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 （autum...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.展开更多
In this paper, the distribution patterns and abundance of pelagic tunicates in the North Yellow Sea of China during the period 2006-2007 were analyzed. Zooplankton samples were obtained with vertical towing from botto...In this paper, the distribution patterns and abundance of pelagic tunicates in the North Yellow Sea of China during the period 2006-2007 were analyzed. Zooplankton samples were obtained with vertical towing from bottom to surface using a WP2 plankton net(200 μm mesh size; mouth area: 0.25 m2). Five species belonging to two classes were identified: Oikopleura dioica, O. longicauda and Fritillaria borealis belonging to class Appendicularia; Salpa fusiformis and Doliolum denticulatum of class Thaliacea. O. dioica and O. longicauda were the dominant species, occurring in the samples of all four seasons, with different distribution patterns. Their maximum abundance were 1664.7 ind. m-3(spring) and 1031.7 ind. m-3(spring) respectively. Following Oikopleura spp. were D. denticulatum, which was found only in autumn with an average abundance of 149.6 ind. m-3, and S. fusiformis, which was detected all the year long except for autumn with low abundance(max. abundance 289.4 ind. m-3 in summer). Only a very small amount of F. borealis was detected in summer samples, with an average abundance of 2.7 ind. m-3. The relationship between tunicates abundances and the environmental factors was analyzed using the stepwise regression model for each species. The variation of appendicularian abundance showed a significant correlation with the surface water temperature and with the concentration of Chl-a. No relationship was found between tunicates abundance and salinity, likely due to the slight changes in surface salinity of the studied area during the four seasons. Salps abundance and that of doliolids were significantly correlated to bottom water temperature, indicating that these two species(S. fusiformis and D. denticulatum) migrate vertically in the water column. In particular D. denticulatum, known to be a warm water species, showed not only an important correlation with water temperature, but also a spatial distribution connected to the warm currents in the North Yellow Sea. The occurrence of D. denticulatum represents an interesting result never found in past research work. Water temperature, algal distribution and currents were the most relevant environmental factors influencing the tunicate abundance and distribution in the North Yellow Sea. Further research is needed in order to get more information on the ecology of these organisms and to better understand their role in the ecosystem including the oceanic food web.展开更多
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 ear...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.展开更多
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 innova...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.展开更多
Conductive papers made from graphene and its derivatives are important for the development of electronic devices; however, elastomer-based matrices usually make it difficult for the conductive sheets to form...Conductive papers made from graphene and its derivatives are important for the development of electronic devices; however, elastomer-based matrices usually make it difficult for the conductive sheets to form continuous conductive networks. In this work, we used tunicate-derived cellulose nanocrystals （TCNC） instead of traditional elastomers as the matrix for polydopamine （PDA）-coated and reduced graphene oxide （GO） to prepare conductive paper, which, at a low concentration, were better for the formation of conductive networks from conductive sheets. It was found that the Young’s modulus of the conductive paper produced via this strategy reached as high as 7 GPa. Meanwhile, owing to the partial reduction of GO during the polymerization of dopamine, the conductivity of the conductive paper reached as high as 1.3×10-5 S/cm when the PDA-coated GO content was 1 wt%, which was much higher than the conductivity of pure GO （-4.60×10-8 S/cm）. This work provides a new strategy for preparing environmentally friendly conductive papers with good mechanical properties and low conductive fller content, which may be used to produce high-performance, low-cost electronic devices.展开更多
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 ...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.展开更多
Heart disease prediction is a critical issue in healthcare,where accurate early diagnosis can save lives and reduce healthcare costs.The problem is inherently complex due to the high dimensionality of medical data,irr...Heart disease prediction is a critical issue in healthcare,where accurate early diagnosis can save lives and reduce healthcare costs.The problem is inherently complex due to the high dimensionality of medical data,irrelevant or redundant features,and the variability in risk factors such as age,lifestyle,andmedical history.These challenges often lead to inefficient and less accuratemodels.Traditional predictionmethodologies face limitations in effectively handling large feature sets and optimizing classification performance,which can result in overfitting poor generalization,and high computational cost.This work proposes a novel classification model for heart disease prediction that addresses these challenges by integrating feature selection through a Genetic Algorithm(GA)with an ensemble deep learning approach optimized using the Tunicate Swarm Algorithm(TSA).GA selects the most relevant features,reducing dimensionality and improvingmodel efficiency.Theselected features are then used to train an ensemble of deep learning models,where the TSA optimizes the weight of each model in the ensemble to enhance prediction accuracy.This hybrid approach addresses key challenges in the field,such as high dimensionality,redundant features,and classification performance,by introducing an efficient feature selection mechanism and optimizing the weighting of deep learning models in the ensemble.These enhancements result in a model that achieves superior accuracy,generalization,and efficiency compared to traditional methods.The proposed model demonstrated notable advancements in both prediction accuracy and computational efficiency over traditionalmodels.Specifically,it achieved an accuracy of 97.5%,a sensitivity of 97.2%,and a specificity of 97.8%.Additionally,with a 60-40 data split and 5-fold cross-validation,the model showed a significant reduction in training time(90 s),memory consumption(950 MB),and CPU usage(80%),highlighting its effectiveness in processing large,complex medical datasets for heart disease prediction.展开更多
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 meta...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.展开更多
The beautiful island of Tobago is the southernmost Caribbean island. The sister island of Trinidad belongs to the Republic of Trinidad and Tobago. Thirty-two species of tunicates were collected from Tobago from depths...The beautiful island of Tobago is the southernmost Caribbean island. The sister island of Trinidad belongs to the Republic of Trinidad and Tobago. Thirty-two species of tunicates were collected from Tobago from depths of 40 m or less and they were listed. Tunicates listed in this work were from collections made in 1956, 1991, 1993, 2002 and 2006 and although specimens were collected from the Atlantic Ocean side of the island and the Caribbean Sea side, all species turned out to be typical Caribbean species.展开更多
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,i...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.展开更多
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...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.展开更多
Workload balancing in cloud computing is not yet resolved,particularly considering Infrastructure as a Service(IaaS)in the cloud network.The problem of being underloaded or overloaded should not occur at the time of t...Workload balancing in cloud computing is not yet resolved,particularly considering Infrastructure as a Service(IaaS)in the cloud network.The problem of being underloaded or overloaded should not occur at the time of the server or host accessing the cloud which may lead to create system crash problem.Thus,to resolve these existing problems,an efficient task scheduling algorithm is required for distributing the tasks over the entire feasible resources,which is termed load balancing.The load balancing approach assures that the entire Virtual Machines(VMs)are utilized appropriately.So,it is highly essential to develop a load-balancing model in a cloud environment based on machine learning and optimization strategies.Here,the computing and networking data is utilized for the analysis to observe the traffic as well as performance patterns.The acquired data is offered to the machine learning decision to select the right server by predicting the performance effectively by employing an Optimal Kernel-based Extreme Learning Machine(OK-ELM)and their parameter is tuned by the developed hybrid approach Population Size-based Mud Ring Tunicate Swarm Algorithm(PS-MRTSA).Further,effective scheduling is performed to resolve the load balancing issues by employing the developed model MR-TSA.Here,the developed approach effectively resolves the multi-objective constraints such as Response time,Resource cost,and energy consumption.Thus,the recommended load balancing model securesan enhanced performance rate than the traditional approaches over several experimental analyses.展开更多
Peyronie's disease(PD)is an inflammatory andfibrotic disease which resultsin disfiguring and often distressing penile curvature deformity,affecting up toone in nine men in the United States,and between 0.3%and 13....Peyronie's disease(PD)is an inflammatory andfibrotic disease which resultsin disfiguring and often distressing penile curvature deformity,affecting up toone in nine men in the United States,and between 0.3%and 13.1%of menglobally.It progresses through an acute phase,associated with pain,as thefibrosis develops.In the quiescent phase,penile pain ceases and deformitystabilizes.The precise etiology remains unknown despite ongoing work toelucidate the biological underpinning.The diagnosis is guided by history andphysical examination.Except for ultrasonography,imaging is not routinelyrecommended.Current management is predicated on symptomatic controland slowing progression in the acute phase,and correction of bothersomecurvature in the stable phase.Most nonsurgical treatment options are poorlysupported by available evidence,with the exceptions of traction therapy andcertain intralesional injections.Surgical treatment,considered only afterstabilization,is guided by severity and the presence or absence of erectilefunction and is highly individualized.Investigations are ongoing into severalareas,including the exact biological mechanisms leading to plaque formationand failure of resolution;the effects of co‐existing systemic disease;the role ofimaging in diagnosis and surgical planning;combination and regenerativenonsurgical therapies;and improvements in surgical techniques.As diag-nostic accuracy improves and targeted treatments become available,man-agement of PD will become progressively tailored to an individual's particulardisease.In this review,we summarize the current knowledge regarding PD,including etiology and epidemiology,diagnosis,management,cutting‐edgeresearch,and future directions in care of this condition.展开更多
文摘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.
基金supported by the National Key Basic Research Project (2005CB422306)National Natural Science Foundation of China (40876066)
文摘In this paper, the distribution patterns and abundance of pelagic tunicates in the North Yellow Sea of China during the period 2006-2007 were analyzed. Zooplankton samples were obtained with vertical towing from bottom to surface using a WP2 plankton net(200 μm mesh size; mouth area: 0.25 m2). Five species belonging to two classes were identified: Oikopleura dioica, O. longicauda and Fritillaria borealis belonging to class Appendicularia; Salpa fusiformis and Doliolum denticulatum of class Thaliacea. O. dioica and O. longicauda were the dominant species, occurring in the samples of all four seasons, with different distribution patterns. Their maximum abundance were 1664.7 ind. m-3(spring) and 1031.7 ind. m-3(spring) respectively. Following Oikopleura spp. were D. denticulatum, which was found only in autumn with an average abundance of 149.6 ind. m-3, and S. fusiformis, which was detected all the year long except for autumn with low abundance(max. abundance 289.4 ind. m-3 in summer). Only a very small amount of F. borealis was detected in summer samples, with an average abundance of 2.7 ind. m-3. The relationship between tunicates abundances and the environmental factors was analyzed using the stepwise regression model for each species. The variation of appendicularian abundance showed a significant correlation with the surface water temperature and with the concentration of Chl-a. No relationship was found between tunicates abundance and salinity, likely due to the slight changes in surface salinity of the studied area during the four seasons. Salps abundance and that of doliolids were significantly correlated to bottom water temperature, indicating that these two species(S. fusiformis and D. denticulatum) migrate vertically in the water column. In particular D. denticulatum, known to be a warm water species, showed not only an important correlation with water temperature, but also a spatial distribution connected to the warm currents in the North Yellow Sea. The occurrence of D. denticulatum represents an interesting result never found in past research work. Water temperature, algal distribution and currents were the most relevant environmental factors influencing the tunicate abundance and distribution in the North Yellow Sea. Further research is needed in order to get more information on the ecology of these organisms and to better understand their role in the ecosystem including the oceanic food web.
文摘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.
基金support for this work from the Deanship of Scientific Research (DSR),University of Tabuk,Tabuk,Saudi Arabia,under grant number S-1440-0262.
文摘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.
基金the National Natural Science Foundation of China (51373131)Fundamental Research Funds for the Central Universities (XDJK2016A017 and XDJK2016C033)+1 种基金Project of Basic Science and Advanced Technology Research, Chongqing Science and Technology Commission (cstc2016, jcyjA0796)the Talent Project of Southwest University (SWU115034)
文摘Conductive papers made from graphene and its derivatives are important for the development of electronic devices; however, elastomer-based matrices usually make it difficult for the conductive sheets to form continuous conductive networks. In this work, we used tunicate-derived cellulose nanocrystals （TCNC） instead of traditional elastomers as the matrix for polydopamine （PDA）-coated and reduced graphene oxide （GO） to prepare conductive paper, which, at a low concentration, were better for the formation of conductive networks from conductive sheets. It was found that the Young’s modulus of the conductive paper produced via this strategy reached as high as 7 GPa. Meanwhile, owing to the partial reduction of GO during the polymerization of dopamine, the conductivity of the conductive paper reached as high as 1.3×10-5 S/cm when the PDA-coated GO content was 1 wt%, which was much higher than the conductivity of pure GO （-4.60×10-8 S/cm）. This work provides a new strategy for preparing environmentally friendly conductive papers with good mechanical properties and low conductive fller content, which may be used to produce high-performance, low-cost electronic devices.
文摘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.
文摘Heart disease prediction is a critical issue in healthcare,where accurate early diagnosis can save lives and reduce healthcare costs.The problem is inherently complex due to the high dimensionality of medical data,irrelevant or redundant features,and the variability in risk factors such as age,lifestyle,andmedical history.These challenges often lead to inefficient and less accuratemodels.Traditional predictionmethodologies face limitations in effectively handling large feature sets and optimizing classification performance,which can result in overfitting poor generalization,and high computational cost.This work proposes a novel classification model for heart disease prediction that addresses these challenges by integrating feature selection through a Genetic Algorithm(GA)with an ensemble deep learning approach optimized using the Tunicate Swarm Algorithm(TSA).GA selects the most relevant features,reducing dimensionality and improvingmodel efficiency.Theselected features are then used to train an ensemble of deep learning models,where the TSA optimizes the weight of each model in the ensemble to enhance prediction accuracy.This hybrid approach addresses key challenges in the field,such as high dimensionality,redundant features,and classification performance,by introducing an efficient feature selection mechanism and optimizing the weighting of deep learning models in the ensemble.These enhancements result in a model that achieves superior accuracy,generalization,and efficiency compared to traditional methods.The proposed model demonstrated notable advancements in both prediction accuracy and computational efficiency over traditionalmodels.Specifically,it achieved an accuracy of 97.5%,a sensitivity of 97.2%,and a specificity of 97.8%.Additionally,with a 60-40 data split and 5-fold cross-validation,the model showed a significant reduction in training time(90 s),memory consumption(950 MB),and CPU usage(80%),highlighting its effectiveness in processing large,complex medical datasets for heart disease prediction.
文摘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.
文摘The beautiful island of Tobago is the southernmost Caribbean island. The sister island of Trinidad belongs to the Republic of Trinidad and Tobago. Thirty-two species of tunicates were collected from Tobago from depths of 40 m or less and they were listed. Tunicates listed in this work were from collections made in 1956, 1991, 1993, 2002 and 2006 and although specimens were collected from the Atlantic Ocean side of the island and the Caribbean Sea side, all species turned out to be typical Caribbean species.
文摘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.
文摘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.
文摘Workload balancing in cloud computing is not yet resolved,particularly considering Infrastructure as a Service(IaaS)in the cloud network.The problem of being underloaded or overloaded should not occur at the time of the server or host accessing the cloud which may lead to create system crash problem.Thus,to resolve these existing problems,an efficient task scheduling algorithm is required for distributing the tasks over the entire feasible resources,which is termed load balancing.The load balancing approach assures that the entire Virtual Machines(VMs)are utilized appropriately.So,it is highly essential to develop a load-balancing model in a cloud environment based on machine learning and optimization strategies.Here,the computing and networking data is utilized for the analysis to observe the traffic as well as performance patterns.The acquired data is offered to the machine learning decision to select the right server by predicting the performance effectively by employing an Optimal Kernel-based Extreme Learning Machine(OK-ELM)and their parameter is tuned by the developed hybrid approach Population Size-based Mud Ring Tunicate Swarm Algorithm(PS-MRTSA).Further,effective scheduling is performed to resolve the load balancing issues by employing the developed model MR-TSA.Here,the developed approach effectively resolves the multi-objective constraints such as Response time,Resource cost,and energy consumption.Thus,the recommended load balancing model securesan enhanced performance rate than the traditional approaches over several experimental analyses.
文摘Peyronie's disease(PD)is an inflammatory andfibrotic disease which resultsin disfiguring and often distressing penile curvature deformity,affecting up toone in nine men in the United States,and between 0.3%and 13.1%of menglobally.It progresses through an acute phase,associated with pain,as thefibrosis develops.In the quiescent phase,penile pain ceases and deformitystabilizes.The precise etiology remains unknown despite ongoing work toelucidate the biological underpinning.The diagnosis is guided by history andphysical examination.Except for ultrasonography,imaging is not routinelyrecommended.Current management is predicated on symptomatic controland slowing progression in the acute phase,and correction of bothersomecurvature in the stable phase.Most nonsurgical treatment options are poorlysupported by available evidence,with the exceptions of traction therapy andcertain intralesional injections.Surgical treatment,considered only afterstabilization,is guided by severity and the presence or absence of erectilefunction and is highly individualized.Investigations are ongoing into severalareas,including the exact biological mechanisms leading to plaque formationand failure of resolution;the effects of co‐existing systemic disease;the role ofimaging in diagnosis and surgical planning;combination and regenerativenonsurgical therapies;and improvements in surgical techniques.As diag-nostic accuracy improves and targeted treatments become available,man-agement of PD will become progressively tailored to an individual's particulardisease.In this review,we summarize the current knowledge regarding PD,including etiology and epidemiology,diagnosis,management,cutting‐edgeresearch,and future directions in care of this condition.
