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%.

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.

Cyberattack Ramifications, The Hidden Cost of a Security Breach

In this in-depth exploration, I delve into the complex implications and costs of cybersecurity breaches. Venturing beyond just the immediate repercussions, the research unearths both the overt and concealed long-term consequences that businesses encounter. This study integrates findings from various research, including quantitative reports, drawing upon real-world incidents faced by both small and large enterprises. This investigation emphasizes the profound intangible costs, such as trade name devaluation and potential damage to brand reputation, which can persist long after the breach. By collating insights from industry experts and a myriad of research, the study provides a comprehensive perspective on the profound, multi-dimensional impacts of cybersecurity incidents. The overarching aim is to underscore the often-underestimated scope and depth of these breaches, emphasizing the entire timeline post-incident and the urgent need for fortified preventative and reactive measures in the digital domain.

地区能源效率与二氧化碳排放的差异性——基于Kaya因素分解

利用绝对差异法、相对差异法和基尼系数对我国28个省区1995-2005年间能源利用效率的差异性进行了比较；引入Theil指数和Kaya因子，深入研究能源消耗导致的地区间人均CO2排放的差别；进一步将28个省区划分为八个经济区，分解Theil指数为区闭和区内贡献。结果显示（1）各省区能源利用效率存在趋同现象。（2）Theil指数表明2003年后人均C02排放差异性在减弱，Kaya因子中贡献最大的是能源强度指标，其次是人均GDP，碳排放系数贡献相对较小。交互作用因子中能源强度和人均GDP之间表现出较大的负影响，即高收入地区能源利用效率高，人均C02排放也多。（3）八个经济区间的差异要大于经济区内部的差异，且一直占据主早地位。文章结论为节能减排提供了参考。

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。

中国20～30岁群体认知沟通能力测评研究

目的检测中国20-30岁群体认知沟通能力。方法 139例正常被试者完成Cookie Theft测验。对于被试产出的语言样本,在8个维度上加以分析,包括总字数（TWN）、错误表述百分比（IS）、不流畅表述百分比（DF）、支持结构百分比（PS）、重复内容百分比（RP）、图片有效信息百分比（CU）、解释图片有效信息百分比（EC）和不相关字词百分比（IR）。结果 8个维度上的评分者一致性信度均较高,分别为rTWN=1;rIS=0.89;rDF=0.85;rPS=0.93;rRP=0.92;rCU=0.98;rEC=0.98;rIR=0.62。结论该常模具有相对较好的信度。

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%.

Application of New Concept for Multiple Intelligences Calculation for Personality and Social Groups Comparison Research

A person is considered as information-energy system with a host of feedbacks. The possibility of determining the statistical characteristics in a multiple intelligences profile of various social groups’ representatives using the vibraimage technology is investigated. Theft and alcohol abuse have been chosen as examples of significant social problems including deviant behavior and the trigger of formation of various socially vulnerable groups. The comparative analysis of conscious and unconscious attitudes in multiple intelligences structure of individuals prone to deviant behavior and the control group allows differentiating professional preferences and the impact of society on different social groups.

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.

Attitudes Towards Shoplifting： A Preliminary Cross-cultural Study of Consumers

The aim of this paper is to explore the consumers＇ attitudes towards shoplifting from a cross-cultural perspective. The research questions are： 1. What may influence offenders to engage in shoplifting？ 2. To which extent consumer attitudes towards shoplifting might reflect cultural issues？ The study adopted a quantitative approach to data collection and was conducted in two stages including different retail contexts. Preliminary analysis of 529 questionnaire responses from UK, US, and Libyan consumers finds that attitudes towards shoplifting are broadly similar despite the different cultural and retail contexts therefore this study reinforces the view that ethical behaviour is culturally bound. However, on closer inspection these findings suggest interesting disparities among the countries in relation to specific attitudes towards the consequences of shoplifting, the impact it has on the social networks of the perpetrator and whether the demographics of the shoplifter play a role in the decision to punish the offender. It also suggests that attitudes towards shoplifting are complex and context specific making it particularly problematic for international retailers to implement universal shoplifting preventative measures.

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.

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.

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.

Real Punishment For Virtual Theft

Virtual property has attained legal status with the recent conviction of several online thieves, but the issue remains a murky one Zhang Bin, a resident of Ningbo, in Zhejiang Province, began selling online game accounts on the Internet in February 2005. Among his customers was a man surnamed Shen, who bought an account for 4,800 yuan. Several days later, Shen discovered that his account had been embezzled, and