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.

Intelligent design:stablecoins(in)stability and collateral during market turbulence

How does stablecoin design affect market behavior during turbulent periods?Stable-coins attempt to maintain a“stable”peg to the US dollar,but do so with widely varying structural designs.The spectacular collapse of the TerraUSD(UST)stablecoin and the linked Terra(LUNA)token in May 2022 precipitated a series of reactions across major stablecoins,with some experiencing a fall in value and others gaining value.Using a Baba,Engle,Kraft and Kroner(1990)(BEKK)model,we examine the reaction to this exogenous shock and find significant contagion effects from the UST collapse,likely partially due to herding behavior among traders.We test the varying reactions among stablecoins and find that stablecoin design differences affect the direction,magnitude,and duration of the response to shocks.We discuss the implications for stablecoin developers,exchanges,traders,and regulators.

An Optimized Novel Trust-Based Security Mechanism Using Elephant Herd Optimization

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.

Financial literacy,behavioral traits,and ePayment adoption and usage in Japan

This study investigates how financial literacy and behavioral traits affect the adoption of electronic payment(ePayment)services in Japan.We construct a financial literacy index using a representative sample of 25,000 individuals from the Bank of Japan’s 2019 Financial Literacy Survey.We then analyze the relationship between this index and the extensive and intensive usage of two types of payment services:electronic money(e-money)and mobile payment apps.Using an instrumental variable approach,we find that higher financial literacy is positively associated with a higher likelihood of adopting ePayment services.The empirical results suggest that individuals with higher financial literacy use payment services more frequently.We also find that risk-averse people are less likely to adopt and use ePayment services,whereas people with herd behavior tend to adopt and use ePayment services more.Our empirical results also suggest that the effects of financial literacy on the adoption and use of ePayment differ among people with different behavioral traits.

Method for Fault Diagnosis and Speed Control of PMSM

In the field of fault tolerance estimation,the increasing attention in electrical motors is the fault detection and diagnosis.The tasks performed by these machines are progressively complex and the enhancements are likewise looked for in the field of fault diagnosis.It has now turned out to be essential to diagnose faults at their very inception;as unscheduled machine downtime can upset deadlines and cause heavy financial burden.In this paper,fault diagnosis and speed control of permanent magnet synchronous motor(PMSM)is proposed.Elman Neural Network(ENN)is used to diagnose the fault of permanent magnet synchronous motor.Both the fault location and fault severity are considered.In this,eccentricity fault may occur in the motor.To control the speed of the permanent magnet synchronous motor,Dolphin Swarm Optimization(DSO)algorithm is used.The proposed work is simulated by using MATLAB in terms of amplitude,speed and torque.The comparison graph of speed vs.torque obtained by the proposed method gives better result compared to the other existing techniques.The proposed work is also compared with Particle Swarm Optimization(PSO)and Elephant Herding Optimization(EHO)algorithm.The proposed usage of Elman Neural Network to detect the fault and the usage of Dolphin Swarm Optimization algorithm to control the speed of the permanent magnet synchronous motor gives better outcome.

Early Detection of Heartbeat from Multimodal Data Using RPA Learning with KDNN-SAE

Heartbeat detection stays central to cardiovascular an electrocardiogram(ECG)is used to help with disease diagnosis and management.Existing Convolutional Neural Network(CNN)-based methods suffer from the less generalization problem thus;the effectiveness and robustness of the traditional heartbeat detector methods cannot be guaranteed.In contrast,this work proposes a heartbeat detector Krill based Deep Neural Network Stacked Auto Encoders(KDNN-SAE)that computes the disease before the exact heart rate by combining features from multiple ECG Signals.Heartbeats are classified independently and multiple signals are fused to estimate life threatening conditions earlier without any error in classification of heart beat.This work contained Training and testing stages,in the preparation part at first the Adaptive Filter Enthalpy-based Empirical Mode Decomposition(EMD)is utilized to eliminate the motion artifact in the signal.At that point,the robotic process automation(RPA)learning part extracts the effective features are extracted,and normalized the value of the feature then estimated utilizing the RPA loss function.At last KDNN-SAE prepared training for the data stored in the dataset.In the subsequent stage,input signal compute motion artifact and RPA Learning the evaluation part determines the detection of Heartbeat.So early diagnosis of heart failures is an essential factor.The results of the experiments show that our proposed method has a high score outcome of 0.9997.Comparable to the CIF,which reaches 0.9990.The CNN and Artificial Neural Network(ANN)had less score 0.95115 and 0.90147.

Time Delay Estimation in Radar System using Fuzzy Based Iterative Unscented Kalman Filter

RSs(Radar Systems)identify and trace targets and are commonly employed in applications like air traffic control and remote sensing.They are necessary for monitoring precise target trajectories.Estimations of RSs are non-linear as the parameters TDEs(time delay Estimations)and Doppler shifts are computed on receipt of echoes where EKFs(Extended Kalman Filters)and UKFs(Unscented Kalman Filters)have not been examined for computations.RSs,certain times result in poor accuracies and SNRs(low signal to noise ratios)especially,while encountering complicated environments.This work proposes IUKFs(Iterated UKFs)to track onlinefilter performances while using optimization techniques to enhance outcomes.The use of cost functions can assist state corrections while lowering costs.A new parameter is optimized using MCEHOs(Mutation Chaotic Elephant Herding Optimizations)by linearly approximating system non-linearity where OIUKFs(Optimized Iterative UKFs)predict a target's unknown parameters.To obtain optimal solutions theoretically,OIUKFs take less iteration,resulting in shorter execution times.The proposed OIUKFs provide numerical approximations which are derivative-free implementations.Simulation evaluation results with estimators show better performances in terms of reduced NMSEs(Normalized Mean Square Errors),RMSEs(Root Mean Squared Errors),SNRs,variances,and better accuracies than current approaches.

Heterogeneous Ensemble Feature Selection Model(HEFSM)for Big Data Analytics

Big Data applications face different types of complexities in classifications.Cleaning and purifying data by eliminating irrelevant or redundant data for big data applications becomes a complex operation while attempting to maintain discriminative features in processed data.The existing scheme has many disadvantages including continuity in training,more samples and training time in feature selections and increased classification execution times.Recently ensemble methods have made a mark in classification tasks as combine multiple results into a single representation.When comparing to a single model,this technique offers for improved prediction.Ensemble based feature selections parallel multiple expert’s judgments on a single topic.The major goal of this research is to suggest HEFSM(Heterogeneous Ensemble Feature Selection Model),a hybrid approach that combines multiple algorithms.The major goal of this research is to suggest HEFSM(Heterogeneous Ensemble Feature Selection Model),a hybrid approach that combines multiple algorithms.Further,individual outputs produced by methods producing subsets of features or rankings or voting are also combined in this work.KNN(K-Nearest Neighbor)classifier is used to classify the big dataset obtained from the ensemble learning approach.The results found of the study have been good,proving the proposed model’s efficiency in classifications in terms of the performance metrics like precision,recall,F-measure and accuracy used.

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.