In the frame of our long-term study of cetacean abundance and distribution in polar marine ecosystems begun in 1979, a drastic increase in the bowbead Balaena mysticetus North Atlantic ＂stock＂ was observed from 2005...In the frame of our long-term study of cetacean abundance and distribution in polar marine ecosystems begun in 1979, a drastic increase in the bowbead Balaena mysticetus North Atlantic ＂stock＂ was observed from 2005 on, by a factor 30 and more： from 0.0002 per count between 1979 and 2003 （one individual, n=5430 cotmts） to 0.06 per count from 2005 to 2014 （34 individuals, n=6000 counts）; the most significant part of the increase occurred from 2007 on. Other large whale species （Mysticeti） showed a similar pattern, mainly blue Balaenoptera musculus, humpback Megaptera novaeangliae and fin whales Balaenoptera physalus. This large and abrupt increase cannot logically be due to population growth, nor to survival of a hidden ＂relic＂ population, nor to a changing geographical distribution within the European Arctic, taking into account the importance of the coverage during this study. Our interpretation is that individuals passed through the Northwest and/or Northeast Passages from the larger Pacific stock into the almost depleted North Atlantic populations coinciding with a period of very low ice coverage -- at the time the lowest ever recorded. In contrast, no clear evolution was detected neither for sperm whale Physeter macrocephalus nor for Minke whale Balaenoptera acusrostrata.展开更多
Find It What do boat captai ns try to do?W hale and dolphin watching is a popular thing to do off the coast of Hualien.*It is an amazing adventure!Imagine this:You are on a boat.Then a big whale jumps out of the water.
1 Move over Simone Biles,because grey whales might just be the next Olympic champions.This conclusion can be drawn from a new study that filmed these amazing animals doing underwater headstands(头倒立)and other moves....1 Move over Simone Biles,because grey whales might just be the next Olympic champions.This conclusion can be drawn from a new study that filmed these amazing animals doing underwater headstands(头倒立)and other moves.2 As part of a seven-year project,scientists used drones(无人驾驶飞机)to observe a group of 200 grey whales off the coasts of Oregon,Washington,northern California and southern Canada.The new study findings,published in Animal Behaviour,revealed that grey whales do headstands by pressing their mouths against the ocean floor while searching for something to eat.Scientists also noticed that when doing headstands,grey whales move like human synchronized swimmers.展开更多
Cetaceans include the largest animals ever to have lived onearth and are uniparous(producing a single calf at each birth)across the infraorder.However,instances of multiple fetuseshave been observed naturally among un...Cetaceans include the largest animals ever to have lived onearth and are uniparous(producing a single calf at each birth)across the infraorder.However,instances of multiple fetuseshave been observed naturally among uniparous mammals,including cetaceans.Despite this,there is no known documented case of twins in cetaceans successfully carried to termin the wild(Perrin and Donovan 1984),and if such casesexist,they would be diffcult to detect.展开更多
Neurodegeneration involves a wide range of neuropathological alterations affecting the integrity,physiology,and architecture of neural cells.Many studies have demonstrated neurodegeneration in different animals.In the...Neurodegeneration involves a wide range of neuropathological alterations affecting the integrity,physiology,and architecture of neural cells.Many studies have demonstrated neurodegeneration in different animals.In the case of Alzheimer's disease(AD),spontaneous animal models should display two neurohistopathological hallmarks:the deposition ofβ-amyloid and the arrangement of neurofibrillary tangles.However,no natural animal models that fulfill these conditions have been reported and most research into AD has been performed using transgenic rodents.Recent studies have also demonstrated that toothed whales-homeothermic,long-lived,top predatory marine mammals-show neuropathological signs of AD-like pathology.The neuropathological hallmarks in these cetaceans could help to better understand their endangered health as well as neurodegenerative diseases in humans.This systematic review analyzes all the literature published to date on this trending topic and the proposed causes for neurodegeneration in these iconic marine mammals are approached in the context of One Health/Planetary Health and translational medicine.展开更多
The Māori people are indigenous to Aotearoa New Zealand,and their language and culture are considered vital components of the nation’s cultural heritage.However,Te Reo Māori is regarded as a lowresource language ou...The Māori people are indigenous to Aotearoa New Zealand,and their language and culture are considered vital components of the nation’s cultural heritage.However,Te Reo Māori is regarded as a lowresource language outside of New Zealand,and its literary works usually rely on English as a pivot language for translation and communication.Therefore,in the process of promoting Māori literature as part of world literature by translating it into non-English languages,the accurate translation of cultural keywords is crucial to prevent dilemmas such as information loss and cultural misappropriation.In this article,we aim to explore effective translation strategies to enhance the international visibility and readership of Māori literature by analysing the rendition of Māori cultural keywords in the Chinese translation of“The Whale Rider”.展开更多
Energy storage power plants are critical in balancing power supply and demand.However,the scheduling of these plants faces significant challenges,including high network transmission costs and inefficient inter-device ...Energy storage power plants are critical in balancing power supply and demand.However,the scheduling of these plants faces significant challenges,including high network transmission costs and inefficient inter-device energy utilization.To tackle these challenges,this study proposes an optimal scheduling model for energy storage power plants based on edge computing and the improved whale optimization algorithm(IWOA).The proposed model designs an edge computing framework,transferring a large share of data processing and storage tasks to the network edge.This architecture effectively reduces transmission costs by minimizing data travel time.In addition,the model considers demand response strategies and builds an objective function based on the minimization of the sum of electricity purchase cost and operation cost.The IWOA enhances the optimization process by utilizing adaptive weight adjustments and an optimal neighborhood perturbation strategy,preventing the algorithm from converging to suboptimal solutions.Experimental results demonstrate that the proposed scheduling model maximizes the flexibility of the energy storage plant,facilitating efficient charging and discharging.It successfully achieves peak shaving and valley filling for both electrical and heat loads,promoting the effective utilization of renewable energy sources.The edge-computing framework significantly reduces transmission delays between energy devices.Furthermore,IWOA outperforms traditional algorithms in optimizing the objective function.展开更多
This study integrates the individual photovoltaic(PV)and thermoelectric generator(TEG)systems into a PV-TEG hybrid system to improve its overall power output by reutilizing the waste heat generated during PV power pro...This study integrates the individual photovoltaic(PV)and thermoelectric generator(TEG)systems into a PV-TEG hybrid system to improve its overall power output by reutilizing the waste heat generated during PV power production to enhance its operational relia-bility.However,stochastic environmental conditions often result in partial shading conditions and nonuniform thermal distribution across the PV-TEG modules,which negatively affect the output characteristics of the system,thus presenting a significant challenge to maintaining their optimal performance.To address these challenges,a novel fitness-distance-balance-based beluga whale optimization(FDBBWO)strategy has been devised for maximizing the power output of the PV-TEG hybrid system under dynamic operation scenar-ios.A broader spectrum of complex and authentic operational contexts has been considered in case studies to examine the effectiveness and feasibility of FDBBWO.For this,real-world datasets collected from different seasons in Hong Kong have been used to validate the practical viability of the proposed strategy.Simulation results reveal that the FDBBWO based maximum power point tracking technique outperforms its competing methods by achieving the highest energy output,with a remarkable increase of up to 134.25%with minimal power fluctuations.For instance,the energy obtained by FDBBWO is 47.45%and 58.34%higher than BWO and perturb and observe methods,respectively,in the winter season.展开更多
The uncertain nature of mapping user tasks to Virtual Machines(VMs) causes system failure or execution delay in Cloud Computing.To maximize cloud resource throughput and decrease user response time,load balancing is n...The uncertain nature of mapping user tasks to Virtual Machines(VMs) causes system failure or execution delay in Cloud Computing.To maximize cloud resource throughput and decrease user response time,load balancing is needed.Possible load balancing is needed to overcome user task execution delay and system failure.Most swarm intelligent dynamic load balancing solutions that used hybrid metaheuristic algorithms failed to balance exploitation and exploration.Most load balancing methods were insufficient to handle the growing uncertainty in job distribution to VMs.Thus,the Hybrid Spotted Hyena and Whale Optimization Algorithm-based Dynamic Load Balancing Mechanism(HSHWOA) partitions traffic among numerous VMs or servers to guarantee user chores are completed quickly.This load balancing approach improved performance by considering average network latency,dependability,and throughput.This hybridization of SHOA and WOA aims to improve the trade-off between exploration and exploitation,assign jobs to VMs with more solution diversity,and prevent the solution from reaching a local optimality.Pysim-based experimental verification and testing for the proposed HSHWOA showed a 12.38% improvement in minimized makespan,16.21% increase in mean throughput,and 14.84% increase in network stability compared to baseline load balancing strategies like Fractional Improved Whale Social Optimization Based VM Migration Strategy FIWSOA,HDWOA,and Binary Bird Swap.展开更多
The Sine and Wormhole Energy Whale Optimization Algorithm(SWEWOA)represents an advanced solution method for resolving Optimal Power Flow(OPF)problems in power systems equipped with Flexible AC Transmission System(FACT...The Sine and Wormhole Energy Whale Optimization Algorithm(SWEWOA)represents an advanced solution method for resolving Optimal Power Flow(OPF)problems in power systems equipped with Flexible AC Transmission System(FACTS)devices which include Thyristor-Controlled Series Compensator(TCSC),Thyristor-Controlled Phase Shifter(TCPS),and Static Var Compensator(SVC).SWEWOA expands Whale Optimization Algorithm(WOA)through the integration of sine and wormhole energy features thus improving exploration and exploitation capabilities for efficient convergence in complex non-linear OPF problems.A performance evaluation of SWEWOA takes place on the IEEE-30 bus test system through static and dynamic loading scenarios where it demonstrates better results than five contemporary algorithms:Adaptive Chaotic WOA(ACWOA),WOA,Chaotic WOA(CWOA),Sine Cosine Algorithm Differential Evolution(SCADE),and Hybrid Grey Wolf Optimization(HGWO).The research shows that SWEWOA delivers superior generation cost reduction than other algorithms by reaching a minimum of 0.9%better performance.SWEWOA demonstrates superior power loss performance by achieving(P_(loss,min))at the lowest level compared to all other tested algorithms which leads to better system energy efficiency.The dynamic loading performance of SWEWOA leads to a 4.38%reduction in gross costs which proves its capability to handle different operating conditions.The algorithm achieves top performance in Friedman Rank Test(FRT)assessments through multiple performance metrics which verifies its consistent reliability and strong stability during changing power demands.The repeated simulations show that SWEWOA generates mean costs(C_(gen,min))and mean power loss values(P_(loss,min))with small deviations which indicate its capability to maintain cost-effective solutions in each simulation run.SWEWOA demonstrates great potential as an advanced optimization solution for power system operations through the results presented in this study.展开更多
Reliable Cluster Head(CH)selectionbased routing protocols are necessary for increasing the packet transmission efficiency with optimal path discovery that never introduces degradation over the transmission reliability...Reliable Cluster Head(CH)selectionbased routing protocols are necessary for increasing the packet transmission efficiency with optimal path discovery that never introduces degradation over the transmission reliability.In this paper,Hybrid Golden Jackal,and Improved Whale Optimization Algorithm(HGJIWOA)is proposed as an effective and optimal routing protocol that guarantees efficient routing of data packets in the established between the CHs and the movable sink.This HGJIWOA included the phases of Dynamic Lens-Imaging Learning Strategy and Novel Update Rules for determining the reliable route essential for data packets broadcasting attained through fitness measure estimation-based CH selection.The process of CH selection achieved using Golden Jackal Optimization Algorithm(GJOA)completely depends on the factors of maintainability,consistency,trust,delay,and energy.The adopted GJOA algorithm play a dominant role in determining the optimal path of routing depending on the parameter of reduced delay and minimal distance.It further utilized Improved Whale Optimisation Algorithm(IWOA)for forwarding the data from chosen CHs to the BS via optimized route depending on the parameters of energy and distance.It also included a reliable route maintenance process that aids in deciding the selected route through which data need to be transmitted or re-routed.The simulation outcomes of the proposed HGJIWOA mechanism with different sensor nodes confirmed an improved mean throughput of 18.21%,sustained residual energy of 19.64%with minimized end-to-end delay of 21.82%,better than the competitive CH selection approaches.展开更多
Although four species of odontocete and four species of baleen whale have been recorded in Prydz Bay,their vocalizations have been rarely investigated.Underwater vocalizations were recorded during March 2017 in Prydz ...Although four species of odontocete and four species of baleen whale have been recorded in Prydz Bay,their vocalizations have been rarely investigated.Underwater vocalizations were recorded during March 2017 in Prydz Bay,Antarctica.Bio-duck sounds,downsweeps,inverted“u”shape signals,whistles,pulsed sounds,and broadband clicks were recorded.Bio-duck sounds and downsweeps were associated with Antarctic minke whales(Balaenoptera bonaerensis)based on visual observations.Similarities between inverted“u”shape signals,biphonic calls,and clicks with vocalizations previously described for killer whales(Orcinus orca)lead us believe the presence of Antarctic killer whales.According to sound structures,signal characteristics,and recording location,Antarctic type C killer whales were the most probable candidates to produce these detected calls.These represent the fi rst detection of inverted“u”shape signals in Antarctic waters,and the fi rst report of Antarctic killer whale in Prydz Bay based on passive acoustic monitoring.The co-existence of Antarctic minke and killer whales may imply that minke whales can detect diff erences between the sounds of mammal-eating and fi sh-eating killer whales.Our descriptions of these underwater vocalizations contribute to the limited body of information regarding the distribution and acoustic behavior of cetaceans in Prydz Bay.展开更多
Localization or positioning scheme in Wireless sensor networks (WSNs) is one of the most challenging andfundamental operations in various monitoring or tracking applications because the network deploys a large areaand...Localization or positioning scheme in Wireless sensor networks (WSNs) is one of the most challenging andfundamental operations in various monitoring or tracking applications because the network deploys a large areaand allocates the acquired location information to unknown devices. The metaheuristic approach is one of themost advantageous ways to deal with this challenging issue and overcome the disadvantages of the traditionalmethods that often suffer from computational time problems and small network deployment scale. This studyproposes an enhanced whale optimization algorithm that is an advanced metaheuristic algorithm based on thesiege mechanism (SWOA) for node localization inWSN. The objective function is modeled while communicatingon localized nodes, considering variables like delay, path loss, energy, and received signal strength. The localizationapproach also assigns the discovered location data to unidentified devices with the modeled objective functionby applying the SWOA algorithm. The experimental analysis is carried out to demonstrate the efficiency of thedesigned localization scheme in terms of various metrics, e.g., localization errors rate, converges rate, and executedtime. Compared experimental-result shows that theSWOA offers the applicability of the developed model forWSNto perform the localization scheme with excellent quality. Significantly, the error and convergence values achievedby the SWOA are less location error, faster in convergence and executed time than the others compared to at least areduced 1.5% to 4.7% error rate, and quicker by at least 4%and 2% in convergence and executed time, respectivelyfor the experimental scenarios.展开更多
Identifying home ranges—those areas traversed by individuals in their normal foraging,mating,and parenting activities—is an important aspect of cetacean study.Understanding these ranges facilitates identification of...Identifying home ranges—those areas traversed by individuals in their normal foraging,mating,and parenting activities—is an important aspect of cetacean study.Understanding these ranges facilitates identification of resource use and conservation.Fin and humpback whales occur in Antarctica during the austral summer,but information regarding their home ranges is limited.Using opportunistically collected whale sighting data from eight consecutive summer seasons spanning 2010–2017,we approximate the home ranges of humpback and fin whales around Drake Passage(DRA),West of Antarctic Peninsula(WAP),South Shetland Islands(SSI),an area northwest of the Weddell Sea(WED),and around the South Orkney Islands(SOI).Approximate home ranges are identified using Kernel Density Estimation(KDE).Most fin whales occurred north and northwest of the SOI,which suggests that waters near these islands support concentrations of this species.Most humpback whales were observed around the SSI,but unlike fin whales,their distributions were highly variable in other areas.KDE suggests spatial segregation in areas where both species exist such as SOI,SSI,and WPA.Partial redundancy analysis(pRDA)suggests that the distributions of these species are more affected by spatial variables(latitude,longitude)than by local scale variables such as sea surface temperature and depth.This study presents a visual approximation of the home ranges of fin and humpback whales,and identifies variation in the effects of space and environmental variables on the distributions of these whales at different spatial scales.展开更多
Since traditional whale optimization algorithms have slow convergence speed,low accuracy and are easy to fall into local optimal solutions,an improved whale optimization algorithm based on mirror selection(WOA-MS)is p...Since traditional whale optimization algorithms have slow convergence speed,low accuracy and are easy to fall into local optimal solutions,an improved whale optimization algorithm based on mirror selection(WOA-MS)is proposed. Specific improvements includes:(1)An adaptive nonlinear inertia weight based on Branin function was introduced to balance global search and local mining.(2) A mirror selection method is proposed to improve the individual quality and speed up the convergence. By optimizing several test functions and comparing the experimental results with other three algorithms,this study verifies that WOA-MS has an excellent optimization performance.展开更多
The current Whale Optimization Algorithm(WOA)has several drawbacks,such as slow convergence,low solution accuracy and easy to fall into the local optimal solution.To overcome these drawbacks,an improved Whale Optimiza...The current Whale Optimization Algorithm(WOA)has several drawbacks,such as slow convergence,low solution accuracy and easy to fall into the local optimal solution.To overcome these drawbacks,an improved Whale Optimization Algorithm(IWOA)is proposed in this study.IWOA can enhance the global search capability by two measures.First,the crossover and mutation operations in Differential Evolutionary algorithm(DE)are combined with the whale optimization algorithm.Second,the cloud adaptive inertia weight is introduced in the position update phase of WOA to divide the population into two subgroups,so as to balance the global search ability and local development ability.ANSYS and Matlab are used to establish the structure model.To demonstrate the application of the IWOA,truss structural optimizations on 52-bar plane truss and 25-bar space truss were performed,and the results were are compared with that obtained by other optimization algorithm.It is verified that,compared with WOA,the IWOA has higher efficiency,fast convergence speed,better solution accuracy and stability.So IWOA can be used in the optimization design of large truss structures.展开更多
Whale optimization algorithm(WOA)tends to fall into the local optimum and fails to converge quickly in solving complex problems.To address the shortcomings,an improved WOA(QGBWOA)is proposed in this work.First,quasi-o...Whale optimization algorithm(WOA)tends to fall into the local optimum and fails to converge quickly in solving complex problems.To address the shortcomings,an improved WOA(QGBWOA)is proposed in this work.First,quasi-opposition-based learning is introduced to enhance the ability of WOA to search for optimal solutions.Second,a Gaussian barebone mechanism is embedded to promote diversity and expand the scope of the solution space in WOA.To verify the advantages of QGBWOA,comparison experiments between QGBWOA and its comparison peers were carried out on CEC 2014 with dimensions 10,30,50,and 100 and on CEC 2020 test with dimension 30.Furthermore,the performance results were tested using Wilcoxon signed-rank(WS),Friedman test,and post hoc statistical tests for statistical analysis.Convergence accuracy and speed are remarkably improved,as shown by experimental results.Finally,feature selection and multi-threshold image segmentation applications are demonstrated to validate the ability of QGBWOA to solve complex real-world problems.QGBWOA proves its superiority over compared algorithms in feature selection and multi-threshold image segmentation by performing several evaluation metrics.展开更多
Due to the development of digital transformation,intelligent algorithms are getting more and more attention.The whale optimization algorithm(WOA)is one of swarm intelligence optimization algorithms and is widely used ...Due to the development of digital transformation,intelligent algorithms are getting more and more attention.The whale optimization algorithm(WOA)is one of swarm intelligence optimization algorithms and is widely used to solve practical engineering optimization problems.However,with the increased dimensions,higher requirements are put forward for algorithm performance.The double population whale optimization algorithm with distributed collaboration and reverse learning ability(DCRWOA)is proposed to solve the slow convergence speed and unstable search accuracy of the WOA algorithm in optimization problems.In the DCRWOA algorithm,the novel double population search strategy is constructed.Meanwhile,the reverse learning strategy is adopted in the population search process to help individuals quickly jump out of the non-ideal search area.Numerical experi-ments are carried out using standard test functions with different dimensions(10,50,100,200).The optimization case of shield construction parameters is also used to test the practical application performance of the proposed algo-rithm.The results show that the DCRWOA algorithm has higher optimization accuracy and stability,and the convergence speed is significantly improved.Therefore,the proposed DCRWOA algorithm provides a better method for solving practical optimization problems.展开更多
Whale optimization algorithm(WOA)is a new population-based meta-heuristic algorithm.WOA uses shrinking encircling mechanism,spiral rise,and random learning strategies to update whale’s positions.WOA has merit in term...Whale optimization algorithm(WOA)is a new population-based meta-heuristic algorithm.WOA uses shrinking encircling mechanism,spiral rise,and random learning strategies to update whale’s positions.WOA has merit in terms of simple calculation and high computational accuracy,but its convergence speed is slow and it is easy to fall into the local optimal solution.In order to overcome the shortcomings,this paper integrates adaptive neighborhood and hybrid mutation strategies into whale optimization algorithms,designs the average distance from itself to other whales as an adaptive neighborhood radius,and chooses to learn from the optimal solution in the neighborhood instead of random learning strategies.The hybrid mutation strategy is used to enhance the ability of algorithm to jump out of the local optimal solution.A new whale optimization algorithm(HMNWOA)is proposed.The proposed algorithm inherits the global search capability of the original algorithm,enhances the exploitation ability,improves the quality of the population,and thus improves the convergence speed of the algorithm.A feature selection algorithm based on binary HMNWOA is proposed.Twelve standard datasets from UCI repository test the validity of the proposed algorithm for feature selection.The experimental results show that HMNWOA is very competitive compared to the other six popular feature selection methods in improving the classification accuracy and reducing the number of features,and ensures that HMNWOA has strong search ability in the search feature space.展开更多
The unstructured growth of abnormal cells in the lung tissue creates tumor.The early detection of lung tumor helps the patients avoiding the death rate and gives better treatment.Various medical image modalities can h...The unstructured growth of abnormal cells in the lung tissue creates tumor.The early detection of lung tumor helps the patients avoiding the death rate and gives better treatment.Various medical image modalities can help the physicians in the diagnosis of disease.Many research works have been proposed for the early detection of lung tumor.High computation time and misidentification of tumor are the prevailing issues.In order to overcome these issues,this paper has proposed a hybrid classifier of Atrous Spatial Pyramid Pooling(ASPP)-Unet architecture withWhale Optimization Algorithm(ASPP-Unet-WOA).To get a fine tuning detection of tumor in the Computed Tomography(CT)of lung image,this model needs pre-processing using Gabor filter.Secondly,feature segmentation is done using Guaranteed Convergence Particle Swarm Optimization.Thirdly,feature selection is done using Binary Grasshopper Optimization Algorithm.This proposed(ASPPUnet-WOA)is implemented in the dataset of National Cancer Institute(NCI)Lung Cancer Database Consortium.Various performance metric measures are evaluated and compared to the existing classifiers.The accuracy of Deep Convolutional Neural Network(DCNN)is 93.45%,Convolutional Neural Network(CNN)is 91.67%,UNet obtains 95.75%and ASPP-UNet-WOA obtains 98.68%.compared to the other techniques.展开更多
文摘In the frame of our long-term study of cetacean abundance and distribution in polar marine ecosystems begun in 1979, a drastic increase in the bowbead Balaena mysticetus North Atlantic ＂stock＂ was observed from 2005 on, by a factor 30 and more： from 0.0002 per count between 1979 and 2003 （one individual, n=5430 cotmts） to 0.06 per count from 2005 to 2014 （34 individuals, n=6000 counts）; the most significant part of the increase occurred from 2007 on. Other large whale species （Mysticeti） showed a similar pattern, mainly blue Balaenoptera musculus, humpback Megaptera novaeangliae and fin whales Balaenoptera physalus. This large and abrupt increase cannot logically be due to population growth, nor to survival of a hidden ＂relic＂ population, nor to a changing geographical distribution within the European Arctic, taking into account the importance of the coverage during this study. Our interpretation is that individuals passed through the Northwest and/or Northeast Passages from the larger Pacific stock into the almost depleted North Atlantic populations coinciding with a period of very low ice coverage -- at the time the lowest ever recorded. In contrast, no clear evolution was detected neither for sperm whale Physeter macrocephalus nor for Minke whale Balaenoptera acusrostrata.
文摘Find It What do boat captai ns try to do?W hale and dolphin watching is a popular thing to do off the coast of Hualien.*It is an amazing adventure!Imagine this:You are on a boat.Then a big whale jumps out of the water.
文摘1 Move over Simone Biles,because grey whales might just be the next Olympic champions.This conclusion can be drawn from a new study that filmed these amazing animals doing underwater headstands(头倒立)and other moves.2 As part of a seven-year project,scientists used drones(无人驾驶飞机)to observe a group of 200 grey whales off the coasts of Oregon,Washington,northern California and southern Canada.The new study findings,published in Animal Behaviour,revealed that grey whales do headstands by pressing their mouths against the ocean floor while searching for something to eat.Scientists also noticed that when doing headstands,grey whales move like human synchronized swimmers.
文摘Cetaceans include the largest animals ever to have lived onearth and are uniparous(producing a single calf at each birth)across the infraorder.However,instances of multiple fetuseshave been observed naturally among uniparous mammals,including cetaceans.Despite this,there is no known documented case of twins in cetaceans successfully carried to termin the wild(Perrin and Donovan 1984),and if such casesexist,they would be diffcult to detect.
文摘Neurodegeneration involves a wide range of neuropathological alterations affecting the integrity,physiology,and architecture of neural cells.Many studies have demonstrated neurodegeneration in different animals.In the case of Alzheimer's disease(AD),spontaneous animal models should display two neurohistopathological hallmarks:the deposition ofβ-amyloid and the arrangement of neurofibrillary tangles.However,no natural animal models that fulfill these conditions have been reported and most research into AD has been performed using transgenic rodents.Recent studies have also demonstrated that toothed whales-homeothermic,long-lived,top predatory marine mammals-show neuropathological signs of AD-like pathology.The neuropathological hallmarks in these cetaceans could help to better understand their endangered health as well as neurodegenerative diseases in humans.This systematic review analyzes all the literature published to date on this trending topic and the proposed causes for neurodegeneration in these iconic marine mammals are approached in the context of One Health/Planetary Health and translational medicine.
基金supported by Victoria University of Wellington 2024 PhD Faculty Grant HSSE(Grant No.:FG-HSSE-12486).
文摘The Māori people are indigenous to Aotearoa New Zealand,and their language and culture are considered vital components of the nation’s cultural heritage.However,Te Reo Māori is regarded as a lowresource language outside of New Zealand,and its literary works usually rely on English as a pivot language for translation and communication.Therefore,in the process of promoting Māori literature as part of world literature by translating it into non-English languages,the accurate translation of cultural keywords is crucial to prevent dilemmas such as information loss and cultural misappropriation.In this article,we aim to explore effective translation strategies to enhance the international visibility and readership of Māori literature by analysing the rendition of Māori cultural keywords in the Chinese translation of“The Whale Rider”.
基金supported by the Changzhou Science and Technology Support Project(CE20235045)Open Subject of Jiangsu Province Key Laboratory of Power Transmission and Distribution(2021JSSPD12)+1 种基金Talent Projects of Jiangsu University of Technology(KYY20018)Postgraduate Research&Practice Innovation Program of Jiangsu Province(SJCX23_1633).
文摘Energy storage power plants are critical in balancing power supply and demand.However,the scheduling of these plants faces significant challenges,including high network transmission costs and inefficient inter-device energy utilization.To tackle these challenges,this study proposes an optimal scheduling model for energy storage power plants based on edge computing and the improved whale optimization algorithm(IWOA).The proposed model designs an edge computing framework,transferring a large share of data processing and storage tasks to the network edge.This architecture effectively reduces transmission costs by minimizing data travel time.In addition,the model considers demand response strategies and builds an objective function based on the minimization of the sum of electricity purchase cost and operation cost.The IWOA enhances the optimization process by utilizing adaptive weight adjustments and an optimal neighborhood perturbation strategy,preventing the algorithm from converging to suboptimal solutions.Experimental results demonstrate that the proposed scheduling model maximizes the flexibility of the energy storage plant,facilitating efficient charging and discharging.It successfully achieves peak shaving and valley filling for both electrical and heat loads,promoting the effective utilization of renewable energy sources.The edge-computing framework significantly reduces transmission delays between energy devices.Furthermore,IWOA outperforms traditional algorithms in optimizing the objective function.
基金supported by National Natural Science Foundation of China(62263014)Yunnan Provincial Basic Research Project(202401AT070344,202301AT070443).
文摘This study integrates the individual photovoltaic(PV)and thermoelectric generator(TEG)systems into a PV-TEG hybrid system to improve its overall power output by reutilizing the waste heat generated during PV power production to enhance its operational relia-bility.However,stochastic environmental conditions often result in partial shading conditions and nonuniform thermal distribution across the PV-TEG modules,which negatively affect the output characteristics of the system,thus presenting a significant challenge to maintaining their optimal performance.To address these challenges,a novel fitness-distance-balance-based beluga whale optimization(FDBBWO)strategy has been devised for maximizing the power output of the PV-TEG hybrid system under dynamic operation scenar-ios.A broader spectrum of complex and authentic operational contexts has been considered in case studies to examine the effectiveness and feasibility of FDBBWO.For this,real-world datasets collected from different seasons in Hong Kong have been used to validate the practical viability of the proposed strategy.Simulation results reveal that the FDBBWO based maximum power point tracking technique outperforms its competing methods by achieving the highest energy output,with a remarkable increase of up to 134.25%with minimal power fluctuations.For instance,the energy obtained by FDBBWO is 47.45%and 58.34%higher than BWO and perturb and observe methods,respectively,in the winter season.
文摘The uncertain nature of mapping user tasks to Virtual Machines(VMs) causes system failure or execution delay in Cloud Computing.To maximize cloud resource throughput and decrease user response time,load balancing is needed.Possible load balancing is needed to overcome user task execution delay and system failure.Most swarm intelligent dynamic load balancing solutions that used hybrid metaheuristic algorithms failed to balance exploitation and exploration.Most load balancing methods were insufficient to handle the growing uncertainty in job distribution to VMs.Thus,the Hybrid Spotted Hyena and Whale Optimization Algorithm-based Dynamic Load Balancing Mechanism(HSHWOA) partitions traffic among numerous VMs or servers to guarantee user chores are completed quickly.This load balancing approach improved performance by considering average network latency,dependability,and throughput.This hybridization of SHOA and WOA aims to improve the trade-off between exploration and exploitation,assign jobs to VMs with more solution diversity,and prevent the solution from reaching a local optimality.Pysim-based experimental verification and testing for the proposed HSHWOA showed a 12.38% improvement in minimized makespan,16.21% increase in mean throughput,and 14.84% increase in network stability compared to baseline load balancing strategies like Fractional Improved Whale Social Optimization Based VM Migration Strategy FIWSOA,HDWOA,and Binary Bird Swap.
文摘The Sine and Wormhole Energy Whale Optimization Algorithm(SWEWOA)represents an advanced solution method for resolving Optimal Power Flow(OPF)problems in power systems equipped with Flexible AC Transmission System(FACTS)devices which include Thyristor-Controlled Series Compensator(TCSC),Thyristor-Controlled Phase Shifter(TCPS),and Static Var Compensator(SVC).SWEWOA expands Whale Optimization Algorithm(WOA)through the integration of sine and wormhole energy features thus improving exploration and exploitation capabilities for efficient convergence in complex non-linear OPF problems.A performance evaluation of SWEWOA takes place on the IEEE-30 bus test system through static and dynamic loading scenarios where it demonstrates better results than five contemporary algorithms:Adaptive Chaotic WOA(ACWOA),WOA,Chaotic WOA(CWOA),Sine Cosine Algorithm Differential Evolution(SCADE),and Hybrid Grey Wolf Optimization(HGWO).The research shows that SWEWOA delivers superior generation cost reduction than other algorithms by reaching a minimum of 0.9%better performance.SWEWOA demonstrates superior power loss performance by achieving(P_(loss,min))at the lowest level compared to all other tested algorithms which leads to better system energy efficiency.The dynamic loading performance of SWEWOA leads to a 4.38%reduction in gross costs which proves its capability to handle different operating conditions.The algorithm achieves top performance in Friedman Rank Test(FRT)assessments through multiple performance metrics which verifies its consistent reliability and strong stability during changing power demands.The repeated simulations show that SWEWOA generates mean costs(C_(gen,min))and mean power loss values(P_(loss,min))with small deviations which indicate its capability to maintain cost-effective solutions in each simulation run.SWEWOA demonstrates great potential as an advanced optimization solution for power system operations through the results presented in this study.
文摘Reliable Cluster Head(CH)selectionbased routing protocols are necessary for increasing the packet transmission efficiency with optimal path discovery that never introduces degradation over the transmission reliability.In this paper,Hybrid Golden Jackal,and Improved Whale Optimization Algorithm(HGJIWOA)is proposed as an effective and optimal routing protocol that guarantees efficient routing of data packets in the established between the CHs and the movable sink.This HGJIWOA included the phases of Dynamic Lens-Imaging Learning Strategy and Novel Update Rules for determining the reliable route essential for data packets broadcasting attained through fitness measure estimation-based CH selection.The process of CH selection achieved using Golden Jackal Optimization Algorithm(GJOA)completely depends on the factors of maintainability,consistency,trust,delay,and energy.The adopted GJOA algorithm play a dominant role in determining the optimal path of routing depending on the parameter of reduced delay and minimal distance.It further utilized Improved Whale Optimisation Algorithm(IWOA)for forwarding the data from chosen CHs to the BS via optimized route depending on the parameters of energy and distance.It also included a reliable route maintenance process that aids in deciding the selected route through which data need to be transmitted or re-routed.The simulation outcomes of the proposed HGJIWOA mechanism with different sensor nodes confirmed an improved mean throughput of 18.21%,sustained residual energy of 19.64%with minimized end-to-end delay of 21.82%,better than the competitive CH selection approaches.
基金Supported by the National Natural Science Foundation of China(No.41906170)the Indian Ocean Ninety-east Ridge Ecosystem and Marine Environment Monitoring and Protection(No.DY135-E2-4)+1 种基金the Cooperation of Top Predators Observation in the Southern Ocean(No.QT4519003)the China-ASEAN Maritime Cooperation Fund。
文摘Although four species of odontocete and four species of baleen whale have been recorded in Prydz Bay,their vocalizations have been rarely investigated.Underwater vocalizations were recorded during March 2017 in Prydz Bay,Antarctica.Bio-duck sounds,downsweeps,inverted“u”shape signals,whistles,pulsed sounds,and broadband clicks were recorded.Bio-duck sounds and downsweeps were associated with Antarctic minke whales(Balaenoptera bonaerensis)based on visual observations.Similarities between inverted“u”shape signals,biphonic calls,and clicks with vocalizations previously described for killer whales(Orcinus orca)lead us believe the presence of Antarctic killer whales.According to sound structures,signal characteristics,and recording location,Antarctic type C killer whales were the most probable candidates to produce these detected calls.These represent the fi rst detection of inverted“u”shape signals in Antarctic waters,and the fi rst report of Antarctic killer whale in Prydz Bay based on passive acoustic monitoring.The co-existence of Antarctic minke and killer whales may imply that minke whales can detect diff erences between the sounds of mammal-eating and fi sh-eating killer whales.Our descriptions of these underwater vocalizations contribute to the limited body of information regarding the distribution and acoustic behavior of cetaceans in Prydz Bay.
基金the VNUHCM-University of Information Technology’s Scientific Research Support Fund.
文摘Localization or positioning scheme in Wireless sensor networks (WSNs) is one of the most challenging andfundamental operations in various monitoring or tracking applications because the network deploys a large areaand allocates the acquired location information to unknown devices. The metaheuristic approach is one of themost advantageous ways to deal with this challenging issue and overcome the disadvantages of the traditionalmethods that often suffer from computational time problems and small network deployment scale. This studyproposes an enhanced whale optimization algorithm that is an advanced metaheuristic algorithm based on thesiege mechanism (SWOA) for node localization inWSN. The objective function is modeled while communicatingon localized nodes, considering variables like delay, path loss, energy, and received signal strength. The localizationapproach also assigns the discovered location data to unidentified devices with the modeled objective functionby applying the SWOA algorithm. The experimental analysis is carried out to demonstrate the efficiency of thedesigned localization scheme in terms of various metrics, e.g., localization errors rate, converges rate, and executedtime. Compared experimental-result shows that theSWOA offers the applicability of the developed model forWSNto perform the localization scheme with excellent quality. Significantly, the error and convergence values achievedby the SWOA are less location error, faster in convergence and executed time than the others compared to at least areduced 1.5% to 4.7% error rate, and quicker by at least 4%and 2% in convergence and executed time, respectivelyfor the experimental scenarios.
基金This work was conducted with logistical and financial support of the Instituto Antártico Argentino.
文摘Identifying home ranges—those areas traversed by individuals in their normal foraging,mating,and parenting activities—is an important aspect of cetacean study.Understanding these ranges facilitates identification of resource use and conservation.Fin and humpback whales occur in Antarctica during the austral summer,but information regarding their home ranges is limited.Using opportunistically collected whale sighting data from eight consecutive summer seasons spanning 2010–2017,we approximate the home ranges of humpback and fin whales around Drake Passage(DRA),West of Antarctic Peninsula(WAP),South Shetland Islands(SSI),an area northwest of the Weddell Sea(WED),and around the South Orkney Islands(SOI).Approximate home ranges are identified using Kernel Density Estimation(KDE).Most fin whales occurred north and northwest of the SOI,which suggests that waters near these islands support concentrations of this species.Most humpback whales were observed around the SSI,but unlike fin whales,their distributions were highly variable in other areas.KDE suggests spatial segregation in areas where both species exist such as SOI,SSI,and WPA.Partial redundancy analysis(pRDA)suggests that the distributions of these species are more affected by spatial variables(latitude,longitude)than by local scale variables such as sea surface temperature and depth.This study presents a visual approximation of the home ranges of fin and humpback whales,and identifies variation in the effects of space and environmental variables on the distributions of these whales at different spatial scales.
基金supported by the Natural Science Foundation of Jiangsu Province (No. BK20151479)the Open Foundation of Graduate Innovation Base in Nanjing University of Aeronautics and Astronautics(No. kfjj20190736)
文摘Since traditional whale optimization algorithms have slow convergence speed,low accuracy and are easy to fall into local optimal solutions,an improved whale optimization algorithm based on mirror selection(WOA-MS)is proposed. Specific improvements includes:(1)An adaptive nonlinear inertia weight based on Branin function was introduced to balance global search and local mining.(2) A mirror selection method is proposed to improve the individual quality and speed up the convergence. By optimizing several test functions and comparing the experimental results with other three algorithms,this study verifies that WOA-MS has an excellent optimization performance.
基金This work was supported by the National Natural Science Foundation of China(Grant No.11872157 and 11532013)the graduate innovative research project of Heilongjiang University of Science and Technology(Grant No.YJSCX2020-214HKD).
文摘The current Whale Optimization Algorithm(WOA)has several drawbacks,such as slow convergence,low solution accuracy and easy to fall into the local optimal solution.To overcome these drawbacks,an improved Whale Optimization Algorithm(IWOA)is proposed in this study.IWOA can enhance the global search capability by two measures.First,the crossover and mutation operations in Differential Evolutionary algorithm(DE)are combined with the whale optimization algorithm.Second,the cloud adaptive inertia weight is introduced in the position update phase of WOA to divide the population into two subgroups,so as to balance the global search ability and local development ability.ANSYS and Matlab are used to establish the structure model.To demonstrate the application of the IWOA,truss structural optimizations on 52-bar plane truss and 25-bar space truss were performed,and the results were are compared with that obtained by other optimization algorithm.It is verified that,compared with WOA,the IWOA has higher efficiency,fast convergence speed,better solution accuracy and stability.So IWOA can be used in the optimization design of large truss structures.
基金the Zhejiang Provincial Natural Science Foundation of China(no.LZ21F020001)the Basic Scientific Research Program of Wenzhou(no.S20220018).
文摘Whale optimization algorithm(WOA)tends to fall into the local optimum and fails to converge quickly in solving complex problems.To address the shortcomings,an improved WOA(QGBWOA)is proposed in this work.First,quasi-opposition-based learning is introduced to enhance the ability of WOA to search for optimal solutions.Second,a Gaussian barebone mechanism is embedded to promote diversity and expand the scope of the solution space in WOA.To verify the advantages of QGBWOA,comparison experiments between QGBWOA and its comparison peers were carried out on CEC 2014 with dimensions 10,30,50,and 100 and on CEC 2020 test with dimension 30.Furthermore,the performance results were tested using Wilcoxon signed-rank(WS),Friedman test,and post hoc statistical tests for statistical analysis.Convergence accuracy and speed are remarkably improved,as shown by experimental results.Finally,feature selection and multi-threshold image segmentation applications are demonstrated to validate the ability of QGBWOA to solve complex real-world problems.QGBWOA proves its superiority over compared algorithms in feature selection and multi-threshold image segmentation by performing several evaluation metrics.
基金supported by Anhui Polytechnic University Introduced Talents Research Fund(No.2021YQQ064)Anhui Polytechnic University ScientificResearch Project(No.Xjky2022168).
文摘Due to the development of digital transformation,intelligent algorithms are getting more and more attention.The whale optimization algorithm(WOA)is one of swarm intelligence optimization algorithms and is widely used to solve practical engineering optimization problems.However,with the increased dimensions,higher requirements are put forward for algorithm performance.The double population whale optimization algorithm with distributed collaboration and reverse learning ability(DCRWOA)is proposed to solve the slow convergence speed and unstable search accuracy of the WOA algorithm in optimization problems.In the DCRWOA algorithm,the novel double population search strategy is constructed.Meanwhile,the reverse learning strategy is adopted in the population search process to help individuals quickly jump out of the non-ideal search area.Numerical experi-ments are carried out using standard test functions with different dimensions(10,50,100,200).The optimization case of shield construction parameters is also used to test the practical application performance of the proposed algo-rithm.The results show that the DCRWOA algorithm has higher optimization accuracy and stability,and the convergence speed is significantly improved.Therefore,the proposed DCRWOA algorithm provides a better method for solving practical optimization problems.
基金This work was supported by the National Natural Science Foundation of China(Grant No.2017YFC0403605 and No.11601419).
文摘Whale optimization algorithm(WOA)is a new population-based meta-heuristic algorithm.WOA uses shrinking encircling mechanism,spiral rise,and random learning strategies to update whale’s positions.WOA has merit in terms of simple calculation and high computational accuracy,but its convergence speed is slow and it is easy to fall into the local optimal solution.In order to overcome the shortcomings,this paper integrates adaptive neighborhood and hybrid mutation strategies into whale optimization algorithms,designs the average distance from itself to other whales as an adaptive neighborhood radius,and chooses to learn from the optimal solution in the neighborhood instead of random learning strategies.The hybrid mutation strategy is used to enhance the ability of algorithm to jump out of the local optimal solution.A new whale optimization algorithm(HMNWOA)is proposed.The proposed algorithm inherits the global search capability of the original algorithm,enhances the exploitation ability,improves the quality of the population,and thus improves the convergence speed of the algorithm.A feature selection algorithm based on binary HMNWOA is proposed.Twelve standard datasets from UCI repository test the validity of the proposed algorithm for feature selection.The experimental results show that HMNWOA is very competitive compared to the other six popular feature selection methods in improving the classification accuracy and reducing the number of features,and ensures that HMNWOA has strong search ability in the search feature space.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work under Grant Number(GRP/303/42)Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2022R203),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘The unstructured growth of abnormal cells in the lung tissue creates tumor.The early detection of lung tumor helps the patients avoiding the death rate and gives better treatment.Various medical image modalities can help the physicians in the diagnosis of disease.Many research works have been proposed for the early detection of lung tumor.High computation time and misidentification of tumor are the prevailing issues.In order to overcome these issues,this paper has proposed a hybrid classifier of Atrous Spatial Pyramid Pooling(ASPP)-Unet architecture withWhale Optimization Algorithm(ASPP-Unet-WOA).To get a fine tuning detection of tumor in the Computed Tomography(CT)of lung image,this model needs pre-processing using Gabor filter.Secondly,feature segmentation is done using Guaranteed Convergence Particle Swarm Optimization.Thirdly,feature selection is done using Binary Grasshopper Optimization Algorithm.This proposed(ASPPUnet-WOA)is implemented in the dataset of National Cancer Institute(NCI)Lung Cancer Database Consortium.Various performance metric measures are evaluated and compared to the existing classifiers.The accuracy of Deep Convolutional Neural Network(DCNN)is 93.45%,Convolutional Neural Network(CNN)is 91.67%,UNet obtains 95.75%and ASPP-UNet-WOA obtains 98.68%.compared to the other techniques.
