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Keywords = SEED subsystem

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25 pages, 24372 KiB  
Article
Data-Driven Machine Learning-Informed Framework for Model Predictive Control in Vehicles
by Edgar Amalyan and Shahram Latifi
Information 2025, 16(6), 511; https://doi.org/10.3390/info16060511 - 19 Jun 2025
Viewed by 675
Abstract
A machine learning framework is developed to interpret vehicle subsystem status from sensor data, providing actionable insights for adaptive control systems. Using the vehicle’s suspension as a case study, inertial data are collected from driving maneuvers, including braking and cornering, to seed a [...] Read more.
A machine learning framework is developed to interpret vehicle subsystem status from sensor data, providing actionable insights for adaptive control systems. Using the vehicle’s suspension as a case study, inertial data are collected from driving maneuvers, including braking and cornering, to seed a prototype XGBoost classifier. The classifier then pseudo-labels a larger exemplar dataset acquired from street and racetrack sessions, which is used to train an inference model capable of robust generalization across both regular and performance driving. An overlapping sliding-window grading approach with reverse exponential weighting smooths transient fluctuations while preserving responsiveness. The resulting real-time semantic mode predictions accurately describe the vehicle’s current dynamics and can inform a model predictive control system that can adjust suspension parameters and update internal constraints for improved performance, ride comfort, and component longevity. The methodology extends to other components, such as braking systems, offering a scalable path toward fully self-optimizing vehicle control in both conventional and autonomous platforms. Full article
(This article belongs to the Special Issue Feature Papers in Information in 2024–2025)
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23 pages, 5219 KiB  
Article
Identification and Evaluation of the Main Constraints on Cotton Production Within a Collective Drip Irrigation System in Southern Xinjiang, China
by Zhanghao Sun, Zhen Wang and Jiusheng Li
Agronomy 2025, 15(4), 760; https://doi.org/10.3390/agronomy15040760 - 21 Mar 2025
Viewed by 585
Abstract
Intensive and large-scale drip irrigation plays a crucial role in ensuring cotton production in Northwest China. However, significant differences in cotton production have occurred at times within large-scale irrigation systems, and quantitative information on the importance and interactions of factors related to cotton [...] Read more.
Intensive and large-scale drip irrigation plays a crucial role in ensuring cotton production in Northwest China. However, significant differences in cotton production have occurred at times within large-scale irrigation systems, and quantitative information on the importance and interactions of factors related to cotton growth and constraints is scarce. In 2018–2019, we monitored six possible constraints (irrigation depth, soil texture, soil salt, soil moisture, soil inorganic nitrogen and soil organic matter) associated with drip irrigation management and seed cotton yields in a collective drip irrigation system (CDIS, composed of several drip irrigation subsystems (DISs)) in southern Xinjiang to assess the importance of different factors and identify the main constraints. In 2023, other more refined field trials were conducted to further evaluate the influencing mechanism of the main constraints on crop growth in one typical DIS within the selected CDIS. The results revealed large yield differences within the CDIS; although the average seed cotton yield was good (2018: 8051 kg ha−1, 2019: 6617 kg ha−1). Excessive irrigation depths (>500 mm) and coarse soil texture (sand content > 70%) were identified as the main constraints, affecting more than 45% of the plant area in the CDIS based on boundary line analysis (a typical analysis method to study the responses between variables) The results from the DISs revealed that the two constraints directly affected the soil moisture and soil inorganic nitrogen in the root zone, which reduced the effectiveness of irrigation and fertilization under drip irrigation. The Structural Equation Model (used to evaluate the causal relationships among multiple variables) revealed that both irrigation depth and soil texture indirectly affect yield by affecting soil inorganic nitrogen and plant N uptake and that soil nitrogen management is critical in resisting yield decline caused by constraints. An optimized irrigation schedule, improved uniformity of the drip irrigation network and adjusted drip fertilization strategies could be used for site-specific management to address the yield decline due to the main constraints and improve water and fertilizer use efficiency under drip irrigation management. Full article
(This article belongs to the Special Issue Improving Irrigation Management Practices for Agricultural Production)
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15 pages, 4345 KiB  
Article
Modal Variability of Ginkgo Seed–Stem System Based on Model Updating
by Jie Zhou, Linyun Xu, Hongping Zhou, Rongshan Zhang, Zhicheng Jia, Fubao Zhang, Yue Zhang, Juan Chen and Cheng Zhang
Forests 2024, 15(1), 178; https://doi.org/10.3390/f15010178 - 15 Jan 2024
Viewed by 1241
Abstract
An accurate simulation model is crucial for the analysis of the correct modal information of the ginkgo seed–stem system (ginkgo subsystem). This underpins the provision of technical rationale for efficient and low-damage precision vibrational harvesting operations in ginkgo cultivation. In this study, [...] Read more.
An accurate simulation model is crucial for the analysis of the correct modal information of the ginkgo seed–stem system (ginkgo subsystem). This underpins the provision of technical rationale for efficient and low-damage precision vibrational harvesting operations in ginkgo cultivation. In this study, based on the modal parameters of the ginkgo subsystem, a finite element model updating method is proposed to correct the elastic modulus of the stem with the natural frequency of the first bending mode. The large difference in the modal results calculated before and after model updating reveals that model updating is a critical step in the finite element analysis of crop subsystems. Then, an uncertainty parameter modeling method is proposed to investigate the modal variability of the ginkgo subsystem by finite element analysis. The results show that the stem length is a key parameter affecting the variability of natural frequencies, and the seed weight is a minor parameter. The variability of the ginkgo seed’s gravity center offset has a negligible effect on the natural frequencies of the system. The first natural frequency of the ginkgo subsystem can be utilized for vibrational harvesting. In addition, since the difference between the upper and lower limits of the first natural frequency of the ginkgo subsystem does not exceed 1 Hz, a specific excitation frequency can cause most ginkgo subsystems to resonate. This result facilitates the determination of precise excitation frequencies for efficient and low-damage ginkgo vibrational harvesting, ensuring both economic and ecological benefits in the management of ginkgo plantations. Full article
(This article belongs to the Special Issue Forest Machinery and Mechanization)
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17 pages, 1964 KiB  
Article
In Silico and In Vitro Investigation of the Distribution and Expression of Key Genes in the Fucose Operon of Escherichia coli
by Nehal A. Saif, Yomna A. Hashem, Heba M. Amin and Ramy K. Aziz
Microorganisms 2023, 11(5), 1265; https://doi.org/10.3390/microorganisms11051265 - 11 May 2023
Viewed by 2555
Abstract
Many gut bacteria degrade polysaccharides, providing nutritional advantages to their hosts. Fucose, a mucin degradation product, was suggested as a communication molecule between the resident microbiota and external pathogens. However, the precise role and variants of the fucose utilization pathway remain to be [...] Read more.
Many gut bacteria degrade polysaccharides, providing nutritional advantages to their hosts. Fucose, a mucin degradation product, was suggested as a communication molecule between the resident microbiota and external pathogens. However, the precise role and variants of the fucose utilization pathway remain to be elucidated. Here, we computationally and experimentally investigated the fucose utilization operon of E. coli. While the operon is conserved among E. coli genomes, a variant pathway, in which an ABC transporter system replaces the fucose permease gene (fucP), was computationally identified in 50 out of 1058 genomes. Comparative genomics and subsystems analysis results were confirmed by polymerase chain reaction-based screening of 40 human E. coli isolates, which indicated the conservation of fucP in 92.5% of the isolates (vs. 7.5% of its suggested alternative, yjfF). The in silico predictions were confirmed by in vitro experiments comparing the growth of E. coli strains K12, BL21, and isogenic fucose-utilization K12 mutants. Additionally, fucP and fucI transcripts were quantified in E. coli K12 and BL21, after in silico analysis of their expression in 483 public transcriptomes. In conclusion, E. coli utilizes fucose by two pathway variants, with measurable transcriptional differences. Future studies will explore this variation’s impact on signaling and virulence. Full article
(This article belongs to the Section Antimicrobial Agents and Resistance)
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20 pages, 3019 KiB  
Article
The Viral Fraction Metatranscriptomes of Lake Baikal
by Sergey Potapov, Andrey Krasnopeev, Irina Tikhonova, Galina Podlesnaya, Anna Gorshkova and Olga Belykh
Microorganisms 2022, 10(10), 1937; https://doi.org/10.3390/microorganisms10101937 - 29 Sep 2022
Cited by 3 | Viewed by 3471
Abstract
This article characterises viral fraction metatranscriptomes (smaller than 0.2 µm) from the pelagic zone of oligotrophic Lake Baikal (Russia). The study revealed the dominance of transcripts of DNA viruses: bacteriophages and algal viruses. We identified transcripts similar to Pithovirus sibericum, a nucleocytoplasmic [...] Read more.
This article characterises viral fraction metatranscriptomes (smaller than 0.2 µm) from the pelagic zone of oligotrophic Lake Baikal (Russia). The study revealed the dominance of transcripts of DNA viruses: bacteriophages and algal viruses. We identified transcripts similar to Pithovirus sibericum, a nucleocytoplasmic large DNA virus (NCLDV) isolated from the permafrost region of Eastern Siberia. Among the families detected were RNA viruses assigned to Retroviridae, Metaviridae, Potyviridae, Astroviridae, and Closteroviridae. Using the PHROG, SEED subsystems databases, and the VOGDB, we indicated that the bulk of transcripts belong to the functional replication of viruses. In a comparative unweighted pair group method with arithmetic mean (UPGMA) analysis, the transcripts from Lake Baikal formed a separate cluster included in the clade with transcripts from other freshwater lakes, as well as marine and oceanic waters, while there was no separation based on the trophic state of the water bodies, the size of the plankton fraction, or salinity. Full article
(This article belongs to the Special Issue Viruses of Plankton)
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14 pages, 1615 KiB  
Article
Functional Diversity of Microbial Communities in the Soybean (Glycine max L.) Rhizosphere from Free State, South Africa
by Titilope Tinu Ajiboye, Ayansina Segun Ayangbenro and Olubukola Oluranti Babalola
Int. J. Mol. Sci. 2022, 23(16), 9422; https://doi.org/10.3390/ijms23169422 - 20 Aug 2022
Cited by 11 | Viewed by 3065
Abstract
The plant microbiome is involved in enhancing nutrient acquisition, plant growth, stress tolerance, and reducing chemical inputs. The identification of microbial functional diversity offers the chance to evaluate and engineer them for various agricultural processes. Using a shotgun metagenomics technique, this study examined [...] Read more.
The plant microbiome is involved in enhancing nutrient acquisition, plant growth, stress tolerance, and reducing chemical inputs. The identification of microbial functional diversity offers the chance to evaluate and engineer them for various agricultural processes. Using a shotgun metagenomics technique, this study examined the functional diversity and metabolic potentials of microbial communities in the rhizosphere of soybean genotype link 678. The dominant genera are Geobacter, Nitrobacter, Burkholderia, Candidatus, Bradyrhizobium and Streptomyces. Twenty-one functional categories were present, with fourteen of the functions being dominant in all samples. The dominant functions include carbohydrates, fatty acids, lipids and isoprenoids, amino acids and derivatives, sulfur metabolism, and nitrogen metabolism. A Kruskal–Wallis test was used to test samples’ diversity differences. There was a significant difference in the alpha diversity. ANOSIM was used to analyze the similarities of the samples and there were significant differences between the samples. Phosphorus had the highest contribution of 64.3% and was more prominent among the soil properties that influence the functional diversity of the samples. Given the functional groups reported in this study, soil characteristics impact the functional role of the rhizospheric microbiome of soybean. Full article
(This article belongs to the Section Molecular Biology)
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19 pages, 3163 KiB  
Article
The Effects of Plant Health Status on the Community Structure and Metabolic Pathways of Rhizosphere Microbial Communities Associated with Solanum lycopersicum
by Afeez Adesina Adedayo, Ayomide Emmanuel Fadiji and Olubukola Oluranti Babalola
Horticulturae 2022, 8(5), 404; https://doi.org/10.3390/horticulturae8050404 - 4 May 2022
Cited by 19 | Viewed by 3871
Abstract
Powdery mildew disease caused by Oidium neolycopersici is one of the major diseases affecting tomato production in South Africa. Interestingly, limited studies exist on how this disease affects the community structure microbial communities associated with tomato plants employing shotgun metagenomics. In this study, [...] Read more.
Powdery mildew disease caused by Oidium neolycopersici is one of the major diseases affecting tomato production in South Africa. Interestingly, limited studies exist on how this disease affects the community structure microbial communities associated with tomato plants employing shotgun metagenomics. In this study, we assess how the health status of a tomato plant affects the diversity of the rhizosphere microbial community. We collected soil samples from the rhizosphere of healthy (HR) and diseased (DR; powdery mildew infected) tomatoes, alongside bulk soil (BR), extracted DNA, and did sequencing using shotgun metagenomics. Our results demonstrated that the rhizosphere microbiome alongside some specific functions were abundant in HR followed by DR and bulk soil (BR) in the order HR > DR > BR. We found eighteen (18) bacterial phyla abundant in HR, including Actinobacteria, Acidobacteria, Aquificae, Bacteroidetes, etc. The dominant fungal phyla include; Ascomycota and Basidiomycota, while the prominent archaeal phyla are Thaumarchaeota, Crenarchaeota, and Euryarchaeota. Three (3) bacteria phyla dominated the DR samples; Bacteroidetes, Gemmatimonadetes, and Thermotoga. Our result also employed the SEED subsystem and revealed that the metabolic pathways involved were abundant in HR. The α-diversity demonstrates that there is no significant difference among the rhizosphere microbiomes across the sites, while β-diversity demonstrated a significant difference. Full article
(This article belongs to the Special Issue Advancements in Soil Health)
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19 pages, 10981 KiB  
Article
Evolution of the Hybrid Aerial Underwater Robotic System (HAUCS) for Aquaculture: Sensor Payload and Extension Development
by Casey J. Den Ouden, Paul S. Wills, Lucas Lopes, Joshua Sanderson and Bing Ouyang
Vehicles 2022, 4(2), 390-408; https://doi.org/10.3390/vehicles4020023 - 21 Apr 2022
Cited by 6 | Viewed by 3932
Abstract
While robotics have been widely used in many agricultural practices such as harvesting, seeding, cattle monitoring, etc., aquaculture farming is an important, fast-growing sector of agriculture that has not seen significant adoption of advanced technologies such as robotics and the Internet of Things [...] Read more.
While robotics have been widely used in many agricultural practices such as harvesting, seeding, cattle monitoring, etc., aquaculture farming is an important, fast-growing sector of agriculture that has not seen significant adoption of advanced technologies such as robotics and the Internet of Things (IoT). In particular, dissolved oxygen (DO) monitoring, a practice in pond aquaculture essential to the health of the fish crops, remains labor-intensive and time-consuming. The Hybrid Aerial Underwater robotiCs System (HAUCS) is an IoT framework that aims to bring transformative changes to pond aquaculture. This paper focuses on the latest development in the HAUCS mobile sensing platform and field deployment. To address some shortcomings with the current implementation, the development of a novel rigid Kirigami-based robotic extension subsystem that can expand the functionality of the HAUCS platform is also being discussed. Full article
(This article belongs to the Special Issue Vehicle Design Processes)
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14 pages, 2831 KiB  
Article
The Immense Functional Attributes of Maize Rhizosphere Microbiome: A Shotgun Sequencing Approach
by Saheed Adekunle Akinola, Ayansina Segun Ayangbenro and Olubukola Oluranti Babalola
Agriculture 2021, 11(2), 118; https://doi.org/10.3390/agriculture11020118 - 2 Feb 2021
Cited by 10 | Viewed by 4070
Abstract
The northwest (NW) province of South Africa is a semi-arid area, often disturbed by soil extremes such as drought and intense temperature. However, many functions possessed by the rhizosphere microbiome are still required, especially those inhabiting arid and semi-arid soils. This study involves [...] Read more.
The northwest (NW) province of South Africa is a semi-arid area, often disturbed by soil extremes such as drought and intense temperature. However, many functions possessed by the rhizosphere microbiome are still required, especially those inhabiting arid and semi-arid soils. This study involves a metagenomic comparison of the major metabolic attributes of two maize rhizosphere soils and their surrounding soils. Here, we hypothesized that there is a considerable difference between the functional diversity of maize rhizosphere and bulk soils and that the rhizosphere soil has distinct functional traits of agricultural importance. A high-throughput sequencing approach was used to assess the metabolic profile of rhizosphere soil microbiota of maize collected from the Gauteng and NW provinces of South Africa. The relative abundance of 13 functional hit categories was significantly different between the sampling sites. The diversity indices showed a considerable difference between the rhizosphere and surrounding soils. The difference in the chemical properties of the sampling sites was responsible for the variation in the microbial functional composition. Nevertheless, the presence of a high relative abundance of functional categories with unknown functions in SEED subsystem-2 coupled with the large number of functional hits conferring a response to soil stressors viz. oxidative stress, heat shock, osmotic stress, and cold shock noticed in the rhizosphere samples may indicate the presence of novel genes at the sampling sites. Exploring the plant growth-promoting traits of microorganisms present at these sites could eliminate the constraint posed by soil stressors on sustainable agriculture. Full article
(This article belongs to the Special Issue Advanced Research of Soil Microbial Functional Diversity)
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31 pages, 710 KiB  
Review
Data Management for the Internet of Things: Design Primitives and Solution
by Mervat Abu-Elkheir, Mohammad Hayajneh and Najah Abu Ali
Sensors 2013, 13(11), 15582-15612; https://doi.org/10.3390/s131115582 - 14 Nov 2013
Cited by 190 | Viewed by 21210
Abstract
The Internet of Things (IoT) is a networking paradigm where interconnected, smart objects continuously generate data and transmit it over the Internet. Much of the IoT initiatives are geared towards manufacturing low-cost and energy-efficient hardware for these objects, as well as the communication [...] Read more.
The Internet of Things (IoT) is a networking paradigm where interconnected, smart objects continuously generate data and transmit it over the Internet. Much of the IoT initiatives are geared towards manufacturing low-cost and energy-efficient hardware for these objects, as well as the communication technologies that provide objects interconnectivity. However, the solutions to manage and utilize the massive volume of data produced by these objects are yet to mature. Traditional database management solutions fall short in satisfying the sophisticated application needs of an IoT network that has a truly global-scale. Current solutions for IoT data management address partial aspects of the IoT environment with special focus on sensor networks. In this paper, we survey the data management solutions that are proposed for IoT or subsystems of the IoT. We highlight the distinctive design primitives that we believe should be addressed in an IoT data management solution, and discuss how they are approached by the proposed solutions. We finally propose a data management framework for IoT that takes into consideration the discussed design elements and acts as a seed to a comprehensive IoT data management solution. The framework we propose adapts a federated, data- and sources-centric approach to link the diverse Things with their abundance of data to the potential applications and services that are envisioned for IoT. Full article
(This article belongs to the Section Sensor Networks)
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