Screen interaction behavior in companion dogs: results from a dog owner survey.

Despite availability of video content marketed for dog (Canis familiaris) entertainment, there is little information on dog behaviors when viewing content, nor describing which content is engaging. The aims of this study were to define demographics of dogs that engage with screens, owner observed behaviors, and perceived content interest. A digital survey was distributed to dog owners (03/2022-03/2023). We collected demographics, home environment, owner-rated behaviors, content interest, and interest in 4 presented videos. We compared the representation of dogs from different purebred dog groups (categorized by job/purpose by the American Kennel Club) with the estimated general purebred dog population. Most respondents (total n=1,246) lived in the USA (89%). Median age was 4 years, 54% were purebred, 51% were female. Most (86%, n=1,077) stated their dog watched screen content. Excitement behaviors were often described: 78% of dogs approached the screen, 76% vocalized. Many owners played videos for their dogs when left alone. Dogs most frequently engaged with animal content; dogs were the most popular animal. Age and visual status influenced the frequency of perceived interaction; age and breed influenced content interest. Within purebred dogs that were stated to watch content, there was a relative over-representation of "sporting" and "herding"-type breeds. A dog's age, visual status, and breed type may influence their interest in video content at home. Because many owners reported excitement in their dogs in reaction to screen content, owners may wish to determine whether video content would be suitable for use when their dogs are left alone.

AOEHO: A New Hybrid Data Replication Method in Fog Computing for IoT Application.

Recently, the concept of the internet of things and its services has emerged with cloud computing. Cloud computing is a modern technology for dealing with big data to perform specified operations. The cloud addresses the problem of selecting and placing iterations across nodes in fog computing. Previous studies focused on original swarm intelligent and mathematical models; thus, we proposed a novel hybrid method based on two modern metaheuristic algorithms. This paper combined the Aquila Optimizer (AO) algorithm with the elephant herding optimization (EHO) for solving dynamic data replication problems in the fog computing environment. In the proposed method, we present a set of objectives that determine data transmission paths, choose the least cost path, reduce network bottlenecks, bandwidth, balance, and speed data transfer rates between nodes in cloud computing. A hybrid method, AOEHO, addresses the optimal and least expensive path, determines the best replication via cloud computing, and determines optimal nodes to select and place data replication near users. Moreover, we developed a multi-objective optimization based on the proposed AOEHO to decrease the bandwidth and enhance load balancing and cloud throughput. The proposed method is evaluated based on data replication using seven criteria. These criteria are data replication access, distance, costs, availability, SBER, popularity, and the Floyd algorithm. The experimental results show the superiority of the proposed AOEHO strategy performance over other algorithms, such as bandwidth, distance, load balancing, data transmission, and least cost path.

Linebreeding as a system of stock breeding to improve the productive qualities of horses of the Kushum breed.

Possessing high meat and dairy productivity and good reproductive and adaptive qualities to year-round grazing, the Kushum breed plays an exceptional role in the improvement of productive qualities in horse herding in Kazakhstan and the former Soviet republics. The aim of the study was to develop breeding methods for creating new highly productive breeding lines for the Kushum breed horses. The leading method was linebreeding, which is based on the systematic use of remarkable animals, the offspring of which will accumulate and develop all the desired qualities and traits. The practical significance of the study lies in the fact that new highly productive breeding lines of the Kushum horse have been created. On this basis, a new intra-breed type of horses with increased live weight, high productivity and adaptive qualities for winter grazing in the Republic of Kazakhstan was created. The results of the study were introduced in farms engaged in breeding Kushum horses. Highly productive stallions of the Kushum breed of new genotypes are sold in horse breeding farms of the republic. These studies are used in the development of a comprehensive plan for selection and breeding work and a scientifically grounded system for conducting productive horse breeding in the Republic of Kazakhstan. The scientific novelty of the study lies in the creation of new highly productive breeding lines of stallions of the Kushum breed Krepysh and Grom, as well as in substantiating the creation of the Samotsvet line with high adaptive qualities.

Moderating Effects of Herding Bias on the Relationship Between Parental Monitoring and Problem Gambling of Youths.

One critical issue in problem gambling is its variation as a function of psychosocial factors. We used empirical data from Nigerian youth Soccer gamblers (N = 238) to explore gambling herding bias as a moderator of the relationship between parental monitoring and problem gambling. Specifically, examine how changes in parental monitoring influence changes in problem gambling, and how this influence is a function of levels of herding bias. Hayes PROCESS macro analysis results revealed that increase in parental monitoring was associated with decrease in problem gambling, whereas increase in herding bias was associated with increase in problem gambling. Herding bias positively moderated the relationship between parental monitoring and problem gambling such that, for respondents who had high and moderate herding bias scores, the relationship between parental monitoring and problem gambling was positive and strong, whereas, for repondents with low herding bias scores, the relationship between parental monitoring and problem gambling was negative. The present study reaffirms the negative and positive influences of parental monitoring and herding bias, respectively, on problem gambling among youths.

Vision-Based Module for Herding with a Sheepdog Robot.

Livestock farming is assisted more and more by technological solutions, such as robots. One of the main problems for shepherds is the control and care of livestock in areas difficult to access where grazing animals are attacked by predators such as the Iberian wolf in the northwest of the Iberian Peninsula. In this paper, we propose a system to automatically generate benchmarks of animal images of different species from iNaturalist API, which is coupled with a vision-based module that allows us to automatically detect predators and distinguish them from other animals. We tested multiple existing object detection models to determine the best one in terms of efficiency and speed, as it is conceived for real-time environments. YOLOv5m achieves the best performance as it can process 64 FPS, achieving an mAP (with IoU of 50%) of 99.49% for a dataset where wolves (predator) or dogs (prey) have to be detected and distinguished. This result meets the requirements of pasture-based livestock farms.

An Efficient and Privacy-Preserving Scheme for Disease Prediction in Modern Healthcare Systems.

With the Internet of Things (IoT), mobile healthcare applications can now offer a variety of dimensionalities and online services. Disease Prediction Systems (DPS) increase the speed and accuracy of diagnosis, improving the quality of healthcare services. However, privacy is garnering an increasing amount of attention these days, especially concerning personal healthcare data, which are sensitive. There are a variety of prevailing privacy preservation techniques for disease prediction that are rendered. Nonetheless, there is a chance of medical users being affected by numerous disparate diseases. Therefore, it is vital to consider multi-label instances, which might decrease the accuracy. Thus, this paper proposes an efficient privacy-preserving (PP) scheme for patient healthcare data collected from IoT devices aimed at disease prediction in the modern Health Care System (HCS). The proposed system utilizes the Log of Round value-based Elliptic Curve Cryptography (LR-ECC) to enhance the security level during data transfer after the initial authentication phase. The authorized healthcare staff can securely download the patient data on the hospital side. Utilizing the Herding Genetic Algorithm-based Deep Learning Neural Network (EHGA-DLNN) can test these data with the trained system to predict the diseases. The experimental results demonstrate that the proposed approach improves prediction accuracy, privacy, and security compared to the existing methods.

Herding intensity and volatility in cryptocurrency markets during the COVID-19.

This paper investigates whether herding is present before and during the COVID-19 pandemic, analyzing intraday data of Bitcoin and eight altcoins. The herding intensity measure of Patterson and Sharma (2006) is calculated for the first time for cryptocurrency markets. Furthermore, we employed a novel Granger causality methodology with a Fourier approximation to determine the relationship between herding and volatility, considering the structural breaks. Our results indicate a significant herding behavior, concentrating during the COVID-19 outbreak. The causality test results show that herding has a significant effect on market volatility. Our results do not support the efficient market hypothesis.

Gradient-based elephant herding optimization for cluster analysis.

Clustering analysis is essential for obtaining valuable information from a predetermined dataset. However, traditional clustering methods suffer from falling into local optima and an overdependence on the quality of the initial solution. Given these defects, a novel clustering method called gradient-based elephant herding optimization for cluster analysis (GBEHO) is proposed. A well-defined set of heuristics is introduced to select the initial centroids instead of selecting random initial points. Specifically, the elephant optimization algorithm (EHO) is combined with the gradient-based algorithm GBO for assigning initial cluster centers across the search space. Second, to overcome the imbalance between the original EHO exploration and exploitation, the initialized population is improved by introducing Gaussian chaos mapping. In addition, two operators, i.e., random wandering and variation operators, are set to adjust the location update strategy of the agents. Nine datasets from synthetic and real-world datasets are adopted to evaluate the effectiveness of the proposed algorithm and the other metaheuristic algorithms. The results show that the proposed algorithm ranks first among the 10 algorithms. It is also extensively compared with state-of-the-art techniques, and four evaluation criteria of accuracy rate, specificity, detection rate, and F-measure are used. The obtained results clearly indicate the excellent performance of GBEHO, while the stability is also more prominent.

Geriatrics and COVID-19.

Since December 2019, a novel coronavirus called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has begun to infect people. The virus first occurred in Wuhan, China, but the whole world is now struggling with the pandemic. Over 13 million confirmed cases and 571,000 deaths have been reported so far, and this number is growing. Older people, who constitute a notable proportion of the world population, are at an increased risk of infection because of altered immunity and chronic comorbidities. Thus, appropriate health care is necessary to control fatalities and spread of the disease in this specific population. The chapter provides an overview of diagnostic methods, laboratory and imaging findings, clinical features, and management of COVID-19 in aged people. Possible mechanisms behind the behavior of SARS-CoV-2 in the elderly include immunosenescence and related impaired antiviral immunity, mature immunity and related hyper-inflammatory responses, comorbidities and their effects on the functioning of critical organs/systems, and the altered expression of angiotensin-converting enzyme 2 (ACE2) that acts as an entry receptor for SARS-CoV-2. This evidence defines the herding behavior of COVID-19 in relation to ACE2 under the influence of immune dysregulation. Then, identifying the immunogenetic factors that affect the disease susceptibility and severity and as well as key inflammatory pathways that have the potential to serve as therapeutic targets needs to remain an active area of research.

Agricultural origins on the Anatolian plateau.

This paper explores the explanations for, and consequences of, the early appearance of food production outside the Fertile Crescent of Southwest Asia, where it originated in the 10th/9th millennia cal BC. We present evidence that cultivation appeared in Central Anatolia through adoption by indigenous foragers in the mid ninth millennium cal BC, but also demonstrate that uptake was not uniform, and that some communities chose to actively disregard cultivation. Adoption of cultivation was accompanied by experimentation with sheep/goat herding in a system of low-level food production that was integrated into foraging practices rather than used to replace them. Furthermore, rather than being a short-lived transitional state, low-level food production formed part of a subsistence strategy that lasted for several centuries, although its adoption had significant long-term social consequences for the adopting community at Boncuklu. Material continuities suggest that Boncuklu's community was ancestral to that seen at the much larger settlement of Çatalhöyük East from 7100 cal BC, by which time a modest involvement with food production had been transformed into a major commitment to mixed farming, allowing the sustenance of a very large sedentary community. This evidence from Central Anatolia illustrates that polarized positions explaining the early spread of farming, opposing indigenous adoption to farmer colonization, are unsuited to understanding local sequences of subsistence and related social change. We go beyond identifying the mechanisms for the spread of farming by investigating the shorter- and longer-term implications of rejecting or adopting farming practices.

A Range-Based Algorithm for Autonomous Navigation of an Aerial Drone to Approach and Follow a Herd of Cattle.

This paper proposes an algorithm that will allow an autonomous aerial drone to approach and follow a steady or moving herd of cattle using only range measurements. The algorithm is also insensitive to the complexity of the herd's movement and the measurement noise. Once arrived at the herd of cattle, the aerial drone can follow it to a desired destination. The primary motivation for the development of this algorithm is to use simple, inexpensive and robust sensing hence range sensors. The algorithm does not depend on the accuracy of the range measurements, rather the rate of change of range measurements. The proposed method is based on sliding mode control which provides robustness. A mathematical analysis, simulations and experimental results with a real aerial drone are presented to demonstrate the effectiveness of the proposed method.

Fire Management in The Boreal Forest of Swedish Sápmi: Prescribed Burning and Consideration of Sami Reindeer Herding During 1920-1970.

Forest owners and Indigenous Sami reindeer herders use the same land in northern Sweden for commercial forestry and winter grazing, respectively. Fire management has been controlled by foresters since the late-19th century, and Sami herders have had to deal with the effects of both fire suppression and prescribed burning. However, the environmental history of fire management and reindeer herding in Sweden has never been thoroughly investigated. We therefore analyzed written archives in order to understand how reindeer herding was considered in planned burning during the mid-20th century, and how the effects of prescribed burning on reindeer herding were interpreted by foresters. We supplemented the interpretation of written sources by including local Sami reindeer herders' insights about prescribed burning. Written records show that reindeer herding was increasingly integrated into the planning process during the 20th century, yet foresters failed to include important aspects of reindeer herding in their interpretation of the effects of prescribed burning. The Sami consider the effects of burning in terms of fodder availability, opportunities for reindeer to graze the fodder, and any impact on the reindeer's movement patterns and thus herd management. The Sami's historical perspective is essential in order to reconstruct a comprehensive picture of the past, and adapt forestry measures effectively in the future.

Testing the Social Bubble Hypothesis on the Early Dynamics of a Scientific Project: The FET Flagship Candidate FuturICT (2010-2013).

We present an analysis of a large emerging scientific project in the light provided by the social bubbles hypothesis (SBH) that we have introduced in earlier papers. The SBH claims that, during an innovation boom or technological revolution, strong social interactions between enthusiastic supporters weave a network of reinforcing feedbacks that leads to widespread endorsement and extraordinary commitment, beyond what would be rationalized by a standard cost-benefit analysis. By probing the (Future and Emerging Technologies) FET Flagship candidate FuturICT project, as it developed in 2010-2013, we aimed at better understanding how a favorable climate was engineered, allowing the dynamics and risk-taking behaviors to evolve. We document that significant risk-taking was indeed clearly found-especially during workshops and meetings, for instance, in the form of the time allocation of participants, who seemed not to mind their precious time being given to the project and who exhibited many signs of enthusiasm. In this sense, the FuturICT project qualifies as a social bubble in the making when considered at the group level. In contrast, risk-perception at the individual level remained high and not everyone involved shared the exuberance cultivated by the promoters of FuturICT. As a consequence, those not unified under the umbrella of the core vision built niches for themselves that were stimulating enough to stay with the project, but not on a basis of blind over-optimism. Our detailed field study shows that, when considering individuals in isolation, the characteristics associated with a social bubble can vary significantly in the presence of other factors besides exaggerated risk-taking.

Transitions in the cryptocurrency market during the COVID-19 pandemic: A network analysis.

In this letter, we identify the transitions of the cryptocurrency market during the pandemic by means of a network analysis. This method allows us to observe that COVID-19 significantly affected cryptocurrencies during a short period of financial panic, from 12 March 2020 to 1 April 2020, giving rise to a remarkable increase of market synchronisation. However, since April 2020, the cryptocurrency market progressively recovered its initial state, since the strong synchronisation, observed as a consequence of COVID-19, continuously disappeared. Therefore, our analysis highlights different market phases, which can be related to some of the phenomena reported in the existing literature.

COVID-19 effect on herding behaviour in European capital markets.

This article investigates whether COVID-19 pandemic had an effect on herding behaviour in Europe. Using a sample from the stock exchanges of France (Paris), Germany (Frankfurt), Italy (Milan), United Kingdom (London) and Spain (Madrid), over the period from January 03, 2000 to June 19, 2020, we found robust evidence that COVID-19 pandemic increased herding behaviour in the capital markets of Europe.

Mixed-species herding levels the landscape of fear.

Prey anti-predator behaviours are influenced by perceived predation risk in a landscape and social information gleaned from herd mates regarding predation risk. It is well documented that high-quality social information about risk can come from heterospecific herd mates. Here, we integrate social information with the landscape of fear to quantify how these landscapes are modified by mixed-species herding. To do this, we investigated zebra vigilance in single- and mixed-species herds across different levels of predation risk (lion versus no lion), and assessed how they manage herd size and the competition-information trade-off associated with grouping behaviour. Overall, zebra performed higher vigilance in high-risk areas. However, mixed-species herding reduced vigilance levels. We estimate that zebra in single-species herds would have to feed for approximately 35 min more per day in low-risk areas and approximately 51 min more in high-risk areas to compensate for the cost of higher vigilance. Furthermore, zebra benefitted from the competition-information trade-off by increasing the number of heterospecifics while keeping the number of zebra in a herd constant. Ultimately, we show that mixed-species herding reduces the effects of predation risk, whereby zebra in mixed-species herds, under high predation risk, perform similar levels of vigilance compared with zebra in low-risk scenarios.

Endangering China's environmental health security goals through negative environmental investor behaviours.

China sees the need to maximise its environmental health security as a major priority in its sustainable development agenda. This is at the heart of China's "ecological civilisation" and "beautiful China" dream. One of the objectives of this dream is to sensitize investors to invest in health and environmental stocks to support environmental health goals. However, both the Shanghai and the Shenzhen stock markets continue to witness contemporaneous movement (herding behaviour) by investors from environmental stock to perceived safer stocks and this is stifling the growth of the environmental health sector due to capital deprivation. Our paper evaluates the significance and potential effect of this herding trend among environmental stocks using a collection of sophisticated econometric models namely, the state-space model, enhanced state-space model, the cross-sectional SD (CSSD) and the cross-sectional absolute deviation (CSAD). The models are used to evaluate firm-level data collected from the 80 environmental stocks indexed by the KGRM MSCI China IMI Environment 10/40 Index. Three of the models confirm the presence of endemic negative (herding) investor behaviour among environmental stocks in China and this threatens the sustainability of environmental stock capital to promote China's environmental health goals. We have proposed measures to ameliorate the risks posed by such negative contemporaneous investor behaviours.

How the cryptocurrency market has performed during COVID 19? A multifractal analysis.

Cryptocurrency markets are complex systems based on speculation. Where investors interact using strategies that generate some biases responsible for endogenous instabilities. This paper investigated the herding biases by quantifying the self-similarity intensity of cryptocurrency returns' during the COVID-19 pandemic. The main purpose of this work was to study the level of cryptocurrency efficiency through multifractal analysis before and after the coronavirus pandemic. The empirical results proved that COVID-19 has a positive impact on the cryptocurrency market efficiency.

Performance of Elephant Herding Optimization and Tree Growth Algorithm Adapted for Node Localization in Wireless Sensor Networks.

Wireless sensor networks, as an emerging paradigm of networking and computing, have applications in diverse fields such as medicine, military, environmental control, climate forecasting, surveillance, etc. For successfully tackling the node localization problem, as one of the most significant challenges in this domain, many algorithms and metaheuristics have been proposed. By analyzing available modern literature sources, it can be seen that the swarm intelligence metaheuristics have obtained significant results in this domain. Research that is presented in this paper is aimed towards achieving further improvements in solving the wireless sensor networks localization problem by employing swarm intelligence. To accomplish this goal, we have improved basic versions of the tree growth algorithm and the elephant herding optimization swarm intelligence metaheuristics and applied them to solve the wireless sensor networks localization problem. In order to determine whether the improvements are accomplished, we have conducted empirical experiments on different sizes of sensor networks ranging from 25 to 150 target nodes, for which distance measurements are corrupted by Gaussian noise. Comparative analysis with other state-of-the-art swarm intelligence algorithms that have been already tested on the same problem instance, the butterfly optimization algorithm, the particle swarm optimization algorithm, and the firefly algorithm, is conducted. Simulation results indicate that our proposed algorithms can obtain more consistent and accurate locations of the unknown target nodes in wireless sensor networks topology than other approaches that have been proposed in the literature.