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Automation Intervention for Monitoring Agriculture Farming Scale: A Review

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Automation Intervention for Monitoring Agriculture Farming S...

International Conference on computing and Networking Technology (ICCNT)

作者： Kapil Joshi Santosh Kumar Shruti Bhatla Vishal Kaushik Harishchander Anandaram Anil Kumar Department of CSE Uttaranchal Institute of Technology Uttaranchal University Dehradun India Department of Mathematics Sharda School of Basic Sciences and Research Sharda University Greater Noida UP India Department of Computer Science and Engineering Graphic Era Hill University Dehradun Uttarakhand India School of Computer Science UPES Dehradun India Centre for Computational Engineering and Networking Amrita Vishwa Vidyapeetham Coimbatore Tamil Nadu India School of Computing DIT University Dehradun India

Agricultural data is only valuable when it has been managed successfully. As data has evolved into the fundamental ingredient in contemporary agriculture to assist farmers with vital decision, latest developments in managing data are causing Smart Agriculture to rise dramatically. With factual information obtained by sensors, significant advantages emerge with the goal of enhancing production and ecology. This type of data-driven farm management is built on information that can boost efficiency by preventing resource waste and contamination of the ecosystem. Data-driven farming, with the assistance of automated solutions using artificial intelligence techniques, lays the groundwork for potential sustainable *** article analyses the current state of sophisticated agricultural systems by rehashing each critical step, from data processing in field crops to variable interest rate implementations, so that farmers can make optimised decision making to save funds while safeguarding the natural environment and converting how meal would be generated to meet the upcoming population explosion.

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Incorporating AI and IoT for Smart Poultry Monitoring and Health Detection

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Incorporating AI and IoT for Smart Poultry Monitoring and He...

Augmented Intelligence and Sustainable Systems (ICAISS), International Conference on

作者： Kapil Joshi Arun Kumar Harishchander Anandaram Anil Kumar Ambika Aggarwal Gunjan Chhabra Department of CSE Uttaranchal Institute of Technology Uttaranchal University Dehradun India Department of Mathematics Sharda School of Basic Sciences and Research Sharda University Greater Noida India Centre for Computational Engineering and Networking Amrita School of Artificial Intelligence Coimbatore India School of Computing DIT University Dehradun India Department of CSE UPES Dehradun India Department of CSE Graphic Era Hill University Dehradun India

One vital sector of animal husbandry, poultry farming, is moving towards scale, intelligence, and automated operations. The emergence of digital technology has significantly improved a number of fields. An extensive analysis of the literature is presented in this paper, with a focus on AI-enabled IoT applications for managing poultry health and well-being. This study primarily centred on chicken welfare, considering modern poultry husbandry is challenged with problems pertaining to standardised criteria for welfare evaluation and reliable monitoring methods, notably for broiler wellness and disease outbreak prevention. There is a compelling rationale for using contemporary technology to continually monitor big farms with millions of birds and increase overall production. This is because there are now increasingly affordable processing resources, IoT sensors, and standard algorithms available. The most important sources of protein in the diet are eggs and poultry, thus poultry farm oversight should prioritize the use of cutting-edge technology solutions. As a result, this study presents a systematic review of the most cutting-edge IoT systems with AI capabilities as well as their most recent developments in the creation of smart systems in this field. The study also explores the benefits and problems that AI and IoT present for chicken farming.

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Investigating institutional influence on graduate program admissions by modelling physics GRE cut-off scores

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arXiv 2020年

作者： Mikkelsen, Nils Johannes Young, Nicholas T. Caballero, Marcos D. Center for Computing in Science Education Department of Physics University of Oslo OsloN-0316 Norway Department of Physics and Astronomy Michigan State University East LansingMI48824 United States Department of Computational Mathematics Science and Engineering Michigan State University East LansingMI48824 United States CREATE for STEM Institute Michigan State University East LansingMI48824 United States

Despite limiting access to applicants from underrepresented racial and ethnic groups, the practice of using hard or soft GRE cut-off scores in physics graduate program admissions is still a popular method for reducing the pool of applicants. The present study considers whether the undergraduate institutions of applicants have any influence on the admissions process by modelling a physics GRE cut-off score with application data from admissions offices of five universities. Two distinct approaches based on inferential and predictive modelling are conducted. While there is some disagreement regarding the relative importance between features, the two approaches largely agree that including institutional information significantly aids the analysis. Both models identify cases where the institutional effects are comparable to factors of known importance such as gender and undergraduate GPA. As the results are stable across many cut-off scores, we advocate against the practice of employing physics GRE cut-off scores in admissions. Copyright © 2020, The Authors. All rights reserved.

关键词： Supervised learning

Improving the accuracy and efficiency of quantum connected moments expansions

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arXiv 2021年

作者： Claudino, Daniel Peng, Bo Bauman, Nicholas P. Kowalski, Karol Humble, Travis S. Oak Ridge National Laboratory Quantum Computing Institute Oak RidgeTN37831 United States Computer Science and Mathematics Oak Ridge National Laboratory Oak RidgeTN37831 United States Pacific Northwest National Laboratory Physical Sciences Division RichlandWA99352 United States Computational Sciences and Engineering Oak Ridge National Laboratory Oak RidgeTN37831 United States

The still-maturing noisy intermediate-scale quantum (NISQ) technology faces strict limitations on the algorithms that can be implemented efficiently. In quantum chemistry, the variational quantum eigensolver (VQE) algorithm has become ubiquitous, using the functional form of the ansatz as a degree of freedom, whose parameters are found variationally in a feedback loop between the quantum processor and its conventional counterpart. Alternatively, a promising new avenue has been unraveled by the quantum variants of techniques grounded on expansions of the moments of the Hamiltonian, among which two stand out: The connected moments expansion (CMX) [Phys. Rev. Lett. 58, 53 (1987)] and the Peeters-Devreese-Soldatov (PDS) functional [J. Phys. A 17, 625 (1984);Int. J. Mod. Phys. B 9, 2899], the latter based on the standard moments . Contrasting with VQE-based methods and provided the quantum circuit prepares a state with non-vanishing overlap with the true ground state, CMX often converges to the ground state energy, while PDS is guaranteed to converge by virtue of being variational. However, for a finite CMX/PDS order, the circuit may significantly impact the energy accuracy. Here we use the ADAPT-VQE algorithm to test shallow circuit construction strategies that are not expected to impede their implementation in the present quantum hardware while granting sizable accuracy improvement in the computed ground state energies. We also show that we can take advantage of the fact that the terms in the connected moments are highly recurring in different powers, incurring a sizable reduction in the number of necessary measurements. By coupling this measurement caching with a threshold that determines whether a given term is to be measured based on its associated scalar coefficient, we observe a further reduction in the number of circuit implementations while allowing for tunable accuracy. Copyright © 2021, The Authors. All rights reserved.

关键词： Quantum chemistry

Introductory physics lab instructors’ perspectives on measurement uncertainty

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Physical Review Physics Education Research 2021年第1期17卷 010133-010133页

作者： Benjamin Pollard Robert Hobbs Rachel Henderson Marcos D. Caballero H. J. Lewandowski Department of Physics University of Colorado Boulder Boulder Colorado 80309 USA JILA National Institute of Standards and Technology and the University of Colorado Boulder Colorado 80309 USA Department of Physics Bellevue College Bellevue Washington 98007 USA Department of Physics & Astronomy and CREATE for STEM Institute Michigan State University East Lansing Michigan 48824 USA Department of Computational Mathematics Science & Engineering Michigan State University East Lansing Michigan 48824 USA Department of Physics and Center for Computing in Science Education University of Oslo 0315 Oslo Norway

Introductory physics lab courses serve as the starting point for students to learn and experience experimental physics at the undergraduate level. They often focus on measurement uncertainty, an essential topic for practicing physicists and a foundation for more advanced lab learning. As such, measurement uncertainty has been a focus when studying and improving introductory physics lab courses. There is a need for a research-based assessment explicitly focused on measurement uncertainty that captures the breadth of learning related to the topic, and that has been developed and documented in an evidence-centered way. In this work, we present the first step in the development of such an assessment, with the goal of establishing the breadth and depth of the domain of measurement uncertainty in introductory physics labs. We conducted and analyzed interviews with introductory physics lab instructors across the US, identifying prevalent concepts and practices related to measurement uncertainty, and their level of emphasis in introductory physics labs. We find that instructors discuss a range of measurement uncertainty topics beyond basic statistical ideas like mean and standard deviation, including those connected to modeling, another lab learning goal. We describe how these findings will be used in the subsequent development of the assessment, called the Survey Of Physics Reasoning On Uncertainty Concepts In Experiments (SPRUCE).

关键词： Assessment Research methodology Faculty Lower undergraduate students

QuaSiMo: A composable library to program hybrid workflows for quantum simulation

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arXiv 2021年

作者： Nguyen, Thien Bassman, Lindsay Lotshaw, Phillip C. Lyakh, Dmitry McCaskey, Alexander Leyton-Ortega, Vicente Pooser, Raphael Elwasif, Wael Humble, Travis S. de Jong, Wibe A. Computer Science & Mathematics Division Oak Ridge National Laboratory Oak RidgeTN37831 United States Computational Research Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States National Center for Computational Sciences Oak Ridge National Laboratory Oak RidgeTN37831 United States Computer Science and Engineering Division Oak Ridge National Laboratory Oak RidgeTN37831 United States Quantum Computing Institute Oak Ridge National Laboratory Oak RidgeTN37831 United States

We present a composable design scheme for the development of hybrid quantum/classical algorithms and workflows for applications of quantum simulation. Our object-oriented approach is based on constructing an expressive set of common data structures and methods that enable programming of a broad variety of complex hybrid quantum simulation applications. The abstract core of our scheme is distilled from the analysis of the current quantum simulation algorithms. Subsequently, it allows a synthesis of new hybrid algorithms and workflows via the extension, specialization, and dynamic customization of the abstract core classes defined by our design. We implement our design scheme using the hardware-agnostic programming language QCOR into the QuaSiMo library. To validate our implementation, we test and show its utility on commercial quantum processors from IBM and Rigetti, running some prototypical quantum simulations. Copyright © 2021, The Authors. All rights reserved.

关键词： Quantum chemistry

Composable programming of hybrid workflows for quantum simulation

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arXiv 2021年

作者： Nguyen, Thien Bassman, Lindsay Lyakh, Dmitry McCaskey, Alexander Leyton-Ortega, Vicente Pooser, Raphael Elwasif, Wael Humble, Travis S. de Jong, Wibe A. Computer Science and Mathematics Division Oak Ridge National Laboratory Oak RidgeTN37831 United States Computational Research Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States National Center for Computational Sciences Oak Ridge National Laboratory Oak RidgeTN37831 United States Computer Science and Engineering Division Oak Ridge National Laboratory Oak RidgeTN37831 United States Quantum Computing Institute Oak Ridge National Laboratory Oak RidgeTN37831 United States

We present a composable design scheme for the development of hybrid quantum/classical algorithms and workflows for applications of quantum simulation. Our object-oriented approach is based on constructing an expressive set of common data structures and methods that enable programming of a broad variety of complex hybrid quantum simulation applications. The abstract core of our scheme is distilled from the analysis of the current quantum simulation algorithms. Subsequently, it allows a synthesis of new hybrid algorithms and workflows via the extension, specialization, and dynamic customization of the abstract core classes defined by our design. We implement our design scheme using the hardware-agnostic programming language QCOR into the QuaSiMo library. To validate our implementation, we test and show its utility on commercial quantum processors from IBM, running some prototypical quantum simulations. Copyright © 2021, The Authors. All rights reserved.

关键词： Quantum chemistry

Tangible reduction in learning sample complexity with large classical samples and small quantum system

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arXiv 2019年

作者： Song, Wooyeong Wieśniak, Marcin Liu, Nana Pawlowski, Marcin Lee, Jinhyoung Kim, Jaewan Bang, Jeongho Department of Physics Hanyang University Seoul04763 Korea Republic of Seoul02792 Korea Republic of Institute of Theoretical Physics and Astrophysics Faculty of Mathematics Physics and Informatics University of Gdańsk Gdańsk80-308 Poland International Centre for Theory of Quantum Technologies University of Gdańsk Gdańsk80-308 Poland Institute of Natural Science Shanghai Jiao Tong University Shanghai200240 China Ministry of Education Key Laboratory in Scientific and Engineering Computing Shanghai Jiao Tong University Shanghai200240 China University of Michigan-Shanghai Jiao Tong University Joint Institute Shanghai200240 China School of Computational Sciences Korea Institute for Advanced Study Seoul02455 Korea Republic of Electronics and Telecommunications Research Institute Daejeon34129 Korea Republic of

Quantum computation requires large classical datasets to be embedded into quantum states in order to exploit quantum parallelism. However, this embedding requires considerable resources in general. It would therefore be desirable to avoid it, if possible, for noisy intermediate-scale quantum (NISQ) implementation. Accordingly, we consider a classical-quantum hybrid architecture, which allows large classical input data, with a relatively small-scale quantum system. This hybrid architecture is used to implement a sampling oracle. It is shown that in the presence of noise in the hybrid oracle, the effects of internal noise can cancel each other out and thereby improve the query success rate. It is also shown that such an immunity of the hybrid oracle to noise directly and tangibly reduces the sample complexity in the framework of computational learning theory. This NISQ-compatible learning advantage is attributed to the oracle’s ability to handle large input features. Copyright © 2019, The Authors. All rights reserved.

关键词： Quantum optics

Ultralow and anisotropic thermal conductivity in semiconductor as2Se3

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arXiv 2017年

作者： González-Romero, Robert L. Antonelli, Alex Chaves, Anderson S. Meléndez, Juan J. Department of Physical Chemical and Natural Systems University Pablo de Olavide Ctra. de Utrera km. 1 Sevilla41013 Spain Instituto de Física Gleb Wataghin and Centre for Computational Engineering & Sciences Universidade Estadual do Campinas Campinas Brazil Department of Physics University of Extremadura Avda. de Elvas s/n Badajoz06006 Spain Institute for Advanced Scientific Computing of Extremadura Badajoz Spain

An ultralow lattice thermal conductivity of 0.14 W· m−1· K−1 along the ~b axis of As2Se3 single crystals was obtained at 300 K by first-principles calculations involving the density functional theory and the resolution of the Boltzmann transport equation. This ultralow lattice thermal conductivity arises from the combination of two mechanisms: 1) a cascade-like fall of the low-lying optical modes, which results in avoided crossings of these with the acoustic modes, low sound velocities and increased scattering rates of the acoustic phonons;and 2) the repulsion between the lone-pair electrons of the As cations and the valence p orbitals of the Se anions, which leads to an increase in the anharmonicity of the bonds. The physical origins of these mechanisms lie on the nature of the chemical bonding in the material and its strong anisotropy. These results, whose validity has been addressed by comparison with SnSe, for which excellent agreement between the theoretical predictions and the experiments is achieved, point out that As2Se3 could exhibit improved thermoelectric properties. Copyright © 2017, The Authors. All rights reserved.

关键词： Chemical bonds