorithms such as NSGA-II,SPEA2 severely deteriorates due to the drastic increase in the Pareto-incomparable solutions. We propose a sorting method which classifies these incomparable solutions into several ordered classes by using the decision maker's( DM) preference information.This is accomplished by designing an interactive evolutionary algorithm and constructing convex cones. This method allows the DMs to drive the search process toward a preferred region of the Pareto optimal front. The performance of the proposed algorithm is assessed for two,three,and four-objective knapsack problems. The results demonstrate the algorithm ' s ability to converge to the most preferred point. The evaluation and comparison of the results indicate that the proposed approach gives better solutions than that of NSGA-II. In addition,the approach is more efficient compared to NSGA-II in terms of the number of generations required to reach the preferred point.

水牛溶菌酶基因家族的鉴定、进化与序列分析

溶菌酶是一种天然的抗菌物质,哺乳动物的乳汁、泪液和唾液等都含有溶菌酶。水牛(Bubalus bubalis)奶比奶牛(Bos taurus)奶含有更多的溶菌酶。水牛溶菌酶基因家族共有16位成员。本研究对水牛溶菌酶基因家族进行了系统分析。结果表明,水牛的溶菌酶基因与奶牛、山羊(Capra hircus)、绵羊(Ovis aries)的亲缘关系近。水牛溶菌酶理化性质分析表明,水牛溶菌酶大部分是碱性的、稳定的、热稳定性好的和亲水的。单核苷酸多态性分析表明,与奶牛溶菌酶相比,水牛溶菌酶基因编码的蛋白质有氨基酸变异和氨基酸序列插入。这些氨基酸的变异和插入可能是水牛溶菌酶与奶牛溶菌酶在抗菌能力上存在差异的原因。水牛乳腺中表达的溶菌酶基因的转录因子结合位点数高于奶牛的,这也可能是水牛乳腺溶菌酶表达量高于奶牛的重要原因。

A New Framework for Scholarship Predictor Using a Machine Learning Approach

Education is the base of the survival and growth of any state,but due to resource scarcity,students,particularly at the university level,are forced into a difficult situation.Scholarships are the most significant financial aid mechanisms developed to overcome such obstacles and assist the students in continuing with their higher studies.In this study,the convoluted situation of scholarship eligibility criteria,including parental income,responsibilities,and academic achievements,is addressed.In an attempt to maximize the scholarship selection process,numerous machine learning algorithms,including Support Vector Machines,Neural Networks,K-Nearest Neighbors,and the C4.5 algorithm,were applied.The C4.5 algorithm,owing to its efficiency in the prediction of scholarship beneficiaries based on extraneous factors,was capable of predicting a phenomenal 95.62%of predictions using extensive data of a well-esteemed government sector university from Pakistan.This percentage is 4%and 15%better than the remainder of the methods tested,and it depicts the extent of the potential for the technique to enhance the scholarship selection process.The Decision Support Systems(DSS)would not only save the administrative cost but would also create a fair and transparent process in place.In a world where accessibility to education is the key,this research provides data-oriented consolidation to ensure that deserving students are helped and allowed to get the financial assistance that they need to reach higher studies and bridge the gap between the demands of the day and the institutions of intellect.