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Obtaining outstanding electricity theft detection (ETD) performance in the realm of advanced metering infrastructure (AMI) and smart grids (SGs) is quite difficult due to various issues. The issues include limited availability of theft data as compared to benign data, neglecting dimensionality reduction, usage of the standalone (single) electricity theft detectors, etc. These issues lead the classification techniques to low accuracy, minimum precision, low F1 score, and overfitting problems. For these reasons, it is extremely crucial to design such a novel strategy that is capable to tackle these issues and yield outstanding ETD performance. In this article, electricity theft happening in SGs is detected using a novel ETD approach. The proposed approach comprises recursive feature elimination (RFE), k nearest neighbor oversampling (KNNOR), bidirectional long short term memory (BiLSTM), and logit boosting (LogitBoost) techniques. Furthermore, three BiLSTM networks and a LogitBoost model are combined to make a BiLSTM-LogitBoost stacking ensemble model. Data preprocessing and feature selection followed by data balancing and electricity theft classification are the four major stages of the model proposed for ETD. It is obvious from the simulations performed using state grid corporation of China (SGCC)’s electricity consumption (EC) data that our proposed model achieves 96.32% precision, 94.33% F1 score, and 89.45% accuracy, which are higher than all the benchmarks employed in this study.

Objective. To compare the knowledge of antibiotic resistance between medical and nonmedical university students of Lahore. Methodology. An observational cross-sectional survey-based study was conducted among students of Lahore, Pakistan, from November 12, 2021, to December 13, 2021. The convenience sampling method was used to select students. Descriptive analysis and chi-square test were performed using Statistical Package for Social Sciences version 25.0. Results. 52.9% medical and 42.25% nonmedical students knew about antibiotics. 24.1% medical and 18.3% nonmedical students do not take antibiotics without a prescription. 40.6% medical and only 19.3% nonmedical students knew about the course of antibiotics. Medical students let the minor ailments recover naturally compared to nonmedical students who visit the doctor more often. Both groups complete the course of antibiotics without a significant difference. 49% medical and 27.9% nonmedical students knew that bacteria can develop resistance against antibiotics. Most nonmedical students responded that antibiotics can work even after resistance. Medical students have better knowledge about the relationship of resistance with overuse and misuse. Conclusion. The knowledge of antibiotics and compliance to therapy of the nonmedical students were less than those of the medical students. Medical students were aware of the pattern of taking antibiotics because of their educational background. There is a dire need for awareness regarding antibiotic use in this group to conserve treatment options for future use.

In this study, a secure and coordinated blockchain based energy trading system for Electric Vehicles (EVs) is presented. The major goals of this study are to provide secure and efficient energy trading between EVs and Charging Stations (CSs), and to ensure efficient coordination between EVs. In this study, a consortium blockchain based energy trading algorithm is presented that handles the essential energy requests, discards the redundant requests and calculates the distance between EVs and CSs. Moreover, Matching Pool (M-Pool) and Pairing Pool (P-Pool) are used to store energy and payment requests. Furthermore, a blockchain based mechanism is proposed for ensuring efficient coordination between EVs at the unsignalized crossroads and intersections. The scheme involves efficient communication between EVs and the timely sharing of important messages. In the proposed work, EVs are authenticated using a Registration Authority (RA) before they become part of a Vehicular Energy Network (VEN). The increase in the number of EVs results in an increase in the number of messages leading to data redundancy issue. To solve the issue, message filtration is performed. The delays incurred in the VEN are also mathematically formulated in this study. In addition, the range anxiety issue is discussed in the proposed work. Besides, a Local Aggregator (LAG) is used as an energy broker to manage energy trading events and transaction validation. To promote the users to take part in the proposed network, they are provided with incentives. The proposed model is tested against selfish mining attack and the security analysis is performed through Oyente tool. The simulation results show that the proposed study excels in providing a secure, efficient and coordinated energy trading and data sharing system for EVs. The results show that due to proper coordination, the risk factor is reduced by almost 25–30%. Moreover, almost 40–50% reduction in time is observed when storing less redundant data.

The increase in resistance pattern of Staphylococcus aureus (S. aureus) has been an emerging threat in therapeutic areas of the dairy industry throughout the globe. The current study was conducted in bovines of district Faisalabad, Pakistan to investigate the phenotypic prevalence of methicillin-resistant (MRSA) and vancomycin-resistant S. aureus (VRSA) in milk samples positive for subclinical mastitis. The study further aimed to assess the associated risk factors and antimicrobial susceptibility pattern against MRSA and VRSA isolates. A total of 385 milk samples (n=193 cattle; n=192 buffalo) collected and screened for subclinical mastitis by surf field mastitis test (SFMT) were further subjected to standard microbiological techniques for the isolation of S. aureus. The positive isolates of S. aureus were phenotypically evaluated for MRSA and VRSA by the disc diffusion method. The study results revealed that out of 385 milk samples, 45.97% (177/385) samples were found positive for subclinical mastitis on SFMT while 37.14% (143/385) samples were confirmed for the presence of S. aureus. Out of these S. aureus isolates, MRSA and VRSA were confirmed in 17.48% (25/143) and 12.58% (18/143) samples respectively. The in-vitro trials of various antibiotics for MRSA and VRSA isolates showed 100% resistance towards Cefoxitin followed by 50% towards Gentamicin, Tylosin, and Trimethoprim + Sulfamethoxazole then 25% to Oxytetracycline, and Fusidic acid while ciprofloxacin, moxifloxacin, and linezolid were found sensitive against study isolates. The public health importance of S. aureus and emerging resistance against antibiotics like methicillin and vancomycin demands regular monitoring of effective use of antimicrobial agents against the isolates of VRSA and MRSA.

This article proposes an energy trading model basedon blockchain to manage and supervise the trading process. In the model, proof-of-energy reputation generation and proof-of-energy reputation consumption consensus mechanisms are proposed to solve the high computational cost and huge monetary investment issues created by the existing consensus mechanisms. Similarly, a mutual verifiable fairness mechanism based on time commitment is presented, which is introduced to prevent cheating attacks in the model. The proposed model's performance is assessed using energy cost, peak-to-average ratio, and trust. The simulation results show that the energy cost of the proposed model decreases by 40%. The results for the load balancing depict that the values of peak-to-average ratio of the proposed model with 20% and 50% peak demand reduction are 6.88 and 3.50, which are lower than 9.17 of the benchmark model. Moreover, the proposed model's results show satisfactory performance for privacy and security of the system.

The bi-directional flow of energy and information in the smart grid makes it possible to record and analyze the electricity consumption profiles of consumers. Because of the increasing rate of inflation over the past few years, people started looking for means to use electricity illegally, termed as electricity theft. Many data analytics techniques are proposed in the literature for electricity theft detection (ETD). These techniques help in the detection of suspected illegal consumers. However, the existing approaches have a low ETD rate either due to improper handling of the imbalanced class problem in a dataset or the selection of inappropriate classifier. In this paper, a robust big data analytics technique is proposed to resolve the aforementioned concerns. Firstly, adaptive synthesis (ADASYN) is applied to handle the imbalanced class problem of data. Secondly convolutional neural network (CNN) and long-short term memory (LSTM) integrated deep siamese network (DSN) are proposed to discriminate the features of both honest and fraudulent consumers. Specifically, the task of feature extraction from weekly energy consumption profiles is handed over to the CNN module while the LSTM module performs the sequence learning. Finally, the DSN contemplates on the shared features provided by the CNN-LSTM and applies final judgment. The data analytics is performed on different train–test ratios of the real-time smart meters’ data. The simulation results validate the proposed model’s effectiveness in terms of high area under the curve, F 1 -Score, precision and recall.

Accurate electricity consumption forecasting in the power grids ensures efficient generation and distribution of electricity. Keeping this in mind, the paper introduces a novel deep learning model, termed Gated-FCN, for short-term load forecasting. The key idea is to introduce an automated feature selection and deep learning model for forecasting. The model includes an eight-layered Fully Convolutional Network (FCN-8) in which the hand-crafted feature selection that requires expert domain knowledge is avoided. Furthermore, the model also reduces noise as it learns internal dependencies and the correlation of the time series. Enhanced Bidirectional Gated Recurrent Unit (EBiGRU) is used in combination with FCN-8 to learn long-term temporal dependencies of the time series. Moreover, a weighted averaging mechanism of multiple snapshot models is adopted in the proposed model to assign optimized weights to BiGRU. At the end of FCN-8 and BiGRU, a fully connected dense layer is used that gives final prediction results. Gated-FCN is an end-to-end forecasting model that does not require any other model for enhancing its forecasting efficiency. Different activation functions are initially analyzed to determine how the proposed model learns complex patterns from the time series data. Later, the activation function having the best accuracy is used for forecasting. The proposed model extracts both spatial and temporal features from the data. Furthermore, this paper also provides predictive and exploratory data analyses to assist policymakers in making optimal decisions regarding power production and dispatch. In order to demonstrate the applicability of the proposed technique, the simulations are performed using nine years’ load consumption data taken from Independent System Operators New England (ISO-NE). The comparison with five state-of-the-art techniques is also provided to prove the fact that Gated-FCN gives the best forecasting accuracy as compared to other benchmark techniques in terms of two performance metrics: Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE).

The drastic increase in real-time vehicle generated data of various types has imparted a great concept of data trading in vehicular networks. Whereas immense usage of Electric Vehicles (EVs) as mobile energy carriers have supported distributed energy trading due to their bidirectional charging and discharging capabilities. The trustless environment of Internet of Electric Vehicles (IoEV), including fuel vehicles and EVs, encounters trading disputes and conflicting interests among trading parties. To address these challenges, we exploit consortium blockchain to maintain transparency and trust in trading activities. Smart contracts are used to tackle trading disputes and illegal actions. Data duplication problem occurs when a dishonest user sell previously traded data multiple times for financial gain. Therefore, data duplication validation is done through previously stored hash-list at roadside units (RSUs) employed with bloom filters for efficient data lookup. Removing data duplication at an earlier stage reduces storage cost. Moreover, an elliptic curve bilinear pairing based digital signature scheme is used to ensure the reliability and integrity of traded data. To ensure persistent availability of traded data, InterPlanetary File System (IPFS) is used, which provides fault-tolerant and a reliable data storage without any single point of failure. On the other hand, the energy trading transactions among EVs face some security and privacy protection challenges. An adversary can infer the energy trading records of EVs, and launch the data linkage attacks. To address this issue, an account generation technique is used that hides the energy trading trends. The new account generation for an EV depends upon its traded volume of energy. The experimental results verify the efficiency of the proposed data and energy trading scheme in IoEV with the reliable and secure data storage.

In this paper a secure energy system is proposed that consists of private and public blockchains for vehicles in sustainable cities and society. The former protects vehicle owners from spatial and temporal information based attacks while the latter provides efficient energy trading in sustainable cities and society. In the proposed system, the dynamic demand based pricing policy for the vehicle owners is proposed using types of vehicles, time of demand and geographical locations. The vehicles’ social welfare and utility are maximized using an optimal scheduling method along with the proposed pricing policy. Also, the vehicle owners’ privacy is protected by applying differential privacy in the proposed consensus energy management algorithm. The numerical analyses show that 89.23% reduction in energy price is achieved as compared to 83.46%, 73.86% and 53.07% for multi-parameter pricing scheme (MPPS), fixed pricing scheme and time-of-use pricing scheme (ToU), respectively. Applying the proposed scheme, the owners can achieve about 81.46% reduction in their operating cost as compared to 80.48%, 69.75% and 68.29% for MPPS, fixed pricing scheme and ToU, respectively. Moreover, the proposed system is 60.32% secure as compared to 39.67% for MPPS system. Furthermore, using less information loss against considerable background knowledge of an attacker, higher privacy protection of vehicles is attained.

Salinity has been considered as the most important factor which adversely affects the plant growth and metabolism in arid and semi-arid areas. Salinity is increasing with times and is expected that 50% of arable land would be salinized till 2050. Present study investigated salinity tolerance and phytoremediation of Na+ and K+ by using halophytes; Cymbopogon jwarancusa, Aeluropus lagopoides, Panicum antidotale and Cenchrus setigerus. Plants were grown for 40 days in hydroponic solution for biomass productions, chlorophyll contents, membrane stability index (MSI) and phytoremediation. Completely Randomized Design (CRD) with four treatments and replications was used to check significance level at 5%. Fresh and dry biomass of each species was increased with increasing salinity level but decreased under highly saline conditions. P. antidotale contained highest fresh/dry weight (20.4 A) and (4.2A) at 140 mM NaCl treatment that decreased at 210 mM NaCl. A. lagopoides showed highest MSI at T3 (46.3 A) significance level that reduced at 210 mM NaCl (20.7 D). K+ concentrations in leaves/roots of all species reduced with elevated salinity (9327.4 A) was the highest K+ in leaves of A. lagopoides which reduced to (3976.2 D) at 210 mM NaCl. Na+ concentrations in the selected halophytes increased with the growing salt levels. Na+ concentration in leaves were (2331.8 D) and (3834.7 ppm) at 0 mM NaCl level while it was (9078 A) and (7494.3A) in P. antidotale and A. lagopoides at 210 mM NaCl respectively. Phytoremediation by halophytic grasses is time saving, economically viable and novel approach towards reclaiming saline soils.

The rapid deployment of Electric Vehicles (EVs) and the integration of renewable energy sources have ameliorated the existing power systems and contributed to the development of greener smart communities. However, load balancing problems, security threats, privacy leakage issues, etc., remain unresolved. Many blockchain-based approaches have been used in literature to solve the aforementioned challenges. However, they are not sufficient to obtain satisfactory results because of the inefficient energy management methods and time-intensiveness of the primitive cryptographic executions on the network devices. In this paper, an efficient and secure blockchain-based Energy Trading (ET) model is proposed. It leverages the contract theory, incentive mechanism, and a reputation system for information asymmetry scenario. In order to motivate the ET entities to trade energy locally and EVs to participate in smart energy management, the proposed incentive provisioning mechanism plays a vital role. Besides, a reputation system improves the reliability and efficiency of the system and discourages the blockchain nodes from acting maliciously. A novel consensus algorithm, i.e., Proof of Work based on Reputation (PoWR), is proposed to reduce transaction confirmation latency and block creation time. Moreover, a shortest route algorithm, i.e., the Dijkstra algorithm, is implemented in order to reduce the traveling distance and energy consumption of the EVs during ET. The performance of the proposed model is evaluated using peak to average ratio, social welfare, utility of local aggregator, etc., as performance metrics. Moreover, privacy and security analyses of the system are also presented.

Unavailability of large training datasets is a bottleneck that needs to be overcome to realize the true potential of deep learning in histopathology applications. Although slide digitization via whole slide imaging scanners has increased the speed of data acquisition, labeling of virtual slides requires a substantial time investment from pathologists. Eye gaze annotations have the potential to speed up the slide labeling process. This work explores the viability and timing comparisons of eye gaze labeling compared to conventional manual labeling for training object detectors. Challenges associated with gaze based labeling and methods to refine the coarse data annotations for subsequent object detection are also discussed. Results demonstrate that gaze tracking based labeling can save valuable pathologist time and delivers good performance when employed for training a deep object detector. Using the task of localization of Keratin Pearls in cases of oral squamous cell carcinoma as a test case, we compare the performance gap between deep object detectors trained using hand-labelled and gaze-labelled data. On average, compared to `Bounding-box' based hand-labeling, gaze-labeling required $57.6\%$ less time per label and compared to `Freehand' labeling, gaze-labeling required on average $85\%$ less time per label., Comment: 12 pages, 10 figures, 2 tables, journal

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.

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.

The computing devices in data centers of cloud and fog remain in continues running cycle to provide services. The long execution state of large number of computing devices consumes a significant amount of power, which emits an equivalent amount of heat in the environment. The performance of the devices is compromised in heating environment. The high powered cooling systems are installed to cool the data centers. Accordingly, data centers demand high electricity for computing devices and cooling systems. Moreover, in Smart Grid (SG) managing energy consumption to reduce the electricity cost for consumers and minimum rely on fossil fuel based power supply (utility) is an interesting domain for researchers. The SG applications are time-sensitive. In this paper, fog based model is proposed for a community to ensure real-time energy management service provision. Three scenarios are implemented to analyze cost efficient energy management for power-users. In first scenario, community&rsquo, s and fog&rsquo, s power demand is fulfilled from the utility. In second scenario,community&rsquo, s Renewable Energy Resources (RES) based Microgrid (MG) is integrated with the utility to meet the demand. In third scenario, the demand is fulfilled by integrating fog&rsquo, s MG, community&rsquo, s MG and the utility. In the scenarios, the energy demand of fog is evaluated with proposed mechanism. The required amount of energy to run computing devices against number of requests and amount of power require cooling down the devices are calculated to find energy demand by fog&rsquo, s data center. The simulations of case studies show that the energy cost to meet the demand of the community and fog&rsquo, s data center in third scenario is 15.09% and 1.2% more efficient as compared to first and second scenarios, respectively. In this paper, an energy contract is also proposed that ensures the participation of all power generating stakeholders. The results advocate the cost efficiency of proposed contract as compared to third scenario. The integration of RES reduce the energy cost and reduce emission of CO 2 . The simulations for energy management and plots of results are performed in Matlab. The simulation for fog&rsquo, s resource management, measuring processing, and response time are performed in CloudAnalyst.

Feline anaplasmosis is considered as an emerging tick-borne disease of zoonotic potential. The aim of current study was to investigate the molecular prevalence of anaplasmosis, associated risk factors, and alterations in hematological parameters of domestic cats from Lahore, Pakistan. Blood samples of 100 domestic cats from district Lahore were examined microscopically and the extracted genomic DNA from each sample was processed for the amplification of 16 S rRNA gene of Anaplasma. PCR confirmed isolates were purified for sequencing. The data regarding the risk factors was collected in a predesigned questionnaire and statistically analyzed by logistic regression analysis. The study found a molecular prevalence of 13% (13/100) among analyzed blood samples. The nucleotide analysis of Anaplasmataceae species sequences amplified by PCR showed high resemblance (99%) with isolates from Korea, Japan, Malaysia, Philippines, and India. The potential risk factors found to be significantly associated (p 0.05) with disease dynamics based on the Chi-Square test were tick infestation on studied animals, previous tick history, tick control status, house hygiene, and housing type. A significant (p 0.05) decrease in the number of platelets, erythrocytes, hemoglobin level, and pack cell volume was observed in cats suffering from anaplasmosis compared to the healthy ones. The current study is the first report of Anaplasma infection in domestic cats of Pakistan. This study will be effectual in designing the control strategies for this disease.

In this work, Electric Vehicles (EVs) are charged using a new and improved charging mechanism called the Mobile-Vehicle-to-Vehicle (M2V) charging strategy. It is further compared with conventional Vehicle-to-Vehicle (V2V) and Grid-to-Vehicle (G2V) charging strategies. In the proposed work, the charging of vehicles is done in a Peer-to-Peer (P2P) manner, the vehicles are charged using Charging Stations (CSs) or Mobile Vehicles (MVs) in the absence of a central entity. CSs are fixed entities situated at certain locations and act as charge suppliers, whereas MVs act as prosumers, which have the capability of charging themselves and also other vehicles. In the proposed system, blockchain technology is used to tackle the issues related with existing systems, such as privacy, security, lack of trust, etc., and also to promote transparency, data immutability, and a tamper-proof nature. Moreover, to store the data related to traffic, roads, and weather conditions, a centralized entity, i.e., Transport System Information Unit (TSIU), is used. It helps in reducing the road congestion and avoids roadside accidents. In the TSIU, an Inter-Planetary File System (IPFS) is used to store the data in a secured manner after removing the data&rsquo, s redundancy through data filtration. Furthermore, four different types of costs are calculated mathematically, which ultimately contribute towards calculating the total charging cost. The shortest distance between a vehicle and the charging entities is calculated using the Great-Circle Distance formula. Moving on, both the time taken to traverse this shortest distance and the time to charge the vehicles are calculated using real-time data of four EVs. Location privacy is also proposed in this work to provide privacy to vehicle users. The power flow and the related energy losses for the above-mentioned charging strategies are also discussed in this work. An incentive provisioning mechanism is also proposed on the basis of timely delivery of credible messages, which further promotes users&rsquo, participation. In the end, simulations are performed and results are obtained that prove the efficiency of the proposed work, as compared to conventional techniques, in minimizing the EVs&rsquo, charging cost, time, and distance.