Synthetic Theft Attacks and Long Short Term Memory-Based Preprocessing for Electricity Theft Detection Using Gated Recurrent Unit

Electricity theft is one of the challenging problems in smart grids. The power utilities around the globe face huge economic loss due to ET. The traditional electricity theft detection (ETD) models confront several challenges, such as highly imbalance distribution of electricity consumption data, curse of dimensionality and inevitable effects of non-malicious factors. To cope with the aforementioned concerns, this paper presents a novel ETD strategy for smart grids based on theft attacks, long short-term memory (LSTM) and gated recurrent unit (GRU) called TLGRU. It includes three subunits: (1) synthetic theft attacks based data balancing, (2) LSTM based feature extraction, and (3) GRU based theft classification. GRU is used for drift identification. It stores and extracts the long-term dependency in the power consumption data. It is beneficial for drift identification. In this way, a minimum false positive rate (FPR) is obtained. Moreover, dropout regularization and Adam optimizer are added in GRU for tackling overfitting and trapping model in the local minima, respectively. The proposed TLGRU model uses the realistic EC profiles of the Chinese power utility state grid corporation of China for analysis and to solve the ETD problem. From the simulation results, it is exhibited that 1% FPR, 97.96% precision, 91.56% accuracy, and 91.68% area under curve for ETD are obtained by the proposed model. The proposed model outperforms the existing models in terms of ETD

AlexNet, AdaBoost and Artificial Bee Colony Based Hybrid Model for Electricity Theft Detection in Smart Grids

Electricity theft (ET) is an utmost problem for power utilities because it threatens public safety, disturbs the normal working of grid infrastructure and increases revenue losses. In the literature, many machine learning (ML), deep learning (DL) and statistical based models are introduced to detect ET. However, these models do not give optimal results due to the following reasons: curse of dimensionality, class imbalance problem, inappropriate hyper-parameter tuning of ML and DL models, etc. Keeping the aforementioned concerns in view, we introduce a hybrid DL model for the efficient detection of electricity thieves in smart grids. AlexNet is utilized to handle the curse of dimensionality issue while the final classification of energy thieves and normal consumers is performed through adaptive boosting (AdaBoost). Moreover, class imbalance problem is resolved using an undersampling technique, named as near miss. Furthermore, hyper-parameters of AdaBoost and AlexNet are tuned using artificial bee colony optimization algorithm. The real smart meters' dataset is used to assess the efficacy of the hybrid model. The substantial amount of simulations proves that the hybrid model obtains the highest classification results as compared to its counterparts. Our proposed model obtains 88%, 86%, 84%, 85%, 78% and 91% accuracy, precision, recall, F1-score, Matthew correlation coefficient and area under the curve receiver operating characteristics, respectively

A systematic review of the relationship between subchondral bone features, pain and structural pathology in peripheral joint osteoarthritis

Introduction: Bone is an integral part of the osteoarthritis (OA) process. We conducted a systematic literature review in order to understand the relationship between non-conventional radiographic imaging of subchondral bone, pain, structural pathology and joint replacement in peripheral joint OA. Methods: A search of the Medline, EMBASE and Cochrane library databases was performed for original articles reporting association between non-conventional radiographic imaging-assessed subchondral bone pathologies and joint replacement, pain or structural progression in knee, hip, hand, ankle and foot OA. Each association was qualitatively characterised by a synthesis of the data from each analysis based upon study design, adequacy of covariate adjustment and quality scoring. Results: In total 2456 abstracts were screened and 139 papers were included (70 cross-sectional, 71 longitudinal analyses; 116 knee, 15 hip, six hand, two ankle and involved 113 MRI, eight DXA, four CT, eight scintigraphic and eight 2D shape analyses). BMLs, osteophytes and bone shape were independently associated with structural progression or joint replacement. BMLs and bone shape were independently associated with longitudinal change in pain and incident frequent knee pain respectively. Conclusion: Subchondral bone features have independent associations with structural progression, pain and joint replacement in peripheral OA in the hip and hand but especially in the knee. For peripheral OA sites other than the knee, there are fewer associations and independent associations of bone pathologies with these important OA outcomes which may reflect fewer studies; for example the foot and ankle were poorly studied. Subchondral OA bone appears to be a relevant therapeutic target. Systematic review: PROSPERO registration number: CRD 4201300500

Non-Technical Losses Detection Using Autoencoder and Bidirectional Gated Recurrent Unit to Secure Smart Grids

Electricity theft is considered one of the most significant reasons of the non technical losses (NTL). It negatively influences the utilities in terms of the power supply quality, grid's safety, and economic loss. Therefore, it is necessary to effectively deal with the electricity theft problem. For detecting electricity theft in smart grids (SGs), an efficient and state-of-the-art approach is designed in the underlying work based on autoencoder and bidirectional gated recurrent unit (AE-BiGRU). The proposed approach consists of six components: (1) data collection, (2) data preparation, (3) data balancing, (4) feature extraction, (5) classification and (6) performance evaluation. Moreover, bidirectional gated recurrent unit (BiGRU) is used for the identification of the anomalies in electricity consumption (EC) patterns caused due to factors like family formation changes, holidays, parties, and so on, which are referred as non-theft factors. The proposed autoencoder-bidirectional gated recurrent unit (AE-BiGRU) model employs the EC data acquired from state grid corporation of China (SGCC) for simulations. Furthermore, it is visualized from the simulation results that 90.1% accuracy and 10.2% false positive rate (FPR) are obtained by the proposed model. The results are better than different existing classifiers, i.e., logistic regression (LR), decision tree (DT), extreme gradient boosting (XGBoost), gated recurrent unit (GRU), etc

A Secure and Efficient Energy Trading Model Using Blockchain for a 5G-Deployed Smart Community

A Smart Community (SC) is an essential part of the Internet of Energy (IoE), which helps to integrate Electric Vehicles (EVs) and distributed renewable energy sources in a smart grid. As a result of the potential privacy and security challenges in the distributed energy system, it is becoming a great problem to optimally schedule EVs' charging with different energy consumption patterns and perform reliable energy trading in the SC. In this paper, a blockchain-based privacy-preserving energy trading system for 5G-deployed SC is proposed. The proposed system is divided into two components: EVs and residential prosumers. In this system, a reputation-based distributed matching algorithm for EVs and a Reward-based Starvation Free Energy Allocation Policy (RSFEAP) for residential homes are presented. A short-term load forecasting model for EVs' charging using multiple linear regression is proposed to plan and manage the intermittent charging behavior of EVs. In the proposed system, identity-based encryption and homomorphic encryption techniques are integrated to protect the privacy of transactions and users, respectively. The performance of the proposed system for EVs' component is evaluated using convergence duration, forecasting accuracy, and executional and transactional costs as performance metrics. For the residential prosumers' component, the performance is evaluated using reward index, type of transactions, energy contributed, average convergence time, and the number of iterations as performance metrics. The simulation results for EVs' charging forecasting gives an accuracy of 99.25%. For the EVs matching algorithm, the proposed privacy-preserving algorithm converges faster than the bichromatic mutual nearest neighbor algorithm. For RSFEAP, the number of iterations for 50 prosumers is 8, which is smaller than the benchmark. Its convergence duration is also 10 times less than the benchmark scheme. Moreover, security and privacy analyses are presented. Finally, we carry out security vulnerability analysis of smart contracts to ensure that the proposed smart contracts are secure and bug-free against the common vulnerabilities' attacks. The results show that the smart contracts are secure against both internal and external attacks

Prognostic model to predict postoperative acute kidney injury in patients undergoing major gastrointestinal surgery based on a national prospective observational cohort study.

Background: Acute illness, existing co-morbidities and surgical stress response can all contribute to postoperative acute kidney injury (AKI) in patients undergoing major gastrointestinal surgery. The aim of this study was prospectively to develop a pragmatic prognostic model to stratify patients according to risk of developing AKI after major gastrointestinal surgery. Methods: This prospective multicentre cohort study included consecutive adults undergoing elective or emergency gastrointestinal resection, liver resection or stoma reversal in 2-week blocks over a continuous 3-month period. The primary outcome was the rate of AKI within 7 days of surgery. Bootstrap stability was used to select clinically plausible risk factors into the model. Internal model validation was carried out by bootstrap validation. Results: A total of 4544 patients were included across 173 centres in the UK and Ireland. The overall rate of AKI was 14·2 per cent (646 of 4544) and the 30-day mortality rate was 1·8 per cent (84 of 4544). Stage 1 AKI was significantly associated with 30-day mortality (unadjusted odds ratio 7·61, 95 per cent c.i. 4·49 to 12·90; P < 0·001), with increasing odds of death with each AKI stage. Six variables were selected for inclusion in the prognostic model: age, sex, ASA grade, preoperative estimated glomerular filtration rate, planned open surgery and preoperative use of either an angiotensin-converting enzyme inhibitor or an angiotensin receptor blocker. Internal validation demonstrated good model discrimination (c-statistic 0·65). Discussion: Following major gastrointestinal surgery, AKI occurred in one in seven patients. This preoperative prognostic model identified patients at high risk of postoperative AKI. Validation in an independent data set is required to ensure generalizability

Even low level of physical activity is associated with reduced mortality among people with metabolic syndrome, a population based study (the HUNT 2 study, Norway)

<p>Abstract</p> <p>Background</p> <p>Low levels of physical activity may increase the risk of developing metabolic syndrome, a cluster of metabolic factors that are associated with the risk of premature death. It has been suggested that physical activity may reduce the impact of factors associated with metabolic syndrome, but it is not known whether physical activity may reduce mortality in people with metabolic syndrome.</p> <p>Methods</p> <p>In a prospective study of 50,339 people, 13,449 had metabolic syndrome at baseline and were followed up for ten years to assess cause-specific mortality. The population was divided into two age groups: those younger than 65 years of age and those older than age 65. Information on their physical activity levels was collected at baseline.</p> <p>Results</p> <p>Metabolic syndrome was associated with higher mortality from all causes (hazard ratio (HR) 1.35, 95% confidence interval (95% CI) 1.20 to 1.52) and from cardiovascular causes (HR 1.78, 95% CI 1.39 to 2.29) in people younger than 65 years old than among other populations. In older people, there was no overall association of metabolic syndrome with mortality. People with metabolic syndrome who reported high levels of physical activity at baseline were at a reduced risk of death from all causes compared to those who reported no physical activity, both in the younger age group (HR 0.52, 95% CI 0.37 to 0.73) and in the older age group (HR 0.59, 95% CI 0.47 to 0.74).</p> <p>Conclusion</p> <p>Among people with metabolic syndrome, physical activity was associated with reduced mortality from all causes and from cardiovascular causes. Compared to inactivity, even low levels of physical activity were associated with reduced mortality.</p

Effect of Blend of Essential Oils on Growth Performance, Carcass Characteristics, Meat Quality, Intestinal Morphology, Serum Biochemistry, and Immune Response of Broiler Chickens

ABSTRACT The purpose of this study was to assess the impact of a blend of essential oils from eucalyptus, citrus, bromohexene HCl, thymole and camphor on the growth performance, carcass characteristics, meat quality, intestinal morphology, serum biochemistry, and immune response of broiler chickens. A total of 240 day-old chicks were divided into four groups, each with six replicates containing ten birds. The experiment was conducted under a completely randomized design (CRD). Different concentrations of the oil blend (0mL/kg, 0.15mL/kg, 0.30mL/kg and 0.45mL/kg) were added to the diet. The findings showed that, in comparison to the other groups, the birds that were given a blend of essential oils at concentrations of 0.30 and 0.45 mL/kg showed improved weight gain, feed efficiency, carcass yield, villus height, crypt depth, and greater immune response against Newcastle disease vaccination (p<0.05). Nonetheless, there was no statistically significant difference in the yields of the breast and thighs, feed consumption, mortality, weights of the liver, wing, heart, and gizzard, or abdominal fat between the treatments. The addition of a blend of essential oils at the doses of 0.30 and 0.45 mL/kg significantly lowered the pH of the meat in comparison to the other groups (p<0.05). All blood biochemical markers, including total serum protein, albumin, globulin, glucose, cholesterol, triglyceride, and uric acid, revealed no variations between the treatments. In conclusion, adding 0.30mL/kg of a blend of essential oils to broiler diets may be the optimum level to improve overall performance without adversely affecting the blood biochemical profile

Pooled analysis of who surgical safety checklist use and mortality after emergency laparotomy

Background: The World Health Organization (WHO) Surgical Safety Checklist has fostered safe practice for 10 years, yet its place in emergency surgery has not been assessed on a global scale. The aim of this study was to evaluate reported checklist use in emergency settings and examine the relationship with perioperative mortality in patients who had emergency laparotomy. Methods: In two multinational cohort studies, adults undergoing emergency laparotomy were compared with those having elective gastrointestinal surgery. Relationships between reported checklist use and mortality were determined using multivariable logistic regression and bootstrapped simulation. Results: Of 12 296 patients included from 76 countries, 4843 underwent emergency laparotomy. After adjusting for patient and disease factors, checklist use before emergency laparotomy was more common in countries with a high Human Development Index (HDI) (2455 of 2741, 89⋅6 per cent) compared with that in countries with a middle (753 of 1242, 60⋅6 per cent; odds ratio (OR) 0⋅17, 95 per cent c.i. 0⋅14 to 0⋅21, P &lt; 0⋅001) or low (363 of 860, 42⋅2 percent; OR 0⋅08, 0⋅07 to 0⋅10, P &lt; 0⋅001) HDI. Checklist use was less common in elective surgery than for emergency laparotomy in high-HDI countries (risk difference −9⋅4 (95 per cent c.i. −11⋅9 to −6⋅9) per cent; P &lt; 0⋅001), but the relationship was reversed in low-HDI countries (+12⋅1 (+7⋅0 to +17⋅3) per cent; P &lt; 0⋅001). In multivariable models, checklist use was associated with a lower 30-day perioperative mortality (OR 0⋅60, 0⋅50 to 0⋅73; P &lt; 0⋅001). The greatest absolute benefit was seen for emergency surgery in low-and middle-HDI countries. Conclusion: Checklist use in emergency laparotomy was associated with a significantly lower perioperative mortality rate. Checklist use in low-HDI countries was half that in high-HDI countries