Enhanced Tunicate Swarm Optimization with Transfer Learning Enabled Medical Image Analysis System

Medical image analysis is an active research topic,with thousands of studies published in the past few years.Transfer learning(TL)including convolutional neural networks(CNNs)focused to enhance efficiency on an innovative task using the knowledge of the same tasks learnt in advance.It has played a major role in medical image analysis since it solves the data scarcity issue along with that it saves hardware resources and time.This study develops an EnhancedTunicate SwarmOptimization withTransfer Learning EnabledMedical Image Analysis System(ETSOTL-MIAS).The goal of the ETSOTL-MIAS technique lies in the identification and classification of diseases through medical imaging.The ETSOTL-MIAS technique involves the Chan Vese segmentation technique to identify the affected regions in the medical image.For feature extraction purposes,the ETSOTL-MIAS technique designs a modified DarkNet-53 model.To avoid the manual hyperparameter adjustment process,the ETSOTLMIAS technique exploits the ETSO algorithm,showing the novelty of the work.Finally,the classification of medical images takes place by random forest(RF)classifier.The performance validation of the ETSOTL-MIAS technique is tested on a benchmark medical image database.The extensive experimental analysis showed the promising performance of the ETSOTL-MIAS technique under different measures.

Seasonal abundance and distribution of pelagic tunicates (Chordata: Thaliacea) in the central South China Sea

A total of 168 macro-zooplankton samples from 42 stations in the central South China Sea （12° ～20° N, 111°～118°E, an area of about 64 × 10^4 km^2 ） were collected in September 1983 （autumn） , April 1984 （spring） , August 1984 （summer） and December 1984 （winter）. Twenty-three species and subspecies of tunicates were found, of which Thalia democratica complex （including T. d. orientalis and T. d. echinata） and Doliolum denticulatum were the dominant species, and accounted for 95.7% , 90. 0%, 91.8% and 90. 5% of the total tunicates found in autumn, winter, spring and summer, respectively. The highest abundance （with a mean of 2.37 ind./m^3 ） occurred in autumn. There are strong correlations between the abundances of the tunicates and those of phytoplankton and chlorophyll a concentration. However, tunicates also aggregate in areas with low primary production in the autumn survey, probably due to the water circulation pattern.

Intelligent Tunicate Swarm-Optimization-Algorithm-Based Lightweight Security Mechanism in Internet of Health Things

Fog computing in the Internet of Health Things(IoHT)is promising owing to the increasing need for energy-and latency-optimized health sector provisioning.Additionally,clinical data(particularly,medical image data)are a delicate,highly protected resource that should be utilized in an effective and responsible manner to fulfil consumer needs.Herein,we propose an energy-efficient fog-based IoHT with a tunicate swarm-optimization-(TSO)-based lightweight Simon cipher to enhance the energy efficiency at the fog layer and the security of data stored at the cloud server.The proposed Simon cipher uses the TSO algorithm to select the optimal keys that will minimize the deterioration of quality between the original and reconstructed(decrypted)images.In this study,the decrypted image quality is preserved by the peak signal-to-noise ratio(PSNR)such that consumers can generate precise medical reports from IoHT devices at the application level.Moreover,a lightweight encryption step is implemented in the fog to improve energy efficiency and reduce additional computations at the cloud server.Experimental results indicate that the TSO-Simon model achieved a high PSNR of 61.37 dB and a pixel change rate of 95.31.

An Improved Tunicate Swarm Algorithm with Best-random Mutation Strategy for Global Optimization Problems

The Tunicate Swarm Algorithm(TSA)inspires by simulating the lives of Tunicates at sea and how food is obtained.This algorithm is easily entrapped to local optimization despite the simplicity and optimal,leading to early convergence compared to some metaheuristic algorithms.This paper sought to improve this algorithm's performance using mutating operators such as the lévy mutation operator,the Cauchy mutation operator,and the Gaussian mutation operator for global optimization problems.Thus,we introduced a version of this algorithm called the QLGCTSA algorithm.Each of these operators has a different performance,increasing the QLGCTSA algorithm performance at a specific optimization operation stage.This algorithm has been run on benchmark functions,including three different compositions,unimodal(UM),and multimodal(MM)groups and its performance evaluate six large-scale engineering problems.Experimental results show that the QLGCTSA algorithm had outperformed other competing optimization algorithms.

Evidence of Bio-Organic Mineralization

The marine black shale formations of different epochs are widely seen in China. They usually come cul as ore-bearing horizon and contain different ore-forming element. Why are there so many different associated metallic elements in the marine black shale ? Evidence shows that the answer is bio-mineralization. The bio-mineralizahon in the process of formation of metalliferous black shales can be divided intp twp stages. The first stage is the bio-achvities occurring in the surface layer aquatic body of the ocean .The ore-forming elements could be absorbed by only a few species of organisms as a bioconcentrator. The second stage is the biogeochemical process. The anaerobic bacteria decomposed the organic remains and the metal elements were activated, and migrated and combined with organic matter or clay minerals or sulfide. After sediments were buried deeply, the organic matter were cracked by high earth temperature.The metal elements in organic compound were continuously acivated, and migrated to form matamorphic minerals or hydrothermal minerals in original rocks or quartz veins. The biogenetic marks in the black shale are changed or distorted beyond recognition and not easy to be found.

Penile Fracture—Report on Three Cases from Cape Coast, Ghana

Introduction: Penile fracture is a urological emergency that occurs when the penis in an erect state suffers a blunt trauma resulting in a rupture of the tunica albuginea of either one or both corpora cavernosa. It is often caused by vigorous sexual intercourse, hence the incidence of penile fracture is under-reported. We therefore present our experience of the clinical presentation and surgical management of penile fracture. Presentation of Cases: We report three cases of penile fracture and all were diagnosed based on their clinical presentation and examination findings. The patients include two middle aged men and one young man, all with history of hearing a popping sound and experiencing sudden onset pain with detumescence of the penis. No radiological investigations were carried out. An emergency surgical repair was done for all patients. Discussion: All the patients had no urethral injury on presentation and underwent immediate surgical repair. The erectile and voiding function of each patient was preserved. Conclusion: The management of penile fracture involves early diagnosis and immediate surgical repair. Early intervention is necessary to preserve penile function.

Management of residual penile curvature after penile prosthesis placement

Residual penile curvature is a common situation following the implantation of a penile prosthesis in patients with Peyronie’s disease.Currently,there is a variety of options for the correction of residual curvature,including penile modeling,plication techniques,as well as tunical incision/excision with or without grafting.A literature search of PubMed and Medline databases was conducted from 1964 until 2020,using search terms for all articles in the English language.In this article,we provide a review of the techniques and the outcomes,according to the published literature.