Improving Supply Chain Strategies towards Mitigating the Effect of Crude Oil Theft and Pipeline Vandalism in the Nigerian Oil and Gas Industry: A Case Study of the Nigerian National Petroleum Corporation (NNPC)

Generically, SCM may be said to include all activities carried out to ensure proper functioning of the supply chain. The activities included in proper management of a supply chain broadly include logistics activities, planning and control of the flow of information and materials in a firm, management of relationships with other organizations and government intervention, However, crude oil theft and pipeline vandalism are established products supply chain disruptors in Nigeria which are rendering the task of running an efficient petroleum supply chain onerous. This paper aims to establish the importance of effective supply chain strategies for companies in the oil and gas industry with special focus on the Nigerian oil and gas sector and the strategies by which the state oil and gas corporation in this sector may mitigate disruptions to its supply chain. This study investigates the enhancement of supply chain strategies towards meeting the challenge of crude oil theft and pipeline vandalism, using the Nigerian National Corporation (NNPC) as a case study. Based on this study, data were collected from two sources: A summary of incident reports obtained from NNPC and an interview with personnel in the PPMC Department. Incident report refers to a report produced when accidents such as equipment failure, injury, loss of life, or fire occur at the work site. Content analysis is utilized to evaluate data obtained from interview responses, CBN financial stability reports, NDIC annual reports, circulars, banking supervision reports and implementation guidelines. The study found out that NNPC should endeavor to sustain its value chain and ward of pipeline vandals and crude oil thieves by engaging in community partnership, detailing security outfits to ensure its pipelines’ right of way and bridging. Concluded that the oil supply chain of the Nigerian National Petroleum Corporation has been plagued by disruptions in the form of crude oil theft and pipeline vandalism which has had debilitating effects on its value.

建立Cookie Theft测验的中国常模

目的建立Cookie Theft测验的中国常模。方法 29名正常被试,17例脑卒中患者完成Cookie Theft测验,在错误表述(IS)、不流畅表述(DF)、支持结构(PS)、重复内容(RP)、图片有效信息(CU)、解释图片有效信息(EC)和不相关字词(IR)7个维度上对语言样本进行分析。结果 7个维度上的重测信度值分别为r(IS)=0.92;r(DF)=0.89;r(PS)=0.98;r(RP)=0.84;r(CU)=0.96;r(EC)=0.88和r(IR)=0.99。17例脑卒中患者中,12例(70.6%)被诊断出。结论该常模具有良好的信效度,具有临床和科研应用价值。

基于Cookie Theft测验的脑小血管病患者认知沟通能力数字化测评

为了克服传统Cookie Theft测验费时费力、评测结果受主观影响等不足,提出了一种数字化测评方法.首先,将Cookie Theft测验转化成总分为28分的数字化量表;接着,分析对比了脑小血管病组和健康组分别在无提示语和有提示语条件下的表现差异情况;最后,综合语音相关特征及信息量相关特征,利用随机森林方法建立了脑小血管病智能诊断模型.实验结果显示,综合使用测试过程中记录的语音相关特征和信息量相关特征,脑小血管病智能诊断模型的识别准确率可达82.4%.

Adaptive electricity theft detection method based on load shape dictionary of customers

With the application of the advanced measurement infrastructure in power grids,data driven electricity theft detection methods become the primary stream for pinpointing electricity thieves.However,owing to anomaly submergence,which shows that the usage patterns of electricity thieves may not always deviate from those of normal users,the performance of the existing usage-pattern-based method could be affected.In addition,the detection results of some unsupervised learning algorithm models are abnormal degrees rather than“0-1”to ascertain whether electricity theft has occurred.The detection with fixed threshold value may lead to deviation and would not be sufficiently flexible to handle the detection for different scenes and users.To address these issues,this study proposes a new electricity theft detection method based on load shape dictionary of users.A corresponding strategy for tunable threshold is proposed to optimize the detection effect of electricity theft,and the efficacy and applicability of the proposed adaptive electricity theft detection method were verified from numerical experiments.

Energy Theft Identification Using Adaboost Ensembler in the Smart Grids

One of the major concerns for the utilities in the Smart Grid(SG)is electricity theft.With the implementation of smart meters,the frequency of energy usage and data collection from smart homes has increased,which makes it possible for advanced data analysis that was not previously possible.For this purpose,we have taken historical data of energy thieves and normal users.To avoid imbalance observation,biased estimates,we applied the interpolation method.Furthermore,the data unbalancing issue is resolved in this paper by Nearmiss undersampling technique and makes the data suitable for further processing.By proposing an improved version of Zeiler and Fergus Net(ZFNet)as a feature extraction approach,we had able to reduce the model’s time complexity.To minimize the overfitting issues,increase the training accuracy and reduce the training loss,we have proposed an enhanced method by merging Adaptive Boosting(AdaBoost)classifier with Coronavirus Herd Immunity Optimizer(CHIO)and Forensic based Investigation Optimizer(FBIO).In terms of low computational complexity,minimized over-fitting problems on a large quantity of data,reduced training time and training loss and increased training accuracy,our model outperforms the benchmark scheme.Our proposed algorithms Ada-CHIO andAda-FBIO,have the low MeanAverage Percentage Error(MAPE)value of error,i.e.,6.8%and 9.5%,respectively.Furthermore,due to the stability of our model our proposed algorithms Ada-CHIO and Ada-FBIO have achieved the accuracy of 93%and 90%.Statistical analysis shows that the hypothesis we proved using statistics is authentic for the proposed technique against benchmark algorithms,which also depicts the superiority of our proposed techniques.

Energy Theft Detection in Smart Grids:Taxonomy,Comparative Analysis,Challenges,and Future Research Directions

Electricity theft is one of the major issues in developing countries which is affecting their economy badly.Especially with the introduction of emerging technologies,this issue became more complicated.Though many new energy theft detection(ETD)techniques have been proposed by utilising different data mining(DM)techniques,state&network(S&N)based techniques,and game theory(GT)techniques.Here,a detailed survey is presented where many state-of-the-art ETD techniques are studied and analysed for their strengths and limitations.Three levels of taxonomy are presented to classify state-of-the-art ETD techniques.Different types and ways of energy theft and their consequences are studied and summarised and different parameters to benchmark the performance of proposed techniques are extracted from literature.The challenges of different ETD techniques and their mitigation are suggested for future work.It is observed that the literature on ETD lacks knowledge management techniques that can be more effective,not only for ETD but also for theft tracking.This can help in the prevention of energy theft,in the future,as well as for ETD.

Spatial Analysis Applied for Gas Theft Modelling in Tabriz City, Iran

Unauthorized use of energy is the major source of the non-technical losses of the energy in developing countries. Gas theft as a kind of energy theft is an increasing issue in a number of countries particularly in developing countries. This study is an attempt to address the issue of gas theft through the deployment of Geographic Information System (GIS) capabilities (Spatial Analysis) to import external factors into the current gas theft detection methods, improve data mining processes, and offer some management solutions. To achieve the intended goals in the study, two types of data sources were collected and analyzed: internal data such as reported instances of gas theft, and some customer properties, and external data such as some demographic data. In order to analyze and modeling the gas theft and the relationships between variables we used Hotspot analysis, Ordinary Least Squares regression (OLS) and Geographically Weighted Regression (GWR) analysis with ArcGIS tools. The results from clustering test indicated that the gas theft is not a random phenomenon in all areas of Tabriz and there are underlying factors. Mapping clusters by the hotspot techniques suggested the locations of clusters and areas at risk. The results of the regression analysis illustrated the importance of external factors clearly. According to the results, we recommend a conceptual GIS framework to select high risk areas as a subset data for a meter data analysis. Results of this research are of great importance for GIS based spatial analysis and can be used as base of future researches.

Comparing the Similarities and Differences between All Versions of Grand Theft Auto

The article investigates the similarities and differences between all versions of Grand Theft Auto as an adventure game with the widest popularity in the last decade. The game is a story collection, a frame for performance, a virtual museum of vernacular culture and a widely circulated pop culture artifact whose double-voiced aesthetic has given rise to diverse interpretive communities. The aim of comparing the differences and similarities between different versions of the game is to be able to evaluate the game from the user’s point of view. With this aim, whether with the verisimilitude that the different versions offer makes GTA a product of an iterative design process or not will be displayed.

An Analysis of the Female Images in Theft from the Perspective of Feminism

In the field of vision of American Literature in the 20th century,Katherine Anne Porter is highly praised by many readers for her flexible artistic style,accurate and vivid description of characters,profound connotation of her works.As a female writer,Porter is good at creating female characters from a unique female perspective,and reveals the inner activities of female characters in self-development and the female individual consciousness and independent consciousness pursued by women through her works.Although she doesn’t consciously involve feminism,we can see that the heroine’s female consciousness is gradually awakening from her portrayal of female characters and her exploration of female inner world.This paper takes Porter’s short story Theft published in the 1990s as an example to analyze the feminist consciousness in her works from the perspective of feminism.

Alternative Solutions to Mitigate Problems due to Neutral Conductors Theft in MV （Medium Voltage） Power Distribution Systems

This paper aims at analyzing the impact of the neutral conductor absence at specific sections over the performance of the power distribution lines, and proposing alternative solutions to mitigate the problems caused by the neutral conductor theft. Simulations are made by the software lnterplan and show that the absence of neutral conductor at specific sections of power distribution lines may increase the neutral-to-ground voltages, which compromises the system＇s safety. The solution developed keeps the technical performance of the power distribution system at satisfactory levels, regarding the voltage profile, or, at least, close to the level before the neutral conductor＇s theft.

Electricity Theft Detection Method Based on Ensemble Learning and Prototype Learning

With the development of advanced metering infrastructure(AMI),large amounts of electricity consumption data can be collected for electricity theft detection.However,the imbalance of electricity consumption data is violent,which makes the training of detection model challenging.In this case,this paper proposes an electricity theft detection method based on ensemble learning and prototype learning,which has great performance on imbalanced dataset and abnormal data with different abnormal level.In this paper,convolutional neural network(CNN)and long short-term memory(LSTM)are employed to obtain abstract feature from electricity consumption data.After calculating the means of the abstract feature,the prototype per class is obtained,which is used to predict the labels of unknown samples.In the meanwhile,through training the network by different balanced subsets of training set,the prototype is representative.Compared with some mainstream methods including CNN,random forest(RF)and so on,the proposed method has been proved to effectively deal with the electricity theft detection when abnormal data only account for 2.5%and 1.25%of normal data.The results show that the proposed method outperforms other state-of-the-art methods.

基于重采样和混合集成学习的不平衡窃电检测

针对电力用户类别不平衡导致窃电检测具有偏向性问题,该文提出一种基于重采样和混合集成学习的不平衡窃电检测模型。首先以Easy-ensemble混合集成学习框架为基础确定最佳采样子集数;然后通过重采样自适应策略,即根据用户用电数据集的不平衡度以及最佳采样子集数确定检测模型的重采样方式,使用电数据达到平衡;最后按照先串行集成减小偏差、后并行集成降低方差的混合集成方式,对重采样后的均衡样本进行窃电检测。算例对比分析表明所提检测模型通过重采样和混合集成有效解决了传统集成算法在不平衡窃电检测中的偏向问题,降低了由于用电数据的不平衡性对集成结果的影响,提高了用户类别不平衡的窃电检测效果,在多种不平衡度下模型的准确率、F1值和G均值均表现优异。

基于多阶段递推数据分析的低压台区窃电检测方法

窃电行为不仅会扰乱正常用电秩序,更会影响电网的供电质量和安全运行。针对窃电检测工作中所面临的用户正常用电行为与窃电行为多样化问题,该文提出一种基于多阶段递推数据分析的低压台区窃电检测方法。该方法第1阶段对嫌疑窃电台区进行判定,针对当日线损不是明显激增的情况,提出基于台区线损综合波动率、总分表电流差异率、线损和电流曲线的突变点时间重合度的三步分析法,为窃电嫌疑用户的检测提供了良好的条件;第2阶段提出基于最优特征集的时间序列相似性度量方法,基于欧氏距离度量曲线间数值特征,同时基于动态时间规整(dynamic time warping,DTW)算法度量曲线间的形态特征,实现窃电嫌疑用户的初步筛选;第3阶段提出基于核函数和惩罚参数优化的支持向量机二次深度检测模型(optimize kernel-function and penalty-parameters support vector machine,OKPSVM),其中惩罚参数采用综合改进的粒子群(improved particle swarm optimization,IPSO)算法。通过算例仿真和实际工程应用,整体优化后的支持向量机模型(IPSO-OKPSVM)能够提高深度窃电检测的精准性和适用性。

基于STM32的智能家居安防系统设计

物联网应用技术快速发展,智能安防产品受到越来越多人的关注,尤其是智能家居安防产品。为了最大程度确保人身以及财产安全,满足人民群众对自身安全和家庭安防的要求,以STM32为主控芯片,结合各类智能传感器、控制器、报警器及智能手机小程序,设计并实现一套基于STM32的家居安防报警系统,该系统能实时、远程监测家居室内外环境状况,及时、有效防止家居安全事故发生,对保护家庭成员生命、财产安全具有重要的现实意义。

多目标优化时域卷积神经网络的窃电行为高准确检测算法

随着当前用户用电行为的多样性及窃电行为隐蔽性的增加,窃电检测检出率高的同时误检率也增加,致使现有算法难以满足高准确窃电稽查的工程实际需求,对此该文提出多目标优化时域卷积神经网络的窃电行为高准确检测算法。首先基于时域卷积神经网络构建深度模型,引入侧输出融合结构提取用户用电信息的高低维特征,并通过注意力机制进行特征融合,提高模型窃电检测准确率;然后通过两阶段训练模型参数,第一阶段基于传统梯度下降算法优化模型权重与阈值,第二阶段基于窃电检测的混淆矩阵建立准确率、检出率与误检率3个维度的目标函数,采用第三代非支配排序遗传算法(non-dominated sorting genetic algorithm,NSGA-Ⅲ)进行模型优化训练,提高窃电检出率并降低误检率,据此建立多目标优化时域卷积神经网络的窃电行为高准确检测算法,并开发窃电检测实验平台实际验证所提算法;最后通过电网实测数据与实验平台验证,结果表明该文所提算法在降低误检率的同时提高10%检出率,比现有算法检出率提高同时误检率降低,更符合当前工程应用高准确窃电的检测实际需求。

接收台湾地区过程中美国军人艾文思侵占黄金案钩沉

抗战胜利后,协助接收台湾地区的美军联络组中校艾文思以职务之便侵占了属于中国的黄金。中方发现后提出引渡艾文思来华受审,为美方所拒绝。中方研判在当时的法律框架下由美方审理更为有利,同意移交美国审理此案。在美国法庭审理过程中,中方积极协助担任公诉人的检察官提供证据,但艾文思巧妙利用美国的陪审制度漏洞而逃脱惩罚。在国民党政权即将崩溃的背景之下,中方通过外交渠道重启审判的努力也无疾而终。本文运用档案资料揭示美国军人侵占中国黄金案件的经过、在美国法庭审理进程及结局,钩沉此一历史事件的真相。

RoGRUT: A Hybrid Deep Learning Model for Detecting Power Trapping in Smart Grids

Electricity theft is a widespread non-technical issue that has a negative impact on both power grids and electricity users.It hinders the economic growth of utility companies,poses electrical risks,and impacts the high energy costs borne by consumers.The development of smart grids is crucial for the identification of power theft since these systems create enormous amounts of data,including information on client consumption,which may be used to identify electricity theft using machine learning and deep learning techniques.Moreover,there also exist different solutions such as hardware-based solutions to detect electricity theft that may require human resources and expensive hardware.Computer-based solutions are presented in the literature to identify electricity theft but due to the dimensionality curse,class imbalance issue and improper hyper-parameter tuning of such models lead to poor performance.In this research,a hybrid deep learning model abbreviated as RoGRUT is proposed to detect electricity theft as amalicious and non-malicious activity.The key steps of the RoGRUT are data preprocessing that covers the problem of class imbalance,feature extraction and final theft detection.Different advanced-level models like RoBERTa is used to address the curse of dimensionality issue,the near miss for class imbalance,and transfer learning for classification.The effectiveness of the RoGRUTis evaluated using the dataset fromactual smartmeters.A significant number of simulations demonstrate that,when compared to its competitors,the RoGRUT achieves the best classification results.The performance evaluation of the proposed model revealed exemplary results across variousmetrics.The accuracy achieved was 88%,with precision at an impressive 86%and recall reaching 84%.The F1-Score,a measure of overall performance,stood at 85%.Furthermore,themodel exhibited a noteworthyMatthew correlation coefficient of 78%and excelled with an area under the curve of 91%.

秦唐间盗罪的变迁:游牧法因素介入下的连续性

中原农耕区的盗罪具有多维性,在“狭义”盗罪外,还有特殊物之盗、特殊主体之盗、特殊手段之盗等。对狭义盗罪,要计赃定罪,实行“实刑主义”,不处死刑。相反,财产性是游牧族群法主要甚至唯一的维度,重财轻人,盗罪至死是草原盗罪的一大特征。在一般情况下,游牧群族法实行“赔偿主义”。盗罪的维度主要由文明类型所决定。秦唐间盗罪的连续性,是在游牧族群法因素介入下的“连续性”。《唐律》规定的倍备制度是游牧法传统在汉地“文明化”的结果。包括盗罪在内的中华法治文明的连续性,乃是游牧文化因素介入下的连续性。农、牧互动以及在这种互动过程中从多元凝聚为一体,是这种连续性背后的秘密。

西夏与唐宋盗窃法比较研究——兼谈西夏对中华法系的继承与发展

“盗窃”是中国最古老的犯罪之一,历代皆有严格法律予以规制,以期维护社会稳定。文章对比研究了《天盛改旧新定律令》《亥年新法》《唐律疏议》《宋刑统》及《庆元条法事类》中关于盗窃罪的条文,发现这些法典均设置了亲属内外有别、宽宥弱者、严惩官盗僧窃以及团伙共谋作案等主体处置措施,涵盖了牲畜、人员、佛神祀物以及官府文书等盗窃对象,量刑上均依据慎刑恤刑、告赏、邻里互助以及重盗重罚等原则。同时,西夏法典融入了大量具有党项民族特色的法律元素,完善了群盗、重盗等制度。概言之,西夏法典在充分吸收中原王朝立法精髓的同时,结合本民族社会经济状况以及司法实践的需要进行了创制和发展,在中华法制史上极具特殊地位。