This paper explores the archaeology of whaling in Arctic prehistory,focusing on the emergence and development of whaling as a central component of cultural ecology among prehistoric Inuit and related societies.Drawing...This paper explores the archaeology of whaling in Arctic prehistory,focusing on the emergence and development of whaling as a central component of cultural ecology among prehistoric Inuit and related societies.Drawing on archaeological evidence from key sites across Alaska,the Chukchi Peninsula,and the Bering Strait region,the study examines how whaling technologies and practices evolved alongside climatic fluctuations,ecological shifts,and social transformations.Integrating ethnographic insights and paleoclimatic data,the study argues that Inuit engagement with whales was not only a subsistence strategy but a long-term,historically contingent relationship that shaped and was shaped by broader cultural systems.展开更多
This paper, for the goal of revealing the mechanism of compromise and change in coordination, will focus on US-Japan conflict over commercial whaling. The regime like the international whaling regulation, where countr...This paper, for the goal of revealing the mechanism of compromise and change in coordination, will focus on US-Japan conflict over commercial whaling. The regime like the international whaling regulation, where countries agree in general but disagree on coordination methods, is known as the Battle of the Sexes in game theory. It has been believed that in regimes presented as the Battle of the Sexes （BoS） situation, once the countries could somehow coordinate their interests and reach an agreement on the specific method of coordination, neither country would have the incentive to withdraw from that agreement. This case study, however, shows that this belief is not always true. From the analysis of this study, it will be concluded that coordination methods change over time even in regimes where the countries agree in general and disagree on coordination methods. In this case, ＂power,＂ ＂institution＂ and ＂consensus＂ are pointed out as incentives to make the two countries accept a specific coordination method.展开更多
This paper is to present a framework to analyse international relations regarding protection and exploitation of an endangered species. The question of how to balance conservation and consumption in order to maintain ...This paper is to present a framework to analyse international relations regarding protection and exploitation of an endangered species. The question of how to balance conservation and consumption in order to maintain the sustainability of resources and nature is not only the central challenge of conservation ecology, but also an international political and economic issue that frequently leads to confrontation between countries. In relation to whales, for example, Japan has long been subjected to criticism by anti-whaling countries such as the United States and Australia, and has faced off against them on the international stage. And, more recently, similar confrontations have begun to appear in relation to tuna and eel. It has been highlighted in recent years that Pacific Bluefin Tuna are becoming endangered, and there is considerable national and international concern with regard to their resource management. This paper first obtains an implication about the course of events that led to the fishing ban. The implication is applied to the case of Pacific Bluefin Tuna. Pacific Bluefin Tuna and the whaling issue reveals points of commonality. The conclusion is that history of the whaling issue implies that Japan will lose the support not only of countries opposed to fishing but also of neutral countries, if Tokyo continues to adopt policies which make light of resource conservation. Even a total ban on the fishing of Pacific Bluefin Tuna may result. This implication from the whaling issue is potentially helpful to predict the development of international relations and conservation regarding other endangered species.展开更多
The Whaling in the Antarctic Case (Australia v. Japan: New Zealand intervening) decided by the International Court of Justice (hereinafter "ICJ" or "the Court") on 31 March 2014 dealt with the inte...The Whaling in the Antarctic Case (Australia v. Japan: New Zealand intervening) decided by the International Court of Justice (hereinafter "ICJ" or "the Court") on 31 March 2014 dealt with the interpretation of specific provisions of the 1946 International Convention for the Regulation of Whaling (ICRW), in particular Article VIII.1, and its complementary instruments, i.e., the Schedule and the Annexes of the International Whaling Commission Scientific Committee. The decision of the Court was a remarkable good one. However, its rigorous reasoning focused almost exclusively on the required purpose of "scientific research" of the JARPA II Programme1 permits as set out in the ICRW, approaching the convention as an autonomous self-contained regime which leaves aside other additional grounds. Nonetheless, it would be beneficial for further jurisdictional developments to strengthen the scope of the ICWR system with the applicable provisions of the United Nations Convention on the Law of the Sea (UNCLOS) and other treaties and institutions impinging on whales and whaling, e.g., CITES, Bonn Convention, Antarctic Treaty System, among others. The query remains concerning the unexplored sources of international law ruling Antarctic spaces and species which are absent in the judgment of the Court but may allow an evolutive interpretation of the ICRW.展开更多
Sound is considered an important aspect of an ecosystem and acoustic methods have emerged as effective tools for ecosystems research.Xincun Lagoon,Hainan Island,is an important ecosystem characterized by dense seagras...Sound is considered an important aspect of an ecosystem and acoustic methods have emerged as effective tools for ecosystems research.Xincun Lagoon,Hainan Island,is an important ecosystem characterized by dense seagrass,which has been declining due to increased human activities,raising great concerns.Previous studies have identified various threats to seagrass,including heavy metal pollution,poor quality water,and so on.In this study,we investigate sources and levels of noise in seagrass beds and attempt to point out potential threats from noise pollution.A line array of six hydrophones was deployed over a period of seven days,from January 15 to January 21,2024.The recordings captured various sounds from marine life,human activities,and natural processes.Biological sounds,such as fish sounds and whale calls,were the most prevalent.Low-frequency noise from wind and tide were often recorded.Xincun Bay hosts more than 1500 fishing vessels;however,due to bad weather conditions that kept most vessels docked during the recording period,only one segment of boat noise was recorded;it lasted for 7 minutes,exhibiting strong energy over a broad frequency band.This event underscores the necessity of long-term monitoring of noise to identify and evaluate not only boat noise but other noise sources,especially ones that are intermittent but strong,that were not encountered during the limited period of observation on which this report is based.展开更多
Existing feature selection methods for intrusion detection systems in the Industrial Internet of Things often suffer from local optimality and high computational complexity.These challenges hinder traditional IDS from...Existing feature selection methods for intrusion detection systems in the Industrial Internet of Things often suffer from local optimality and high computational complexity.These challenges hinder traditional IDS from effectively extracting features while maintaining detection accuracy.This paper proposes an industrial Internet ofThings intrusion detection feature selection algorithm based on an improved whale optimization algorithm(GSLDWOA).The aim is to address the problems that feature selection algorithms under high-dimensional data are prone to,such as local optimality,long detection time,and reduced accuracy.First,the initial population’s diversity is increased using the Gaussian Mutation mechanism.Then,Non-linear Shrinking Factor balances global exploration and local development,avoiding premature convergence.Lastly,Variable-step Levy Flight operator and Dynamic Differential Evolution strategy are introduced to improve the algorithm’s search efficiency and convergence accuracy in highdimensional feature space.Experiments on the NSL-KDD and WUSTL-IIoT-2021 datasets demonstrate that the feature subset selected by GSLDWOA significantly improves detection performance.Compared to the traditional WOA algorithm,the detection rate and F1-score increased by 3.68%and 4.12%.On the WUSTL-IIoT-2021 dataset,accuracy,recall,and F1-score all exceed 99.9%.展开更多
Amidst the growing global emphasis on nuclear safety,the integrity of nuclear reactor systems has garnered attention in the aftermath of consequential events.Moreover,the rapid development of artificial intelligence t...Amidst the growing global emphasis on nuclear safety,the integrity of nuclear reactor systems has garnered attention in the aftermath of consequential events.Moreover,the rapid development of artificial intelligence technology has provided immense opportunities to enhance the safety and economy of nuclear energy.However,data-driven deep learning techniques often lack interpretability,which hinders their applicability in the nuclear energy sector.To address this problem,this study proposes a hybrid data-driven and knowledge-driven artificial intelligence model based on physics-informed neural networks to accurately compute the neutron flux distribution inside a nuclear reactor core.Innovative techniques,such as regional decomposition,intelligent k_(eff)(effective multiplication factor)search,and k_(eff)inversion,have been introduced for the calculation.Furthermore,hyperparameters of the model are automatically optimized using a whale optimization algorithm.A series of computational examples are used to validate the proposed model,demonstrating its applicability,generality,and high accuracy in calculating the neutron flux within the nuclear reactor.The model offers a dependable strategy for computing the neutron flux distribution in nuclear reactors for advanced simulation techniques in the future,including reactor digital twinning.This approach is data-light,requires little to no training data,and still delivers remarkably precise output data.展开更多
国际捕鲸委员会成立于第二次世界大战之后,最初的宗旨是有效利用鲸鱼资源。但是随着鲸鱼种类的濒临灭绝,1986年,该委员会做出决议,禁止对这一海洋哺乳动物进行商业性捕猎。随着执行保护措施后鲸鱼数量的增加,捕杀与保护之间的矛盾有所...国际捕鲸委员会成立于第二次世界大战之后,最初的宗旨是有效利用鲸鱼资源。但是随着鲸鱼种类的濒临灭绝,1986年,该委员会做出决议,禁止对这一海洋哺乳动物进行商业性捕猎。随着执行保护措施后鲸鱼数量的增加,捕杀与保护之间的矛盾有所激化。能否在商业利益和动物保护两者中间找到一个平衡点呢?To kill,or not to kill,that is a question.展开更多
Load forecasting is of great significance to the development of new power systems.With the advancement of smart grids,the integration and distribution of distributed renewable energy sources and power electronics devi...Load forecasting is of great significance to the development of new power systems.With the advancement of smart grids,the integration and distribution of distributed renewable energy sources and power electronics devices have made power load data increasingly complex and volatile.This places higher demands on the prediction and analysis of power loads.In order to improve the prediction accuracy of short-term power load,a CNN-BiLSTMTPA short-term power prediction model based on the Improved Whale Optimization Algorithm(IWOA)with mixed strategies was proposed.Firstly,the model combined the Convolutional Neural Network(CNN)with the Bidirectional Long Short-Term Memory Network(BiLSTM)to fully extract the spatio-temporal characteristics of the load data itself.Then,the Temporal Pattern Attention(TPA)mechanism was introduced into the CNN-BiLSTM model to automatically assign corresponding weights to the hidden states of the BiLSTM.This allowed the model to differentiate the importance of load sequences at different time intervals.At the same time,in order to solve the problem of the difficulties of selecting the parameters of the temporal model,and the poor global search ability of the whale algorithm,which is easy to fall into the local optimization,the whale algorithm(IWOA)was optimized by using the hybrid strategy of Tent chaos mapping and Levy flight strategy,so as to better search the parameters of the model.In this experiment,the real load data of a region in Zhejiang was taken as an example to analyze,and the prediction accuracy(R2)of the proposed method reached 98.83%.Compared with the prediction models such as BP,WOA-CNN-BiLSTM,SSA-CNN-BiLSTM,CNN-BiGRU-Attention,etc.,the experimental results showed that the model proposed in this study has a higher prediction accuracy.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
China Launches“Blue Whale”-World’s First High-speed Uncrewed Submersible.The"Blue Whale,"a cutting-edge high-speed submersible unmanned surface vessel,was launched on April 28 in Zhuhai,south China's ...China Launches“Blue Whale”-World’s First High-speed Uncrewed Submersible.The"Blue Whale,"a cutting-edge high-speed submersible unmanned surface vessel,was launched on April 28 in Zhuhai,south China's Guangdong Province.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
Recent studies employing deep learning to solve the traveling salesman problem(TSP)have mainly focused on learning construction heuristics.Such methods can improve TSP solutions,but still depend on additional programs...Recent studies employing deep learning to solve the traveling salesman problem(TSP)have mainly focused on learning construction heuristics.Such methods can improve TSP solutions,but still depend on additional programs.However,methods that focus on learning improvement heuristics to iteratively refine solutions remain insufficient.Traditional improvement heuristics are guided by a manually designed search strategy and may only achieve limited improvements.This paper proposes a novel framework for learning improvement heuristics,which automatically discovers better improvement policies for heuristics to iteratively solve the TSP.Our framework first designs a new architecture based on a transformer model to make the policy network parameterized,which introduces an action-dropout layer to prevent action selection from overfitting.It then proposes a deep reinforcement learning approach integrating a simulated annealing mechanism(named RL-SA)to learn the pairwise selected policy,aiming to improve the 2-opt algorithm's performance.The RL-SA leverages the whale optimization algorithm to generate initial solutions for better sampling efficiency and uses the Gaussian perturbation strategy to tackle the sparse reward problem of reinforcement learning.The experiment results show that the proposed approach is significantly superior to the state-of-the-art learning-based methods,and further reduces the gap between learning-based methods and highly optimized solvers in the benchmark datasets.Moreover,our pre-trained model M can be applied to guide the SA algorithm(named M-SA(ours)),which performs better than existing deep models in small-,medium-,and large-scale TSPLIB datasets.Additionally,the M-SA(ours)achieves excellent generalization performance in a real-world dataset on global liner shipping routes,with the optimization percentages in distance reduction ranging from3.52%to 17.99%.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
Diagnosing gastrointestinal tract diseases is a critical task requiring accurate and efficient methodologies.While deep learning models have significantly advanced medical image analysis,challenges such as imbalanced ...Diagnosing gastrointestinal tract diseases is a critical task requiring accurate and efficient methodologies.While deep learning models have significantly advanced medical image analysis,challenges such as imbalanced datasets and redundant features persist.This study proposes a novel framework that customizes two deep learning models,NasNetMobile and ResNet50,by incorporating bottleneck architectures,named as NasNeck and ResNeck,to enhance feature extraction.The feature vectors are fused into a combined vector,which is further optimized using an improved Whale Optimization Algorithm to minimize redundancy and improve discriminative power.The optimized feature vector is then classified using artificial neural network classifiers,effectively addressing the limitations of traditional methods.Data augmentation techniques are employed to tackle class imbalance,improving model learning and generalization.The proposed framework was evaluated on two publicly available datasets:Hyper-Kvasir and Kvasir v2.The Hyper-Kvasir dataset,comprising 23 gastrointestinal disease classes,yielded an impressive 96.0%accuracy.On the Kvasir v2 dataset,which contains 8 distinct classes,the framework achieved a remarkable 98.9%accuracy,further demonstrating its robustness and superior classification performance across different gastrointestinal datasets.The results demonstrate the effectiveness of customizing deep models with bottleneck architectures,feature fusion,and optimization techniques in enhancing classification accuracy while reducing computational complexity.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
文摘This paper explores the archaeology of whaling in Arctic prehistory,focusing on the emergence and development of whaling as a central component of cultural ecology among prehistoric Inuit and related societies.Drawing on archaeological evidence from key sites across Alaska,the Chukchi Peninsula,and the Bering Strait region,the study examines how whaling technologies and practices evolved alongside climatic fluctuations,ecological shifts,and social transformations.Integrating ethnographic insights and paleoclimatic data,the study argues that Inuit engagement with whales was not only a subsistence strategy but a long-term,historically contingent relationship that shaped and was shaped by broader cultural systems.
文摘This paper, for the goal of revealing the mechanism of compromise and change in coordination, will focus on US-Japan conflict over commercial whaling. The regime like the international whaling regulation, where countries agree in general but disagree on coordination methods, is known as the Battle of the Sexes in game theory. It has been believed that in regimes presented as the Battle of the Sexes （BoS） situation, once the countries could somehow coordinate their interests and reach an agreement on the specific method of coordination, neither country would have the incentive to withdraw from that agreement. This case study, however, shows that this belief is not always true. From the analysis of this study, it will be concluded that coordination methods change over time even in regimes where the countries agree in general and disagree on coordination methods. In this case, ＂power,＂ ＂institution＂ and ＂consensus＂ are pointed out as incentives to make the two countries accept a specific coordination method.
文摘This paper is to present a framework to analyse international relations regarding protection and exploitation of an endangered species. The question of how to balance conservation and consumption in order to maintain the sustainability of resources and nature is not only the central challenge of conservation ecology, but also an international political and economic issue that frequently leads to confrontation between countries. In relation to whales, for example, Japan has long been subjected to criticism by anti-whaling countries such as the United States and Australia, and has faced off against them on the international stage. And, more recently, similar confrontations have begun to appear in relation to tuna and eel. It has been highlighted in recent years that Pacific Bluefin Tuna are becoming endangered, and there is considerable national and international concern with regard to their resource management. This paper first obtains an implication about the course of events that led to the fishing ban. The implication is applied to the case of Pacific Bluefin Tuna. Pacific Bluefin Tuna and the whaling issue reveals points of commonality. The conclusion is that history of the whaling issue implies that Japan will lose the support not only of countries opposed to fishing but also of neutral countries, if Tokyo continues to adopt policies which make light of resource conservation. Even a total ban on the fishing of Pacific Bluefin Tuna may result. This implication from the whaling issue is potentially helpful to predict the development of international relations and conservation regarding other endangered species.
文摘The Whaling in the Antarctic Case (Australia v. Japan: New Zealand intervening) decided by the International Court of Justice (hereinafter "ICJ" or "the Court") on 31 March 2014 dealt with the interpretation of specific provisions of the 1946 International Convention for the Regulation of Whaling (ICRW), in particular Article VIII.1, and its complementary instruments, i.e., the Schedule and the Annexes of the International Whaling Commission Scientific Committee. The decision of the Court was a remarkable good one. However, its rigorous reasoning focused almost exclusively on the required purpose of "scientific research" of the JARPA II Programme1 permits as set out in the ICRW, approaching the convention as an autonomous self-contained regime which leaves aside other additional grounds. Nonetheless, it would be beneficial for further jurisdictional developments to strengthen the scope of the ICWR system with the applicable provisions of the United Nations Convention on the Law of the Sea (UNCLOS) and other treaties and institutions impinging on whales and whaling, e.g., CITES, Bonn Convention, Antarctic Treaty System, among others. The query remains concerning the unexplored sources of international law ruling Antarctic spaces and species which are absent in the judgment of the Court but may allow an evolutive interpretation of the ICRW.
基金supported financially by the Director General’s Scientific Research Fund of Guangzhou Marine Geological Survey(Grant Number:2023GMGSJZJJ00029).
文摘Sound is considered an important aspect of an ecosystem and acoustic methods have emerged as effective tools for ecosystems research.Xincun Lagoon,Hainan Island,is an important ecosystem characterized by dense seagrass,which has been declining due to increased human activities,raising great concerns.Previous studies have identified various threats to seagrass,including heavy metal pollution,poor quality water,and so on.In this study,we investigate sources and levels of noise in seagrass beds and attempt to point out potential threats from noise pollution.A line array of six hydrophones was deployed over a period of seven days,from January 15 to January 21,2024.The recordings captured various sounds from marine life,human activities,and natural processes.Biological sounds,such as fish sounds and whale calls,were the most prevalent.Low-frequency noise from wind and tide were often recorded.Xincun Bay hosts more than 1500 fishing vessels;however,due to bad weather conditions that kept most vessels docked during the recording period,only one segment of boat noise was recorded;it lasted for 7 minutes,exhibiting strong energy over a broad frequency band.This event underscores the necessity of long-term monitoring of noise to identify and evaluate not only boat noise but other noise sources,especially ones that are intermittent but strong,that were not encountered during the limited period of observation on which this report is based.
基金supported by the Major Science and Technology Programs in Henan Province(No.241100210100)Henan Provincial Science and Technology Research Project(No.252102211085,No.252102211105)+3 种基金Endogenous Security Cloud Network Convergence R&D Center(No.602431011PQ1)The Special Project for Research and Development in Key Areas of Guangdong Province(No.2021ZDZX1098)The Stabilization Support Program of Science,Technology and Innovation Commission of Shenzhen Municipality(No.20231128083944001)The Key scientific research projects of Henan higher education institutions(No.24A520042).
文摘Existing feature selection methods for intrusion detection systems in the Industrial Internet of Things often suffer from local optimality and high computational complexity.These challenges hinder traditional IDS from effectively extracting features while maintaining detection accuracy.This paper proposes an industrial Internet ofThings intrusion detection feature selection algorithm based on an improved whale optimization algorithm(GSLDWOA).The aim is to address the problems that feature selection algorithms under high-dimensional data are prone to,such as local optimality,long detection time,and reduced accuracy.First,the initial population’s diversity is increased using the Gaussian Mutation mechanism.Then,Non-linear Shrinking Factor balances global exploration and local development,avoiding premature convergence.Lastly,Variable-step Levy Flight operator and Dynamic Differential Evolution strategy are introduced to improve the algorithm’s search efficiency and convergence accuracy in highdimensional feature space.Experiments on the NSL-KDD and WUSTL-IIoT-2021 datasets demonstrate that the feature subset selected by GSLDWOA significantly improves detection performance.Compared to the traditional WOA algorithm,the detection rate and F1-score increased by 3.68%and 4.12%.On the WUSTL-IIoT-2021 dataset,accuracy,recall,and F1-score all exceed 99.9%.
文摘Amidst the growing global emphasis on nuclear safety,the integrity of nuclear reactor systems has garnered attention in the aftermath of consequential events.Moreover,the rapid development of artificial intelligence technology has provided immense opportunities to enhance the safety and economy of nuclear energy.However,data-driven deep learning techniques often lack interpretability,which hinders their applicability in the nuclear energy sector.To address this problem,this study proposes a hybrid data-driven and knowledge-driven artificial intelligence model based on physics-informed neural networks to accurately compute the neutron flux distribution inside a nuclear reactor core.Innovative techniques,such as regional decomposition,intelligent k_(eff)(effective multiplication factor)search,and k_(eff)inversion,have been introduced for the calculation.Furthermore,hyperparameters of the model are automatically optimized using a whale optimization algorithm.A series of computational examples are used to validate the proposed model,demonstrating its applicability,generality,and high accuracy in calculating the neutron flux within the nuclear reactor.The model offers a dependable strategy for computing the neutron flux distribution in nuclear reactors for advanced simulation techniques in the future,including reactor digital twinning.This approach is data-light,requires little to no training data,and still delivers remarkably precise output data.
文摘国际捕鲸委员会成立于第二次世界大战之后,最初的宗旨是有效利用鲸鱼资源。但是随着鲸鱼种类的濒临灭绝,1986年,该委员会做出决议,禁止对这一海洋哺乳动物进行商业性捕猎。随着执行保护措施后鲸鱼数量的增加,捕杀与保护之间的矛盾有所激化。能否在商业利益和动物保护两者中间找到一个平衡点呢?To kill,or not to kill,that is a question.
文摘Load forecasting is of great significance to the development of new power systems.With the advancement of smart grids,the integration and distribution of distributed renewable energy sources and power electronics devices have made power load data increasingly complex and volatile.This places higher demands on the prediction and analysis of power loads.In order to improve the prediction accuracy of short-term power load,a CNN-BiLSTMTPA short-term power prediction model based on the Improved Whale Optimization Algorithm(IWOA)with mixed strategies was proposed.Firstly,the model combined the Convolutional Neural Network(CNN)with the Bidirectional Long Short-Term Memory Network(BiLSTM)to fully extract the spatio-temporal characteristics of the load data itself.Then,the Temporal Pattern Attention(TPA)mechanism was introduced into the CNN-BiLSTM model to automatically assign corresponding weights to the hidden states of the BiLSTM.This allowed the model to differentiate the importance of load sequences at different time intervals.At the same time,in order to solve the problem of the difficulties of selecting the parameters of the temporal model,and the poor global search ability of the whale algorithm,which is easy to fall into the local optimization,the whale algorithm(IWOA)was optimized by using the hybrid strategy of Tent chaos mapping and Levy flight strategy,so as to better search the parameters of the model.In this experiment,the real load data of a region in Zhejiang was taken as an example to analyze,and the prediction accuracy(R2)of the proposed method reached 98.83%.Compared with the prediction models such as BP,WOA-CNN-BiLSTM,SSA-CNN-BiLSTM,CNN-BiGRU-Attention,etc.,the experimental results showed that the model proposed in this study has a higher prediction accuracy.
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Project supported by the National Natural Science Foundation of China(Grant Nos.72101046 and 61672128)。
文摘Recent studies employing deep learning to solve the traveling salesman problem(TSP)have mainly focused on learning construction heuristics.Such methods can improve TSP solutions,but still depend on additional programs.However,methods that focus on learning improvement heuristics to iteratively refine solutions remain insufficient.Traditional improvement heuristics are guided by a manually designed search strategy and may only achieve limited improvements.This paper proposes a novel framework for learning improvement heuristics,which automatically discovers better improvement policies for heuristics to iteratively solve the TSP.Our framework first designs a new architecture based on a transformer model to make the policy network parameterized,which introduces an action-dropout layer to prevent action selection from overfitting.It then proposes a deep reinforcement learning approach integrating a simulated annealing mechanism(named RL-SA)to learn the pairwise selected policy,aiming to improve the 2-opt algorithm's performance.The RL-SA leverages the whale optimization algorithm to generate initial solutions for better sampling efficiency and uses the Gaussian perturbation strategy to tackle the sparse reward problem of reinforcement learning.The experiment results show that the proposed approach is significantly superior to the state-of-the-art learning-based methods,and further reduces the gap between learning-based methods and highly optimized solvers in the benchmark datasets.Moreover,our pre-trained model M can be applied to guide the SA algorithm(named M-SA(ours)),which performs better than existing deep models in small-,medium-,and large-scale TSPLIB datasets.Additionally,the M-SA(ours)achieves excellent generalization performance in a real-world dataset on global liner shipping routes,with the optimization percentages in distance reduction ranging from3.52%to 17.99%.
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 Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia through the Researchers Supporting Project PNURSP2025R333.
文摘Diagnosing gastrointestinal tract diseases is a critical task requiring accurate and efficient methodologies.While deep learning models have significantly advanced medical image analysis,challenges such as imbalanced datasets and redundant features persist.This study proposes a novel framework that customizes two deep learning models,NasNetMobile and ResNet50,by incorporating bottleneck architectures,named as NasNeck and ResNeck,to enhance feature extraction.The feature vectors are fused into a combined vector,which is further optimized using an improved Whale Optimization Algorithm to minimize redundancy and improve discriminative power.The optimized feature vector is then classified using artificial neural network classifiers,effectively addressing the limitations of traditional methods.Data augmentation techniques are employed to tackle class imbalance,improving model learning and generalization.The proposed framework was evaluated on two publicly available datasets:Hyper-Kvasir and Kvasir v2.The Hyper-Kvasir dataset,comprising 23 gastrointestinal disease classes,yielded an impressive 96.0%accuracy.On the Kvasir v2 dataset,which contains 8 distinct classes,the framework achieved a remarkable 98.9%accuracy,further demonstrating its robustness and superior classification performance across different gastrointestinal datasets.The results demonstrate the effectiveness of customizing deep models with bottleneck architectures,feature fusion,and optimization techniques in enhancing classification accuracy while reducing computational complexity.
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.
