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Abstract Energy management strategies are crucial to efficiently scheduling appliances and preventing peak generation due to increased energy demand. It is essential to manage the demand and supply of energy based on the consumer’s consumption patterns using various heuristic optimization techniques. Additionally, the end-user is more concerned with minimizing electricity costs and reducing peak-to-average ratios (PARs). This work proposes an arithmetic Harris hawks optimization (AHHO) as a new approach for improving the Harris hawks algorithm to optimize residential demand response (DR) load management in a smart grid. Our method employs arithmetic and lightweight flight operators based on the Lévy flight distribution to generate diverse design solutions and improve the HHO’s exploration capabilities. We consider 15 smart appliances and categorize them based on how much energy they use, computing the electricity price using real-time pricing (RTP) and critical peak pricing (CPP). While maintaining user satisfaction within operational and power limits, the objective is to decrease energy costs and PAR. We evaluate the effectiveness of our proposed AHHO approach against nine cutting-edge algorithms using both RTP and CPP schemes. The findings demonstrate that our suggested approach performs better than the other algorithms because it achieved cost savings of 42.10% and 30% under RTP and CPP schemes, respectively. Meanwhile, it also reduced PAR by 55.17% and 50% under RTP and CPP schemes, respectively.

This article presents an investigation of sub-synchronous resonance (SSR) issues in doubly-fed induction generator (DFIG) based wind plant and proposes an optimal control scheme for its mitigation. The article firstly reviews the causes of SSR, namely compensation level and wind speed in DFIG-based wind plant. The proportional resonance controller with harmonic compensators (PR+HC) is designed to mitigate SSR without compromising total harmonic distortions (THD) in the power signal. The series compensated IEEE first benchmarked model energized with a 100MW DFIG-based wind plant is used as a platform in MATLAB/Simulink for the implementation of the proposed scheme. The comparative analysis of the designed PR+HC controller is carried out with the conventional Proportional Integral (PI) controller to validate the optimal response of PR+HC controller. In addition, high performance of PR+HC in coordination with a flexible AC transmission (FACT) device in terms of damped oscillations and low THD is achieved. The overall simulation results are analyzed in both time domain and frequency domain to authenticate the damped, low harmonic, and smooth response of proposed scheme under varying compensation level and wind speeds.

Abstract Smart devices in various application areas are becoming increasingly prevalent for efficient handling of multiple critical activities. One such area of interest is high-security militarized environments. Due to military zones’ harsh and unpredictable nature, monitoring devices deployed in such environments must operate without power interruption for extended time periods. Therefore, it is essential to choose an appropriate application design for operating these “things” in the internet of things (IoT) environment such that energy can be conserved throughout the operating span of an application. This paper presents two application modules and analyzes their performance in terms of energy conservation considering a military-based IoT-Fog architecture. The two modules are: A sequential application module, and a master-worker application module. Experimental results show that the master-worker module incurs lower energy consumption and communication overhead than the sequential application module. Significantly, the master-worker module exhibits a lower delay in tuple execution by almost four milliseconds while also accounting for lower simulation time and higher network utilization. The module achieves significant savings in energy consumption, making it more effective in handling smart devices.

Thorax disease is a life-threatening disease caused by bacterial infections that occur in the lungs. It could be deadly if not treated at the right time, so early diagnosis of thoracic diseases is vital. The suggested study can assist radiologists in more swiftly diagnosing thorax disorders and in the rapid airport screening of patients with a thorax disease, such as pneumonia. This paper focuses on automatically detecting and localizing thorax disease using chest X-ray images. It provides accurate detection and localization using DenseNet-121 which is foundation of our proposed framework, called Z-Net. The proposed framework utilizes the weighted cross-entropy loss function (W-CEL) that manages class imbalance issue in the ChestX-ray14 dataset, which helped in achieving the highest performance as compared to the previous models. The 112,120 images contained in the ChestX-ray14 dataset (60,412 images are normal, and the rest contain thorax diseases) were preprocessed and then trained for classification and localization. This work uses computer-aided diagnosis (CAD) system that supports development of highly accurate and precise computer-aided systems. We aim to develop a CAD system using a deep learning approach. Our quantitative results show high AUC scores in comparison with the latest research works. The proposed approach achieved the highest mean AUC score of 85.8%. This is the highest accuracy documented in the literature for any related model.

Voltage instability in a power system produces low-frequency oscillations (LFOs), causing adverse effects in power distribution. Intelligent control schemes can overcome the limitations of fixed-parameter structures in power system stabilizers (PSS). Flexible alternating current transmission system (FACTS) control along with some supplementary control have remarkable potential in damping the oscillations. This paper proposes an adaptive neurofuzzy based recurrent wavelet control (ANRWC) scheme to enhance the power system stability. The proposed scheme utilizes recurrent Gaussian as antecedent part’s membership function and recurrent wavelet function in consequent parts. Our scheme uses gradient descent, adadelta, adaptive moment estimation (ADAM) and proximal gradient descent algorithms for optimization in which parameters of the scheme are updated using a back-propagation algorithm. A multi-machine power system is used for testing the controller. We evaluate the proposed control scheme in comparison to conventional lead-lag control and an adaptive neurofuzzy takagi sugeno kang (ANFTSK) control scheme. For comparison, we calculate the performance indices (PIs) for different controllers. Both quantitative and qualitative evaluations assert the effectiveness of the proposed control as compared to other schemes.

In the current era, data is growing exponentially due to advancements in smart devices. Data scientists apply a variety of learning-based techniques to identify underlying patterns in the medical data to address various health-related issues. In this context, automated disease detection has now become a central concern in medical science. Such approaches can reduce the mortality rate through accurate and timely diagnosis. COVID-19 is a modern virus that has spread all over the world and is affecting millions of people. Many countries are facing a shortage of testing kits, vaccines, and other resources due to significant and rapid growth in cases. In order to accelerate the testing process, scientists around the world have sought to create novel methods for the detection of the virus. In this paper, we propose a hybrid deep learning model based on a convolutional neural network (CNN) and gated recurrent unit (GRU) to detect the viral disease from chest X-rays (CXRs). In the proposed model, a CNN is used to extract features, and a GRU is used as a classifier. The model has been trained on 424 CXR images with 3 classes (COVID-19, Pneumonia, and Normal). The proposed model achieves encouraging results of 0.96, 0.96, and 0.95 in terms of precision, recall, and f1-score, respectively. These findings indicate how deep learning can significantly contribute to the early detection of COVID-19 in patients through the analysis of X-ray scans. Such indications can pave the way to mitigate the impact of the disease. We believe that this model can be an effective tool for medical practitioners for early diagnosis.

Parkinson’s disease (PD) is a chronic and progressive neurological disease that mostly shakes and compromises the motor system of the human brain. Patients with PD can face resting tremors, loss of balance, bradykinesia, and rigidity problems. Complex patterns of PD, i.e., with relevance to other neurological diseases and minor changes in brain structure, make the diagnosis of this disease a challenge and cause inaccuracy of about 25% in the diagnostics. The research community utilizes different machine learning techniques for diagnosis using handcrafted features. This paper proposes a computer-aided diagnostic system using a convolutional neural network (CNN) to diagnose PD. CNN is one of the most suitable models to extract and learn the essential features of a problem. The dataset is obtained from Parkinson’s Progression Markers Initiative (PPMI), which provides different datasets (benchmarks), such as T2-weighted MRI for PD and other healthy controls (HC). The mid slices are collected from each MRI. Further, these slices are registered for alignment. Since the PD can be found in substantia nigra (i.e., the midbrain), the midbrain region of the registered T2-weighted MRI slice is selected using the freehand region of interest technique with a 33 × 33 sized window. Several experiments have been carried out to ensure the validity of the CNN. The standard measures, such as accuracy, sensitivity, specificity, and area under the curve, are used to evaluate the proposed system. The evaluation results show that CNN provides better accuracy than machine learning techniques, such as naive Bayes, decision tree, support vector machine, and artificial neural network.

In a smart grid, efficient load management can help balance and reduce the burden on the national power grid and also minimize local operational electricity cost. Robust optimization is a technique that is increasingly used in home energy management systems, where it is applied in the scheduling of household loads through demand side control. In this work, interruptible loads and thermostatically controlled loads are analyzed to obtain optimal schedules in the presence of uncertainty. Firstly, the uncertain parameters are represented as different intervals, and then in order to control the degree of conservatism, these parameters are divided into various robustness levels. The conventional scheduling problem is transformed into a deterministic scheduling problem by translating the intervals and robustness levels into constraints. We then apply Harris’ hawk optimization together with integer linear programming to further optimize the load scheduling. Cost and trade-off schemes are considered to analyze the financial consequences of several robustness levels. Results show that the proposed method is adaptable to user requirements and robust to the uncertainties.

Attackers increasingly seek to compromise organizations and their critical data with advanced stealthy methods, often utilising legitimate tools. In the main, organisations employ reactive approaches for cyber security, focused on rectifying immediate incidents and preventing repeat attacks, through protections such as vulnerability assessment and penetration testing (VAPT) security information and event management (SIEM), firewalls, anti-spam/anti-malware solutions and system patches. Such system have weaknesses in addressing modern modern stealthy attacks. Proactive approaches, have been seen as part of the solution to this problem. However, approaches such as VAPT have limited scope and only works with threats that have already been discovered. Promising methods such as threat hunting are gaining momentum, enabling organisations to identify and rapidly respond to any potential attacks, though they have been criticised for their significant cost. In this paper, we present a novel hybrid model for uncovering tactics, techniques, and procedures (TTPs) through offensive security, specifically threat hunting via adversary emulation. The proposed technique is based on a novel approach of inducing adversary emulation (mapping each respective phase) model inside the threat hunting approach. The experimental results show that the proposed approach uses threat hunting via adversary emulation and has countervailing effects on hunting advance level threats. Moreover, the threat detection ability of the proposed approach utilizes minimum resources. The proposed approach can be used to develop the offensive security-aware environment for organizations to uncover advanced attack mechanisms and test their ability for attack detection.

Highly reliable and flexible control is required for distributed generation (DG) to efficiently connect to the grid. Smart inverters play a key role in the control and integration of DG into the power grid and provide advanced functionalities. In this paper, an energy-based single-phase voltage-source smart inverter (SPV-SSI) of 5 kVA is designed and analyzed in detail. SPV-SSI is capable of supplying the power to local load and the utility load up to the rated capacity of the inverter, injecting the power into the grid, storing the energy in lead-acid battery bank, controlling the voltage at the point of common coupling (PCC) during voltage sags or faults, and making decisions on real-time pricing information obtained from the utility grid through advanced metering. The complete design of smart inverter in dq frame, bi-directional DC-DC buck-boost converter, IEEE standard 1547 based islanding and recloser, and static synchronous compensator (STATCOM) functionalities is presented in this paper. Moreover, adaptive controllers, i.e., fuzzy proportional-integral (F-PI) controller and fuzzy-sliding mode controller (F-SMC) are designed. The performances of F-PI controller and F-SMC are superior, stable, and robust compared with those of conventionally tuned PI controllers for voltage control loop (islanded mode) and current control loop (grid-con-nected mode).

The agriculture sector plays a crucial role in supplying nutritious and high-quality food. Plant disorders significantly impact crop productivity, resulting in an annual loss of 33%. The early and accurate detection of plant disorders is a difficult task for farmers and requires specialized knowledge, significant effort, and labor. In this context, smart devices and advanced artificial intelligence techniques have significant potential to pave the way toward sustainable and smart agriculture. This paper presents a deep learning-based android system that can diagnose ginger plant disorders such as soft rot disease, pest patterns, and nutritional deficiencies. To achieve this, state-of-the-art deep learning models were trained on a real dataset of 4,394 ginger leaf images with diverse backgrounds. The trained models were then integrated into an Android-based mobile application that takes ginger leaf images as input and performs the real-time detection of crop disorders. The proposed system shows promising results in terms of accuracy, precision, recall, confusion matrices, computational cost, Matthews correlation coefficient (MCC), mAP, and F1-score.

The Internet of Medical Things (IoMT) has revolutionized Ambient Assisted Living (AAL) by interconnecting smart medical devices. These devices generate a large amount of data without human intervention. Learning-based sophisticated models are required to extract meaningful information from this massive surge of data. In this context, Deep Neural Network (DNN) has been proven to be a powerful tool for disease detection. Pulmonary Embolism (PE) is considered the leading cause of death disease, with a death toll of 180,000 per year in the US alone. It appears due to a blood clot in pulmonary arteries, which blocks the blood supply to the lungs or a part of the lung. An early diagnosis and treatment of PE could reduce the mortality rate. Doctors and radiologists prefer Computed Tomography (CT) scans as a first-hand tool, which contain 200 to 300 images of a single study for diagnosis. Most of the time, it becomes difficult for a doctor and radiologist to maintain concentration going through all the scans and giving the correct diagnosis, resulting in a misdiagnosis or false diagnosis. Given this, there is a need for an automatic Computer-Aided Diagnosis (CAD) system to assist doctors and radiologists in decision-making. To develop such a system, in this paper, we proposed a deep learning framework based on DenseNet201 to classify PE into nine classes in CT scans. We utilized DenseNet201 as a feature extractor and customized fully connected decision-making layers. The model was trained on the Radiological Society of North America (RSNA)-Pulmonary Embolism Detection Challenge (2020) Kaggle dataset and achieved promising results of 88%, 88%, 89%, and 90% in terms of the accuracy, sensitivity, specificity, and Area Under the Curve (AUC), respectively.

The Internet of Things (IoT) has emerged as a promising paradigm to enhance the living standard of human life by employing varied smart devices including smart phones, smart watches, sensors, on-board units and other networking equipment. However, these devices consume a considerable amount of energy to perform their operations that has a significant impact on the environment, product cost and life of the device. Given this fact, energy-efficient solutions for smart environments have gained great attention from researchers and the industrial community. In this context, a novel fog-based multi-level energy-efficient framework for IoT-enabled smart environments has been proposed. To achieve this, the proposed framework adds additional two layers in the existing IoT-fog-cloud architecture - sensors-based energy-efficient hardware layer and policy layer, to monitor the energy consumption and to enable the energy-aware decision making. Initially, the main sources of energy consumption in an IoT-enabled smart environment are identified. Further, the energy requirements of a device to perform a specific task are estimated. Moreover, the alternative devices to perform the same task using less energy are searched out. Finally, a device or a set of devices, to process the job consuming lower energy while ensuring the job requirements, is selected. To validate the proposed framework, four case studies are considered - smart parking, smart hospital specifically ICU, smart agriculture and smart airport. Simulations are conducted using iFogsim toolkit and results show that a significant amount of energy can be conserved by employing the proposed framework.

The transport sector has proven to be the largest contributor to global CO2 emissions. To reduce CO2 emissions and improve mileage, the existing research has proposed different fuel models for vehicles such as Plug-in Hybrid Electric Vehicles (PHEVs), Electric Vehicles (EVs), solar and hydrogen Vehicles. However, these vehicles suffer from a range of issues and solutions are required to increase range, and improve charging. In this context, we propose A Novel Hybrid Fuel Framework for Modern Vehicles, to reduce CO2 emissions and increase vehicle mileage, by managing energy resources efficiently through the application of Fuzzy Logic. It considers three different energy sources i.e., gasoline, solar and electric power, to charge a vehicle, and suggest a modification in the architecture of EVs is made for the availability of all these energy resources. We use Visual Studio to implement fuzzy logic based algorithm designed to simulate the proposed system and added a small gasoline engine to the existing architecture of EVs to provide energy resources that overcome charging issues during long-range travel. We use the Statistical Package for Social Sciences (SPSS) tool to evaluate the performance of the proposed framework for CO2 emissions and fuel efficiency. The proposed framework achieves the best mileage of 57.6 Kilometers per liter (Km/l) with a 660 Cubic Centimeter (CC) gasoline engine which is 111.11% more efficient than existing frameworks. Moreover, CO2 emissions through our proposed framework are 41.52 Grams per Kilometer (G/Km) which are 53% lower than current frameworks. The proposed framework also improves the charging duration of batteries i.e., a 10 Kilowatt-Hour (KwH) battery can be charged in 1 hour and 15 minutes.

Juniperus spp. are used as medicinal plants in many countries like Bosnia, Lebanon, and Turkey. In folk medicines, these plants have been used for treating skin and respiratory tract diseases, urinary problems, rheumatism and gall bladder stones. The objectives of this work were to synthesize silver nanoparticles (AgNPs) using a coniferous tree, Juniperus procera leaf extract and testing the synthesized AgNPs for its antimicrobial potentials, hemolytic activity, toxicity and the proliferative effects against normal and activated rat splenic cells. Leaf extract was prepared using acetone and ethanol as solvents. AgNPs were prepared using the acetone extract. AgNPs were validated using UV–Vis spectroscopy and scanning electron microscopy (SEM). Functional groups in the extract were identified using Fourier Transform Infrared (FT-IR) spectroscopy. SEM images of AgNPs showed spherical and cubic shapes with a uniform size distribution with an average size of 30–90 nm. FT-IR spectroscopy showed the presence of many functional groups in the plant extract. AgNPs showed promising antimicrobial activity against tested bacteria and fungus. AgNPs also expressed a stimulating activity towards the rat splenic cells in a dose dependent manner. Acetone as solvent was safer on cells than ethanol. Green synthesized AgNPs using J. procera might be used as a broad-spectrum therapeutic agent against microorganisms and as an immunostimulant agent. Keywords: Juniperus procera, AgNPs, Splenic cells proliferation, Antibacterial, Antifungal, Plant extract

Travel time prediction is essential to intelligent transportation systems directly affecting smart cities and autonomous vehicles. Accurately predicting traffic based on heterogeneous factors is highly beneficial but remains a challenging problem. The literature shows significant performance improvements when traditional machine learning and deep learning models are combined using an ensemble learning approach. This research mainly contributes by proposing an ensemble learning model based on hybridized feature spaces obtained from a bidirectional long short-term memory module and a bidirectional gated recurrent unit, followed by support vector regression to produce the final travel time prediction. The proposed approach consists of three stages–initially, six state-of-the-art deep learning models are applied to traffic data obtained from sensors. Then the feature spaces and decision scores (outputs) of the model with the highest performance are fused to obtain hybridized deep feature spaces. Finally, a support vector regressor is applied to the hybridized feature spaces to get the final travel time prediction. The performance of our proposed heterogeneous ensemble using test data showed significant improvements compared to the baseline techniques in terms of the root mean square error (53.87±3.50), mean absolute error (12.22±1.35) and the coefficient of determination (0.99784±0.00019). The results demonstrated that the hybridized deep feature space concept could produce more stable and superior results than the other baseline techniques.

Unmanned ground vehicles (UGVs) are becoming the foremost part of rescue teams for protecting human lives from severe disasters and reducing human casualties by informing them about the risks ahead, such as mine detection and clearance. In mine detection, a centralized system is required so that the UGVs can communicate with each other efficiently to disseminate the mine detection messages (MDMs) to incoming vehicles of military and civilians. Therefore, in this piece of research, a novel unmanned ground and aerial vehicle (UGAV)-based mine-detection-vehicle routing (MDVR) protocol has been proposed, mainly for the mine detection and clearance teams using a vehicular ad hoc network (VANET). The protocol disseminates the MDMs using UGVs and unmanned aerial vehicles (UAVs) in combination to overcome the limitations of only inter-UGV communication. The proposed protocol performs cluster-based multicast communication in real time using UAVs so that the dynamic mobility of UGVs cannot affect the performance of MDM dissemination. Hence, the proposed scheme is adaptable because any failure in message delivery can cause a high level of destruction. The proposed cluster-based scheme can adapt to any real-time scenario by introducing the level-based cluster-head election scheme (LBCHE), which works concerning its assigned priority for reducing the delay incurred in MDMs dissemination. The simulation of the proposed protocol in the network simulator (NS) shows that the overhead and delay are reduced in MDMs dissemination. At the same time, the throughput, packet delivery ratio, and stability increased compared to the other competing protocols.

Abstract With the evolution of fog computing, processing takes place locally in a virtual platform rather than in a centralized cloud server. Fog computing combined with cloud computing is more efficient as fog computing alone does not serve the purpose. Inefficient resource management and load balancing leads to degradation in quality of service as well as energy losses. Traffic overhead is increased because all the requests are sent to the main server causing delays which cannot be tolerated in healthcare scenarios. To overcome this problem, the authors are consolidating fog computing resources so that requests are handled by foglets and only critical requests are sent to the cloud for processing. Servers are placed locally in each city to handle the nearby requests in order to utilize the resources efficiently along with load balancing among all the servers, which leads to reduced latency and traffic overhead with the improved quality of service.

Currently, the electrical energy crisis is an important issue in Pakistan. Due to the shortage of electrical power, inhabitants of the country are facing 10–12 h of blackouts in urban areas and 14–20 h in rural areas daily. The current demand for electrical power is 15,000 MW, which is expected to increase further to 49,078 MW by 2050. Conventional energy sources are unable to meet this demand. This paper discusses the potential of Small Hydro Power Plants (SHPPs) to partially overcome the shortage of electricity. Overall, 60,000 MW of hydroelectric resources have been identified in Pakistan. whereas, approximately 11% of the identified resources are operational, producing 7228 MW of electric power. The energy crisis can be easily overcome by installing SHPPs. The use of SHPPs has been estimated to save 120 million tons of coal or 83.3 billion liters of oil in a year. Thus, these plants are environmentally friendly and make a low contribution to global warming. Worldwide, SHPPs provide employment to 0.2 million people. Pakistan Council of Renewable Energy Technology (PCRET) and Sarhad Rural Support Program (SRSP) has installed 1100 SHPPs, with a total capacity of 42.507 MW, which fulfills the electrical energy demand of approximately 0.7 million people in Pakistan.

Abstract Edge-of-Things (EoT) emerged as a novel computing and storage paradigm to overcome the limitations of IoT-cloud environment by providing cloud-like services at edge of the network. EoT offers a vast area for research and development as the invention has laid out great opportunities to experiment the possibilities for handling large data sets produced by the growing Internet-of-Things (IoT). The EoT offers a framework that lies between the cloud-to-end to perform the processing and cater the storage demands of the IoT applications. However, the exponential increase in EoT infrastructure resulted into extreme energy consumption. This paper finds the opportunity to address the issue of energy consumption in IoT-EoT environment by introducing dynamic speed scaling mechanism in EoT devices. The proposed approach is rigorously evaluated, and the verification is acquired through the simulations carried out on the simulator, iFogSim. The results show significant improvement in energy conservation by dynamically scaling the processor frequency of EoT devices according to the load variations in IoT traffic.

In the telecom sector, a huge volume of data is being generated on a daily basis due to a vast client base. Decision makers and business analysts emphasized that attaining new customers is costlier than retaining the existing ones. Business analysts and customer relationship management (CRM) analyzers need to know the reasons for churn customers, as well as, behavior patterns from the existing churn customers' data. This paper proposes a churn prediction model that uses classification, as well as, clustering techniques to identify the churn customers and provides the factors behind the churning of customers in the telecom sector. Feature selection is performed by using information gain and correlation attribute ranking filter. The proposed model first classifies churn customers data using classification algorithms, in which the Random Forest (RF) algorithm performed well with 88.63% correctly classified instances. Creating effective retention policies is an essential task of the CRM to prevent churners. After classification, the proposed model segments the churning customer's data by categorizing the churn customers in groups using cosine similarity to provide group-based retention offers. This paper also identified churn factors that are essential in determining the root causes of churn. By knowing the significant churn factors from customers' data, CRM can improve productivity, recommend relevant promotions to the group of likely churn customers based on similar behavior patterns, and excessively improve marketing campaigns of the company. The proposed churn prediction model is evaluated using metrics, such as accuracy, precision, recall, f-measure, and receiving operating characteristics (ROC) area. The results reveal that our proposed churn prediction model produced better churn classification using the RF algorithm and customer profiling using k-means clustering. Furthermore, it also provides factors behind the churning of churn customers through the rules generated by using the attribute-selected classifier algorithm.

Underwater Wireless Sensor Networks (UWSNs) serve as a proficient source to monitor aquatic environment. However, data communications and information routing within these systems offer many challenges. To ensure sufficient network lifetime, energy efficiency in routing protocols serves as a major concern in UWSNs. This paper presents an energy competent cooperative routing scheme known as Region Based Courier-nodes Mobility with Incremental Cooperative (RBCMIC) routing. The proposed scheme uses broadcast nature of wireless nodes and performs an incremental cooperative routing. A rigorous evaluation and verification of the proposed scheme with current state-of-the-art yield improved energy efficiency, resulting in extended network lifetime. The results show that an overall improvement of 20% is witnessed in energy usage, whereas a notable 89% improvement is achieved in end-to-end delay in comparison to DEADS protocol.

Emergencies, by definition, are unpredictable and rapid response is a key requirement in emergency management. Globally, a significant number of deaths occur each year, caused by excessive delays in rescue activities. Vehicles embedded with sophisticated technologies, along with roads equipped with advanced infrastructure, can play a vital role in the timely identification and notification of roadside incidents. However, such infrastructure and technologically-rich vehicles are rarely available in less developed countries. Hence, in such countries, low-cost solutions are required to address the issue. Systems based on the Internet of Things (IoT) have begun to be used to detect and report roadside incidents. The majority of the systems designed for this purpose involve the use of the cloud to compute, manage, and store information. However, the centralization and remoteness of cloud resources can result in an increased delay that raises serious concerns about its feasibility in emergency situations; in life-threatening situations, all delays should be minimized where feasible. To address the problem of latency, fog computing has emerged as a middleware paradigm that brings the cloud-like resources closer to end devices. In light of this, the research proposed here leverages the advantages of sophisticated features of smartphones and fog computing to propose and develop a low-cost and delay-aware accident detection and response system, which we term Emergency Response and Disaster Management System (ERDMS). An Android application is developed that utilizes smartphone sensors for the detection of incidents. When an accident is detected, a plan of action is devised. Initially, a nearby hospital is located using the Global Positioning System (GPS). The emergency department of the hospital is notified about the accident that directs an ambulance to the accident site. In addition, the family contacts of the victim are also informed about the accident. All the required computation is performed on the nearby available fog nodes. Moreover, the proposed scheme is simulated using iFogSim to evaluate and compare the performance using fog nodes and cloud data centers.

Dealing with air pollution presents a major environmental challenge in smart city environments. Real-time monitoring of pollution data enables local authorities to analyze the current traffic situation of the city and make decisions accordingly. Deployment of the Internet of Things-based sensors has considerably changed the dynamics of predicting air quality. Existing research has used different machine learning tools for pollution prediction; however, comparative analysis of these techniques is required to have a better understanding of their processing time for multiple datasets. In this paper, we have performed pollution prediction using four advanced regression techniques and present a comparative study to determine the best model for accurately predicting air quality with reference to data size and processing time. We have conducted experiments using Apache Spark and performed pollution estimation using multiple datasets. The Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) have been used as evaluation criteria for the comparison of these regression models. Furthermore, the processing time of each technique through standalone learning and through fitting the hyperparameter tuning on Apache Spark has also been calculated to find the best-fit model in terms of processing time and lowest error rate.

Fog computing has emerged as a revolutionary paradigm to serve the massive data generated in the Internet of Things (IoT) environments. It can be considered a derivative of cloud computing that provides cloud-like services at the edge of the network. As such, it helps address the, often significant, issue of delays encountered when using cloud systems for the IoT. According to the literature, inefficient scheduling of user tasks in fog computing can actually result in higher delays than cloud computing. Hence, the real benefits of fog computing can only be obtained by applying effective job scheduling strategies. In fact, task scheduling is an NP-hard problem and requires optimal and efficient techniques to address issues of latency, response time, and the efficient resource utilization of resources available at the edge of the network. Given this, we propose a novel bio-inspired hybrid algorithm (NBIHA) which is a hybrid of modified particle swarm optimization (MPSO) and modified cat swarm optimization (MCSO). In the proposed scheme, the MPSO is used to schedule the tasks among fog devices and the hybrid of the MPSO and MCSO is used to manage resources at the fog device level. In the proposed approach, the resources are assigned and managed on the basis of the demand of incoming requests. The main objective of the proposed work is to reduce the average response time and to optimize resource utilization by efficiently scheduling the tasks and managing the fog resources available. The simulations are performed using iFogSim. The evaluation results show that the proposed approach (NBIHA) shows promising results in terms of energy consumption, execution time, and average response time in comparison to the state-of-the-art scheduling techniques.

We present a case of multifocal plasmacytoma, one of the rarest hematological malignancies, with an unusual presentation of cervical myelopathy. A 60-year-old male presented with features of cervicalgia and radiculomyelopathy. There was a history of significant weight loss over 2 months with no sphincter involvement. Neurological examination showed spastic quadriparesis with extensor plantars. Radiological workup showed multiple lytic lesions from C3 to C6 with cord compression and myelomalacic changes. The patient underwent three-level corpectomy (C5–C7) with cord decompression. Reconstruction was done with an expandable titanium cage, and anterior cervical plating from C4 to T1 was done. The histopathology and immunohistochemistry markers enabled in clinching this rare diagnosis of multifocal plasmacytoma. It is essential to distinguish early between various entities among plasmacytoma as each one has a different clinical course and prognosis. Multiple solitary plasmacytoma presenting de novo with cord compression and myelopathy is an extremely rare presentation.

Plants’ diseases cannot be avoided because of unpredictable climate patterns and environmental changes. The plants like ginger get affected by various pests, conditions, and nutritional deficiencies. Therefore, it is essential to identify such causes early and perform the cure to get the desired production rate. Deep learning-based methods are helpful for the identification and classification of problems in this domain. This paper presents deep artificial neural network and deep learning-based methods for the early detection of diseases, pest patterns, and nutritional deficiencies. We have used a real-field dataset consisting of healthy and affected ginger plant leaves. The results show that the convolutional neural network (CNN) has achieved the highest accuracy of 99% for disease rhizomes detection. For pest pattern leaves, VGG-16 models showed the highest accuracy of 96%. For nutritional deficiency-affected leaves, ANN has achieved the highest accuracy (96%). The experimental results achieved are comparable with other existing techniques in the literature. In addition, the results demonstrated the potential in improving the yield of ginger using the proposed disease detection methods and an essential consideration for the design of real-time disease detection applications. However, the results are specific to the dataset used in this work and may yield different results for the other datasets.

Geminiviruses are insect-transmissible, economically vital group of plant viruses, which cause significant losses to crop production and ornamental plants across the world. During this study, infectious clones of three devastating begomoviruses, i.e., Cotton leaf curl Multan virus (CLCuMuV), Ramie mosaic virus (RamV) and Corchorus yellow vein Vietnam virus (CoYVV) were constructed by following novel protocol. All infectious clones were confirmed by cloning and sequencing. All of the infectious clones were agro-inoculated in Agrobacterium. After the agro-infiltrations, all clones were injected into Nicotiana benthamiana and jute plants under controlled condition. After 28 days of inoculation, plants exhibited typical symptoms of their corresponding viruses. All the symptomatic and asymptomatic leaves were collected from inoculated plants for further analysis. The southern blot analysis was used to confirm the infection of studied begomoviruses. At the end, all the products were sequenced and analyzed.

The application of wireless body area network (WBAN) in health sector is crucial. It prolongs the life expectancy and improves the quality of life by enabling remote health monitoring. Despite many advantages, it may result and exacerbates various extant health issues especially in the context of mobile multimedia. Due to high data rate of mobile multimedia applications, heat generated from sensor devices is considerably high and results in thermal dissipation. The thermal impact of sensors on human body has largely been a neglected topic. The paper rigorously analyzes the specific absorption rate (SAR) impact on human body. Moreover, the paper largely contributes by comprehensively evaluating SAR impact under various circumstances and dependency factors. The analysis and evaluation show promising results that can significantly help the successful design and deployment of WBAN strategies. The paper also provides great guidance to numerous practitioners of this domain.

Technology is an extremely potent tool that can be leveraged for human development and social good. Owing to the great importance of environment and human psychology in driving human behaviour, and the ubiquity of technology in modern life, there is a need to leverage the insights and capabilities of both fields together for nudging people towards a behaviour that is optimal in some sense (personal or social). In this regard, the field of persuasive technology, which proposes to infuse technology with appropriate design and incentives using insights from psychology, behavioural economics, and human-computer interaction holds a lot of promise. Whilst persuasive technology is already being developed and is at play in many commercial applications, it can have the great social impact in the field of Information and Communication Technology for Development (ICTD) which uses Information and Communication Technology (ICT) for human developmental ends such as education and health. In this paper we will explore what persuasive technology is and how it can be used for the ends of human development. To develop the ideas in a concrete setting, we present a case study outlining how persuasive technology can be used for human development in Pakistan, a developing South Asian country, that suffers from many of the problems that plague typical developing country.

Advertising over smart devices is one of the growing trends in the information technology domain. Most of the Android application (app) developers generate revenue through the use of ads, but on the other hand, the end users get the free app. However, the excessive number of ads infers hidden costs with respect to energy consumption, network utilization, and user comfort. These factors affect the app rating and reviews. Consequently, developers require a technique to balance app performance through optimized mobile ad usage. Therefore, in this paper, we extend an existing work and propose an energy-efficient method that uses gamma correction to reduce the hidden costs of a mobile app. In this approach, gamma correction efficiently balances the app performance by minimizing the size of ads. The size of the mobile ad is reduced by adjusting its pixels, reducing background color, and illuminating the content of the ad. After several experiments, it is deduced that our proposed approach efficiently saves mobile battery and developers can apply this approach to improve app rank and feedback.

The novelty behind the research in this paper is to investigate the Super Twisting Sliding Mode Controller (ST-SMC) for efficiently injecting both active and reactive power under normal and abnormal operating conditions for a three-phase grid-connected photovoltaic (PV) system. The ST-SMC is aimed to inject sinusoidal current to the grid with low Total Harmonic Distortion (THD), to avoid chattering with easy real implementation, and to enhance the quality of disturbance rejection and sensitivity to parameter variation. The test under normal conditions includes initialization, steady state behavior, dynamic behavior, and interrupting the injection of acting and reactive power while the abnormal conditions consists of voltage sag, voltage swell, frequency variation, DC-link variation, and inclusion of 5th harmonics, etc. The phase lock loop used for synchronization is based on a synchronous reference frame that works well under distorted grids and nonideal. Automatic code is generated in PSIM 9.1 for hardware implementation in the DSP board TMS32F28335 from Texas Instruments while code composer studio 6.2.0 is used for debugging. The real time testing is executed using Typhoon Hardware in Loop (HIL) 402 device on the DSP board. The results authenticate the fastness, effectiveness, and robustness for both steady state and dynamic behavior under various scenarios of the designed controller.

The modular multilevel converter (MMC) has been prominently used in medium- and high-power applications. This paper presents the control of output and circulating current of MMC using sliding mode control (SMC). The design of the proposed controller and the relation between control parameters and validity condition are based on the system dynamics. The proposed designed controller enables the system to track its reference values. The controller is designed to control both output current and circulating current along with suppression of second harmonics contents in circulating current. Furthermore, the capacitor voltage and energy of the converter are also regulated. The control of output current is carried out in d q -axis as well as in α β − a x i s with first-order switching law. However, a second-order switching law-based super twisting algorithm is used for controlling circulating current and suppression of its second harmonics contents. The stability of the controlled system is numerically calculated and verified by Lyapunov stability conditions. Moreover, the simulation results of the proposed controller are critically compared with the conventional proportional resonant (PR) controller to verify the effectiveness of the proposed control strategy. The proposed controller attains faster dynamic response and minimizes steady-state error comparatively. The simulation of the MMC model is carried out in MATLAB/Simulink.

Identification of the transcription start sites (TSSs) of a virus is of great importance to understand and dissect the mechanism of viral genome transcription but this often requires costly and laborious experiments. Many segmented negative-sense RNA viruses (sNSVs) cleave capped leader sequences from a large variety of mRNAs and use these cleaved leaders as primers for transcription in a conserved process called cap snatching. The recent developments in high-throughput sequencing have made it possible to determine most, if not all, of the capped RNAs snatched by a sNSV. Here, we show that rice stripe tenuivirus (RSV), a plant-infecting sNSV, co-infects Nicotiana benthamiana with two different begomoviruses and snatches capped leader sequences from their mRNAs. By determining the 5′ termini of a single RSV mRNA with high-throughput sequencing, the 5′ ends of almost all the mRNAs of the co-infecting begomoviruses could be identified and mapped on their genomes. The findings in this study provide support for the using of the cap snatching of sNSVs as a tool to map viral TSSs.

In this paper, a model reference controller (MRC) based on a neural network (NN) is proposed for damping oscillations in electric power systems. Variation in reactive load, internal or external perturbation/faults, and asynchronization of the connected machine cause oscillations in power systems. If the oscillation is not damped properly, it will lead to a complete collapse of the power system. An MRC base unified power flow controller (UPFC) is proposed to mitigate the oscillations in 2-area, 4-machine interconnected power systems. The MRC controller is using the NN for training, as well as for plant identification. The proposed NN-based MRC controller is capable of damping power oscillations; hence, the system acquires a stable condition. The response of the proposed MRC is compared with the traditionally used proportional integral (PI) controller to validate its performance. The key performance indicator integral square error (ISE) and integral absolute error (IAE) of both controllers is calculated for single phase, two phase, and three phase faults. MATLAB/Simulink is used to implement and simulate the 2-area, 4-machine power system.

A wireless sensor network (WSN) has achieved significant importance in tracking different physical or environmental conditions using wireless sensor nodes. Such types of networks are used in various applications including smart cities, smart building, military target tracking and surveillance, natural disaster relief, and smart homes. However, the limited power capacity of sensor nodes is considered a major issue that hampers the performance of a WSN. A plethora of research has been conducted to reduce the energy consumption of sensor nodes in traditional WSN, however the limited functional capability of such networks is the main constraint in designing sophisticated and dynamic solutions. Given this, software defined networking (SDN) has revolutionized traditional networks by providing a programmable and flexible framework. Therefore, SDN concepts can be utilized in designing energy-efficient WSN solutions. In this paper, we exploit SDN capabilities to conserve energy consumption in a traditional WSN. To achieve this, an energy-aware multihop routing protocol (named EASDN) is proposed for software defined wireless sensor network (SDWSN). The proposed protocol is evaluated in a real environment. For this purpose, a test bed is developed using Raspberry Pi. The experimental results show that the proposed algorithm exhibits promising results in terms of network lifetime, average energy consumption, the packet delivery ratio, and average delay in comparison to an existing energy efficient routing protocol for SDWSN and a traditional source routing algorithm.

Internet of Things-enabled Intelligent Transportation Systems (ITS) are gaining significant attention in academic literature and industry, and are seen as a solution to enhancing road safety in smart cities. Due to the ever increasing number of vehicles, a significant rise in the number of road accidents has been observed. Vehicles embedded with a plethora of sensors enable us to not only monitor the current situation of the vehicle and its surroundings but also facilitates the detection of incidents. Significant research, for example, has been conducted on accident rescue, particularly on the use of Information and Communication Technologies (ICT) for efficient and prompt rescue operations. The majority of such works provide sophisticated solutions that focus on reducing response times. However, such solutions can be expensive and are not available in all types of vehicles. Given this, we present a novel Internet of Things-based accident detection and reporting system for a smart city environment. The proposed approach aims to take advantage of advanced specifications of smartphones to design and develop a low-cost solution for enhanced transportation systems that is deployable in legacy vehicles. In this context, a customized Android application is developed to gather information regarding speed, gravitational force, pressure, sound, and location. The speed is a factor that is used to help improve the identification of accidents. It arises because of clear differences in environmental conditions (e.g., noise, deceleration rate) that arise in low speed collisions, versus higher speed collisions). The information acquired is further processed to detect road incidents. Furthermore, a navigation system is also developed to report the incident to the nearest hospital. The proposed approach is validated through simulations and comparison with a real data set of road accidents acquired from Road Safety Open Repository, and shows promising results in terms of accuracy.

This paper addresses the output current and circulating current control of the modular multi-level converter (MMC). The challenging task of MMCs is the control of output current and circulating current. Existing control structures for output and circulating current achieve control objectives with comparatively complex controllers and the designed parameters for the controller is also difficult. In this paper, an adaptive proportional integral (API) controller is designed to control the output current and the circulating current. The output current is regulated in α β axes while the circulating current is regulated in the a b c stationary frame to enhance MMC performance. The output and circulating current control results using an API controller are compared with the conventional proportional resonant (PR) controller in terms of transient response, stability, optimal performance, and reference tracking for results verification. The API control architecture significantly improve transient response, stability, and have excellent reference tracking capability. Moreover, it controls output current and converges the circulating current to a desired value. The control structure is designed for a three-phase MMC system, simulated and analyzed in MATLAB-Simulink.

High penetration of large capacity wind turbines into power grid has led to serious concern about its influence on the dynamic behaviors of the power system. Unbalanced grid voltage causing DC-voltage fluctuations and DC-link capacitor large harmonic current which results in degrading reliability and lifespan of capacitor used in voltage source converter. Furthermore, due to magnetic saturation in the generator and non-linear loads distorted active and reactive power delivered to the grid, violating grid code. This paper provides a detailed investigation of dynamic behavior and transient characteristics of Doubly Fed Induction Generator (DFIG) during grid faults and voltage sags. It also presents novel grid side controllers, Adaptive Proportional Integral Controller (API) and Proportional Resonant with Resonant Harmonic Compensator (PR+RHC) which eliminate the negative impact of unbalanced grid voltage on the DC-capacitor as well as achieving harmonic filtering by compensating harmonics which improve power quality. Proposed algorithm focuses on mitigation of harmonic currents and voltage fluctuation in DC-capacitor making capacitor more reliable under transient grid conditions as well as distorted active and reactive power delivered to the electric grid. MATLAB/Simulink simulation of 2 MW DFIG model with 1150 V DC-linked voltage has been considered for validating the effectiveness of proposed control algorithms. The proposed controllers performance authenticates robust, ripples free, and fault-tolerant capability. In addition, performance indices and Total Harmonic Distortions (THD) are also calculated to verify the robustness of the designed controller.

The unique characteristics of underwater environment such as long propagation delay, limited bandwidth, energy-constraint and non-uniform topology are big challenges in designing a routing protocol for underwater wireless sensor networks (UWSNs). In this paper, a novel routing scheme is proposed through which two mobile sinks are used for efficient collection of sensed data packets. Moreover, a new metric “Mobile Sink Utility Ratio (MUR)” is also introduced that helps in measuring the usage of mobile sink in the collection of data packets. The proposed scheme is rigorously evaluated and compared with current state-of-the-art routing protocols. The simulation of the proposed scheme shows promising results in terms of throughput, network lifetime, packet drop ratio and MUR.

The energy-efficient and reliable delivery of data packets in resource constraint underwater wireless sensor networks (UWSNs) is one of the key considerations to enhance the network lifetime. The traditional re-transmissions approach consumes the node battery and increases the communication overhead, which results in congestion and affects the reliable data packet delivery in the network. To ensure the reliability and conserve the node battery, in this paper, we propose adaptive forwarding layer multipath power control routing protocol to reduce the energy dissipation, achieve the data reliability and avoid the energy hole problem. In order to achieve the reliability, tree based topology is exploited to direct multiple copies of the data packet towards the surface through cross nodes in the network. The energy dissipation is reduced by a substantial amount with the selection of low noise path between the source and the destination including the information of neighbors of the potential forwarder node. Extensive simulation results show that our proposed work outperforms the compared existing scheme in terms of energy efficiency and packet received ratio (PRR).