Prolapse Incidence in Swine Breeding Herds Is a Cause for Concern

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.

Design and prototyping of the readout electronics for the transition radiation detector in the high energy cosmic radiation detection facility

The high energy cosmic-radiation detection(HERD)facility is planned to launch in 2027 and scheduled to be installed on the China Space Station.It serves as a dark matter particle detector,a cosmic ray instrument,and an observatory for high-energy gamma rays.A transition radiation detector placed on one of its lateral sides serves dual purpose,(ⅰ)calibrating HERD's electromagnetic calorimeter in the TeV energy range,and(ⅱ)serving as an independent detector for high-energy gamma rays.In this paper,the prototype readout electronics design of the transition radiation detector is demonstrated,which aims to accurately measure the charge of the anodes using the SAMPA application specific integrated circuit chip.The electronic performance of the prototype system is evaluated in terms of noise,linearity,and resolution.Through the presented design,each electronic channel can achieve a dynamic range of 0–100 fC,the RMS noise level not exceeding 0.15 fC,and the integral nonlinearity was<0.2%.To further verify the readout electronic performance,a joint test with the detector was carried out,and the results show that the prototype system can satisfy the requirements of the detector's scientific goals.

Chaotic Elephant Herd Optimization with Machine Learning for Arabic Hate Speech Detection

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.

Does Herd Immunity Reduce the Risk of Contracting COVID-19?

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.

黄冈市某奶牛场DHI技术应用情况报告

通过湖北省黄冈市某奶牛场一年的DHI(Dairy herd improvement)测定结果,对生产和管理中存在的问题进行分析,以便更好地应用这一技术。结果表明,头胎牛占参测牛群的64%,产奶量及乳脂率的全年波动较大,平均SCC、产犊间隔和产奶时间分别为64万个、459 d和316 d,全年综合奶损失量772 t。热应激等问题是制约该奶牛场生产发展的瓶颈。

相互作用herding模型的非线性行为和动力学特性

相互作用herding模型定性上能很好地呈现一些真实的经济规律,但定量上与真实的市场还有一定的距离,特别是收益绝对值的自关联衰退得太快.通过数值模拟研究发现该模型在它的参数取某些特定值时具有特有的非线性行为和动力学特性.此时,它不仅重现该模型原有的动力学特性,并能展现出更接近真实的市场规律,其中收益绝对值表现出长程关联,呈现出幂指数分布,它的幂明显变小,落在真实的市场规律范围内.

基于HErD分布的信号交叉口车辆到达规律动态预测

为掌握信号交叉口实时优化信号控制和交通诱导所需的车流到达信号交叉口的随机时变规律，利用HErD分布具有无限逼近任意非负随机变量的特性，提出基于HErD分布的信号交叉口车辆到达时间间隔变化规律动态预测的方法，并以一个交叉口的2个进口道进行实例验证分析。实验结果为：其中方向一的拟合度范围为86．17％～93．51％，平均拟合度高达90．32％，方向二的拟合度范围为93．24％～98．52％，平均拟合度高达96．93％，其高精度的拟合结果表明基于HErD分布的信号交叉口车辆到达规律动态预测具有科学性和实用性。

一个具有双重反馈作用的异类经纪人herding模型

一个具有双重反馈作用的异类经纪人herding模型被提出.该模型认为,金融市场是由不同大小和行为倾向的经纪人集团组成.当两个不同行为倾向的经纪人集团相遇时,他们有可能发生一次交易;当两个同种行为倾向的经纪人集团相遇时,他们可能合并成一个更大的经纪人集团.交易或合并的成功率跟市场上一次波动程度有关.当两个经纪人集团发生一次交易后,处于亏损方的经纪人集团有可能分化和翻转,分化和翻转的概率取决于经纪人集团的亏损程度和市场信念等因素.数值计算表明,该模型能较好地反映市场的经济行为和动力学特性.

信念调节着市场的演化

本文提出了一个带有双重反馈作用的异质herding模型,该模型假设金融市场是由不同大小和意愿的经纪人集团组成,两个不同集团发生交易或合并的成功率依赖上一次交易的市场波动程度,交易后亏损方发生翻转和分化的概率跟亏损程度有关。我们认为翻转和分化的概率也跟经纪人集团对市场的信念有关。为此,我们给经纪人集团一个参数k去表示他们对市场的信念程度。数值计算表明在我们的模型中动力学行为明显随k变化而变化,即信念能调节市场的演化。

Histopathological and molecular study of Neospora caninum infection in bovine aborted fetuses

Objective:To estimate the extent to which abortion in dairy cows was associated with of Neospom caninum(N.caninum) and to determine the risk factors of neosporosis in dairy farms from 9 provinces in Iran.Methods:Polymerase chain reaction(PCR) test was used to detect Neospora infection in the brain of 395 bovine aborted fetuses from 9 provinces of Iran.In addition,the brains of aborted fetuses were taken for histopathological examination.To identify the risk factors associated with neosporosis,data analysis was performed by SAS.Results:N.caninum was detected in 179(45%) out of 395 fetal brain samples of bovine aborted fetuses using PCR.Among the PCR-positive brain samples,only 56 samples were suited for histopathological examination.The characteristic lesions of Neospora infection including non-suppurative encephalitis were found in 16(28%) of PCR-positive samples.The risk factors including season,parity of dam,history of bovine virus diarrhea and infectious bovine rhinotracheitis infection in herd,cow's milk production,herd size and fetal appearance did not show association with the infection.This study showed that Neospora caused abortion was significantly more in the second trimester of pregnancy than other periods.In addition,a significant association was observed between Neospora infection and stillbirth.Conclusions:The results showed N.caninum infection was detected in high percentage of aborted fetuses.In addition,at least one fourth of abortions caused by Neospora infection.These results indicate increasing number of abortions associated with the protozoa more than reported before in Iran.

Prospective Alternatives of Antibiotics in Animal Diet

Antibiotics have been used in animal feeding for long history.In recent years,much attention has been received for their negative effects on animal and human being as well.Technology has been focused on alternatives of antibotics,such as probiotics,oligosaccharides,acidifiers,Chinese herds,chemical drugs,and other environmental measures.Their mechanism,effects,related factors and their prospect in the future were discussed in this paper.

链接变量循环的Hash函数结构

现有的Hash函数基本上都是根据Merkle-Damg°ard结构设计的。基于Merkle-Damg°ard结构易受到长度扩展攻击、多碰撞攻击、Herding攻击等这些缺陷,设计了一种链接变量循环的Hash结构,该结构是基于宽管道Hash结构的,具有大的内部状态,可以有效抵抗上述针对Merkle-Damg°ard结构的攻击。结构具有可分析的安全性,可以提高Hash函数的性能,尤其是基于数学困难问题的Hash算法,增加了消息块对Hash值的作用。

Network correlation between investor’s herding behavior and overconfidence behavior

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.

Study of Digital Management System of Milking Process on Large-Sized Dairy Farm

This study was to supply the systemic and full milking process data to support the implementation of both dairy herd improvement （DHI） and digital feeding of dairy cattle. This study designed the relational structured database and developed a set of digital management information system on milking process of intensive dairy farm using Visual Basic 6.0, Access databases, and Crystal report combining the milking characteristics of a grown cow, such as quality and sanitation testing indexes of raw milk. The system supplies a series of convenient, intelligent input interfaces of crude datum, and can count, analyze, and graphically show milking datum based on different types and different parities of cows or herds in a specific duration, and can dynamically produce some important derived data, such as days of grown cow, daily average of milk production of grown cow, days of cow milk production, and daily average of milking cow production; and can carry out all-pervasive data mining. With the help of system analysis and software design techniques, it is possible to realize precision farming for a dairy cattle herd based on whole digital management of milking process and realtime prediction on nutrient requirements and ration of dairy cattle, as well as dairy herd improvement.

Artificial Intelligence Based Prostate Cancer Classification Model Using Biomedical Images

Medical image processing becomes a hot research topic in healthcare sector for effective decision making and diagnoses of diseases.Magnetic resonance imaging(MRI)is a widely utilized tool for the classification and detection of prostate cancer.Since the manual screening process of prostate cancer is difficult,automated diagnostic methods become essential.This study develops a novel Deep Learning based Prostate Cancer Classification(DTL-PSCC)model using MRI images.The presented DTL-PSCC technique encompasses EfficientNet based feature extractor for the generation of a set of feature vectors.In addition,the fuzzy k-nearest neighbour(FKNN)model is utilized for classification process where the class labels are allotted to the input MRI images.Moreover,the membership value of the FKNN model can be optimally tuned by the use of krill herd algorithm(KHA)which results in improved classification performance.In order to demonstrate the good classification outcome of the DTL-PSCC technique,a wide range of simulations take place on benchmark MRI datasets.The extensive comparative results ensured the betterment of the DTL-PSCC technique over the recent methods with the maximum accuracy of 85.09%.

Herding behavior in Ramadan and financial crises: the case of the Pakistani stock market

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.

Viral evolution and transmission effectiveness

Different viruses transmit among hosts with different degrees of efficiency. A basic reproductive number(R0) indicates an average number of cases getting infected from a single infected case. R0 can vary widely from a little over 1 to more than 10. Low R0 is usually found among rapidly evolving viruses that are often under a strong positive selection pressure, while high R0 is often found among viruses that are highly stable. The reason for the difference between antigenically diverse viruses with low R0, such as influenza A virus, and antigenically stable viruses with high R0, such as measles virus, is not clear and has been a subject of great interest. Optimization of transmissibility fitness considering intra-host dynamics and inter-host transmissibility was shown to result in strategies for tradeoff between transmissibility and diversity. The nature of transmission, targeting either a na?ve children population or an adult population with partial immunity, has been proposed as a contributing factor for the difference in the strategies used by the two groups of viruses. The R0 determines the levels of threshold heard immunity. Lower R0 requires lowerherd immunity to terminate an outbreak. Therefore, it can be assumed that the outbreak saturation can be reached more readily when the R0 is low. In addition, one may assume that when the outbreak saturation is reached, herd immunity may provide a strong positive selection pressure that could possibly result in an occurrence of escape mutants. Studies of these hypotheses will give us an important insight into viral evolution. This review discusses the above hypotheses as well as some possible mechanistic explanation for the difference in transmission efficiency of

Speculative bubbles and herding in cryptocurrencies

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.

Quality Analysis of Dairy Herd Improvement Data from Henan Province

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.

Process of Analyzing Pig Inbreeding Coefficient Using SAS Program

The paternity index is one of the important parameters which paternity determination depends on.Inbreeding is an indispensable and effective means to improve herds and breeds and breed new strains and breeds.It can fix good traits and improve herd genetic uniformity.The INBREED module of SAS statistical analysis software can be used to calculate the inbreeding coefficients of the offspring and their parents in the pig herd pedigree.In this study,we used actual data as an example to compile and operate an SAS program for calculating the inbreeding coefficients of a pig herd.Compared with the dedicated software for calculating inbreeding coefficients developed in recent years,such as BASIC+database dBASE,Visual Basic+database SQ L Serve method,DFREMLI,MTDF EMLI,VCE,ASREML,DMU,GBS and Herdsman,calculating inbreeding coefficients with SAS programs has the advantages of low cost,simple programming language,and easy operation.For livestock breeders who are not provided with special computing software,the use of SAS to calculate the inbreeding coefficients of pigs is of great significance to planned breed selection and assortative mating.