Herd immunity is often considered a measure to protect a whole community or population from disease if the vaccination threshold is met. Using the demographic and COVID-19 infection data from the state of Pennsylvania...Herd immunity is often considered a measure to protect a whole community or population from disease if the vaccination threshold is met. Using the demographic and COVID-19 infection data from the state of Pennsylvania, United States, the study aimed to determine if herd immunity by vaccination is an effective way to reduce the spread of the COVID-19 virus. The Pennsylvania counties were split into two groups based on qualification of herd immunity: counties that met the COVID-19 herd immunization rate of 70% and counties that did not. The ANOVA test was used to analyze the difference between the groups with and without herd immunity by the COVID-19 vaccine. The results demonstrated that there was no significant statistical difference between counties that did achieve and those that did not achieve the herd immunity threshold for the COVID-19 vaccine. On the other hand, it was observed that there had been a significant decrease in positive cases between 2020 and 2023. This decline can be attributed to the overall protection by the vaccination and adaptability to the disease, not specifically due to herd immunity alone. Ultimately, these outcomes suggest that herd immunity cannot reduce the risk of contracting COVID-19. Increased efforts to get vaccinated should be implemented to protect the general community and a wider scope of age.展开更多
In recent years,the usage of social networking sites has considerably increased in the Arab world.It has empowered individuals to express their opinions,especially in politics.Furthermore,various organizations that op...In recent years,the usage of social networking sites has considerably increased in the Arab world.It has empowered individuals to express their opinions,especially in politics.Furthermore,various organizations that operate in the Arab countries have embraced social media in their day-to-day business activities at different scales.This is attributed to business owners’understanding of social media’s importance for business development.However,the Arabic morphology is too complicated to understand due to the availability of nearly 10,000 roots and more than 900 patterns that act as the basis for verbs and nouns.Hate speech over online social networking sites turns out to be a worldwide issue that reduces the cohesion of civil societies.In this background,the current study develops a Chaotic Elephant Herd Optimization with Machine Learning for Hate Speech Detection(CEHOML-HSD)model in the context of the Arabic language.The presented CEHOML-HSD model majorly concentrates on identifying and categorising the Arabic text into hate speech and normal.To attain this,the CEHOML-HSD model follows different sub-processes as discussed herewith.At the initial stage,the CEHOML-HSD model undergoes data pre-processing with the help of the TF-IDF vectorizer.Secondly,the Support Vector Machine(SVM)model is utilized to detect and classify the hate speech texts made in the Arabic language.Lastly,the CEHO approach is employed for fine-tuning the parameters involved in SVM.This CEHO approach is developed by combining the chaotic functions with the classical EHO algorithm.The design of the CEHO algorithm for parameter tuning shows the novelty of the work.A widespread experimental analysis was executed to validate the enhanced performance of the proposed CEHOML-HSD approach.The comparative study outcomes established the supremacy of the proposed CEHOML-HSD model over other approaches.展开更多
Although the 2022 cryptocurrency market crash prompted despair among investors,the rallying cry,“wagmi”(We’re all gonna make it.)emerged among cryptocurrency enthusiasts in the aftermath.Did cryptocurrency enthusia...Although the 2022 cryptocurrency market crash prompted despair among investors,the rallying cry,“wagmi”(We’re all gonna make it.)emerged among cryptocurrency enthusiasts in the aftermath.Did cryptocurrency enthusiasts respond to this crash differently compared to traditional investors?Using natural language processing techniques applied to Twitter data,this study employed a difference-in-differences method to determine whether the cryptocurrency market crash had a differential effect on investor sentiment toward cryptocurrency enthusiasts relative to more traditional investors.The results indicate that the crash affected investor sentiment among cryptocurrency enthusiastic investors differently from traditional investors.In particular,cryptocurrency enthusiasts’tweets became more neutral and,surprisingly,less negative.This result appears to be primarily driven by a deliberate,collectivist effort to promote positivity within the cryptocurrency community(“wagmi”).Considering the more nuanced emotional content of tweets,it appears that cryptocurrency enthusiasts expressed less joy and surprise in the aftermath of the cryptocurrency crash than traditional investors.Moreover,cryptocurrency enthusiasts tweeted more frequently after the cryptocurrency crash,with a relative increase in tweet frequency of approximately one tweet per day.An analysis of the specific textual content of tweets provides evidence of herding behavior among cryptocurrency enthusiasts.展开更多
This study explains the role of economic uncertainty as a bridge between business cycles and investors’herding behavior.Starting with a conventional stochastic differential equation representing the evolution of stoc...This study explains the role of economic uncertainty as a bridge between business cycles and investors’herding behavior.Starting with a conventional stochastic differential equation representing the evolution of stock returns,we provide a simple theoretical model and empirically demonstrate it.Specifically,the growth rate of gross domestic product and the power law exponent are used as proxies for business cycles and herding behavior,respectively.We find stronger herding behavior during recessions than during booms.We attribute this to economic uncertainty,which leads to strong behavioral bias in the stock market.These findings are consistent with the predictions of the quantum model.展开更多
The dairy herd improvement data from Henan Province were analyzed statistically to establish screening criteria for relevant data, thereby laying a foundation for genetic evaluation of dairy cows. With the 2 152 451 t...The dairy herd improvement data from Henan Province were analyzed statistically to establish screening criteria for relevant data, thereby laying a foundation for genetic evaluation of dairy cows. With the 2 152 451 test-day records about 155 893 Chinese Holstein dairy cows collected by the Henan Dairy Herd Improvement Center from January 2008 to April 2016, the dynamics of test times during a complete lactation, test interval during a complete lactation, days in milk （DIM） of first test-day record, daughter descendant number and herd number of bull, age at first calving and pedigree integrity rate among different years and different herd sizes were analyzed by MEANS order of SAS 9.4. In addition, the data that were applicable to genetic evaluation were screened by SQL program. The results showed that during 2008-2015, the number of cow individuals participating in DHI in Henan Province increased from 7 379 to 93 706; the test-day milk yield increased from 19.91 to 24.05 kg; the somatic cell count reduced from 411.09×10^3 to 277.08×10^3 cells/ml; the percentage of cows with DIM ranging from 5-305 d reached 70.92%; the average test times increased from 3.20 to 6.31 times; the test interval decreased from 70.22 to 33.83 d; the dairy cows with age at first calving of 25 months were dominant, accounting for 12.57%; the bulls whose daughter descendant number was 20 or more and the daughters were distributed in 10 or more farms accounted for 6.05%; the one-generation pedigree integrity rate was 82.54%; the percentage of data that could be used for genetic evaluation was screened as 20.67%, which was lower than the results of other similar studies.展开更多
Routing strategies and security issues are the greatest challenges in Wireless Sensor Network(WSN).Cluster-based routing Low Energy adaptive Clustering Hierarchy(LEACH)decreases power consumption and increases net-wor...Routing strategies and security issues are the greatest challenges in Wireless Sensor Network(WSN).Cluster-based routing Low Energy adaptive Clustering Hierarchy(LEACH)decreases power consumption and increases net-work lifetime considerably.Securing WSN is a challenging issue faced by researchers.Trust systems are very helpful in detecting interfering nodes in WSN.Researchers have successfully applied Nature-inspired Metaheuristics Optimization Algorithms as a decision-making factor to derive an improved and effective solution for a real-time optimization problem.The metaheuristic Elephant Herding Optimizations(EHO)algorithm is formulated based on ele-phant herding in their clans.EHO considers two herding behaviors to solve and enhance optimization problem.Based on Elephant Herd Optimization,a trust-based security method is built in this work.The proposed routing selects routes to destination based on the trust values,thus,finding optimal secure routes for transmitting data.Experimental results have demonstrated the effectiveness of the proposed EHO based routing.The Average Packet Loss Rate of the proposed Trust Elephant Herd Optimization performs better by 35.42%,by 1.45%,and by 31.94%than LEACH,Elephant Herd Optimization,and Trust LEACH,respec-tively at Number of Nodes 3000.As the proposed routing is efficient in selecting secure routes,the average packet loss rate is significantly reduced,improving the network’s performance.It is also observed that the lifetime of the network is enhanced with the proposed Trust Elephant Herd Optimization.展开更多
It is generally accepted that herding behavior and overconfidence behavior are unrelated or even mutually exclusive.However,these behaviors can both lead to some similar market anomalies,such as excessive trading volu...It is generally accepted that herding behavior and overconfidence behavior are unrelated or even mutually exclusive.However,these behaviors can both lead to some similar market anomalies,such as excessive trading volume and volatility in the stock market.Due to the limitation of traditional time series analysis,we try to study whether there exists network relevance between the investor’s herding behavior and overconfidence behavior based on the complex network method.Since the investor’s herding behavior is based on market trends and overconfidence behavior is based on past performance,we convert the time series data of market trends into a market network and the time series data of the investor’s past judgments into an investor network.Then,we update these networks as new information arrives at the market and show the weighted in-degrees of the nodes in the market network and the investor network can represent the herding degree and the confidence degree of the investor,respectively.Using stock transaction data of Microsoft,US S&P 500 stock index,and China Hushen 300 stock index,we update the two networks and find that there exists a high similarity of network topological properties and a significant correlation of node parameter sequences between the market network and the investor network.Finally,we theoretically derive and conclude that the investor’s herding degree and confidence degree are highly related to each other when there is a clear market trend.展开更多
In order to solve the problem that the krill herd(KH)algorithm is premature due to the decrease of population diversity,a new hybrid vortex search KH(VSKH)algorithm has been developed to deal with the global optimizat...In order to solve the problem that the krill herd(KH)algorithm is premature due to the decrease of population diversity,a new hybrid vortex search KH(VSKH)algorithm has been developed to deal with the global optimization problem.The improvement is that a new hybrid vortex search(HVS)operator is added into the updating process of the krill for the purpose of dealing with optimization problems more efficiently.Using 20 benchmark functions for comparison experiments,the results show that the VSKH algorithm has higher accuracy.展开更多
This study examines herding behavior in the Pakistani Stock Market under different market conditions,focusing on the Ramadan effect and Crisis period by using data from 2004 to 2014.Two regression models of Christie a...This study examines herding behavior in the Pakistani Stock Market under different market conditions,focusing on the Ramadan effect and Crisis period by using data from 2004 to 2014.Two regression models of Christie and Huang(Financ Analysts J 51:31-37,1995)and Chang et al.,(J Bank Finance 24:1651-1679,2000)are used for herding estimations.Results based on daily stock data reveal that there is an absence of herding behavior during rising(up)and falling(down)market as well as during high and low volatility in market.While herding behavior is detected during low trading volume days.Yearly analysis shows that herding existed during 2005,2006 and 2007,while it is not evident during rest of the period.However,herding behavior is not detected during Ramadan.Furthermore,during financial crisis of 2007-08,Pakistani Stock Market exhibits herding behavior due to higher uncertainty and information asymmetry.展开更多
This study investigates speculative bubbles in the cryptocurrency market and factors affecting bubbles during the COVID-19 pandemic.Our results indicate that each cryptocurrency covered in the study presented bubbles....This study investigates speculative bubbles in the cryptocurrency market and factors affecting bubbles during the COVID-19 pandemic.Our results indicate that each cryptocurrency covered in the study presented bubbles.Moreover,we found that explosive behavior in one currency leads to explosivity in other cryptocurrencies.During the pandemic,herd behavior was evident among investors;however,this diminishes during bubbles,indicating that bubbles are not explained by herd behavior.Regarding cryptocurrency and market-specific factors,we found that Google Trends and volume are positively associated with predicting speculative bubbles in time-series and panel probit regressions.Hence,investors should exercise caution when investing in cryptocurrencies and follow both crypto currency and market-related factors to estimate bubbles.Alternative liquidity,volatility,and Google Trends measures are used for robustness analysis and yield similar results.Overall,our results suggest that bubble behavior is common in the cryptocurrency market,contradicting the efficient market hypothesis.展开更多
Grassland is most important in China due to its multi-functions. However, about 90% of total usable grassland in China has been degraded and the degradation expands at a rate of 2 million ha per year. Western China co...Grassland is most important in China due to its multi-functions. However, about 90% of total usable grassland in China has been degraded and the degradation expands at a rate of 2 million ha per year. Western China covers 6.12 million square kilometers and 63.8% of the total national area with a distribution of 50 minority nationalities and 75% of the minority national population. Ecological environment there is very vulnerable with more than 90% areas of the annually increased degradation taking place. Under the current tenure arrangement, the individual herder households become the main and direct users of grassland, their decision-making on grassland management may have crucial impact on ecological environment as well as their livelihoods. Thus, it is necessary to examine the determinants of their grassland management behaviors. This study applies 231 household field data from 6 provinces of western China and uses econometric models to explore the major constraints for restricting the herd households' grassland management behaviors. Main results show that under the current tenure and other governance measures, institutional factors, market price and herder's farm and household's characteristics affect the grassland management behaviors.展开更多
The development of buffalo milk industry in China encounters the problems of small high yield populations and insufficient excellent provenance. There- fore, it is necessary to carry out dairy herd improvement （DHI）...The development of buffalo milk industry in China encounters the problems of small high yield populations and insufficient excellent provenance. There- fore, it is necessary to carry out dairy herd improvement （DHI） to increase dairy buffalo herd productivity. This paper reviewed the situation and problems of DHI in dairy buffalo, and the corresponding opinions and suggestions were put forward.展开更多
Smallholder dairy farming in Africa is classified into rural, peri-urban and urban systems. The major classification criterion is demographic. Dairy systems are extensively characterized, but not based on rigorous sta...Smallholder dairy farming in Africa is classified into rural, peri-urban and urban systems. The major classification criterion is demographic. Dairy systems are extensively characterized, but not based on rigorous statistical analyses. We validated this classification based on herd genetic structure and identify determinants of within-system variations, taking Ethiopia as a case study. Discriminant function analysis correctly classified 38% - 50.6% of the 360 sampled farms into the three systems. Multinomial logistic regression analysis showed that rural and peri-urban farmers were 1.26 (P < 0.1) to 1.45 (P < 0.001) times more likely to keep local and low grade crossbreds and fewer high grade crosses (P < 0.05;odds ratio = 2.35) than the urban farmers. In the rural system, proportion of high grade crosses declined and low grades increased over generations, whereas in urban system the reverse was observed. Access to breeding services and land resources significantly determined the adoption of crossbred dairy herd within systems. In conclusion, considering farms within systems as a uniform unit to target development interventions may not be appropriate and thus farm topologies and system specific determinants of farmers’ breeding strategies need to be considered to design and introduce appropriate breeding interventions.展开更多
This paper analyses the herding behaviour among exchanges around the expiration of bitcoin futures traded on the Chicago Mercantile Exchange(CME).The database extends from December 2017 to October 2020,taking as a ref...This paper analyses the herding behaviour among exchanges around the expiration of bitcoin futures traded on the Chicago Mercantile Exchange(CME).The database extends from December 2017 to October 2020,taking as a reference the main exchanges that trade bitcoin(Binance,Bitfinex,Bitstamp,Coinbase,itBit,Kraken,and Gemini)and using hourly closing prices and trading volumes in bitcoin and US dollars.Adapting the proposal of Chang,Cheng and Khorana(2000)(CCK)to test conditional herding,we obtain results that indicate that the herding effect is significant during the week before expiration.After expiration,the herding effect lasts for a few hours and disappears.Information overload originating,among other causes,from sophisticated investors’strategies may generate this mimetic behaviour.The results show the relevance of intraday data applied to specific events such as expiration since the unconditional analysis shows,in general,anti-herding behaviour throughout the period of study.展开更多
Beginning in the fall of 2014 there has been a general and widespread increase in the incidence of prolapse in the U.S. swine herd. The purpose of this manuscript is to review the incidence, causative factors and trea...Beginning in the fall of 2014 there has been a general and widespread increase in the incidence of prolapse in the U.S. swine herd. The purpose of this manuscript is to review the incidence, causative factors and treatment of rectal, vaginal, uterine and preputial prolapses. Rectal and vaginal prolapses are most common in swine when compared to other prolapse types. The cause of prolapses supports a fixation mechanism failure overcome by pressure on or weakening of support tissue. The fundamental factors affecting the incidence for prolapses are many and include factors related to nutrition, physiology, hormones, genetics, environment and other disease factors such as chronic diarrhea, cough, and dystocia. Treatment of prolapsed swine includes surgical and therapeutic management that can lead to complete recovery. However, in most cases, euthanasia is the final result. Economic loss was calculated at approximately $5220 dollars/year/1000 sows.展开更多
A model to explain the dynamic characters of earnings management was developed based on the interactionamong several firms’ disclosure policies. Under the condition of incomplete information, each firm’s earnings ma...A model to explain the dynamic characters of earnings management was developed based on the interactionamong several firms’ disclosure policies. Under the condition of incomplete information, each firm’s earnings man-agement will be influenced by the earnings disclosure policies of other firms. It can lead to "herd behavior" of earningsmanagement. This paper studies the relationship between earnings manipulation and rights issue policy based on thedistribution of earnings after management. The results indicate that Chinese listed companies trend towards controllingROE in the narrow ranges just above 6% and 10% .Therefore, "herd behavior" exists in the earnings management.展开更多
This paper explores some behavioral factors that may explain the formation of speculative bubbles in financial markets. The study adopts an experimental approach focused on the agents’ behavior when facing a “true...This paper explores some behavioral factors that may explain the formation of speculative bubbles in financial markets. The study adopts an experimental approach focused on the agents’ behavior when facing a “true” bubble and is incentivized to herd and/or receive information about the market sentiment. For this purpose, a straightforward laboratory experiment that reproduces the dotcom market bubble and asks subjects to forecast asset prices in a true dynamic information scenario. The experiment was conducted in the laboratory of the Faculty of Economics at the University of Salamanca and involved 137 undergraduate students in the degree of economics. The results show that incentives to the herding behavior increase the forecasted volatility and thus contribute to the bubble inflation. Nevertheless, this effect may be offset by giving information to the agents about the expected market trend. Therefore, under herding effects, it is the absence of clear signals about market sentiments what inflates the bubble.展开更多
文摘Herd immunity is often considered a measure to protect a whole community or population from disease if the vaccination threshold is met. Using the demographic and COVID-19 infection data from the state of Pennsylvania, United States, the study aimed to determine if herd immunity by vaccination is an effective way to reduce the spread of the COVID-19 virus. The Pennsylvania counties were split into two groups based on qualification of herd immunity: counties that met the COVID-19 herd immunization rate of 70% and counties that did not. The ANOVA test was used to analyze the difference between the groups with and without herd immunity by the COVID-19 vaccine. The results demonstrated that there was no significant statistical difference between counties that did achieve and those that did not achieve the herd immunity threshold for the COVID-19 vaccine. On the other hand, it was observed that there had been a significant decrease in positive cases between 2020 and 2023. This decline can be attributed to the overall protection by the vaccination and adaptability to the disease, not specifically due to herd immunity alone. Ultimately, these outcomes suggest that herd immunity cannot reduce the risk of contracting COVID-19. Increased efforts to get vaccinated should be implemented to protect the general community and a wider scope of age.
基金Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2024R263)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.This study is supported via funding from Prince Sattam bin Abdulaziz University Project Number(PSAU/2024/R/1445).
文摘In recent years,the usage of social networking sites has considerably increased in the Arab world.It has empowered individuals to express their opinions,especially in politics.Furthermore,various organizations that operate in the Arab countries have embraced social media in their day-to-day business activities at different scales.This is attributed to business owners’understanding of social media’s importance for business development.However,the Arabic morphology is too complicated to understand due to the availability of nearly 10,000 roots and more than 900 patterns that act as the basis for verbs and nouns.Hate speech over online social networking sites turns out to be a worldwide issue that reduces the cohesion of civil societies.In this background,the current study develops a Chaotic Elephant Herd Optimization with Machine Learning for Hate Speech Detection(CEHOML-HSD)model in the context of the Arabic language.The presented CEHOML-HSD model majorly concentrates on identifying and categorising the Arabic text into hate speech and normal.To attain this,the CEHOML-HSD model follows different sub-processes as discussed herewith.At the initial stage,the CEHOML-HSD model undergoes data pre-processing with the help of the TF-IDF vectorizer.Secondly,the Support Vector Machine(SVM)model is utilized to detect and classify the hate speech texts made in the Arabic language.Lastly,the CEHO approach is employed for fine-tuning the parameters involved in SVM.This CEHO approach is developed by combining the chaotic functions with the classical EHO algorithm.The design of the CEHO algorithm for parameter tuning shows the novelty of the work.A widespread experimental analysis was executed to validate the enhanced performance of the proposed CEHOML-HSD approach.The comparative study outcomes established the supremacy of the proposed CEHOML-HSD model over other approaches.
文摘Although the 2022 cryptocurrency market crash prompted despair among investors,the rallying cry,“wagmi”(We’re all gonna make it.)emerged among cryptocurrency enthusiasts in the aftermath.Did cryptocurrency enthusiasts respond to this crash differently compared to traditional investors?Using natural language processing techniques applied to Twitter data,this study employed a difference-in-differences method to determine whether the cryptocurrency market crash had a differential effect on investor sentiment toward cryptocurrency enthusiasts relative to more traditional investors.The results indicate that the crash affected investor sentiment among cryptocurrency enthusiastic investors differently from traditional investors.In particular,cryptocurrency enthusiasts’tweets became more neutral and,surprisingly,less negative.This result appears to be primarily driven by a deliberate,collectivist effort to promote positivity within the cryptocurrency community(“wagmi”).Considering the more nuanced emotional content of tweets,it appears that cryptocurrency enthusiasts expressed less joy and surprise in the aftermath of the cryptocurrency crash than traditional investors.Moreover,cryptocurrency enthusiasts tweeted more frequently after the cryptocurrency crash,with a relative increase in tweet frequency of approximately one tweet per day.An analysis of the specific textual content of tweets provides evidence of herding behavior among cryptocurrency enthusiasts.
基金supported by the National Research Foundation of Korea grant funded by the Korean government(No.2022R1A2C100425811,Kwangwon Ahn).
文摘This study explains the role of economic uncertainty as a bridge between business cycles and investors’herding behavior.Starting with a conventional stochastic differential equation representing the evolution of stock returns,we provide a simple theoretical model and empirically demonstrate it.Specifically,the growth rate of gross domestic product and the power law exponent are used as proxies for business cycles and herding behavior,respectively.We find stronger herding behavior during recessions than during booms.We attribute this to economic uncertainty,which leads to strong behavioral bias in the stock market.These findings are consistent with the predictions of the quantum model.
基金Supported by Science and Technology Open Cooperation Project of Henan Province(162106000017)Science and Technology People-benefiting Plan Project of Henan Province(152207110004)Puyang Science and Technology Plan Project(150109)~~
文摘The dairy herd improvement data from Henan Province were analyzed statistically to establish screening criteria for relevant data, thereby laying a foundation for genetic evaluation of dairy cows. With the 2 152 451 test-day records about 155 893 Chinese Holstein dairy cows collected by the Henan Dairy Herd Improvement Center from January 2008 to April 2016, the dynamics of test times during a complete lactation, test interval during a complete lactation, days in milk （DIM） of first test-day record, daughter descendant number and herd number of bull, age at first calving and pedigree integrity rate among different years and different herd sizes were analyzed by MEANS order of SAS 9.4. In addition, the data that were applicable to genetic evaluation were screened by SQL program. The results showed that during 2008-2015, the number of cow individuals participating in DHI in Henan Province increased from 7 379 to 93 706; the test-day milk yield increased from 19.91 to 24.05 kg; the somatic cell count reduced from 411.09×10^3 to 277.08×10^3 cells/ml; the percentage of cows with DIM ranging from 5-305 d reached 70.92%; the average test times increased from 3.20 to 6.31 times; the test interval decreased from 70.22 to 33.83 d; the dairy cows with age at first calving of 25 months were dominant, accounting for 12.57%; the bulls whose daughter descendant number was 20 or more and the daughters were distributed in 10 or more farms accounted for 6.05%; the one-generation pedigree integrity rate was 82.54%; the percentage of data that could be used for genetic evaluation was screened as 20.67%, which was lower than the results of other similar studies.
文摘Routing strategies and security issues are the greatest challenges in Wireless Sensor Network(WSN).Cluster-based routing Low Energy adaptive Clustering Hierarchy(LEACH)decreases power consumption and increases net-work lifetime considerably.Securing WSN is a challenging issue faced by researchers.Trust systems are very helpful in detecting interfering nodes in WSN.Researchers have successfully applied Nature-inspired Metaheuristics Optimization Algorithms as a decision-making factor to derive an improved and effective solution for a real-time optimization problem.The metaheuristic Elephant Herding Optimizations(EHO)algorithm is formulated based on ele-phant herding in their clans.EHO considers two herding behaviors to solve and enhance optimization problem.Based on Elephant Herd Optimization,a trust-based security method is built in this work.The proposed routing selects routes to destination based on the trust values,thus,finding optimal secure routes for transmitting data.Experimental results have demonstrated the effectiveness of the proposed EHO based routing.The Average Packet Loss Rate of the proposed Trust Elephant Herd Optimization performs better by 35.42%,by 1.45%,and by 31.94%than LEACH,Elephant Herd Optimization,and Trust LEACH,respec-tively at Number of Nodes 3000.As the proposed routing is efficient in selecting secure routes,the average packet loss rate is significantly reduced,improving the network’s performance.It is also observed that the lifetime of the network is enhanced with the proposed Trust Elephant Herd Optimization.
基金Project supported by the Youth Program of the National Social Science Foundation of China(Grant No.18CJY057)。
文摘It is generally accepted that herding behavior and overconfidence behavior are unrelated or even mutually exclusive.However,these behaviors can both lead to some similar market anomalies,such as excessive trading volume and volatility in the stock market.Due to the limitation of traditional time series analysis,we try to study whether there exists network relevance between the investor’s herding behavior and overconfidence behavior based on the complex network method.Since the investor’s herding behavior is based on market trends and overconfidence behavior is based on past performance,we convert the time series data of market trends into a market network and the time series data of the investor’s past judgments into an investor network.Then,we update these networks as new information arrives at the market and show the weighted in-degrees of the nodes in the market network and the investor network can represent the herding degree and the confidence degree of the investor,respectively.Using stock transaction data of Microsoft,US S&P 500 stock index,and China Hushen 300 stock index,we update the two networks and find that there exists a high similarity of network topological properties and a significant correlation of node parameter sequences between the market network and the investor network.Finally,we theoretically derive and conclude that the investor’s herding degree and confidence degree are highly related to each other when there is a clear market trend.
基金Supported by the National Natural Science Foundation of China(11871383,71471140)。
文摘In order to solve the problem that the krill herd(KH)algorithm is premature due to the decrease of population diversity,a new hybrid vortex search KH(VSKH)algorithm has been developed to deal with the global optimization problem.The improvement is that a new hybrid vortex search(HVS)operator is added into the updating process of the krill for the purpose of dealing with optimization problems more efficiently.Using 20 benchmark functions for comparison experiments,the results show that the VSKH algorithm has higher accuracy.
文摘This study examines herding behavior in the Pakistani Stock Market under different market conditions,focusing on the Ramadan effect and Crisis period by using data from 2004 to 2014.Two regression models of Christie and Huang(Financ Analysts J 51:31-37,1995)and Chang et al.,(J Bank Finance 24:1651-1679,2000)are used for herding estimations.Results based on daily stock data reveal that there is an absence of herding behavior during rising(up)and falling(down)market as well as during high and low volatility in market.While herding behavior is detected during low trading volume days.Yearly analysis shows that herding existed during 2005,2006 and 2007,while it is not evident during rest of the period.However,herding behavior is not detected during Ramadan.Furthermore,during financial crisis of 2007-08,Pakistani Stock Market exhibits herding behavior due to higher uncertainty and information asymmetry.
文摘This study investigates speculative bubbles in the cryptocurrency market and factors affecting bubbles during the COVID-19 pandemic.Our results indicate that each cryptocurrency covered in the study presented bubbles.Moreover,we found that explosive behavior in one currency leads to explosivity in other cryptocurrencies.During the pandemic,herd behavior was evident among investors;however,this diminishes during bubbles,indicating that bubbles are not explained by herd behavior.Regarding cryptocurrency and market-specific factors,we found that Google Trends and volume are positively associated with predicting speculative bubbles in time-series and panel probit regressions.Hence,investors should exercise caution when investing in cryptocurrencies and follow both crypto currency and market-related factors to estimate bubbles.Alternative liquidity,volatility,and Google Trends measures are used for robustness analysis and yield similar results.Overall,our results suggest that bubble behavior is common in the cryptocurrency market,contradicting the efficient market hypothesis.
基金Ford Foundation (1105-1408)Natural Science Foundation of China (71273268) for providing funding supports
文摘Grassland is most important in China due to its multi-functions. However, about 90% of total usable grassland in China has been degraded and the degradation expands at a rate of 2 million ha per year. Western China covers 6.12 million square kilometers and 63.8% of the total national area with a distribution of 50 minority nationalities and 75% of the minority national population. Ecological environment there is very vulnerable with more than 90% areas of the annually increased degradation taking place. Under the current tenure arrangement, the individual herder households become the main and direct users of grassland, their decision-making on grassland management may have crucial impact on ecological environment as well as their livelihoods. Thus, it is necessary to examine the determinants of their grassland management behaviors. This study applies 231 household field data from 6 provinces of western China and uses econometric models to explore the major constraints for restricting the herd households' grassland management behaviors. Main results show that under the current tenure and other governance measures, institutional factors, market price and herder's farm and household's characteristics affect the grassland management behaviors.
基金Supported by Scientific Innovation Program of Guangxi Aquatic,Animal Husbandry and Veterinary Bureau(1304519)
文摘The development of buffalo milk industry in China encounters the problems of small high yield populations and insufficient excellent provenance. There- fore, it is necessary to carry out dairy herd improvement （DHI） to increase dairy buffalo herd productivity. This paper reviewed the situation and problems of DHI in dairy buffalo, and the corresponding opinions and suggestions were put forward.
文摘Smallholder dairy farming in Africa is classified into rural, peri-urban and urban systems. The major classification criterion is demographic. Dairy systems are extensively characterized, but not based on rigorous statistical analyses. We validated this classification based on herd genetic structure and identify determinants of within-system variations, taking Ethiopia as a case study. Discriminant function analysis correctly classified 38% - 50.6% of the 360 sampled farms into the three systems. Multinomial logistic regression analysis showed that rural and peri-urban farmers were 1.26 (P < 0.1) to 1.45 (P < 0.001) times more likely to keep local and low grade crossbreds and fewer high grade crosses (P < 0.05;odds ratio = 2.35) than the urban farmers. In the rural system, proportion of high grade crosses declined and low grades increased over generations, whereas in urban system the reverse was observed. Access to breeding services and land resources significantly determined the adoption of crossbred dairy herd within systems. In conclusion, considering farms within systems as a uniform unit to target development interventions may not be appropriate and thus farm topologies and system specific determinants of farmers’ breeding strategies need to be considered to design and introduce appropriate breeding interventions.
文摘This paper analyses the herding behaviour among exchanges around the expiration of bitcoin futures traded on the Chicago Mercantile Exchange(CME).The database extends from December 2017 to October 2020,taking as a reference the main exchanges that trade bitcoin(Binance,Bitfinex,Bitstamp,Coinbase,itBit,Kraken,and Gemini)and using hourly closing prices and trading volumes in bitcoin and US dollars.Adapting the proposal of Chang,Cheng and Khorana(2000)(CCK)to test conditional herding,we obtain results that indicate that the herding effect is significant during the week before expiration.After expiration,the herding effect lasts for a few hours and disappears.Information overload originating,among other causes,from sophisticated investors’strategies may generate this mimetic behaviour.The results show the relevance of intraday data applied to specific events such as expiration since the unconditional analysis shows,in general,anti-herding behaviour throughout the period of study.
文摘Beginning in the fall of 2014 there has been a general and widespread increase in the incidence of prolapse in the U.S. swine herd. The purpose of this manuscript is to review the incidence, causative factors and treatment of rectal, vaginal, uterine and preputial prolapses. Rectal and vaginal prolapses are most common in swine when compared to other prolapse types. The cause of prolapses supports a fixation mechanism failure overcome by pressure on or weakening of support tissue. The fundamental factors affecting the incidence for prolapses are many and include factors related to nutrition, physiology, hormones, genetics, environment and other disease factors such as chronic diarrhea, cough, and dystocia. Treatment of prolapsed swine includes surgical and therapeutic management that can lead to complete recovery. However, in most cases, euthanasia is the final result. Economic loss was calculated at approximately $5220 dollars/year/1000 sows.
文摘A model to explain the dynamic characters of earnings management was developed based on the interactionamong several firms’ disclosure policies. Under the condition of incomplete information, each firm’s earnings man-agement will be influenced by the earnings disclosure policies of other firms. It can lead to "herd behavior" of earningsmanagement. This paper studies the relationship between earnings manipulation and rights issue policy based on thedistribution of earnings after management. The results indicate that Chinese listed companies trend towards controllingROE in the narrow ranges just above 6% and 10% .Therefore, "herd behavior" exists in the earnings management.
文摘This paper explores some behavioral factors that may explain the formation of speculative bubbles in financial markets. The study adopts an experimental approach focused on the agents’ behavior when facing a “true” bubble and is incentivized to herd and/or receive information about the market sentiment. For this purpose, a straightforward laboratory experiment that reproduces the dotcom market bubble and asks subjects to forecast asset prices in a true dynamic information scenario. The experiment was conducted in the laboratory of the Faculty of Economics at the University of Salamanca and involved 137 undergraduate students in the degree of economics. The results show that incentives to the herding behavior increase the forecasted volatility and thus contribute to the bubble inflation. Nevertheless, this effect may be offset by giving information to the agents about the expected market trend. Therefore, under herding effects, it is the absence of clear signals about market sentiments what inflates the bubble.
