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13 pages, 2862 KiB  
Article
Solving Flexible Job-Shop Scheduling Problems Based on Quantum Computing
by Kaihan Fu, Jianjun Liu, Miao Chen and Huiying Zhang
Entropy 2025, 27(2), 189; https://doi.org/10.3390/e27020189 - 13 Feb 2025
Viewed by 465
Abstract
Flexible job-shop scheduling problems (FJSPs) represent one of the most complex combinatorial optimization challenges. Modern production systems and control processes demand rapid decision-making in scheduling. To address this challenge, we propose a quantum computing approach for solving FJSPs. We propose a quadratic unconstrained [...] Read more.
Flexible job-shop scheduling problems (FJSPs) represent one of the most complex combinatorial optimization challenges. Modern production systems and control processes demand rapid decision-making in scheduling. To address this challenge, we propose a quantum computing approach for solving FJSPs. We propose a quadratic unconstrained binary optimization (QUBO) model to minimize the makespan of FJSPs, with the scheduling scheme encoded in the ground state of the Hamiltonian operator. The model is solved using a coherent Ising machine (CIM). Numerical experiments are conducted to evaluate and validate the performance and effectiveness of the CIM. The results demonstrate that quantum computing holds significant potential for solving FJSPs more efficiently than traditional computational methods. Full article
(This article belongs to the Special Issue Quantum Information: Working towards Applications)
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<p>Gantt chart of a solution for the example (3 jobs × 3 machines FJSP, makespan 16). Orange represents job 1, blue represents job 2, and green represents job 3.</p>
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<p>Structure and principle of a coherent Ising machine.</p>
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<p>Diagram of the total energy value of the Hamiltonian of benchmark SSFJS05.</p>
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<p>Diagram of the maximum cut of the optical quantum computer: (<b>a</b>) 17 × 17 Ising matrix; (<b>b</b>) 85 × 85 Ising matrix; (<b>c</b>) 160 × 160 Ising matrix; (<b>d</b>) 193 × 193 Ising matrix; (<b>e</b>) 127 × 127 Ising matrix. The blue or green dot on the circumference indicates the phase state of the optical qubit after coherence, the blue indicates that the phase is positive (spin variable σ is “1”), and the green indicates that the phase is negative (spin variable σ is “−1”).</p>
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<p>Gantt chart solved by the optical quantum computer of benchmark SSFJS05. Red represents job 1, yellow represents job 2, and orange represents job 3.</p>
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16 pages, 2867 KiB  
Article
Mixed Thermal and Renewable Energy Generation Optimization in Non-Interconnected Regions via Boolean Mapping
by Pavlos Nikolaidis
Thermo 2024, 4(4), 445-460; https://doi.org/10.3390/thermo4040024 - 23 Oct 2024
Cited by 1 | Viewed by 672
Abstract
Global efforts aiming to shift towards renewable energy and smart grid configurations require accurate unit commitment schedules to guarantee power balance and ensure feasible operation under different complex constraints. Intelligent systems utilizing hybrid and high-level techniques have arisen as promising solutions to provide [...] Read more.
Global efforts aiming to shift towards renewable energy and smart grid configurations require accurate unit commitment schedules to guarantee power balance and ensure feasible operation under different complex constraints. Intelligent systems utilizing hybrid and high-level techniques have arisen as promising solutions to provide optimum exploration–exploitation trade-offs at the expense of computational complexity. To ameliorate this requirement, which is extremely expensive in non-interconnected renewable systems, radically different approaches based on enhanced priority schemes and Boolean encoding/decoding have to take place. This compilation encompasses various mappings that convert multi-valued clausal forms into Boolean expressions with equivalent satisfiability. Avoiding any need to introduce prior parameter settings, the solution utilizes state-of-the-art advancements in the field of artificial intelligence models, namely Boolean mapping. It allows for the efficient identification of the optimal configuration of a non-convex system with binary and discontinuous dynamics in the fewest possible trials, providing impressive performance. In this way, Boolean mapping becomes capable of providing global optimum solutions to unit commitment utilizing fully tractable procedures without deteriorating the computational time. The results, considering a non-interconnected power system, show that the proposed model based on artificial intelligence presents advantageous performance in terms of generating cost and complexity. This is particularly important in isolated networks, where even a-not-so great deviation between production and consumption may reflect as a major disturbance in terms of frequency and voltage. Full article
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<p>Demonstration of a conventional generating unit [<a href="#B11-thermo-04-00024" class="html-bibr">11</a>].</p>
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<p>(<b>a</b>) Input–output curve of a generating unit and (<b>b</b>) the net heat rate characteristic of a steam turbine generator unit [<a href="#B11-thermo-04-00024" class="html-bibr">11</a>].</p>
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<p>Combined cycle configuration.</p>
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<p>Dynamic exploration of the produced binary combinations per committed unit.</p>
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<p>Flow diagram of the comprehensive BM optimization paradigm.</p>
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<p>A representation of the weekly MW load demand per season.</p>
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<p>PV power output impact on net electricity demand.</p>
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<p>Annual electricity demand distinguished in RES power supplied, base load, and peak load.</p>
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<p>Actual implementation of the proposed BM optimization loop.</p>
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<p>Optimal economic dispatch of the proposed approach.</p>
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25 pages, 433 KiB  
Article
Polar Codes with Differential Phase Shift Keying for Selective Detect-and-Forward Multi-Way Relaying Systems
by Ruilin Ji and Harry Leib
Network 2024, 4(3), 313-337; https://doi.org/10.3390/network4030015 - 8 Aug 2024
Viewed by 1002
Abstract
Relaying with network coding forms a basis for a variety of collaborative communication systems. A linear block coding framework for multi-way relaying using network codes introduced in the literature shows great promise for understanding, analyzing, and designing such systems. So far, this technique [...] Read more.
Relaying with network coding forms a basis for a variety of collaborative communication systems. A linear block coding framework for multi-way relaying using network codes introduced in the literature shows great promise for understanding, analyzing, and designing such systems. So far, this technique has been used with low-density parity check (LDPC) codes and belief propagation (BP) decoding. Polar codes have drawn significant interest in recent years because of their low decoding complexity and good performance. Our paper considers the use of polar codes also as network codes with differential binary phase shift keying (DBPSK), bypassing the need for channel state estimation in multi-way selective detect-and-forward (DetF) cooperative relaying. We demonstrate that polar codes are suitable for such applications. The encoding and decoding complexity of such systems for linear block codes is analyzed using maximum likelihood (ML) decoding for LDPC codes with log-BP decoding and polar codes with successive cancellation (SC) as well as successive cancellation list (SCL) decoding. We present Monte-Carlo simulation results for the performance of such a multi-way relaying system, employing polar codes with different lengths and code rates. The results demonstrate a significant performance gain compared to an uncoded scheme. The simulation results show that the error performance of such a system employing polar codes is comparable to LDPC codes with log-BP decoding, while the decoding complexity is much lower. Furthermore, we consider a hard threshold technique at user terminals for determining whether a relay transmits or not. This technique makes the system practical without increasing the complexity and can significantly reduce the degradation from intermittent relay transmissions that is associated with such a multi-way relaying protocol. Full article
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Graphical abstract
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<p>Relation between transmission time slots and phases.</p>
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<p>Encoder of a <math display="inline"><semantics> <mrow> <mo>(</mo> <mn>8</mn> <mo>,</mo> <mn>5</mn> <mo>)</mo> </mrow> </semantics></math> polar code.</p>
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<p>BEC channel model.</p>
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<p>Receiver model of the terminals with the hard threshold.</p>
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<p>Frame error rate (FER) performance of a <math display="inline"><semantics> <mrow> <mo>(</mo> <mn>1024</mn> <mo>,</mo> <mn>512</mn> <mo>)</mo> </mrow> </semantics></math> polar code with successive cancellation (SC) and successive cancellation list (SCL) decoders over an additive white Gaussian noise (AWGN) channel and comparison with the results from ref. [<a href="#B60-network-04-00015" class="html-bibr">60</a>] (marked as [ref]).</p>
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<p>Bit error rate (BER) performance of a <math display="inline"><semantics> <mrow> <mo>(</mo> <mn>8</mn> <mo>,</mo> <mn>5</mn> <mo>)</mo> </mrow> </semantics></math> polar code with the successive cancellation (SC) and maximum likelihood (ML) decoders.</p>
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<p>Bit error rate (BER) performance of three long polar codes when all relays transmit.</p>
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<p>Bit error rate (BER) performance of a <math display="inline"><semantics> <mrow> <mo>(</mo> <mn>512</mn> <mo>,</mo> <mn>416</mn> <mo>)</mo> </mrow> </semantics></math> polar code with successive cancellation (SC) decoding.</p>
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<p>Bit error rate (BER) performance of a <math display="inline"><semantics> <mrow> <mo>(</mo> <mn>512</mn> <mo>,</mo> <mn>416</mn> <mo>)</mo> </mrow> </semantics></math> polar code with successive cancellation list (SCL) decoding.</p>
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<p>Bit error rate (BER) comparison of <math display="inline"><semantics> <msub> <mover accent="true"> <mi mathvariant="bold">m</mi> <mo>^</mo> </mover> <mi>T</mi> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mover accent="true"> <mi mathvariant="bold">m</mi> <mo>^</mo> </mover> <mi>A</mi> </msub> </semantics></math> of a <math display="inline"><semantics> <mrow> <mo>(</mo> <mn>512</mn> <mo>,</mo> <mn>416</mn> <mo>)</mo> </mrow> </semantics></math> polar code with successive cancellation (SC) decoding.</p>
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<p>Bit error rate (BER) comparison of <math display="inline"><semantics> <msub> <mover accent="true"> <mi mathvariant="bold">m</mi> <mo>^</mo> </mover> <mi>T</mi> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mover accent="true"> <mi mathvariant="bold">m</mi> <mo>^</mo> </mover> <mi>A</mi> </msub> </semantics></math> of a <math display="inline"><semantics> <mrow> <mo>(</mo> <mn>512</mn> <mo>,</mo> <mn>416</mn> <mo>)</mo> </mrow> </semantics></math> polar code with successive cancellation list (SCL) decoding.</p>
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27 pages, 2909 KiB  
Article
Extracting Geoscientific Dataset Names from the Literature Based on the Hierarchical Temporal Memory Model
by Kai Wu, Zugang Chen, Xinqian Wu, Guoqing Li, Jing Li, Shaohua Wang, Haodong Wang and Hang Feng
ISPRS Int. J. Geo-Inf. 2024, 13(7), 260; https://doi.org/10.3390/ijgi13070260 - 21 Jul 2024
Viewed by 1374
Abstract
Extracting geoscientific dataset names from the literature is crucial for building a literature–data association network, which can help readers access the data quickly through the Internet. However, the existing named-entity extraction methods have low accuracy in extracting geoscientific dataset names from unstructured text [...] Read more.
Extracting geoscientific dataset names from the literature is crucial for building a literature–data association network, which can help readers access the data quickly through the Internet. However, the existing named-entity extraction methods have low accuracy in extracting geoscientific dataset names from unstructured text because geoscientific dataset names are a complex combination of multiple elements, such as geospatial coverage, temporal coverage, scale or resolution, theme content, and version. This paper proposes a new method based on the hierarchical temporal memory (HTM) model, a brain-inspired neural network with superior performance in high-level cognitive tasks, to accurately extract geoscientific dataset names from unstructured text. First, a word-encoding method based on the Unicode values of characters for the HTM model was proposed. Then, over 12,000 dataset names were collected from geoscience data-sharing websites and encoded into binary vectors to train the HTM model. We conceived a new classifier scheme for the HTM model that decodes the predictive vector for the encoder of the next word so that the similarity of the encoders of the predictive next word and the real next word can be computed. If the similarity is greater than a specified threshold, the real next word can be regarded as part of the name, and a successive word set forms the full geoscientific dataset name. We used the trained HTM model to extract geoscientific dataset names from 100 papers. Our method achieved an F1-score of 0.727, outperforming the GPT-4- and Claude-3-based few-shot learning (FSL) method, with F1-scores of 0.698 and 0.72, respectively. Full article
(This article belongs to the Topic Geocomputation and Artificial Intelligence for Mapping)
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<p>General idea of the paper.</p>
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<p>HTM structure [<a href="#B57-ijgi-13-00260" class="html-bibr">57</a>,<a href="#B58-ijgi-13-00260" class="html-bibr">58</a>]. (<b>A</b>) HTM has a three-level hierarchy. The smallest unit is an HTM cell. In each layer, there are a large number of cells, multiple cells form mini-columns, and multiple mini-columns form regions. (<b>B</b>) The end-to-end HTM system includes an encoder, HTM SP, HTM TM, and a classifier. (<b>C</b>) An HTM neuron has one proximal dendrite and several distal dendrites, and dendrites have different functions. Proximal dendrites receive feedforward inputs, while distal dendrites receive contextual information from nearby cells in the layer. (<b>D</b>) All cells in the same mini-column share the same synapses that receive feedforward inputs, which means they receive the same information. (<b>E</b>) Each layer of the HTM model consists of several mini-columns of cells that can read and form synaptic connections with input data.</p>
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<p>Example of encoding words in names of a geoscientific dataset using our method.</p>
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<p>Structure of HTM spatial pooler [<a href="#B58-ijgi-13-00260" class="html-bibr">58</a>].</p>
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<p>Structure of HTM temporal memory [<a href="#B63-ijgi-13-00260" class="html-bibr">63</a>,<a href="#B64-ijgi-13-00260" class="html-bibr">64</a>]. Cells in the TM process can exist in three states: inactive, active, or predictive. When a cell does not receive any feedforward input, it is in an inactive state (purple triangle), and when it receives feedforward input, it is in an active state (green triangle). Sufficient lateral activity in a contextual dendrite leads to a predictive state (red triangle).</p>
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<p>Prediction accuracy with different model sizes and training iteration times.</p>
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<p>Comparison of the five methods on precision, recall, and F1-score.</p>
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13 pages, 335 KiB  
Article
Binary Encoding-Based Federated Learning for Traffic Sign Recognition in Autonomous Driving
by Yian Wen, Yun Zhou and Kai Gao
Mathematics 2024, 12(14), 2229; https://doi.org/10.3390/math12142229 - 17 Jul 2024
Viewed by 930
Abstract
Autonomous driving involves collaborative data sensing and traffic sign recognition. Emerging artificial intelligence technology has brought tremendous advances to vehicular networks. However, it is challenging to guarantee privacy and security when using traditional centralized machine learning methods for traffic sign recognition. It is [...] Read more.
Autonomous driving involves collaborative data sensing and traffic sign recognition. Emerging artificial intelligence technology has brought tremendous advances to vehicular networks. However, it is challenging to guarantee privacy and security when using traditional centralized machine learning methods for traffic sign recognition. It is urgent to introduce a distributed machine learning approach to protect private data of connected vehicles. In this paper, we propose a local differential privacy-based binary encoding federated learning approach. The binary encoding techniques and random perturbation methods are used in distributed learning scenarios to enhance the efficiency and security of data transmission. For the vehicle layer in this approach, the model is trained locally, and the model parameters are uploaded to the central server through encoding and perturbing. The central server designs the corresponding decoding, correction scheme, and regression statistical method for the received binary string. Then, the model parameters are aggregated and updated in the server and transmitted to the vehicle until the learning model is trained. The performance of the proposed approach is verified using the German Traffic Sign Recognition Benchmark data set. The simulation results show that the convergence of the approach is better with the increase in the learning cycle. Compared with baseline methods, such as the convolutional neural network, random forest, and backpropagation, the proposed approach achieves higher accuracy in the process of traffic sign recognition, with an increase of 6%. Full article
(This article belongs to the Special Issue Artificial Intelligence Security and Machine Learning)
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<p>Architecture diagram of the proposed BCFL-LDP for traffic sign recognition in autonomous driving. The framework consists of two parts: the vehicle layer of the local training model and the central server for aggregating model parameters.</p>
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<p>The variation of global model training accuracy of four different methods with a different number of training vehicles is illustrated. As the number of vehicles participating in the training increases, the training accuracy of each method increases. At the same time, the proposed method maintains the highest accuracy regardless of the number of training vehicles.</p>
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<p>The convergence time variation of four different methods with different number of training vehicles is illustrated. When the number of training vehicles is 10, the convergence time gap between the proposed BCFL-LDP and the three baseline methods starts to become small.</p>
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<p>The change trend of model loss with an increasing number of learning rounds was compared between the proposed method and the three baseline methods. When the number of learning rounds was 100, the loss of the four methods was below 0.5 and tends to converge, and the loss of the proposed method was the lowest.</p>
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<p>The variation trend of model accuracy with the increase of learning rounds was compared between the proposed method and the three baseline methods. As the number of learning rounds increased, the model accuracy of each method increased and gradually converged, and the proposed BCFL-LDP always achieved the highest accuracy.</p>
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28 pages, 8640 KiB  
Article
Ecological Trait-Based Digital Categorization of Microbial Genomes for Denitrification Potential
by Raphael D. Isokpehi, Yungkul Kim, Sarah E. Krejci and Vishwa D. Trivedi
Microorganisms 2024, 12(4), 791; https://doi.org/10.3390/microorganisms12040791 - 13 Apr 2024
Viewed by 2412
Abstract
Microorganisms encode proteins that function in the transformations of useful and harmful nitrogenous compounds in the global nitrogen cycle. The major transformations in the nitrogen cycle are nitrogen fixation, nitrification, denitrification, anaerobic ammonium oxidation, and ammonification. The focus of this report is the [...] Read more.
Microorganisms encode proteins that function in the transformations of useful and harmful nitrogenous compounds in the global nitrogen cycle. The major transformations in the nitrogen cycle are nitrogen fixation, nitrification, denitrification, anaerobic ammonium oxidation, and ammonification. The focus of this report is the complex biogeochemical process of denitrification, which, in the complete form, consists of a series of four enzyme-catalyzed reduction reactions that transforms nitrate to nitrogen gas. Denitrification is a microbial strain-level ecological trait (characteristic), and denitrification potential (functional performance) can be inferred from trait rules that rely on the presence or absence of genes for denitrifying enzymes in microbial genomes. Despite the global significance of denitrification and associated large-scale genomic and scholarly data sources, there is lack of datasets and interactive computational tools for investigating microbial genomes according to denitrification trait rules. Therefore, our goal is to categorize archaeal and bacterial genomes by denitrification potential based on denitrification traits defined by rules of enzyme involvement in the denitrification reduction steps. We report the integration of datasets on genome, taxonomic lineage, ecosystem, and denitrifying enzymes to provide data investigations context for the denitrification potential of microbial strains. We constructed an ecosystem and taxonomic annotated denitrification potential dataset of 62,624 microbial genomes (866 archaea and 61,758 bacteria) that encode at least one of the twelve denitrifying enzymes in the four-step canonical denitrification pathway. Our four-digit binary-coding scheme categorized the microbial genomes to one of sixteen denitrification traits including complete denitrification traits assigned to 3280 genomes from 260 bacteria genera. The bacterial strains with complete denitrification potential pattern included Arcobacteraceae strains isolated or detected in diverse ecosystems including aquatic, human, plant, and Mollusca (shellfish). The dataset on microbial denitrification potential and associated interactive data investigations tools can serve as research resources for understanding the biochemical, molecular, and physiological aspects of microbial denitrification, among others. The microbial denitrification data resources produced in our research can also be useful for identifying microbial strains for synthetic denitrifying communities. Full article
(This article belongs to the Special Issue Microbial Nitrogen Cycle)
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<p>A screenshot of an Integrated Microbial Genomes and Microbiomes (IMG/M) webpage displaying microbial genomes with annotation for a KEGG Orthology (KO) term identifier. The example shown is for nitrous oxide reductase with KO identifier K00376, retrieving 8193 genomes.</p>
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<p>A screenshot of the design of a visual analytics resource for constructing a dataset of microbial genomes from the dataset retrieved from the bioinformatics resource (IMG/M). The example shown is for nitrous oxide reductase with KEGG Orthology identifier K00376. The filters in the design allow for the display of a dataset with options for taxonomic domain and genome sequencing status.</p>
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<p>Distribution of denitrification patterns and denitrification traits assigned to a set of 62,624 microbial genomes consisting of 866 archaeal and 61,758 bacterial genomes. “Null” means an absence of annotation.</p>
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<p>Distribution of denitrification patterns, denitrification traits, and ecosystem types for 179 bacterial genomes annotated with the ecosystem of the host-associated and ecosystem category of Mollusca. The five genomes assigned to the oyster ecosystem type were from four strains of <span class="html-italic">Roseibium album</span> (CECT 5094, CECT 5095, CECT 5096, and CECT 7551) and <span class="html-italic">Ruegeria denitrificans</span> CECT 5091.</p>
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<p>A screenshot of a visual analytics resource to support interaction with the dataset on denitrification potential of archaeal and bacterial genomes with an emphasis on filtering by ecosystem options. The interaction worksheet provides options and links to external resources (IMG/M website, Google Search and Google Scholar). The insert box on the left was obtained from clicking the sequencing status symbol associated with <span class="html-italic">Marionobacter denitrificans</span> JB02H27, a bacteria isolated from marine sediment and known to reduce nitrite and nitrate to gaseous nitrogen [<a href="#B54-microorganisms-12-00791" class="html-bibr">54</a>]. The webpage link to the interactive version of the visual analytics resource is available in the <a href="#app1-microorganisms-12-00791" class="html-app">Supplementary Materials</a> section.</p>
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<p>A screenshot of a visual analytics resource to support human interaction with the dataset on denitrification potential of archaeal and bacterial genomes with emphasis on filtering by taxonomic options. The interaction worksheet provides options as well as connection to external resources (IMG/M website, Google Search and Google Scholar). The insert image with GTDB-Tk taxonomic assignments was obtained by clicking the sequencing status symbol associated with <span class="html-italic">Roseibium aestuarii</span> SYSU M00256-3, a bacteria isolated from an estuary and known to be unable to reduce nitrate [<a href="#B55-microorganisms-12-00791" class="html-bibr">55</a>]. The webpage link to the interactive version of the visual analytics resource is available in the <a href="#app1-microorganisms-12-00791" class="html-app">Supplementary Materials</a> section.</p>
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<p>Three stages of interactive data investigation for the denitrification potential of bacterial genera associated with the Eastern oyster (<span class="html-italic">Crassostrea virginica</span>). We obtained the list of nine genera from the study of bacteria associated with the gill tissues of the Pacific oyster (<span class="html-italic">Crassostrea gigas</span>) and Eastern oyster [<a href="#B52-microorganisms-12-00791" class="html-bibr">52</a>].</p>
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<p>Ecosystem classifications and denitrification potential patterns of 127 <span class="html-italic">Arcobacteraceae</span> genomes. The association of <span class="html-italic">Arcobacteraceae</span> with multi-ecosystem habitats including human, animal, plants, and the environment presents a bacteria family for research on synthetic denitrifying communities.</p>
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<p>Ecosystem categories assigned to 3280 bacterial genomes with complete denitrification potential. The phyla Campylobacterota and Pseudomonadota have genera associated with Mollusca (shellfish).</p>
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<p>Evidence from binary numbering patterns indicating that three Campylobacterota genera (<span class="html-italic">Caminibacter</span>, <span class="html-italic">Lebetimonas</span>, and <span class="html-italic">Nautilia</span>) do not encode the gene for nitrous oxide reductase. The last digit of the “Denitrification Pattern” and “Denitrifying Enzymes Pattern” is “0”.</p>
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<p>Genomes of the genera in phylum Campylobacterota (<span class="html-italic">Nitratifractor</span>, <span class="html-italic">Nitratiruptor</span>, <span class="html-italic">Sulfurimonas</span>, and <span class="html-italic">Sulfurovum</span>) that have the complete denitrification pattern (“1111”) in the microbial denitrification potential dataset. The nitrous oxide reductase activity of strains from the taxonomic class campylobacteria associated with deep-sea hydrothermal vents was reported by Fukushi et al. [<a href="#B62-microorganisms-12-00791" class="html-bibr">62</a>].</p>
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<p>Visual interfaces for selecting and exploring searches for scholarly articles with gene symbols of enzymes for denitrification. (<b>a</b>) The list of functional annotation identifiers and gene symbol for enzymes in the canonical denitrification pathway. Selecting the square for each gene symbol displays the Google Scholar search options. (<b>b</b>) The list of search text for Google Scholar to retrieve up-to-date journal articles and other scholarly literature. (<b>c</b>) An example of part of the retrieved results for the search text “(‘absence of nosZ’ denitrification)”. The selected journal article provides insights into the evolutionary history of the incomplete denitrification pathway of the bacteria genus, <span class="html-italic">Thermus</span>.</p>
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<p>An example of the denitrification trait inferences according to rules of the end products of reduction reactions of nitrate, nitrite, nitric oxide, and nitrous oxide. The presence or absence of protein families (italicized gene symbols) maps to complete and incomplete denitrification traits. The grey and white colors indicate presence and absence respectively of the gene(s) for the denitrification step. The source of the image is an open-access article by Karaoz and Brodie [<a href="#B10-microorganisms-12-00791" class="html-bibr">10</a>].</p>
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<p>Comparison of the categorizations of <span class="html-italic">Thermus</span> strains in this report (Isokpehi et al., 2024) and those of Jiao et al. (2022) [<a href="#B63-microorganisms-12-00791" class="html-bibr">63</a>]. Among the 23 <span class="html-italic">Thermus</span> strains, eight strains are not included in our microbial denitrification potential dataset because they do not have at least one of the genes for the 12 enzymes for denitrification. In the categorization by Jiao et al. [<a href="#B63-microorganisms-12-00791" class="html-bibr">63</a>], the filled symbols indicate presence of genes for denitrification enzymes. The numbers before the Genome Name in both images is to show agreement of absence of genes for the denitrification enzymes by the categorizations by Isokpehi et al. (2024) and Jiao et al. [<a href="#B63-microorganisms-12-00791" class="html-bibr">63</a>]. In addition, the list of <span class="html-italic">Thermus</span> strains include Type strains (with superscript “T”). The open access image by Jiao et al. is available at <a href="https://doi.org/10.1002/mlf2.12009" target="_blank">https://doi.org/10.1002/mlf2.12009</a> (accessed on 23 March 2024).</p>
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<p>The categories of denitrification-potential patterns for 866 archaeal genomes. The genomes encode at least 1 of the 12 enzymes in the canonical four-step denitrification pathway according to the MicroTrait rules (<a href="#microorganisms-12-00791-f0A1" class="html-fig">Figure A1</a>). The 12-digit binary numbers represent the presence (“1”) or absence (“0”) of the following enzymes: narG, narH, narI, napA, napB, nirK, nirS, norB, norC, norV, norW, and nosZ. A pattern of interest can be used to search the interactive version of <a href="#microorganisms-12-00791-f005" class="html-fig">Figure 5</a> available at <a href="https://public.tableau.com/app/profile/qeubic/viz/microbial_denitrifiers/abstract/" target="_blank">https://public.tableau.com/app/profile/qeubic/viz/microbial_denitrifiers/abstract/</a> (accessed on 23 March 2024).</p>
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23 pages, 423 KiB  
Article
Secure Groups for Threshold Cryptography and Number-Theoretic Multiparty Computation
by Berry Schoenmakers and Toon Segers
Cryptography 2023, 7(4), 56; https://doi.org/10.3390/cryptography7040056 - 9 Nov 2023
Viewed by 2067
Abstract
In this paper, we introduce secure groups as a cryptographic scheme representing finite groups together with a range of operations, including the group operation, inversion, random sampling, and encoding/decoding maps. We construct secure groups from oblivious group representations combined with cryptographic protocols, implementing [...] Read more.
In this paper, we introduce secure groups as a cryptographic scheme representing finite groups together with a range of operations, including the group operation, inversion, random sampling, and encoding/decoding maps. We construct secure groups from oblivious group representations combined with cryptographic protocols, implementing the operations securely. We present both generic and specific constructions, in the latter case specifically for number-theoretic groups commonly used in cryptography. These include Schnorr groups (with quadratic residues as a special case), Weierstrass and Edwards elliptic curve groups, and class groups of imaginary quadratic number fields. For concreteness, we develop our protocols in the setting of secure multiparty computation based on Shamir secret sharing over a finite field, abstracted away by formulating our solutions in terms of an arithmetic black box for secure finite field arithmetic or for secure integer arithmetic. Secure finite field arithmetic suffices for many groups, including Schnorr groups and elliptic curve groups. For class groups, we need secure integer arithmetic to implement Shanks’ classical algorithms for the composition of binary quadratic forms, which we will combine with our adaptation of a particular form reduction algorithm due to Agarwal and Frandsen. As a main result of independent interest, we also present an efficient protocol for the secure computation of the extended greatest common divisor. The protocol is based on Bernstein and Yang’s constant-time 2-adic algorithm, which we adapt to work purely over the integers. This yields a much better approach for multiparty computation but raises a new concern about the growth of the Bézout coefficients. By a careful analysis, we are able to prove that the Bézout coefficients in our protocol will never exceed 3max(a,b) in absolute value for inputs a and b. We have integrated secure groups in the Python package MPyC and have implemented threshold ElGamal and threshold DSA in terms of secure groups. We also mention how our results support verifiable multiparty computation, allowing parties to jointly create a publicly verifiable proof of correctness for the results accompanying the results of a secure computation. Full article
(This article belongs to the Special Issue Cyber Security, Cryptology and Machine Learning)
16 pages, 1121 KiB  
Article
Event-Triggered State Estimation for Uncertain Systems with Binary Encoding Transmission Scheme
by Zun Li, Binqiang Xue and Youyuan Chen
Mathematics 2023, 11(17), 3679; https://doi.org/10.3390/math11173679 - 26 Aug 2023
Viewed by 1150
Abstract
This paper proposes an event-triggered state estimation method for parameter-uncertain systems with a binary encoding transmission scheme. Firstly, a binary encoding transmission scheme is introduced between the state estimator and the system to improve the efficiency of network communication. Secondly, an event-triggering mechanism [...] Read more.
This paper proposes an event-triggered state estimation method for parameter-uncertain systems with a binary encoding transmission scheme. Firstly, a binary encoding transmission scheme is introduced between the state estimator and the system to improve the efficiency of network communication. Secondly, an event-triggering mechanism (ETM) is designed to ensure the accuracy of state estimation and reduce the computational burden of the state estimator. At the event-triggered moments, considering the uncertainty of the system, the binary encoding transmission scheme, and the ETM, a moving horizon estimator (MHER) is designed using the robust least squares optimization method to obtain optimal state estimation. At the no-event-triggered moments, the state estimation of the system is computed based on an open-loop state estimator (OLER). Furthermore, stability analysis showed that the state estimation error of the proposed method is bounded. Finally, the practical value of the proposed in this paper is confirmed through numerical simulation. Full article
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<p>Event-triggered state estimation method.</p>
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<p>Binary encoding transmission scheme.</p>
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<p>Schematic diagram of the ETM.</p>
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<p>Event-triggered situations at <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mo>=</mo> <mn>0.21</mn> </mrow> </semantics></math>.</p>
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<p>Estimation situations of the state <math display="inline"><semantics> <msub> <mi>x</mi> <mn>1</mn> </msub> </semantics></math>.</p>
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<p>Estimation situations of the state <math display="inline"><semantics> <msub> <mi>x</mi> <mn>2</mn> </msub> </semantics></math>.</p>
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<p>State estimation error of the three methods.</p>
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18 pages, 2954 KiB  
Article
Low-Complexity Fast CU Classification Decision Method Based on LGBM Classifier
by Yanjun Wang, Yong Liu, Jinchao Zhao and Qiuwen Zhang
Electronics 2023, 12(11), 2488; https://doi.org/10.3390/electronics12112488 - 31 May 2023
Cited by 1 | Viewed by 1467
Abstract
At present, the latest video coding standard is Versatile Video Coding (VVC). Although the coding efficiency of VVC is significantly improved compared to the previous generation, standard High-Efficiency Video Coding (HEVC), it also leads to a sharp increase in coding complexity. VVC significantly [...] Read more.
At present, the latest video coding standard is Versatile Video Coding (VVC). Although the coding efficiency of VVC is significantly improved compared to the previous generation, standard High-Efficiency Video Coding (HEVC), it also leads to a sharp increase in coding complexity. VVC significantly improves HEVC by adopting the quadtree with nested multi-type tree (QTMT) partition structure, which has been proven to be very effective. This paper proposes a low-complexity fast coding unit (CU) partition decision method based on the light gradient boosting machine (LGBM) classifier. Representative features were extracted to train a classifier matching the framework. Secondly, a new fast CU decision framework was designed for the new features of VVC, which could predict in advance whether the CU was divided, whether it was divided by quadtree (QT), and whether it was divided horizontally or vertically. To solve the multi-classification problem, the technique of creating multiple binary classification problems was used. Subsequently, a multi-threshold decision-making scheme consisting of four threshold points was proposed, which achieved a good balance between time savings and coding efficiency. According to the experimental results, our method achieved a significant reduction in encoding time, ranging from 47.93% to 54.27%, but only improved the Bjøntegaard delta bit-rate (BDBR) by 1.07%~1.57%. Our method showed good performance in terms of both encoding time reduction and efficiency. Full article
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<p>The division of QTMT.</p>
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<p>The diagram depicts the process of intra coding in VVC.</p>
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<p>The new fast CU decision framework.</p>
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<p>Feature importance ranking of the top 11 features of the classifiers.</p>
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<p>AUC_ROC curve for each classifier. (<b>a</b>) NS_Classifier. (<b>b</b>) QT_Classifier. (<b>c</b>) HV_Classifier. (<b>d</b>) HBP_Classifier. (<b>e</b>) VBP_Classifier.</p>
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<p>Coding performance of classifier.</p>
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<p>Framework for training LGBM classifier for CU partition decisions and evaluating the performance of VTM encoder.</p>
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<p>Comparison of the number of blocks processed.</p>
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<p>Performance comparison of BDBR and ∆T at different threshold points.</p>
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19 pages, 3239 KiB  
Article
Boosted Binary Quantum Classifier via Graphical Kernel
by Yuan Li and Duan Huang
Entropy 2023, 25(6), 870; https://doi.org/10.3390/e25060870 - 29 May 2023
Viewed by 1401
Abstract
In terms of the logical structure of data in machine learning (ML), we apply a novel graphical encoding method in quantum computing to build the mapping between feature space of sample data and two-level nested graph state that presents a kind of multi-partite [...] Read more.
In terms of the logical structure of data in machine learning (ML), we apply a novel graphical encoding method in quantum computing to build the mapping between feature space of sample data and two-level nested graph state that presents a kind of multi-partite entanglement state. By implementing swap-test circuit on the graphical training states, a binary quantum classifier to large-scale test states is effectively realized in this paper. In addition, for the error classification caused by noise, we further explored the subsequent processing scheme by adjusting the weights so that a strong classifier is formed and its accuracy is greatly boosted. In this paper, the proposed boosting algorithm demonstrates superiority in certain aspects as demonstrated via experimental investigation. This work further enriches the theoretical foundation of quantum graph theory and quantum machine learning, which may be exploited to assist the classification of massive-data networks by entangling subgraphs. Full article
(This article belongs to the Special Issue Quantum Machine Learning 2022)
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<p>Expression of the inputting quantum state. Here, every <math display="inline"><semantics> <mrow> <mrow> <mo>|</mo> </mrow> <msub> <mi>x</mi> <mi>j</mi> </msub> <mrow> <mo>〉</mo> </mrow> </mrow> </semantics></math> in subspace <math display="inline"><semantics> <msub> <mi mathvariant="script">S</mi> <mi>j</mi> </msub> </semantics></math> is a quantum sample state in subspace <math display="inline"><semantics> <msub> <mi mathvariant="script">S</mi> <mi>j</mi> </msub> </semantics></math> described by the superposition of orthogonal eigenstates <math display="inline"><semantics> <mrow> <mrow> <mo>|</mo> </mrow> <msub> <mi>a</mi> <mi>k</mi> </msub> <mrow> <mo>〉</mo> </mrow> </mrow> </semantics></math>. Furthermore, <math display="inline"><semantics> <msub> <mi>c</mi> <mrow> <mi>j</mi> <mi>k</mi> </mrow> </msub> </semantics></math> are corresponding coefficients for <math display="inline"><semantics> <mrow> <mn>1</mn> <mo>≤</mo> <mi>j</mi> <mo>≤</mo> <mi>N</mi> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mn>1</mn> <mo>≤</mo> <mi>k</mi> <mo>≤</mo> <mi>m</mi> </mrow> </semantics></math>.</p>
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<p>Illustration of using quantum feature maps based on graph state <span class="html-italic">G</span> for machine learning. According to the existing samples, the input data describing different shapes can be classified into the labels in <span class="html-italic">Y</span>. Here, the different shapes depicted in yellow, purple and green are different classes of classical samples, as well as the blue square represents the input data to be classified.</p>
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<p>A two-level nested graph <math display="inline"><semantics> <mrow> <mi>G</mi> <mo>=</mo> <mo>[</mo> <msub> <mi>G</mi> <mn>5</mn> </msub> <mrow> <mo>[</mo> <msub> <mi>G</mi> <mn>3</mn> </msub> <mo>]</mo> </mrow> <mo>]</mo> </mrow> </semantics></math> which corresponds to graph state <math display="inline"><semantics> <mrow> <mo>|</mo> <mi>G</mi> <mo>〉</mo> </mrow> </semantics></math>. Here, the subgraph <math display="inline"><semantics> <msub> <mi>G</mi> <mn>3</mn> </msub> </semantics></math> is formed by three grey entangling dots, and graph <span class="html-italic">G</span> entangled by the five subgraphs.</p>
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<p>The circuit of classifier in quantum communication for two correspondents (Alice and Bob). The test state is prepared with operator ‘<math display="inline"><semantics> <mrow> <mi>T</mi> <mi>s</mi> </mrow> </semantics></math>’ at Alice’s side, of which features are indexed by ‘<math display="inline"><semantics> <mrow> <mi>I</mi> <mi>n</mi> <mi>d</mi> </mrow> </semantics></math>’. By entangling an ancilla state operated by operator ‘<math display="inline"><semantics> <mrow> <mi>A</mi> <mi>n</mi> <mi>c</mi> </mrow> </semantics></math>’ with the training state gained by ‘<math display="inline"><semantics> <mrow> <mi>T</mi> <mi>r</mi> </mrow> </semantics></math>’ at receiver Bob’s side. As a result, the class label may be derived from ‘<math display="inline"><semantics> <mrow> <mi>L</mi> <mi>a</mi> <mi>b</mi> </mrow> </semantics></math>’ after measuring.</p>
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<p>Illustration of quantum graph segmentation and graphical kernel. A graph state <math display="inline"><semantics> <mrow> <mrow> <mo>|</mo> </mrow> <msup> <mo>Φ</mo> <mi>g</mi> </msup> <mrow> <mo>〉</mo> </mrow> </mrow> </semantics></math> is formed by graphs <math display="inline"><semantics> <msub> <mi>G</mi> <mi>t</mi> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>G</mi> <mi>q</mi> </msub> </semantics></math> which correspond to training state <math display="inline"><semantics> <mrow> <mrow> <mo>|</mo> </mrow> <msub> <mi>G</mi> <mi>t</mi> </msub> <mrow> <mo>〉</mo> </mrow> </mrow> </semantics></math> and query state <math display="inline"><semantics> <mrow> <mrow> <mo>|</mo> </mrow> <msub> <mi>G</mi> <mi>q</mi> </msub> <mrow> <mo>〉</mo> </mrow> </mrow> </semantics></math> with a mapping <math display="inline"><semantics> <mi mathvariant="script">C</mi> </semantics></math>, where the two states are entangled by entanglement matrix (graph) <math display="inline"><semantics> <msub> <mo>Γ</mo> <mrow> <mi>s</mi> <mo>,</mo> <mi>t</mi> </mrow> </msub> </semantics></math>. Here, <math display="inline"><semantics> <msub> <mi>v</mi> <mi>i</mi> </msub> </semantics></math> for <math display="inline"><semantics> <mrow> <mn>1</mn> <mo>≤</mo> <mi>i</mi> <mo>≤</mo> <mn>4</mn> </mrow> </semantics></math> in the circuit are the vertices in graphs <math display="inline"><semantics> <msub> <mi>G</mi> <mi>t</mi> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>G</mi> <mi>q</mi> </msub> </semantics></math>. In the circuit of kernel, the first register is the ancilla state, and the second is the training state. Furthermore, the third register is the input data as the query state. Correspondingly, the fourth register and final register are label and index states, respectively, so that the label results may be obtained with phase measurement <math display="inline"><semantics> <msub> <mi>M</mi> <mi>z</mi> </msub> </semantics></math>.</p>
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<p>Curves of probability with 200 test points in interval [0, <math display="inline"><semantics> <mrow> <mn>2</mn> <mi>π</mi> <mo>/</mo> <mn>3</mn> </mrow> </semantics></math>] resort to 8192 shots, which varies in the range [0.26, 0.74].</p>
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<p>Curves of probability with 200 test points in interval [0, <math display="inline"><semantics> <mrow> <mi>π</mi> <mo>/</mo> <mn>6</mn> </mrow> </semantics></math>] resort to 8192 shots, which varies in the range [0.34, 0.66]. Here, we take the smaller angular distance.</p>
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<p>Convergence trend after adopting iterative cycles. The probability <span class="html-italic">p</span> of classification while the parameter <math display="inline"><semantics> <mi>κ</mi> </semantics></math> is taken as <math display="inline"><semantics> <mrow> <mi>κ</mi> <mo>=</mo> <mn>12</mn> <mo>/</mo> <mn>20</mn> <mo>,</mo> <mn>1</mn> <mo>/</mo> <mn>2</mn> <mo>,</mo> <mn>1</mn> <mo>/</mo> <mn>3</mn> </mrow> </semantics></math>, respectively, in the quantum classifier, the accuracy approaches 1 with different velocities in terms of the number <span class="html-italic">T</span> of adopted iterations.</p>
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<p>Metrics comparison of quantum boosting (Qboosting) and QKNN with classical KNN, SVM and Decision Trees via use of the UCI Skin dataset.</p>
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18 pages, 4963 KiB  
Article
m6Aminer: Predicting the m6Am Sites on mRNA by Fusing Multiple Sequence-Derived Features into a CatBoost-Based Classifier
by Ze Liu, Pengfei Lan, Ting Liu, Xudong Liu and Tao Liu
Int. J. Mol. Sci. 2023, 24(9), 7878; https://doi.org/10.3390/ijms24097878 - 26 Apr 2023
Cited by 5 | Viewed by 1855
Abstract
As one of the most important post-transcriptional modifications, m6Am plays a fairly important role in conferring mRNA stability and in the progression of cancers. The accurate identification of the m6Am sites is critical for explaining its biological significance and developing its application in [...] Read more.
As one of the most important post-transcriptional modifications, m6Am plays a fairly important role in conferring mRNA stability and in the progression of cancers. The accurate identification of the m6Am sites is critical for explaining its biological significance and developing its application in the medical field. However, conventional experimental approaches are time-consuming and expensive, making them unsuitable for the large-scale identification of the m6Am sites. To address this challenge, we exploit a CatBoost-based method, m6Aminer, to identify the m6Am sites on mRNA. For feature extraction, nine different feature-encoding schemes (pseudo electron–ion interaction potential, hash decimal conversion method, dinucleotide binary encoding, nucleotide chemical properties, pseudo k-tuple composition, dinucleotide numerical mapping, K monomeric units, series correlation pseudo trinucleotide composition, and K-spaced nucleotide pair frequency) were utilized to form the initial feature space. To obtain the optimized feature subset, the ExtraTreesClassifier algorithm was adopted to perform feature importance ranking, and the top 300 features were selected as the optimal feature subset. With different performance assessment methods, 10-fold cross-validation and independent test, m6Aminer achieved average AUC of 0.913 and 0.754, demonstrating a competitive performance with the state-of-the-art models m6AmPred (0.905 and 0.735) and DLm6Am (0.897 and 0.730). The prediction model developed in this study can be used to identify the m6Am sites in the whole transcriptome, laying a foundation for the functional research of m6Am. Full article
(This article belongs to the Section Molecular Genetics and Genomics)
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<p>The framework of m6Aminer. (<b>A</b>). Utilize CD-HIT to remove the redundant sequences and build the benchmark datasets. (<b>B</b>). Form an initial feature space and select the top 300 features according to their importance. (<b>C</b>). Construct and optimize the CatBoost-based model. (<b>D</b>). Predict the m6Am sites using m6Aminer.</p>
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<p>Box plots of the average metrics with different classifiers on 10 sub-training datasets. (<b>A</b>) ACC. (<b>B</b>) AUC. (<b>C</b>) Sn. (<b>D</b>) Sp. (<b>E</b>) F1. (<b>F</b>) MCC.</p>
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<p>Feature ranking and selection. (<b>A</b>) The importance of 1120 features on a sub-training dataset; (<b>B</b>) The top 20 features ranked using the ExtraTreesClassifier algorithm; (<b>C</b>) The performance of the CatBoost-based model using different combinations of feature subsets on the 10 sub-training datasets.</p>
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<p>Comparison of the ROC curves for three models on the independent testing datasets.</p>
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<p>Comparison of the precision–recall curves for three models on the independent testing datasets.</p>
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<p>The m6Aminer web server.</p>
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23 pages, 6004 KiB  
Article
Channel Allocation Algorithm Based on Swarm Intelligence for a Wireless Monitoring Network
by Na Xia, Yu Li, Ke Zhang, Peipei Wang, Linmei Luo, Lei Chen and Jun Yang
Electronics 2023, 12(8), 1840; https://doi.org/10.3390/electronics12081840 - 12 Apr 2023
Cited by 1 | Viewed by 1534
Abstract
In wireless networks, multiple monitoring nodes are used to collect users’ transmission data in real time, which can be used for fault diagnosis and analytical feedback of the wireless network. Due to the limited number of monitoring nodes, key issues include how to [...] Read more.
In wireless networks, multiple monitoring nodes are used to collect users’ transmission data in real time, which can be used for fault diagnosis and analytical feedback of the wireless network. Due to the limited number of monitoring nodes, key issues include how to optimize and schedule the channel resources of each node to cover more users, obtain more network data, and maximize the quality of network monitoring. In this paper, a channel allocation algorithm based on swarm intelligence—“discrete bacterial foraging optimization”—is proposed based on the classic bacterial foraging optimization algorithm. The position of each dimension in the iterative process is discretized to binary 0 or 1 to encode and express the channel allocation problem of wireless monitoring networks, and the channel allocation scheme is optimized by location updates guided by bacterial foraging. Many simulation and practical experiments have proved the effectiveness of the algorithm, and it also has low complexity and provable convergence. Compared with similar algorithms, this algorithm improves monitoring quality by 1.428% while boosting speed by up to 32.602%. The algorithm has lower complexity, higher performance, and can converge to the optimal solution at a faster rate. Full article
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<p>Wireless network and wireless monitoring network.</p>
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<p>Undirected bipartite graph <math display="inline"><semantics> <mrow> <msup> <mi>G</mi> <mi>R</mi> </msup> </mrow> </semantics></math>.</p>
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<p>Undirected bipartite graph <math display="inline"><semantics> <mi>G</mi> </semantics></math>.</p>
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<p>Searching space of the channel allocation problem.</p>
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<p>2D binary coding for a channel allocation scheme.</p>
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<p>2D accumulation coding for a channel allocation scheme (Case 1).</p>
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<p>2D binary coding satisfying the constraint condition.</p>
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<p>2D accumulation coding for a channel allocation scheme (Case 2).</p>
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<p>Two chemotactic distances and changing probability of <math display="inline"><semantics> <mrow> <mn>0</mn> <mo>↔</mo> <mn>1</mn> </mrow> </semantics></math>.</p>
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<p>Chemotactic distances and position changing probability.</p>
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<p>Revised process after updating the encodings. (<b>a</b>) indicates the coding after updating bacterial positions at a certain time, (<b>b</b>) corresponds to the cumulative coding table after Step 2 correction, (<b>c</b>) corresponds to the coding table after the Step 3 correction, (<b>d</b>) represents the cumulative coding table in Case 1, (<b>e</b>) represents the cumulative coding table in Case 2, (<b>f</b>) represents the coding table after the reassignment of radios for the multiplexed channel in (<b>e</b>).</p>
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<p>Convergence experiment in Case 1.</p>
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<p>Convergence experiment in Case 2.</p>
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<p>Convergence experiment.</p>
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<p>Performance comparison of the four algorithms.</p>
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<p>Iterative process of the three algorithms.</p>
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<p>Results of the practical network experiment.</p>
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15 pages, 799 KiB  
Article
Reversible Data Hiding in Encrypted Images with Extended Parametric Binary Tree Labeling
by Quan Feng, Lu Leng, Chin-Chen Chang, Ji-Hwei Horng and Meihong Wu
Appl. Sci. 2023, 13(4), 2458; https://doi.org/10.3390/app13042458 - 14 Feb 2023
Cited by 11 | Viewed by 1793
Abstract
Images uploaded to the cloud may be confidential or related to personal private information, so they need to be encrypted before uploading to the cloud storage. At the service provider side, appending additional information is usually required for transmission or database management. Reversible [...] Read more.
Images uploaded to the cloud may be confidential or related to personal private information, so they need to be encrypted before uploading to the cloud storage. At the service provider side, appending additional information is usually required for transmission or database management. Reversible data hiding in encrypted images (RDHEI) serves as a technical solution. Recent RDHEI schemes successfully utilize the spatial correlation between image pixel values to vacate spare room for data hiding, however, the data payload can be further improved. This paper proposes a RDHEI scheme based on extended parameter binary tree labeling, which replaces non-reference pixel values with their prediction errors in a reduced length to vacate space. We further encode the prediction error of non-embeddable pixels to fit the space left from labeling. Thus, the space required to store the pixel bits replaced by labeling codes is saved. Experimental results show that the data payload of the extended parametric binary tree labeling outperforms state-of-the-art schemes. The embedding rates for the commonly applied datasets, including Bossbase, BOWS-2, and UCID, are 3.2305 bpp, 3.1619 bpp, and 2.8113 bpp, respectively. Full article
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<p>Binary code distribution based on a complete binary tree.</p>
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<p>An example of flag bit selection when <math display="inline"><semantics> <mrow> <mi>β</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math> to 7.</p>
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<p>An example of flag bit selection when <math display="inline"><semantics> <mrow> <mi>β</mi> <mo>=</mo> <mn>2</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math> to 7.</p>
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<p>Procedure of the proposed method.</p>
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<p>Median-edge detector.</p>
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<p>Example of the prediction error.</p>
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<p>Image encryption.</p>
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<p>Pixel grouping.</p>
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<p>The labeled encrypted image generation process.</p>
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<p>Testing images: (<b>a</b>) Man, (<b>b</b>) Mit.</p>
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<p>The numbers of the pixels with two different prediction errors.</p>
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<p>AI and secret data hiding.</p>
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<p>The test images: (<b>a</b>) Lena, (<b>b</b>) Man, (<b>c</b>) Jetplane, (<b>d</b>) Baboon.</p>
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<p>The different phases of EPBTL on Lena image: (<b>a</b>) Original image, (<b>b</b>) Encrypted image, (<b>c</b>) Embebded encrypted image, (<b>d</b>) Recovered image.</p>
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<p>The histograms of images at different phases of EPBTL on Lena: (<b>a</b>) Histogram of the original image, (<b>b</b>) Histogram of the encrypted image, (<b>c</b>) Histogram of the embebded encrypted image, (<b>d</b>) Histogram of the recovered image.</p>
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<p>The comparison of the embedding rates of the different schemes on the test images includes Puteaux et al. [<a href="#B23-applsci-13-02458" class="html-bibr">23</a>], Wang et al. [<a href="#B25-applsci-13-02458" class="html-bibr">25</a>], Yi et al. [<a href="#B26-applsci-13-02458" class="html-bibr">26</a>], Wu et al. [<a href="#B27-applsci-13-02458" class="html-bibr">27</a>] and Proposed method.</p>
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<p>The comparison of the average embedding rates of the different schemes on the datasets includes Puteaux et al. [<a href="#B23-applsci-13-02458" class="html-bibr">23</a>], Wang et al. [<a href="#B25-applsci-13-02458" class="html-bibr">25</a>], Yi et al. [<a href="#B26-applsci-13-02458" class="html-bibr">26</a>], Wu et al. [<a href="#B27-applsci-13-02458" class="html-bibr">27</a>] and Proposed method.</p>
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16 pages, 5361 KiB  
Article
PA-Tran: Learning to Estimate 3D Hand Pose with Partial Annotation
by Tianze Yu, Luke Bidulka, Martin J. McKeown and Z. Jane Wang
Sensors 2023, 23(3), 1555; https://doi.org/10.3390/s23031555 - 31 Jan 2023
Cited by 3 | Viewed by 2677
Abstract
This paper tackles a novel and challenging problem—3D hand pose estimation (HPE) from a single RGB image using partial annotation. Most HPE methods ignore the fact that the keypoints could be partially visible (e.g., under occlusions). In contrast, we propose a deep-learning framework, [...] Read more.
This paper tackles a novel and challenging problem—3D hand pose estimation (HPE) from a single RGB image using partial annotation. Most HPE methods ignore the fact that the keypoints could be partially visible (e.g., under occlusions). In contrast, we propose a deep-learning framework, PA-Tran, that jointly estimates the keypoints status and 3D hand pose from a single RGB image with two dependent branches. The regression branch consists of a Transformer encoder which is trained to predict a set of target keypoints, given an input set of status, position, and visual features embedding from a convolutional neural network (CNN); the classification branch adopts a CNN for estimating the keypoints status. One key idea of PA-Tran is a selective mask training (SMT) objective that uses a binary encoding scheme to represent the status of the keypoints as observed or unobserved during training. In addition, by explicitly encoding the label status (observed/unobserved), the proposed PA-Tran can efficiently handle the condition when only partial annotation is available. Investigating the annotation percentage ranging from 50–100%, we show that training with partial annotation is more efficient (e.g., achieving the best 6.0 PA-MPJPE when using about 85% annotations). Moreover, we provide two new datasets. APDM-Hand, is for synthetic hands with APDM sensor accessories, which is designed for a specific hand task. PD-APDM-Hand, is a real hand dataset collected from Parkinson’s Disease (PD) patients with partial annotation. The proposed PA-Tran can achieve higher estimation accuracy when evaluated on both proposed datasets and a more general hand dataset. Full article
(This article belongs to the Special Issue Women’s Special Issue Series: Sensors)
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<p>Biological characteristics of the human hand skeleton: (<b>a</b>) Illustration of the DoF of the hand; (<b>b</b>) Indices of the hand joints.</p>
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<p>Overview of the proposed PA-Tran framework. Given an input image <math display="inline"><semantics> <mi mathvariant="script">I</mi> </semantics></math>, we extract the image features using a convolution neural network. Then the image features are passed into two separate branches: the regression branch <math display="inline"><semantics> <mrow> <mi>r</mi> <mi>e</mi> <mi>g</mi> <mo>(</mo> <mo>·</mo> <mo>)</mo> </mrow> </semantics></math> and the classification branch <math display="inline"><semantics> <mrow> <mi>c</mi> <mi>l</mi> <mi>a</mi> <mo>(</mo> <mo>·</mo> <mo>)</mo> </mrow> </semantics></math>. <math display="inline"><semantics> <mrow> <mi>c</mi> <mi>l</mi> <mi>a</mi> <mo>(</mo> <mo>·</mo> <mo>)</mo> </mrow> </semantics></math> will generate the status embedding for <math display="inline"><semantics> <mrow> <mi>r</mi> <mi>e</mi> <mi>g</mi> <mo>(</mo> <mo>·</mo> <mo>)</mo> </mrow> </semantics></math> and masks for <math display="inline"><semantics> <mrow> <mi>S</mi> <mi>M</mi> <mi>T</mi> </mrow> </semantics></math> to learn the interaction between labels. The structures of <math display="inline"><semantics> <mrow> <mi>r</mi> <mi>e</mi> <mi>g</mi> <mo>(</mo> <mo>·</mo> <mo>)</mo> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>c</mi> <mi>l</mi> <mi>a</mi> <mo>(</mo> <mo>·</mo> <mo>)</mo> </mrow> </semantics></math> are detailed in <a href="#sec3dot2-sensors-23-01555" class="html-sec">Section 3.2</a>.</p>
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<p>The structure of the <math display="inline"><semantics> <mrow> <mi>r</mi> <mi>e</mi> <mi>g</mi> <mo>(</mo> <mo>·</mo> <mo>)</mo> </mrow> </semantics></math> branch. The input is the concatenation of feature embedding, position embedding, and status embedding. Sequential transformer blocks are adopted to reduce the dimension of the hidden embedding progressively. The final output is the coordinates of the keypoints.</p>
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<p>Examples of finger-tapping animation frames with motion blur.</p>
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<p>Examples of hand-movement animation frames with motion blur.</p>
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<p>Examples of APDM-Hand images from different views and backgrounds.</p>
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<p>Examples of PD-APDM-Hand, which is collected from real Parkinson’s Disease patients when taking the UPDRS test.</p>
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<p>Qualititive result on APDM-Hand dataset: (<b>a</b>) Ground truth; (<b>b</b>) METRO; (<b>c</b>) PA-Tran.</p>
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<p>Qualititive results on PD-APDM-Hand dataset: (<b>a</b>) PD subject 1; (<b>b</b>) PD subject 2.</p>
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<p>Hand pose estimation with motion blur.</p>
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19 pages, 3434 KiB  
Article
Evolutionary Optimization of Drone-Swarm Deployment for Wireless Coverage
by Xiao Zhang, Xin Xiang, Shanshan Lu, Yu Zhou and Shilong Sun
Drones 2023, 7(1), 8; https://doi.org/10.3390/drones7010008 - 23 Dec 2022
Cited by 4 | Viewed by 2771
Abstract
The need for longer lasting and wider wireless coverage has driven the transition from a single drone to drone swarms. Unlike the single drone, drone swarms can collaboratively achieve full coverage over a target area. However, the existing literature on the drones’ wireless [...] Read more.
The need for longer lasting and wider wireless coverage has driven the transition from a single drone to drone swarms. Unlike the single drone, drone swarms can collaboratively achieve full coverage over a target area. However, the existing literature on the drones’ wireless coverage has largely overlooked one important fact: that the network lifetime is determined by the minimum leftover energy among all drones. Hence, the maximum energy consumption is minimized in our drone-swarms deployment problem (DSDP), which aims to balance the energy consumption of all drones and maximize the full-coverage network lifetime. We present a genetic algorithm that encodes the solutions as chromosomes and simulates the biological evolution process in search of a favorable solution. Specifically, an integer code scheme is adopted to encode the sequence of the drones’ deployment. With the order of the drones’ sequence determined by the coding process, we introduce a feasibility checking operator with binary search to improve the performance. By relaxing the constraint of full coverage as an objective of coverage rate, we study the tradeoffs between energy consumption, number of drones, and coverage rate of the target area. By taking advantage of the MOEA/D framework with neighboring subproblems searching, we present a drone-swarms deployment algorithm based on MOEA/D (DSDA-MOEA/D) to find the best tradeoff between these objectives. Extensive simulations were conducted to evaluate the performance of the proposed algorithms. Full article
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<p>System model for deploying drones to provide wireless coverage to the target interval <math display="inline"><semantics> <mrow> <mi>L</mi> <mo>=</mo> <mo>[</mo> <mn>0</mn> <mo>,</mo> <mi>L</mi> <mo>]</mo> </mrow> </semantics></math>, where drone <math display="inline"><semantics> <msub> <mi>d</mi> <mi>i</mi> </msub> </semantics></math> with coverage radius <math display="inline"><semantics> <msub> <mi>r</mi> <mi>i</mi> </msub> </semantics></math> is deployed from <math display="inline"><semantics> <msub> <mi>x</mi> <mi>i</mi> </msub> </semantics></math> to <math display="inline"><semantics> <msub> <mi>y</mi> <mi>i</mi> </msub> </semantics></math> at operating altitude <math display="inline"><semantics> <msub> <mi>h</mi> <mi>i</mi> </msub> </semantics></math>.</p>
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<p>Example of offspring reproduction: crossover.</p>
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<p>Example of offspring reproduction: mutation.</p>
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<p>Deployment of six drones to provide wireless coverage to the target area.</p>
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<p>Generating the weight coefficients tree for <math display="inline"><semantics> <mrow> <mi>p</mi> <mo>=</mo> <mn>5</mn> </mrow> </semantics></math>, where <math display="inline"><semantics> <mi>λ</mi> </semantics></math> can be {0, 0.2, 0.4, 0.6, 0.8, 1}. When <math display="inline"><semantics> <mi>λ</mi> </semantics></math> = 0, <math display="inline"><semantics> <mi>β</mi> </semantics></math> can be selected from {0, 0.2, 0.4, 0.6, 0.8, 1}, and so on, until all weight vectors are obtained.</p>
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<p>Example of the coding structure of a solution: <math display="inline"><semantics> <msub> <mi>d</mi> <mn>1</mn> </msub> </semantics></math> located at (1,1) has hovering height of 500, <math display="inline"><semantics> <msub> <mi>d</mi> <mn>2</mn> </msub> </semantics></math> located at (1,8) has hovering height of 800, and so on.</p>
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<p>Example of crossover of two individuals.</p>
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<p>The best fitness and the mean fitness of the population size of each generation with HGA from different distributions. (<b>a</b>) beta distribution, (<b>b</b>) exponential distribution, (<b>c</b>) gamma distribution, (<b>d</b>) random distribution.</p>
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<p>The best fitness and the mean fitness of the population size of each generation with HGA from different distributions. (<b>a</b>) beta distribution, (<b>b</b>) exponential distribution, (<b>c</b>) gamma distribution, (<b>d</b>) random distribution.</p>
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<p>In the case of the random distribution, the results of the deployment scheme of three algorithms. (<b>a</b>) Comparison of the results of the best fitness. (<b>b</b>) Mean fitness.</p>
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<p>The 50 groups of optimal fitness and mean fitness by the proposed algorithm. (<b>a</b>,<b>b</b>) show 50 groups of experimental results: best fitness and mean fitness of three algorithms for beta distribution, (<b>c</b>,<b>d</b>) for exponential distribution, (<b>e</b>,<b>f</b>) for gamma distribution, and (<b>g</b>,<b>h</b>) for random.</p>
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<p>Non-dominated fronts found by DDA-MOEA/D for drone deployment at the (<b>a</b>) 20th, (<b>b</b>) 40th, (<b>c</b>) 60th and (<b>d</b>) 80th generations.</p>
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<p>Non-dominated fronts found by: (<b>a</b>) DDA-MOEA/D, (<b>b</b>) SPEAII, and (<b>c</b>) NSGAII.</p>
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<p>Non-dominated fronts found by: (<b>a</b>) DDA-MOEA/D, (<b>b</b>) SPEAII, and (<b>c</b>) NSGAII.</p>
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<p>Running times of DDA-MOEA/D, SPEAII and NSGAII.</p>
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