Sampling Points Research Articles

Mapping the dynamics of intensive forage acreage during 2008–2022 in Google Earth Engine using time series Landsat images and a phenology-based algorithm

The increasing demand for livestock feed in China has led to a remarkable increase in forage acreage, particularly in forage fields under intensive agricultural management. However, there are no maps currently available to demonstrate and document the spatial and temporal patterns of the continuous expansion of intensive forage. Here we proposed a pixel- and phenology-based approach to map intensive forage by utilizing time series Landsat images and the Google Earth Engine platform. Given the challenge of accurately identifying intensive forage fields in diverse regions using fixed multi-temporal Landsat data, along with the distinct phenological characteristics of intensive forage fields that undergo multiple annual harvests, we proposed an innovative data mining approach based on dynamic monthly composite images (DMCI). This approach aims to enhance the algorithm’s efficiency and precision in identifying intensive forage. As a pilot study, we analyzed all available Landsat images (1732 scenes) from 2008 to 2022 in a county located in the farming-pastoral ecotone of northern China and tracked the historical dynamics of intensive forage expansion over five epochs at three-year intervals. The accuracy assessment of the intensive forage maps for the five epochs using validation sample points showed that the overall accuracy and Mathews correlation coefficient ranged from 97.1 % to 98.9 % and 0.93 to 0.98 respectively. The intensive forage acreage in the study area exhibited a dramatic expansion from 2008 to 2022, particularly after the 2012s. This study demonstrates the potential of our DMCI- and phenology-based algorithm and time series Landsat images for tracking the dynamics of intensive forage acreage at 30-m resolution in temperate single-cropping regions.

An Investigation into the Pole–Slot Ratio and Optimization of a Low-Speed and High-Torque Permanent Magnet Motor

At present, low-speed high-torque permanent magnet motors are widely used in the sampling industry, manufacturing industry and energy industry. However, the research on low-speed high-torque permanent magnet motors is far from enough. The primary difficulty in the initial design of low-speed high-torque permanent magnet motors is the selection of pole–slot ratio. The pole–slot ratio has a great influence on the electromagnetic performance such as torque ripple and the maximum output torque of low-speed motors. Choosing the appropriate pole–slot ratio scheme can make the design of a low-speed motor more efficient. In addition, the optimization design of the motor is also a necessary process. At present, there are many studies on optimization algorithms. However, the research on sample point sampling and surrogate model fitting is not enough. Choosing the appropriate sample point sampling method and surrogate model fitting method can help one obtain a more accurate surrogate model, which lays a foundation for the optimization of the motor. Based on the above analysis, this paper first selects four representative pole–slot ratio schemes for comprehensive comparison of their electromagnetic performances. Secondly, two sample point sampling methods and three surrogate model fitting methods are combined to obtain six surrogate models, and the accuracy of the six surrogate models is compared and analyzed. Finally, a 37kW,160rpm prototype is made, and the comparison of the surrogate model optimization prediction results, the finite element simulation calculation results and the measured results is carried out to further prove the accuracy of the selected surrogate model. The work performed in this paper provides a certain reference value for the initial design and optimization experiment design of low-speed high-torque permanent magnet motor.

Machine Learning-Based Fine Classification of Agricultural Crops in the Cross-Border Basin of the Heilongjiang River between China and Russia

The transboundary region along the Heilongjiang River, encompassing the Russian Far East and Northeast China, possesses abundant agricultural natural resources crucial for global food security. In the face of the challenge of disruptions in the global food supply chain, the precise monitoring and exploitation of agricultural resources in the Heilongjiang Basin becomes imperative. This study employed deep learning to classify crop status in 2023 in the Heilongjiang Basin using Sentinel-2 satellite remote sensing images at a 10 m resolution. Various vegetation indices, including the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Water Index (NDWI), the Enhanced Vegetation Index (EVI), the Modified Soil Adjusted Vegetation Index (MSAVI), and others, were computed and analyzed for different crops. The Google Earth Engine (GEE) platform was utilized for validation point sampling based on plot objects. The random forest (RF) classification method was successfully employed to classify and identify major crops in the study area (wheat, maize, rice, and soybean), as well as wetlands, tree cover, grassland, water, and constructed land, with an overall classification accuracy of 86%. Tree cover dominated the land cover, constituting 62%, while wheat, maize, rice, and soybeans accounted for 7% of the total area. Of these, soybeans occupied the largest area (57,646.60 hectares), followed by rice (53,209.53 hectares), maize (39,998.37 hectares), and wheat (8782.31 hectares). This study demonstrated that sample selection based on plot objects facilitates efficient sample labeling, providing insights into crop classification in other, potentially larger, areas. This method simultaneously distinguishes wetland, cultivated land, and forest features, supporting further integrated investigations for more natural resources.

Tracking bioaerosol exposure among municipal solid waste workers using hematological and inflammatory biomarkers

High levels of bioaerosols may exist in the air of municipal solid waste (MSW) management facilities, constituting a significant occupational hazard for workers. In this study, we investigated the potential association between exposure to bioaerosols and inflammatory biomarkers among municipal solid waste workers (MSWWs) at both the landfill site and the municipal solid waste transfer station (MSWTS), in comparison to a control group without exposure. Air sampling was conducted at six points around the landfill, two points at the MSWTS, and one point in a public park (as a control area) during the spring and summer of 2019. The results of our study revealed that airborne pathogens were highly prevalent at the sampling points, especially in the active zone and leachate collection pond. Aspergillus species were the predominant fungal species detected in this study, with the highest occurrence observed for Aspergillus flavus (83.3%), Aspergillus niger, and Aspergillus fumigatus (75.0%). Furthermore, Staphylococcus species accounted for over 75% of the total bacterial bioaerosols detected across all study areas. The blood test results of workers revealed a significant increase in platelets (PLT), immunoglobulin G (IgG), white blood cells (WBC), neutrophils, basophils, and high-sensitivity C-reactive protein (hs-CRP) compared to the control group. Conversely, platelet distribution width (PDW), mean platelet volume (MPV), and platelet-large cell ratio (P-LCR) in the exposed subjects exhibited a decreasing trend compared to the control group. These findings suggest a potential association between exposure to bioaerosols, particularly in the vicinity of open dumpsites, and elevated levels of hematologic and inflammatory markers in circulation. Furthermore, the influence of smoking status and confounding factors appears to be significant in both the control and exposure groups.

Integrated occurrence of contaminants of emerging concern, including microplastics, in urban and agricultural watersheds in the State of São Paulo, Brazil

Contaminants of emerging concern (CECs), including microplastics, have been the focus of many studies due to their environmental impact, affecting biota and human health. The diverse land uses and occupation of watersheds are important parameters driving the occurrence of these contaminants. CECs such as pesticides, drugs, hormones, and industrial-origin substances were analyzed in urban/industrial (Atibaia) and agricultural (Preto/Turvo) watersheds located in São Paulo state, Brazil. A total of 24 CECs were investigated, and, as a result, only 5 (caffeine, carbendazim, atrazine, ametrine and 2-hydroxytrazine) were responsible for 81.73 % of the statistical difference between watersheds contamination profile. The Atibaia watershed presented considerable concentrations of caffeine (ranging from 75 to 2025 ng L−1), while carbendazim (44 to 1144 ng L−1) and atrazine (3 to 266 ng L−1) presented highest levels in Preto/Turvo watershed. In all sampling points, the cumulative potential aquatic life risk assessed by the NORMAN database indicates some level of environmental concern associated to pesticides and caffeine (risk quotient >1). Microplastics had been analyzed in both watersheds, being the white/transparent fragments in size between 100 and 250 μm the most detected in this study. The estimated abundance in the Atibaia watershed ranged from 349 to 2898 items m−3 presenting some influence of pluviosity, while in Rio Preto/Turvo ranged from 169 to 6370 items m−3, being more abundant in the dam area without a clear influence of pluviosity. In both basins, polyethylene and polypropylene were the most detected polymers, probably due to the intense use of single-use plastics in urban areas. Possibly, due to the distinct physic-chemical properties of microplastics and organic CECs, no correlations were observed between their occurrence, which makes us conclude that they have different transport mechanism, behavior, and fate in the environment.

Assessing the accuracy of remote sensing data products: A multi-granular spatial sampling method

Over the past two decades, remote sensing data have demonstrated significant potential across various fields. Accuracy assessment, as a crucial component, plays a vital role in ensuring the effective application of remote sensing data products. To assess the accuracy of remote sensing data products, this paper proposed a multi-granular spatial sampling method (MG-SSM). In the proposed MG-SSM, the assessed remote sensing data products were stratified into different layers based on spatial heterogeneity, quantified by calculating the aggregation index. Subsequently, multi-granular sampling units were defined for each layer based on spatial correlation, quantified using Moran’s I and Z-score. Finally, the accuracy of remote sensing data products was assessed by comparing it with the reference data. Using the classification results of Landsat 9 OLI/TIRS with a 30-m spatial resolution as assessed data, and the classification results of Sentinel-2 MSI with a 10-m spatial resolution as reference data, and employing overall accuracy (OA) and the Kappa coefficient as assessment metrics, the performance of MG-SSM was compared against other sampling methods, including SRS, ST, SY, SSS, and GLCM. The performances of SRS, ST, SY, SSS, GLCM, and MG-SSM were 74.41%, 75.39%, 76.57%, 78.47%, 76.77%, and 67.36% in terms of OA, and 0.6, 0.61, 0.64, 0.66, 0.64, and 0.46 in terms of the Kappa coefficient, respectively. The results indicated that MG-SSM exhibited the lowest values for overall accuracy and the Kappa coefficient, aligning with the objective of assessing the accuracy of remote sensing data products. This outcome is attributed to MG-SSM’s strategy of distributing more sample points in areas with lower aggregation of land-cover classes, which are more prone to classification errors. Collectively, the proposed MG-SSM enhanced the representativeness and reduced the information redundancy of the sample points, making it an efficient sampling method for assessing the accuracy of remote sensing data products.

Wastewater monitoring of SARS-CoV-2 gene for COVID-19 epidemiological surveillance in Tucumán, Argentina.

Wastewater-based epidemiology provides temporal and spatial information about the health status of a population. The objective of this study was to analyze and report the epidemiological dynamics of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in the province of Tucumán, Argentina during the second and third waves of coronavirus disease 2019 (COVID-19) between April 2021 and March 2022. The study aimed to quantify SARS-CoV-2 RNA in wastewater, correlating it with clinically reported COVID-19 cases. Wastewater samples (n = 72) were collected from 16 sampling points located in three cities of Tucumán (San Miguel de Tucumán, Yerba Buena y Banda del Río Salí). Detection of viral nucleocapsid markers (N1 gene) was carried out using one-step reverse transcription-quantitative polymerase chain reaction (RT-qPCR). Viral loads were determined for each positive sample using a standard curve. A positive correlation (p < 0.05) was observed between viral load (copies/mL) and the clinically confirmed COVID-19 cases reported at specific sampling points in San Miguel de Tucumán (SP4, SP7, and SP8) in both months, May and June. Indeed, the high viral load concurred with the peaks of COVID-19 cases. This method allowed us to follow the behavior of SARS-CoV-2 infection during epidemic outbreaks. Thus, wastewater monitoring is a valuable epidemiological indicator that enables the anticipation of increases in COVID-19 cases and tracking the progress of the pandemic. SARS-CoV-2 genome-based surveillance should be implemented as a routine practice to prepare for any future surge in infections.

A Novel Consolidation Analysis Framework: Universal Function Approximators Regularized by Physical Principles

Analytical and numerical techniques are widely used to analyse and interpret soil consolidation problems. An important limitation is the requirement for significant geotechnical knowledge and expertise to derive ‘true’ solutions. This study proposes an alternative: a universal function approximator regularized with known physical principles. The proposed approach here advances previous work to solve one-dimensional (1D) consolidation considering both self-weight and large strains, as well as two-dimensional (2D) consolidation by vertical drains. Initial and boundary conditions of the studied consolidation problems are first strongly and weakly enforced for comparison. A neural network is adopted here as the function approximator. To boost prediction accuracy, a novel strategy is proposed to adaptively sample data points for training. An estimate of epistemic uncertainty is achieved using the confidence interval of ensembled multiple outputs. The results show that the proposed approach accurately predicts the behaviours of complex consolidation processes. Results also indicate that regularization using weak physical constraints can alleviate the imbalance of back-propagated gradients of different loss terms and, in turn, achieve higher accuracy. The proposed method is generic, mesh-free, more robust and can be applied to a wide range of geotechnical problems.

Dynamic accuracy measurement method for star trackers using a time-synchronized high-accuracy turntable

Star trackers are typically used in a spacecraft to provide absolute attitude information to the on-board attitude control system so as to promote high accuracy. The performance of the star tracker is rather important. Attitude incorrectness provided by star trackers may lead to bad navigation with big deviations, even failure of satellites. Therefore, how to realize and verify the accuracy is crucial. As a matter of fact, it is difficult to validate accuracy of star trackers on the ground, especially for star trackers under highly dynamic conditions. In this paper, an accuracy measurement method for star trackers under dynamic conditions is proposed, utilizing a high-accuracy swing table to provide reference to compare. To this end, a swing table, star tracker, and the test equipment are synchronized, in order to reduce systematic errors. As the motion trajectory of the swing table can be set beforehand, the initial attitude of the star tracker can be predicted through a set of coordinate transformations. As a result, the star tracker is able to keep tracking, regardless of the angular velocity of the swing table. This makes the statistical sample points more sufficient and the results more reliable. Moreover, it can evaluate the angular velocity of star trackers up to 20°/s. In comparison with the conventional method with simulated stars, this method utilizes real navigation stars as observation targets making the measurement results much closer to the on-orbit performance. Lastly, but much more importantly, it can also verify the performance of a star tracker in one experiment, such as sensitivity, static performance, capture probability, and so on. Experimental results demonstrate that the proposed method is effective, especially for highly dynamic star trackers. Such a measurement environment is close to the in-orbit conditions, and it can satisfy the stringent requirement for star trackers under high dynamics.

Deep reinforcement learning-based reactive trajectory planning method for UAVs

In order to improve the ability of avoiding dynamic threats during the flight of unmanned aerial vehicles (UAVs), a deep reinforcement learning-based reactive trajectory planning method is proposed in this paper. Firstly, a constrained Rapidly-exploring Random Tree-Connect algorithm (C-RRT-Connect) is proposed as the basic algorithm of reactive trajectory planning to globally plan for avoiding static obstacles in the environment. The C-RRT-Connect algorithm introduces the idea of target attraction to constrain the optimal growth point in the RRT-Connect algorithm. Then, based on the global trajectory, the local optimization is carried out according to the dynamic threats detected by the UAV during the flight. According to the real-time relative state between the UAV and the detected dynamic threat, the reaction sampling points and directional coefficients for avoiding the corresponding dynamic threat are generated online via the action network trained with the depth deterministic policy gradient algorithm (DDPG). And then the local trajectory is adjusted to modify the flight trajectory of the UAV to achieve reactive obstacle avoidance. The simulation experiment firstly compares the global trajectory planning performance of C-RRT-Connect and RRT-Connect in static environment, and secondly compares the local trajectory planning performance of DDPG algorithm and the artificial potential field method in dynamic environment. The experimental results show that in static environment, C-RRT-Connect algorithm has faster searching speed, less invalid samples and higher searching trajectory quality than RRT-Connect algorithm; In a dynamic environment, DDPG algorithm reduces the average running time by about 26% compared with the artificial potential field method, and has a stronger ability to evade dynamic threats in real time.

Land Use Impact on Water Quality and Phytoplankton Community Structure in Danjiangkou Reservoir

The composition and intensity of land use significantly influence the aquatic ecological environment, further affecting the physicochemical attributes of the water body, and indirectly modulating the phytoplankton community structure. This study centers around the Danjiangkou Reservoir, investigating the correlation between land use, water environment, and phytoplankton alongside varying intensities of anthropogenic activities, based on the review of land use, phytoplankton, and water quality data of 2021. Firstly, an analysis was conducted over five circular buffer zones generated around sampling points with radii set at 500 m, 1000 m, 1500 m, 2000 m, and 2500 m, wherein the intensity of human activity was categorized into low, medium, and high, in accordance with the human activity intensity level score (HAILS). This study proceeded to explore the correlation between land use and the water environment across different scales, compared phytoplankton density and community structure differences across varied levels of human activity, and analyzed the association between phytoplankton communities in the Danjiangkou Reservoir and environmental variables under various intensities. The findings showed: The land use within the 500 m circular buffer zone has the greatest impact on the water quality of the Danjiangkou Reservoir, especially with the increase in human activities leading to elevated nutrient levels in the water, thereby promoting the growth and reproduction of algae, and increasing the risk of algal blooms. This research scrutinizes the relationship between land use, water environment, and plankton under varying human activity intensities, serving as a foundation for environmental management sectors to make informed decisions and promote the sustainable development of the catchment water environment.

HB-RRT:A path planning algorithm for mobile robots using Halton sequence-based rapidly-exploring random tree

Path planning remains crucial for efficient robot operation. A Halton Biased Rapidly-exploring Random Tree (HB-RRT) path planning algorithm is introduced in this study. The Halton sequence, known for its uniform distribution and low discrepancy, is employed for sampling. Issues arising from the pseudo-random sequence in the standard RRT algorithm, leading to uneven distribution of sampling points, are addressed. A mouse-inspired goal-oriented strategy and a candidate sampling pool strategy are incorporated to enhance the sampling point quality, thereby addressing the challenge of insufficient memory during node expansion. Path optimization is further achieved through a multi-level planning approach, which aims to minimize redundancy. A subsequent smoothing of the path is conducted using a cubic B-spline method. Comparisons with the RRT, Bionic Target Bias-RRT, and Informed-RRT* algorithms, through both numerical simulations and real-world testing, confirm the superiority of the HB-RRT algorithm in terms of planning time, path length, and overall path quality.

Comparison of Molecular and Morphological Identification Methods for Anguina Seed Gall Nematodes in Oregon Grasses Grown for Seed

Oregon's grass seed industry specializes in the production of forage grasses, including annual ryegrass ( Lolium multiflorum) and orchardgrass ( Dactylis glomerata). These species are hosts of seed gall nematodes (SGNs): Anguina funesta and Anguina sp. SGNs cause yield-limiting seed galls and can also vector toxic Rathayibacter bacteria. Trade partners have strict phytosanitary regulations leading to rejection of seed lots infested with SGNs. Current best practices for SGN detection focus on postharvest seed evaluation. Methods to evaluate fields before harvest could improve risk management decisions. In this study, we evaluated timing, collection, and detection methods to generate new recommendations for SGN detection throughout the growing season. Fields of annual ryegrass (21) and orchardgrass (7) were sampled in the 2022 and 2023 growing seasons at tillering (March), flowering (May), harvest (July), and germination (November). At each time point, tillers, seed heads, or soil samples were collected. Nematodes were extracted from soil, tiller, and seed head samples using traditional nematology methods. Alternatively, SGN-specific real-time and conventional PCR protocols were evaluated on DNA extracted from tillers or seed heads. Direct enumeration of SGN from tillers with traditional nematology methods resulted in positive detections in 11 to 19% of fields, depending on sample time and year, as opposed to 33 to 44% of fields when using molecular methods. SGNs were detected in 40% of fields using both methods when evaluating seed head samples. This study indicates the utility of incorporating molecular methods for risk evaluations of SGNs and provides recommendations for accurate SGN detection throughout the growing season. [Formula: see text] The author(s) have dedicated the work to the public domain under the Creative Commons CC0 “No Rights Reserved” license by waiving all of his or her rights to the work worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law, 2024.

A two-group canonical variate analysis biplot for an optimal display of both means and cases

AbstractCanonical variate analysis (CVA) entails a two-sided eigenvalue decomposition. When the number of groups, J, is less than the number of variables, p, at most $$J-1$$ J - 1 eigenvalues are not exactly zero. A CVA biplot is the simultaneous display of the two entities: group means as points and variables as calibrated biplot axes. It follows that with two groups the group means can be exactly represented in a one-dimensional biplot but the individual samples are approximated. We define a criterion to measure the quality of representing the individual samples in a CVA biplot. Then, for the two-group case we propose an additional dimension for constructing an optimal two-dimensional CVA biplot. The proposed novel CVA biplot maintains the exact display of group means and biplot axes, but the individual sample points satisfy the optimality criterion in a unique simultaneous display of group means, calibrated biplot axes for the variables, and within group samples. Although our primary aim is to address two-group CVA, our proposal extends immediately to an optimal three-dimensional biplot when encountering the equally important case of comparing three groups in practice.

Microplastics removal efficiency and risk analysis of wastewater treatment plants in Oman

Microplastics (MPs) have recently been documented as an emerging pollutant that poses a critical threat to environment. Wastewater treatment plants (WWTPs) are commonly regarded as significant contributors to the presence of MPs. This study aimed to assess the MPs load of three wastewater treatment facilities in Oman using various treatments, including MBR, SBR, and CAS. Wastewater samples from influent, effluent, and sludge were collected and analyzed to determine the concentration, morphology, size, color, and polymer type of the MPs. A set of sieves with a mesh size range of 1 mm–45 μm was used to for filtration. Oxidation treatment was applied for all samples using Fenton's reagent, followed by density separation by sodium chloride solution. The Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy (ATR- FTIR) method was utilized to test 10% from each sampling point to confirm the polymer types of the MPs. The pollution load index (PLI) and hazard index (HI) have been employed to assess the risk associated with the chemical toxicity and concentration of detected particles. The PROMETHEE method was used to rank the risk of sampling sites based on different criteria that posed potential ecological and human health risks. The results indicate that the average concentrations of 0.99 MP/L, 1.38 MP/L, and 0.93 MP/L were detected in the final treated effluent of WWTP A, WWTP B, and WWTP C, respectively. These concentrations correspond to overall removal efficiencies of 82.5%, 77.4%, and 79.2% for WWTP A, WWTP B, and WWTP C, respectively Most MPs found in tertiary effluent were smaller particles (425 μm) and fiber-shaped. The major types of MPs were polypropylene (PP), low-density polyethylene (LDPE), polyurethane (PU), polyethylene terephthalate (PET), and Polyvinyl chloride (PVC). This study showed that treated effluent and sludge release significant MPs into the environment.

Discovery of novel cholinesterase inhibitors easily crossing the blood-brain barrier via structure-property relationship investigation: Methylenedioxy-cinnamicamides containing tertiary amine side chain.

In the present investigation, a series of dimethoxy or methylenedioxy substituted-cinnamamide derivatives containing tertiary amine moiety (N. N-Dimethyl, N, N-diethyl, Pyrrolidine, Piperidine, Morpholine) were synthesized and evaluated for cholinesterase inhibition and blood-brain barrier (BBB) permeability. Although their chemical structures are similar, their biological activities exhibit diversity. The results showed that all compounds except for those containing morpholine group exhibited moderate to potent acetylcholinesterase inhibition. Preliminary screening of BBB permeability shows that methylenedioxy substituted compounds have better brain permeability than the others. Compound 10c, containing methylenedioxy and pyrrolidine side chain, showed a better acetylcholinesterase inhibition (IC50: 1.52±0.19 μmol/L) and good blood-brain barrier permeability. Further pharmacokinetic investigation of compound 10c using ultra high performance liquid chromatography-mass/mass spectrometry (UPLC-MS/MS) in mice showed that compound 10c in brain tissue reached its peak concentration (857.72 ± 93.56 ng/g) after dosing 30 min. Its half-life in the serum is 331 min (5.52 h), and the CBrain/CSerum at various sampling points is ranged from 1.65 to 4.71(Mean: 2.76) within 24 hours. This investigation provides valuable information on the chemistry and pharmacological diversity of cinnamic acid derivatives and may be beneficial for the discovery of central nervous system drugs.

A combinatorial problem related to the classical probability

In the classical probability model, let f(n) be the maximum number of pairwise independent events for the sample space with n sample points. The determination of f(n) is equivalent to the problem of determining the maximum cardinality of specific intersecting families on the set {1,2,…,n}. We show that f(n)≤n+1, and f(n)=n+1 if there exists a Hadamard matrix of order n.

Graph Sampling for Map Comparison

Comparing two road maps is a basic operation that arises in a variety of situations. A map comparison method that is commonly used, mainly in the context of comparing reconstructed maps to ground truth maps, is based on graph sampling . The essential idea is to first compute a set of point samples on each map, and then to match pairs of samples—one from each map—in a one-to-one fashion. For deciding whether two samples can be matched, different criteria, e.g., based on distance or orientation, can be used. The total number of matched pairs gives a measure of how similar the maps are. Since the work of Biagioni and Eriksson [11, 12], graph sampling methods have become widely used. However, there are different ways to implement each of the steps, which can lead to significant differences in the results. This means that conclusions drawn from different studies that seemingly use the same comparison method, cannot necessarily be compared. In this work we present a unified approach to graph sampling for map comparison. We present the method in full generality, discussing the main decisions involved in its implementation. In particular, we point out the importance of the sampling method (GEO vs. TOPO) and that of the matching definition, discussing the main options used, and proposing alternatives for both key steps. We experimentally evaluate the different sampling and matching options considered on map datasets and reconstructed maps. Furthermore, we provide a code base and an interactive visualization tool to set a standard for future evaluations in the field of map construction and map comparison.

Examining the performance of Laguerre-based and nonlinear predictive control models for wheeled mobile robots when facing obstacles

This focuses on the problem of controlling the formation of a team of rotating robots that are not homonomic and are not able to interact in a barrier environment. The virtual structure formation control strategy is obtained here to calculate each path of the robot separately while the reference path of the virtual center of the formation is generated by artificial potential fields. In this presentation, we will introduce a new control algorithm that utilizes Model predictive control and nonlinear system dynamics. Three traditional model prediction control controllers (MPC): logger-based MPC, nonlinear MPC, and traditional MPC are used to apply the control algorithm for the nonlinear system. Model predictive control is used to calculate the torques required to track the path by using the dynamic model of the Rotary moving robot. A proposed control rule is the most effective approach to solving the formation and tracking problems. Model Predictive Control will be introduced through the introduction of basic ideas and terms. Traditional MPC, logger-based MPC, and non-linear MPC are among the three MPC methods to be offered. The discrete time display of the power plant under control is taken into account by developing MPC methods for linear or nonlinear systems for practical implementation. The model predictor controller optimizes a cost function to calculate an optimal control sequence at any sampling point. While ignoring the rest, the system only achieves the first control action of this sequence. During subsequent sampling, the optimization problem is resolvable with the use of updated process measurements and a different horizon. The cost function formula is determined by the controller’s purpose. An error function often plays a role in determining the objective by revealing the difference between the desired and actual response. The shape of the formation can be preserved by the law of control by utilizing information from other robots, which can be assured through a new cost function. In the current conditions of disruption, the proposed controllers can also achieve the goal of tracking the path and maintaining formation, according to the results.

Synthesis, Antimicrobial Assessment of Chalcones and their Pyrimidine Derivatives

The aim of the study was to transform chalcones synthesized to their respective pyrimidine derivatives which was successful. The synthesized compounds were subjected to antimicrobial assay against bacteria and fungi organisms screened, however sample ZB had inhibitory effect against bacterial strains. Sample ZB is active against S. aureus and E. coli with an MIC of 5 and 1 mg/mL with a zone of inhibition 11 and 23 mm respectively. When compared to sample B had activity against E. coli with a minimum inhibitory concentration of 0.1 mg/mL and zone of inhibition of 20 mm. Modification of 4-(2-hydroxyphenyl)-6-(2,3,4-trimethoxyphenyl)pyrimidin-2-one to pyrimidin-2-one increases spectrum of activity against gram positive bacterial S. aureus. Sample A inhibits B. subtilis and P. marneffei with an MIC of 10 mg/mL with a zone of inhibition of 14 mm and 20 mm respectively. Transformation of Sample A to 2-aminopyrimidine drastically abolish the antibacterial and antifungal effect. Sample ZA is inactive against bacteria and fungi organisms, when compared to ZB which inhibits S. aureus and E. coli. The spectral analysis revealed that the samples are in line with literature and had a melting point (100-103oC and 115-117oC) for sample ZA and ZB respectively. The starting materials had a melting point (55-57oC and 95-100oC) for Sample A and B respectively.