Giant Scrotal Lymphoedema: A Case Series.

Giant scrotal lymphoedema is a rare condition caused by obstruction, aplasia, or hypoplasia of lymphatic vessels draining the external genitalia. While this condition can be congenital or acquired, the most common acquired cause of such lymphatic obstruction worldwide is lymphatic filariasis (LF). We present a case series analysis of three patients of giant scrotal lymphoedema who were successfully treated for the condition in the Department of General Surgery, King George's Medical University (KGMU), Lucknow, with satisfactory post-operative recovery and minimal recurrence. The first patient was a 45-year-old who had been living with the condition for 10 years, and the resected scrotal tissue weighed 35 kg. The second patient was a 45-year-old who was diagnosed with filariasis five years back before the condition set in, and the resected scrotal tissue weighed 32 kg. The third patient was a 22-year-old young man who had been diagnosed with the condition 10 years back, and the resected scrotal tissue weighed 25 kg. Proper pre-operative evaluation was conducted in all three patients to establish the diagnosis of scrotal lymphoedema. The urethral catheterisation was conducted, which additionally helped to identify penile tissue intraoperatively. Careful exploration of scrotal tissue was conducted along with delineation of the penis from scrotal oedema. The surgical approach involved debulking scrotal lymphoedema with the reconstruction of scrotal skin while preserving penile tissue. Patients with giant scrotal lymphoedema face the social stigma that creates physical disability. Hence, they end up seeking medical help from tertiary care centres after the disease has reached advanced stages and fibrosis has set in. However, single-stage debulking, along with reconstructive surgery (referred to as reduction scrotoplasty), yields promising results even in cases of very bulky scrotal lymphoedema, weighing up to 35 kg, as per our study.

VGG-COVIDNet: A Novel model for COVID detection from X-Ray and CT Scan images.

In this research work, a new deep learning model named VGG-COVIDNet has been proposed which can classify COVID-19 cases from normal cases over X-Rays and CT scan images of lungs. Medical practitioners use the X-Rays and CT scan images of lungs to identify whether a person is infected from COVID or not. In present times, it is very important to give real time COVID prediction with high reliability of results. Deep learning models equipped with machine learning support have been found very influential in accurate prediction of COVID or Non-COVID cases in real time. However, there are some limitations associated with the performance of these model which are model size, achieving good balance of model size and accuracy, and making a single model fitting well for both X-Ray and CT Scan image datasets. Keeping in mind these performance constraints, this new model (VGG-COVIDNet) has been proposed for real time prediction of COVID cases with good balance of model size and accuracy working well for both type of datasets (CT Scan and X-Ray). In order to control model size, an improved version of VGG-16 architecture has been proposed which contains only 13 convolutional layers and 5 fully connected layers. Multiple dropout layers have been added in the proposed architecture which can drop some percentage of features and applies random transformations to decrease the model over-fitting issue. Keeping in mind the primary goal to increase the model accuracy the proposed model has been trained on different datasets with ReLU activation function which is one of the best non-linear activation functions. Four different capacity datasets with CT scan and X-Ray images have been used to validate the performance of proposed model. The proposed model gives an overall accuracy of more than 90% on both types of input datasets i.e. X-Ray and CT Scan.

O-WCNN: an optimized integration of spatial and spectral feature map for arrhythmia classification.

The regular monitoring and accurate diagnosis of arrhythmia are critically important, leading to a reduction in mortality rate due to cardiovascular diseases (CVD) such as heart stroke or cardiac arrest. This paper proposes a novel convolutional neural network (CNN) model for arrhythmia classification. The proposed model offers the following improvements compared with traditional CNN models. Firstly, the multi-channel model can concatenate spectral and spatial feature maps. Secondly, the structural unit is composed of a depthwise separable convolution layer followed by activation and batch normalization layers. The structural unit offers effective utilization of network parameters. Also, the optimization of hyperparameters is done using Hyperopt library, based on Sequential Model-Based Global Optimization algorithm (SMBO). These improvements make the network more efficient and accurate for arrhythmia classification. The proposed model is evaluated using tenfold cross-validation following both subject-oriented inter-patient and class-oriented intra-patient evaluation protocols. Our model achieved 99.48% and 99.46% accuracy in VEB (ventricular ectopic beat) and SVEB (supraventricular ectopic beat) class classification, respectively. The model is compared with state-of-the-art models and has shown significant performance improvement.

Smart systems and data-driven services in healthcare.

The modern development of Medicine and Healthcare is primarily based on the automation of various processes to support the correct and timely medical decisions. A doctor or other medical staff's fast, accurate, reliable diagnosis, prevention, and treatment processes are the keys to quality patient care. This aim can be achieved by developing reliable, intelligent Healthcare and medicine service systems for various purposes. The Special Issue covers science-intensive real-time solutions that underlie smart systems and data-driven services in Healthcare and Medicine.

Mathematical modelling of bio-inspired frog leap optimization algorithm for transmission expansion planning.

Bio-inspired computing has progressed so far to deal with real-time multi-objective optimization problems. The Transmission expansion planning of the modern electricity grid requires finding the best and optimal routes for electricity transmission from the generation point to the endpoint while satisfying all the power and load constraints. Further, the transmission expansion cost allocation becomes a critical and pragmatic issue in the deregulated electricity industry. The prime objective is to minimize the total investment and expansion costs while considering N-1 contingency. The most optimal transmission expansion planning problem's solution is calculated using the objective function and the constraints. This optimal solution provides the total number and best locations for the candidates. The presented paper details the mathematical modeling of the shuffled frog leap algorithm with various modifications applied to the method to refine the results and finally proposes an enhanced novel approach to solve the transmission expansion planning problem. The proposed algorithm produces the expansion plans based on target-based evolution. The presented algorithm is rigorously tested on the standard Garver dataset and IEEE 24 bus system. The empirical results of the proposed algorithm led to better expansion plans while effectively considering typical electrical constraints along with modern and realistic constraints.

A Deep Intelligent Attack Detection Framework for Fog-Based IoT Systems.

Fog computing provides a multitude of end-based IoT system services. End IoT devices exchange information with fog nodes and the cloud to handle client undertakings. During the process of data collection between the layer of fog and the cloud, there are more chances of crucial attacks or assaults like DDoS and many more security attacks being compromised by IoT end devices. These network (NW) threats must be spotted early. Deep learning (DL) assumes an unmistakable part in foreseeing the end client behavior by extricating highlights and grouping the foe in the network. Yet, because of IoT devices' compelled nature in calculation and storage spaces, DL cannot be managed on those. Here, a framework for fog-based attack detection is proffered, and different attacks are prognosticated utilizing long short-term memory (LSTM). The end IoT gadget behaviour can be prognosticated by installing a trained LSTMDL model at the fog node computation module. The simulations are performed using Python by comparing LSTMDL model with deep neural multilayer perceptron (DNMLP), bidirectional LSTM (Bi-LSTM), gated recurrent units (GRU), hybrid ensemble model (HEM), and hybrid deep learning model (CNN + LSTM) comprising convolutional neural network (CNN) and LSTM on DDoS-SDN (Mendeley Dataset), NSLKDD, UNSW-NB15, and IoTID20 datasets. To evaluate the performance of the binary classifier, metrics like accuracy, precision, recall, f1-score, and ROC-AUC curves are considered on these datasets. The LSTMDL model shows outperforming nature in binary classification with 99.70%, 99.12%, 94.11%, and 99.88% performance accuracies on experimentation with respective datasets. The network simulation further shows how different DL models present fog layer communication behaviour detection time (CBDT). DNMLP detects communication behaviour (CB) faster than other models, but LSTMDL predicts assaults better.

EDLDR: An Ensemble Deep Learning Technique for Detection and Classification of Diabetic Retinopathy.

Diabetic retinopathy (DR) is an ophthalmological disease that causes damage in the blood vessels of the eye. DR causes clotting, lesions or haemorrhage in the light-sensitive region of the retina. Person suffering from DR face loss of vision due to the formation of exudates or lesions in the retina. The detection of DR is critical to the successful treatment of patients suffering from DR. The retinal fundus images may be used for the detection of abnormalities leading to DR. In this paper, an automated ensemble deep learning model is proposed for the detection and classification of DR. The ensembling of a deep learning model enables better predictions and achieves better performance than any single contributing model. Two deep learning models, namely modified DenseNet101 and ResNeXt, are ensembled for the detection of diabetic retinopathy. The ResNeXt model is an improvement over the existing ResNet models. The model includes a shortcut from the previous block to next block, stacking layers and adapting split-transform-merge strategy. The model has a cardinality parameter that specifies the number of transformations. The DenseNet model gives better feature use efficiency as the dense blocks perform concatenation. The ensembling of these two models is performed using normalization over the classes followed by maximum a posteriori over the class outputs to compute the final class label. The experiments are conducted on two datasets APTOS19 and DIARETDB1. The classifications are carried out for both two classes and five classes. The images are pre-processed using CLAHE method for histogram equalization. The dataset has a high-class imbalance and the images of the non-proliferative type are very low, therefore, GAN-based augmentation technique is used for data augmentation. The results obtained from the proposed method are compared with other existing methods. The comparison shows that the proposed method has higher accuracy, precision and recall for both two classes and five classes. The proposed method has an accuracy of 86.08 for five classes and 96.98% for two classes. The precision and recall for two classes are 0.97. For five classes also, the precision and recall are high, i.e., 0.76 and 0.82, respectively.

CNGOD-An improved convolution neural network with grasshopper optimization for detection of COVID-19.

The world is facing the pandemic situation due to a beta corona virus named Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). The disease caused by this virus known as Corona Virus Disease 2019 (COVID-19) has affected the entire world. The current diagnosis methods are laboratory based and require specialized testing kits for performing the test. Therefore, to overcome the limitations of testing kits a diagnosis method from chest X-ray images is proposed in this paper. Chest X-ray images can be easily obtained by X-ray machines that are readily available at medical centres. The radiological examinations augmented with chest X-ray images is an effective way of disease diagnosis. The automated analysis of the chest X-ray images requires a highly efficient method for identifying COVID-19 from these images. Thus, a novel deep convolution neural network (CNN) optimized using Grasshopper Optimization Algorithm (GOA) is proposed. The deep learning model comprises depth wise separable convolutions that independently look at cross channel and spatial correlations. The optimization of deep learning models is a complex task due the multiple layers and their non-linearities. In image classification problems optimizers like Adam, SGD etc. get stuck in local minima. Thus, in this paper a metaheuristic optimization algorithm is used to optimize the network. Grasshoper Optimization Algorithm (GOA) is a metaheuristic algorithm that mimics the behaviour of grasshoppers for food search. This algorithm is a fast converging and is capable of exploration and exploitation of large search spaces. Maximum Probability Based Cross Entropy Loss (MPCE) loss function is used as it minimizes the back propogation error of cross entropy and improves the training. The experimental results show that the proposed method gives high classification accuracy. The interpretation of results is augmented with class activation maps. Grad-CAM visualization algorithm is used for class activation maps.

Phytostabilization of coal mine overburden waste, exploiting the phytoremedial efficacy of lemongrass under varying level of cow dung manure.

A pot study was performed to assess the phytoremedial potential of Cymbopogon citratus (D.C.) Staf. for reclamation of coal mine overburden dump wastes, emphasizing the outcome of amendment practices using cow dung manure (CM) and garden soil mixtures on the revegetation of over-burden wastes (OB). Wastes amendment with cow dung manure and garden soil resulted in a significant increase in soil health and nutrient status along with an increment in the phytoavailability of Zn and Cu which are usually considered as micronutrients, essential for plant growth. A significant increment in the total biomass of lemongrass by 38.6% under CM20 (OB: CM 80:20) was observed along with improved growth parameters under amended treatments as compared to OB (100% waste). Furthermore, the proportionate increases in the assimilative rate, water use efficiency, and chlorophyll fluorescence have been observed with the manure application rates. Lemongrass emerged out to be an efficient metal-tolerant herb species owing to its high metal-tolerance index (>100%). Additionally, lemongrass efficiently phytostablized Pb and Ni in the roots. Based on the strong plant performances, the present study highly encourages the cultivation of lemongrass in coal mining dumpsites for phytostabilization coupled with cow-dung manure application (20% w/w).

Single-pass incremental force updates for adaptively restrained molecular dynamics.

Adaptively restrained molecular dynamics (ARMD) allows users to perform more integration steps in wall-clock time by switching on and off positional degrees of freedoms. This article presents new, single-pass incremental force updates algorithms to efficiently simulate a system using ARMD. We assessed different algorithms for speedup measurements and implemented them in the LAMMPS MD package. We validated the single-pass incremental force update algorithm on four different benchmarks using diverse pair potentials. The proposed algorithm allows us to perform simulation of a system faster than traditional MD in both NVE and NVT ensembles. Moreover, ARMD using the new single-pass algorithm speeds up the convergence of observables in wall-clock time. © 2017 Wiley Periodicals, Inc.

Applicability of Zeolite Based Systems for Ammonia Removal and Recovery From Wastewater.

Ammonia discharged in industrial effluents bears deleterious effects and necessitates remediation. Integrated systems devoted to recovery of ammonia in a useful form and remediation of the same addresses the challenges of waste management and its utilization. A comparative performance evaluation study was undertaken to access the suitability of different zeolite based systems (commercial zeolites and zeolites synthesized from fly ash) for removal of ammonia followed by its subsequent release. Four main parameters which were studied to evaluate the applicability of such systems for large scale usage are cost-effectiveness, ammonia removal efficiency, performance on regeneration, and ammonia release percentage. The results indicated that synthetic zeolites outperformed zeolites synthesized from fly ash, although the later proved to be more efficient in terms of total cost incurred. Process technology development in this direction will be a trade-of between cost and ammonia removal and release efficiencies.