Similarity-based laboratory study of CO_(2) huff-n-puff in tight conglomerate cores

Tight conglomerate reservoirs are featured with extremely low permeability,strong heterogeneity and poor water injectivity.CO_(2) huff-n-puff has been considered a promising candidate to enhance oil recovery in tight reservoirs,owing to its advantages in reducing oil viscosity,improving mobility ratio,quickly replenishing formation pressure,and potentially achieving a miscible state.However,reliable inhouse laboratory evaluation of CO_(2) huff-n-puff in natural conglomerate cores is challenging due to the inherent high formation pressure.In this study,we put forward an equivalent method based on the similarity of the miscibility index and Grashof number to acquire a lab-controllable pressure that features the flow characteristics of CO_(2) injection in a tight conglomerate reservoir.The impacts of depletion degree,pore volume injection of CO_(2) and soaking time on ultimate oil recovery in tight cores from the Mahu conglomerate reservoir were successfully tested at an equivalent pressure.Our results showed that oil recovery decreased with increased depletion degree while exhibiting a non-monotonic tendency(first increased and then decreased)with increased CO_(2) injection volume and soaking time.The lower oil recoveries under excess CO_(2) injection and soaking time were attributed to limited CO_(2) dissolution and asphaltene precipitation.This work guides secure and reliable laboratory design of CO_(2) huff-n-puff in tight reservoirs with high formation pressure.

ASTERIX Category017数据格式分析与应用

文章介绍了ASTERIX标准协议的结构框架,依据实例详细给出了ASTERIX Category017数据格式的解析过程,并阐述了该格式协议在S模式二次雷达协同监视组网中的重要应用。

A Study on the Correlation Between LIWC Word Categories and Chinese Composition Writing Performance of Fourth,Fifth,and Sixth Grade Students

This study focuses on the analysis of the Chinese composition writing performance of fourth,fifth,and sixth grade students in 16 selected schools in Longhua District,Shenzhen during the spring semester of 2023.Using LIWC(Linguistic Inquiry and Word Count)as a text analysis tool,the study explores the impact of LIWC categories on writing performance which is scaled by score.The results show that the simple LIWC word categories have a significant positive influence on the composition scores of lower-grade students;while complex LIWC word categories have a significant negative influence on the composition scores of lower-grade students but a significant positive influence on the composition scores of higher-grade students.Process word categories have a positive influence on the composition scores of all three grades,but the impact of complex process word categories increases as the grade level rises.

“Instruction-Category” Approach of Test Suite Construction for Oven Embedded System

Being different from testing for popular GUI software, the “instruction-category” approach is proposed for testing embedded system. This approach is constructed by three steps including refining items, drawing instruction-brief and instruction-category, and constructing test suite. Consequently, this approach is adopted to test oven embedded system, and detail process is deeply discussed. As a result, the factual result indicates that the “instruction-category” approach can be effectively applied in embedded system testing as a black-box method for conformity testing.

Mapping Soil Salinity Using a Similarity-based Prediction Approach:A Case Study in Huanghe River Delta,China

Spatial distribution of soil salinity can be estimated based on its environmental factors because soil salinity is strongly affected and indicated by environmental factors. Different with other properties such as soil texture, soil salinity varies with short-term time. Thus, how to choose powerful environmental predictors is especially important for soil salinity. This paper presents a similarity-based prediction approach to map soil salinity and detects powerful environmental predictors for the Huanghe(Yellow) River Delta area in China. The similarity-based approach predicts the soil salinities of unsampled locations based on the environmental similarity between unsampled and sampled locations. A dataset of 92 points with salt data at depth of 30–40 cm was divided into two subsets for prediction and validation. Topographical parameters, soil textures, distances to irrigation channels and to the coastline, land surface temperature from Moderate Resolution Imaging Spectroradiometer(MODIS), Normalized Difference Vegetation Indices(NDVIs) and land surface reflectance data from Landsat Thematic Mapper(TM) imagery were generated. The similarity-based prediction approach was applied on several combinations of different environmental factors. Based on three evaluation indices including the correlation coefficient(CC) between observed and predicted values, the mean absolute error and the root mean squared error we found that elevation, distance to irrigation channels, soil texture, night land surface temperature, NDVI, and land surface reflectance Band 5 are the optimal combination for mapping soil salinity at the 30–40 cm depth in the study area(with a CC value of 0.69 and a root mean squared error value of 0.38). Our results indicated that the similarity-based prediction approach could be a vital alternative to other methods for mapping soil salinity, especially for area with limited observation data and could be used to monitor soil salinity distributions in the future.

Soil polygon disaggregation through similarity-based prediction with legacy pedons

Conventional soil maps generally contain one or more soil types within a single soil polygon.But their geographic locations within the polygon are not specified.This restricts current applications of the maps in site-specific agricultural management and environmental modelling.We examined the utility of legacy pedon data for disaggregating soil polygons and the effectiveness of similarity-based prediction for making use of the under-or over-sampled legacy pedon data for the disaggregation.The method consisted of three steps.First,environmental similarities between the pedon sites and each location were computed based on soil formative environmental factors.Second,according to soil types of the pedon sites,the similarities were aggregated to derive similarity distribution for each soil type.Third,a hardening process was performed on the maps to allocate candidate soil types within the polygons.The study was conducted at the soil subgroup level in a semi-arid area situated in Manitoba,Canada.Based on 186 independent pedon sites,the evaluation of the disaggregated map of soil subgroups showed an overall accuracy of 67% and a Kappa statistic of 0.62.The map represented a better spatial pattern of soil subgroups in both detail and accuracy compared to a dominant soil subgroup map,which was commonly used in practice.Incorrect predictions mainly occurred in the agricultural plain area and the soil subgroups that are very similar in taxonomy,indicating that new environmental covariates need to be developed.We concluded that the combination of legacy pedon data with similarity-based prediction is an effective solution for soil polygon disaggregation.

Association between cardiorespiratory fitness level and insulin resistance in adolescents with various obesity categories

BACKGROUND An association between cardiorespiratory fitness(CRF)and insulin resistance in obese adolescents,especially in those with various obesity categories,has not been systematically studied.There is a lack of knowledge about the effects of CRF on insulin resistance in severely obese adolescents,despite their continuous rise.AIM To investigate the association between CRF and insulin resistance in obese adolescents,with special emphasis on severely obese adolescents.METHODS We performed a prospective,cross-sectional study that included 200 pubertal adolescents,10 years to 18 years of age,who were referred to a tertiary care center due to obesity.According to body mass index(BMI),adolescents were classified as mildly obese(BMI 100% to 120% of the 95^(th)percentile for age and sex)or severely obese(BMI≥120% of the 95^(th)percentile for age and sex or≥35 kg/m^(2),whichever was lower).Participant body composition was assessed by bioelectrical impedance analysis.A homeostatic model assessment of insulin resistance(HOMA-IR)was calculated.Maximal oxygen uptake(VO_(2)max)was determined from submaximal treadmill exercise test.CRF was expressed as VO_(2)max scaled by total body weight(TBW)(mL/min/kg TBW)or by fat free mass(FFM)(mL/min/kg FFM),and then categorized as poor,intermediate,or good,according to VO_(2)max terciles.Data were analyzed by statistical software package SPSS(IBM SPSS Statistics for Windows,Version 24.0).P<0.05 was considered statistically significant.RESULTS A weak negative correlation between CRF and HOMA-IR was found[Spearman’s rank correlation coefficient(rs)=-0.28,P<0.01 for CRF_(TBW);(r_(s))=-0.21,P<0.01 for CRF_(FFM)].One-way analysis of variance(ANOVA)revealed a significant main effect of CRF on HOMA-IR[F(2200)=6.840,P=0.001 for CRF_(TBW);F_((2200))=3.883,P=0.022 for CRF_(FFM)].Subsequent analyses showed that obese adolescents with poor CRF had higher HOMA-IR than obese adolescents with good CRF(P=0.001 for CRF_(TBW);P=0.018 for CRF_(FFM)).Two-way ANOVA with Bonferroni correction confirmed significant effect of interaction of CRF level and obesity category on HOMA-IR[F_((2200))=3.292,P=0.039 for CRF_(TBW)].Severely obese adolescents had higher HOMA-IR than those who were mildly obese,with either good or poor CRF.However,HOMA-IR did not differ between severely obese adolescents with good and mildly obese adolescents with poor CRF.CONCLUSION CRF is an important determinant of insulin resistance in obese adolescents,regardless of obesity category.Therefore,CRF assessment should be a part of diagnostic procedure,and its improvement should be a therapeutic goal.

Profile of the Functional Categories of Food Additives in Industrial Foods Marketed in Senegal

Food additives, whether natural or artificial substances, are widely used around the world to improve the sensory quality of products, extend their shelf life and make them more competitive. However, the abusive and uncontrolled consumption of food additives is the cause of numerous illnesses and diseases such as poisoning, allergies, diabetes and numerous cancers. So, in addition to setting up control and regulatory bodies, it is becoming essential to keep a constant watch on the presence of food additives on the market. The aim of this study is to highlight the main categories of food additives in food products frequently sold on the Senegalese market. The methodology of the study is based on the identification of food additives from the information given on the labels of food packaging. Information was collected in markets in two (2) major regions of Senegal: Dakar and Saint-Louis. The results of our study show the presence of 153 food additives on the labels of 514 samples collected. Moreover, the frequency and diversity of additives depended on the food category. On the other hand, beyond their important technological and functional roles, some additives such as aspartame and monosodium glutamate have been implicated in pathologies, and others, such as titanium dioxide, are the subject of much controversy and even withdrawal in certain legislations for their impacts deemed potentially negative on consumer health.

Refined Sparse Representation Based Similar Category Image Retrieval

Given one specific image,it would be quite significant if humanity could simply retrieve all those pictures that fall into a similar category of images.However,traditional methods are inclined to achieve high-quality retrieval by utilizing adequate learning instances,ignoring the extraction of the image’s essential information which leads to difficulty in the retrieval of similar category images just using one reference image.Aiming to solve this problem above,we proposed in this paper one refined sparse representation based similar category image retrieval model.On the one hand,saliency detection and multi-level decomposition could contribute to taking salient and spatial information into consideration more fully in the future.On the other hand,the cross mutual sparse coding model aims to extract the image’s essential feature to the maximumextent possible.At last,we set up a database concluding a large number of multi-source images.Adequate groups of comparative experiments show that our method could contribute to retrieving similar category images effectively.Moreover,adequate groups of ablation experiments show that nearly all procedures play their roles,respectively.

A Hybrid Ensemble Learning Approach Utilizing Light Gradient Boosting Machine and Category Boosting Model for Lifestyle-Based Prediction of Type-II Diabetes Mellitus

Addressing classification and prediction challenges, tree ensemble models have gained significant importance. Boosting ensemble techniques are commonly employed for forecasting Type-II diabetes mellitus. Light Gradient Boosting Machine (LightGBM) is a widely used algorithm known for its leaf growth strategy, loss reduction, and enhanced training precision. However, LightGBM is prone to overfitting. In contrast, CatBoost utilizes balanced base predictors known as decision tables, which mitigate overfitting risks and significantly improve testing time efficiency. CatBoost’s algorithm structure counteracts gradient boosting biases and incorporates an overfitting detector to stop training early. This study focuses on developing a hybrid model that combines LightGBM and CatBoost to minimize overfitting and improve accuracy by reducing variance. For the purpose of finding the best hyperparameters to use with the underlying learners, the Bayesian hyperparameter optimization method is used. By fine-tuning the regularization parameter values, the hybrid model effectively reduces variance (overfitting). Comparative evaluation against LightGBM, CatBoost, XGBoost, Decision Tree, Random Forest, AdaBoost, and GBM algorithms demonstrates that the hybrid model has the best F1-score (99.37%), recall (99.25%), and accuracy (99.37%). Consequently, the proposed framework holds promise for early diabetes prediction in the healthcare industry and exhibits potential applicability to other datasets sharing similarities with diabetes.

基于微信平台的健康信息行为研究:基本特征、主题范畴与展望

目的/意义梳理基于微信平台的健康信息行为研究现状,把握研究动态、拓宽媒介视角。方法/过程系统搜集国内外文献,运用内容分析法统计其发文趋势、研究方法和研究对象,归纳分析主题范畴并展望未来发展方向。结果/结论问卷调查法和访谈法广泛应用于微信平台健康信息行为研究中;老年微信用户和公众号功能成为热点研究对象;主要研究主题可归纳为健康信息使用行为、健康信息采纳行为、健康信息传播行为和持续使用行为4个范畴。未来应进一步丰富研究方法、细分研究对象、厘清主题内涵及外延。

End-to-end aspect category sentiment analysis based on type graph convolutional networks

For the existing aspect category sentiment analysis research,most of the aspects are given for sentiment extraction,and this pipeline method is prone to error accumulation,and the use of graph convolutional neural network for aspect category sentiment analysis does not fully utilize the dependency type information between words,so it cannot enhance feature extraction.This paper proposes an end-to-end aspect category sentiment analysis(ETESA)model based on type graph convolutional networks.The model uses the bidirectional encoder representation from transformers(BERT)pretraining model to obtain aspect categories and word vectors containing contextual dynamic semantic information,which can solve the problem of polysemy;when using graph convolutional network(GCN)for feature extraction,the fusion operation of word vectors and initialization tensor of dependency types can obtain the importance values of different dependency types and enhance the text feature representation;by transforming aspect category and sentiment pair extraction into multiple single-label classification problems,aspect category and sentiment can be extracted simultaneously in an end-to-end way and solve the problem of error accumulation.Experiments are tested on three public datasets,and the results show that the ETESA model can achieve higher Precision,Recall and F1 value,proving the effectiveness of the model.

基于“学科-科教-产教”多维融合的能源动力拔尖创新人才培养模式探索与实践

在我国“双碳”战略和能源战略的指导下,能源动力类别研究生培养尤为重要。而目前我国能源动力类别研究生培养所面临的主要问题是能源动力类别研究生培养模式与新时代拔尖创新人才要求不匹配,主要问题包括学科背景单一,课程体系陈旧;教学内容陈旧,知识体系不新;实践能力不强,实践环节薄弱。该文针对上述三方面问题开展研究,提出“学科-科教-产教”融合的多维融合思想,构建创新型能源动力类别研究生培养模式,该模式有助于将能源动力类别研究生培养成为新时代拔尖创新人才,满足新时代的迫切需要。该文所提出的思想和培养模式对于能源动力类别研究生培养具有重要的参考价值。

“数据”可否纳入知识产权客体范畴?

《民法总则》(草案)第108条将“数据信息”纳入知识产权客体范畴,虽然该规定最终未获通过,但却引发了学界和业界的热议,数据可否纳入知识产权客体范畴的讨论方兴未艾。根据“信息链”理论,事实、数据、信息、知识和情报之间是呈现一种由下至上逐步提炼加工的链式关系,数据与信息、知识、情报之间依次包含。但数据并不符合知识产权客体的一般特征,若将“数据”整体直接纳入知识产权客体的范畴,会冲击已有的权能和体系,影响知识产权客体家族的统一性和协调性。应对数据进行分类保护,在现有知识产权保护基础之上,增设数据产权保护模式,数据主体就个人原始数据享有数据人格权和数据财产权;企业就其加工处理取得的衍生数据享有数据财产权,企业这种财产权利取得的门槛低于知识产权取得,不必要求独创性、创造性或显著性等知识产权客体的一般特征,宜采取弱保护模式。

新时代法学基本范畴的重构——构建中国自主法学知识体系要论

在新时代背景下对法学基本范畴的内涵和体系进行重构,是构建中国自主法学知识体系的“成事之要”。“政治新时代”和“科技新时代”的到来,为法学基本范畴的重构提供了背景和动因。法学基本范畴从法学学科体系看构成了近代法律科学的“通用语汇”,从法学学术体系看可谓是法学理论创新的“思想芯片”,从法学话语体系看形塑了中国法学对外传播的“基本语法”。为了重构法学基本范畴,一方面要进行理论上的融会贯通,即贯通马克思主义、中华传统优秀法治文明和西方优秀法治文化三个传统,另一方面要对历史和当下的法治实践予以归纳提炼,并通过建构性解释上升为法学概念和范畴。重构的核心任务在于构筑兼具普遍性与中国性的法学基本范畴体系,为此既要加强对原创性概念和范畴的研究,也要加强对普遍法学概念和范畴的新内涵的研究,并按科学严密的内在结构构筑起以“法”和“法治”为核心、兼具静态性和动态性的二元法学基本范畴体系。

中国式现代化理论的标识性范畴与体系化构建

中国式现代化范畴既有从探索提出到形成确立的逻辑演进过程,也有从简单范畴到范畴体系的逻辑展开过程。中国式现代化是新时代党的理论创新的标识性范畴,但作为一个科学理论体系,中国式现代化理论自身也包含着一系列标识性范畴,这些范畴揭示了中国式现代化的内涵、特征和本质,使中国式现代化更加清晰、科学、可感可行。中国式现代化理论的标识性范畴为构建中国式现代化理论的科学体系,提炼中国式现代化理论的基本原理,提升中国式现代化理论的话语说服力和影响力,进而推进中国式现代化理论的体系化、学理化,奠定了坚实的概念基石和理论基础。提炼并阐明中国式现代化理论的标识性范畴,是构建具有学理性的中国式现代化理论体系的基础工作和必要环节。

网络刑法的发展回溯与理论供源

传统刑法体系遭遇网络犯罪形态后频现应答疲态,预防性立法由此显身,开启由网络犯罪策动刑法转型的号角,但网络刑法的理论根基尚不稳。网络刑法的演进是网络社会发展与网络犯罪迸发共同作用下真实的渐显;网络空间社会思维是网络刑法的建构基石,衍生丰富的刑法学意义。网络安全秩序价值的相对优位与网络自由的并重、网络安全法益的统领地位与刑法任务变更、预防性理念的内在觉醒与审慎运行,是网络刑法的根基之本,框定网络刑法的发展脉络。刑法知识迁移是渐进的,网络犯罪、网络归责、网络制裁堪当生成网络刑法知识范畴的参照性“母版”,未来可创设网络刑法典以实现知识结构的创造性立法固化。

职业教育科教融汇的动力机制

职业教育科教融汇是科学知识与技术知识、科学技术与教育要素、科学研究与教学过程融合汇聚的过程,是优化职业教育类型定位的新方向,也是建设教育强国、科技强国、人才强国的迫切需要和题中之义。职业教育科教融汇动力机制由引力、行动力、支持力和阻力构成,四种要素相互作用、相互影响,决定科教融汇的方向和速度。当前由于体制机制错位缺位、院校能力整体偏弱、办学主体共建不足、办学实践路径依赖等原因,职业教育科教融汇动力机制存在引力有限、行动力式微、支持力薄弱、阻力突显等阻滞困境。为保障职业教育科教融汇长远可持续发展,需要建立健全体制机制,深化科教融汇发展格局;提升核心办学能力,强化科教融汇内生力量;协同多元主体,聚合科教融汇外部供给;创新发展路径,生成科教融汇特色内容。

劳动关系微分化视角下网约工劳动权益的法律保障

在算法技术与平台经济相得益彰共同推进数字经济向纵深方向发展的过程中,劳动关系日益呈现出微分化的演化趋势,使得以网约工为代表的新业态从业人员陷入了“身份不明确”的主体性危机与“权益保障难”的双重困境,不仅对传统的劳动从属基础性理论提出了新的挑战,而且也日益凸显现行法律制度供给不足的现状。基于网约工群体经济从属性、人格从属性与组织从属性微分化趋势之系统理论分析,科学审视与证成微分化视阈下具有劳动从属性的网约工“第三类劳动者”之身份,并将“内在经济与人格从属程度的差距均以外在劳动量的差别为表现形式”这一从属性微分化之核心作为识别与判定标准,对具有不同强度从属性的“第三类劳动者”给予不同程度的劳动法倾斜保护,希冀能平衡平台经济有序发展与网约工劳动权益合法保障之间的关系,为健全和完善我国劳动法律制度体系提供符合数字经济内在运行规律的科学立法选择。

从整体主义哲学看中国古代哲学超越性概念与范畴的翻译

中国古代哲学超越性概念与范畴的翻译问题,关系到中国哲学在世界哲学之林如何自我定位以及与世界哲学如何对话。对中国古代哲学是否存在超越性概念与范畴以及如何翻译这一问题,文化上的排外论、兼容论和多元论有不同的回答和主张。多元论中的普遍主义强调文化共同性,特殊主义则强调个体的独特性。立足普遍性可以发现各种文化之间的联系,但不是为了寻求一种把独特性沦为背景的世界语言;立足特殊性能够尊重各种文化的独特性,但是不能由此认为它们必然彼此互不相干。标举中国哲学独特性的特殊论者,倾向于放弃西方哲学传统中被认为具有高度超越意义的概念和范畴。整体主义是普遍性与特殊性的统一,整体主义哲学翻译观认为,中西哲学传统中有着一些共通的问题意识和思维范畴。中国哲学中的超越性范畴是确然存在的,诸如“天”“道”“自然”等普遍的人类经验和问题范畴,大可以寻找最相似的译名,这是“同”;而对这些范畴提供具体的解释和论述,就是在提供中国哲学对这些普遍问题的独特回应。哲学概念和范畴翻译中体现的这种“同-异”张力,尊重了中国哲学对共同问题的特有回应这一事实。这种名同实异、异趣同调的翻译主张能帮助中国哲学实现与世界哲学真正平等的对话与交流,为人类整体智慧的提升做出贡献。