Cloud-based mathematical models for self-organizing swarms of UAVs : design and analysis

Unmanned aerial vehicle (UAV) swarms have gained significant attention for their potential applications in various fields. The effective coordination and control of UAV swarms require the development of robust mathematical models that can capture their complex dynamics. The paper introduces mathematical models and relevant paradigms based on the design and analysis of self-organizing swarms of UAVs. The logical and technological construction of the model relies on the theorems developed by authors for obtaining full information exchange during the swarm quasi-random walk. The suggested rotor-router model interprets the discrete-time walk accompanied by the deterministic evolution of configurations of rotors randomly placed on the vertices of the swarm graph. The recommended optimal and fault-tolerant gossip/broadcast schemes support the resilience of swarm to internal failures and external attacks, and cryptographic protocols approve the security. The proposed cloud network topology serves as the implementation framework for the model, encompassing various connectivity options to ensure the expected behavior of the UAV swarms.Peer reviewe

U.S. consumers' acceptance and willingness to buy irradiated food

Due to the character of the original source materials and the nature of batch digitization, quality control issues may be present in this document. Please report any quality issues you encounter to [email protected], referencing the URI of the item.Includes bibliographical references (leaves 95-102).Issued also on microfiche from Lange Micrographics.A consumer intercept survey was conducted to evaluate consumers' attitude and awareness, as well as their willingness to accept irradiated food products. The primary data for this analysis were collected in Spring 2001. A low level of awareness of food irradiation exists despite the recent increase in news stories about irradiation technology. This study reveals that most consumers are not familiar with irradiation technology, which attributes to the fact that the public is very ambivalent in their decisions regarding irradiated foods. Education programs seem to have positive effects on shaping consumer opinion about irradiation, which can improve the safety of food products. Thus, the results of this study provide useful information required for the development and implementation of effective consumer educational programs. The study identifies the current profiles of consumers who are willing to purchase irradiated food products and who are willing to pay a premium for irradiated beef products in the marketplace. A number of socio-economic variables were hypothesized to be related to consumer willingness to buy and pay more for irradiated beef. The estimates of willingness to buy were obtained using a probit model. Willingness to pay more for irradiated food products was estimated using ordered probit with a sample selection model. Standard errors of the marginal effects of the ordered probit model were estimated using the bootstrap method. About 80% of the respondents were willing to purchase irradiated beef products and about 58% were willing to pay a premium for irradiated beef. This finding suggests that those who think that improper handling contributes to food poisoning are more likely to buy and pay a premium of 50 cents per pound for irradiated beef than others. Those who trust the irradiation technology are also more likely to purchase and pay a premium of between 5 to 50 cents per pound for irradiated beef. The results of this study provide information important not only to food retailers, but also to other players in the supply chain

The livestock mandatory price reporting system: Lamb industry perceptions and impact

Due to the character of the original source materials and the nature of batch digitization, quality control issues may be present in this document. Please report any quality issues you encounter to [email protected], referencing the URI of the item.Includes bibliographical references (leaves 111-114).Issued also on microfiche from Lange Micrographics.The livestock mandatory price reporting system was approved by Congress in July of 1999, signed into law on October 22, 1999, and launched on April 2, 2001. In announcing the implementation of the mandatory price reporting system, the USDA claimed that it would provide information on 80% to 95% of the volume of all cattle, boxed beef, slaughter hogs, sheep and lamb meat, and imported lamb meat transactions. The new law had three explicit objectives: (1) facilitate price discovery, (2) make livestock markets more open, and (3) provide all market participants with market information that can be easily understood (USDA 2001a). This study attempts to determine if the new mandatory price reporting system had achieved these objectives in the perspective of the sheep and lamb industry during the period of the study. Survey questionnaires were developed to determine the perceptions of lamb producers and feeders regarding the effect of the mandatory price reporting system on: (1) the price discovery process, (2) the openness and transparency of lamb buying and selling transactions in the market, and (3) the quantity, accuracy, availability, and timeliness of information needed to make production and marketing decisions. First, the cross-tabulation or descriptive categorical analysis provided the basis for an aggregate evaluation of the perceptions of the survey respondents. Second, a logit regression analysis based on the ordinal regression models was built to consider the relationship between the perceptions of the responding sheep and lamb producers and feeders regarding the new mandatory price reporting system and key characteristics of those respondents (such as age, gender, years of experience, size of operations, etc.). During the period of the study, most respondents believed that the new Mandatory Price Reporting Act has not facilitated or enhanced their power to negotiate prices. Although producers from bigger ranches and feeders from larger capacity ranches reported some improvement in the price discovery process, most respondents believed that packers and feeders were still manipulating the sheep and lambs markets. Neither producers nor feeders perceived any improvements in the availability and openness of private transactions. The mandatory livestock price reports about the sheep and lamb market prices are considered the least useful information source for majority of respondents, as well as the most difficult to understand. The most useful and understandable source is perceived to be the voluntary USDA market reports and other ranchers and feeders

Pair correlations in sandpile model: A Check of logarithmic conformal field theory

We compute the correlations of two height variables in the two-dimensional Abelian sandpile model. We extend the known result for two minimal heights to the case when one of the heights is bigger than one. We find that the most dominant correlation log r/r^4 exactly fits the prediction obtained within the logarithmic conformal approach. Comment: 5 page

Towards the NNLL Precision in the Decay Bˉ→Xsγ\bar B \rightarrow X_s \gamma

The present NLL prediction for the decay rate of the rare inclusive process {bar B} {yields} X{sub s}{gamma} has a large uncertainty due to the charm mass renormalization scheme ambiguity. We estimate that this uncertainty will be reduced by a factor of 2 at the NNLL level. This is a strong motivation for the on-going NNLL calculation, which will thus significantly increase the sensitivity of the observable {bar B} {yields} X{sub s}{gamma} to possible new degrees of freedom beyond the SM. We also give a brief status report of the NNLL calculation

Air temperature forecasting using artificial neural network for Ararat valley

The air temperature is a critical factor in many societal challenges to protect human health and the environment. Moreover, a vital climatic parameter, the temperature has a direct impact on evaporation, frost, and snow melting. Temperature predictions are based mainly on numerical and statistical models. Sometimes it is a challenge to improve the weather forecast accuracy. The article aims to implement a weather prediction technique based on machine learning methods and approaches to improve the hourly air temperature prediction for up to 24 hours in the Ararat valley (Armenia). Due to intense heat and low relative humidity, the high temperatures and hot winds occur between 120 and 160 days per year in Ararat valley, as one of the aridest regions of Armenia. The approach utilizes the earth observation data received from several meteorological stations and the large satellite analysis-ready datasets at different frequencies and resolutions. The experiments have been conducted with multiple neural networks to forecast air temperatures for 24 hours that happened over the Ararat valley. The suggested model has 87.31% and 75.57% accuracies to predict the temperature for the next 3 and 24 hours, which is sufficient to be used alongside the current state-of-the-art techniques