Multifractal and joint multifractal analysis of soil invertebrate fauna, altitude, and organic carbon

ABSTRACT The objectives of this study were to evaluate the degree of multifractality of the spatial distribution of altitude, organic carbon concentration, and invertebrate fauna diversity, and to characterize the degree of joint multifractal association among these variables. Soil sampling was performed every 20 m across a 2,540 m transect, with a total of 128 sampling points in a sugarcane area in Goiana municipality, Pernambuco State. For each sampling point, the altitude, organic carbon concentration, and macrofauna diversity (diversity indices and functional groups) were evaluated. Spatial distributions of altitude, organic carbon concentration, and macrofauna diversity were characterized by the generalized dimension spectrum (Dq) and singularity spectrums [f(α) versus α], which presented multifractal behavior with different degrees of heterogeneity in scales. Joint multifractal analysis was useful for revealing the relationships at multiple scales between the studied variables, as demonstrated by the non-detected associations using traditional statistical methods. To quantify the spatial variability of edaphic fauna based on the multiple scales and association sets in the joint dimension, the impact of agricultural production systems on biological diversity can be described. All of the studied variables displayed a multifractal behavior with greater or lower heterogeneity degree depending on the variable, with altitude and organic carbon being the most homogeneous attributes.

Low-Cost Clamp-On Photometers (ClampOD) and Tube Photometers (TubeOD) for Online Cell Density Determination

Optical density (OD) measurement is the gold standard to estimate microbial cell density in aqueous systems. Recording microbial growth curves is essential to assess substrate utilization, gauge sensitivity to inhibitors or toxins, or determine the perfect sampling point. Manual sampling for cuvette-photometer-based measurements can cause disturbances and impact growth, especially for strictly anaerobic or thermophilic microbes. For slow growing microbes, manual sampling can cause data gaps that complicate analysis. Online OD measurement systems provide a solution, but are often expensive and ill-suited for applications such as monitoring microbial growth in custom or larger anaerobic vessels. Furthermore, growth measurements of thermophilic cultures are limited by the heat sensitivity of complex electronics. Here, we present two simple, low-cost, self-assembled photometers—a “TubeOD” for online measurement of anaerobic and thermophilic cultures in Hungate tubes and a “ClampOD” that can be attached to virtually any transparent growth vessel. Both OD-meters can be calibrated in minutes. We detail the manufacturing and calibration procedure and demonstrate continuous acquisition of high quality cell density data of a variety of microbes, including strict anaerobes, a thermophile, and gas-utilizing strains in various glassware. When calibrated and operated within their detection limits (ca. 0.3–90% of the photosensor voltage range), these self-build OD-meters can be used for continuous measurement of microbial growth in a variety of applications, thereby, simplifying and enhancing everyday lab operations.

Estimation of greenhouse gas emissions of a tropical reservoir in Colombia

Tropical reservoirs are generally flooded in soils with a high content of organic matter. This, combined with high temperatures, favors the generation of carbon dioxide (CO2) and methane (CH4) by biological degradation, contributing to the impact on climate change. A tropical reservoir in Colombia was monitored for 7 years in the pre-fill, fill and post-fill stages, for the last of these during the day and night. Emissions from diffusive fluxes at the surface of the water were measured using a floating static chamber, while inverted funnel methodology was used to measure the fluxes by bubbling. The samples collected in the field were analyzed in the laboratory using a gas chromatograph with a mass detector. The results showed average emissions of 70,892.51 ± 41,079.16-ton CO2eq/year for pre-filling; 178,254.53 ± 105,838.01-ton CO2eq/year for filling; and 466,946.57-ton CO2eq/year for post-filling (for 5 years), concluding that the weather conditions and the filling percentage (Area surface and volume) had an impact on the generation of greenhouse gases at filling and post-filling stages, as did the organic matter present in the area of influence of the sampling point. Higher greenhouse gas emissions were found during the day compared to the results at night, indicating that temperature affects these processes, especially in tropical reservoirs. This study, currently unique in Colombia, will allow directing efforts towards mitigating the impacts of greenhouse gas emissions in tropical reservoirs.

Landscape Perspective Distance-Included Angle Shape Distribution Analysis Based on 3D CAD Model Retrieval Algorithm

In view of the results obtained in the retrieval process of the 3D CAD model, which can show the differences in the local feature details of the model, the 3D CAD model retrieval algorithm is introduced into the analysis of the perspective distance-angle shape distribution of the garden landscape in this paper. Random sampling is performed on the surface of the constructed 3D CAD model, combined with the test distance between the sampling point and the neighboring points, and the corresponding garden landscape perspective distance-angle shape distribution characteristics in this area are calculated in order to achieve the similarity of the CAD model high-speed retrieval. Finally, experimental research shows that the algorithm proposed in this paper is better than the overall shape distribution algorithm and the spherical harmonic algorithm in the search performance of the CAD model, and it can effectively improve the recognition ability of the local detailed features of the 3D CAD model.

Semantic segmentation of large-scale point clouds based on dilated nearest neighbors graph

Three-dimensional (3D) semantic segmentation of point clouds is important in many scenarios, such as automatic driving, robotic navigation, while edge computing is indispensable in the devices. Deep learning methods based on point sampling prove to be computation and memory efficient to tackle large-scale point clouds (e.g. millions of points). However, some local features may be abandoned while sampling. In this paper, We present one end-to-end 3D semantic segmentation framework based on dilated nearest neighbor encoding. Instead of down-sampling point cloud directly, we propose a dilated nearest neighbor encoding module to broaden the network’s receptive field to learn more 3D geometric information. Without increase of network parameters, our method is computation and memory efficient for large-scale point clouds. We have evaluated the dilated nearest neighbor encoding in two different networks. The first is the random sampling with local feature aggregation. The second is the Point Transformer. We have evaluated the quality of the semantic segmentation on the benchmark 3D dataset S3DIS, and demonstrate that the proposed dilated nearest neighbor encoding exhibited stable advantages over baseline and competing methods.

Research on compact propulsion system dynamic model based on deep neural network

The on-board dynamic model is the basis of numerous advanced control technologies for modern aero-engine, and its accuracy and real-time performance are two crucial indicators. Since the simulation accuracy declines with the deviation from the ground point of the conventional compact propulsion system dynamic model (CPSDM), an improved CPSDM (ICPSDM) based on deep neural network is proposed in this paper. First, the K-means algorithm is utilized to get the sampling point, and the engine simulation data is collected in the cluster centers. Then, the batch normalize deep neural network (BN-DNN) is applied to build the ICPSDM, which can shorten the training time tremendously. The simulation results show that, in the same simulation environment, the accuracy of turbine inlet temperature T 4 , fan surge margin SMc, thrust F, and specific fuel consumption SFC calculated by ICPSDM are 11.04, 3.17, 1.89, and 1.98 times of CPSDM, respectively, at off-design point, and the real-time performance of ICPSDM is more than 30 times faster than the component-level model.

Overall Parameters Design of Air-Launched Rockets Using Surrogate Based Optimization Method

In this paper, the design and optimization method of rocket parameters based on the surrogate model and the trajectory simulation system of the 3-DOF air-launched rockets were established. The Gaussian kernel width determination method based on the relationship between local density and width is used to ensure the efficiency and reliability of the optimization method, and at the same time greatly reduces the amount of calculation. An adaptive sampling point updating method was established, which includes three stages: location sampling, exploration sampling, and potential optimal sampling of the potential feasible region. The adaptive sampling is realized by the distance constraint. Based on the precision of the surrogate model, the convergence end criterion was established, which can achieve efficient and reliable probabilistic global optimization. The objective function of the optimization problem was deduced to determine the maximum load mass and reasonable constraints were set to ensure that the rocket could successfully enter orbit. For solid engine rockets with the same take-off mass as Launcherone, the launch altitude and target orbit were optimized and analyzed, and verified by 3-DOF trajectory simulation. The surrogate-based optimization algorithm solved the problem of the overall parameter design optimization of the air-launched rocket and it provides support for the design of air-launched solid rockets.

Chemical and Microbiological Air Quality in a Municipal Solid Waste Landfill and Its Surroundings, in South-Eastern Romania

The aim of this study was to assess the microbiological and chemical air quality in a municipal solid waste landfill and its inhabited surroundings, in a particular context in which Romania struggles with the incapacity to comply with its environmental commitments. The research was conducted on a landfill near the capital Bucharest between November 2018 and September 2019. To evaluate the chemical (oxygen, carbon dioxide, methane, hydrogen sulfide, ammonia and carbon monoxide–MX6 iBrid™ Détector multigas) and microbiological (airborne bacteria and fungi–aspiration method) parameters, eight sampling points were established, located both on the perimeter of the landfill and within its surroundings. CO and CH4 were not detected in any of the sampling points, during the study period; O2 was in normal values 20.09–21.05%; CO2 had a maximum average concentration of 620 ± 215; H2S had values between 0.1 and 5.0 ppm only in the sampling points inside the landfill; NH3 was present only once in a single sampling point with values between 1.0 and 3.0 ppm. The microbiological results provide an overview of the total plate count and total fungal count, with no significant differences between the level of contamination inside the landfill and within its surroundings (p > 0.05). Ten bacterial species and fungi from six genera have been identified. It was also found that the number of microorganisms in the air was significantly lower during the winter, spring and early summer months compared with the late summer and autumn months (p < 0.05).

Analysis of Water Chemistry Physics for Fish Cultivation Activities in The Tributary Komering River, Banyuasin District

Residents use the Komering tributary that flows through Pangkalan Gelebak Village in Banyuasin Regency for fish farming activities. Villagers tried to cultivate catfish and tilapia, but it did not work because they died. Water as a medium for fish farming activities is an important factor that must be considered. Water quality in waters can affect aquaculture activities and fish growth. The study was conducted to determine the water quality (physics and chemistry) in the Komering River tributary as a basis for the cultivation environment. The research was conducted in the Komering tributary of Pangkalan Gelebak Village, Banyuasin Regency, in September, October, and November 2019. The sampling point is determined using a purposive sampling method in four sampling points. The temperature measurements range from 27-32.7ºC, depth 0.4-9 m, brightness 0.2-1.1 m, and turbidity 12.62-69.2 NTU. Only the depth of the four physical parameters is unsuitable for cultivation activities. During the dry season, the water depth between the riverbed and cage nets does not reach 1m. Chemical parameter measurement results; dissolved oxygen 1.7 – 7 mg/l, pH 6.5 – 7, alkalinity 34 - 46 mg/l CaCO3, nitrate 0.892 - 2.278 mg/l, nitrite 0.032 - 0.217 mg/l, ammonia 0.02-0.398 mg/ l and ortho-phosphate 0.054-0.283 mg/l. The value of nitrate, ammonia, and orthophosphate indicated high values so than unsuitable for fish farming activities. Agricultural activities around creeks and domestic waste disposal are causes of poor water quality.

Research and Implementation of High-Speed Data Streams Symbol Synchronization Algorithm Using Training Sequence in IMDD-OOFDM System

We propose a symbol synchronization algorithm for high-speed data streams in IMDD-OOFDM system using a training sequence. Sampling point phase offset approximately sustains within ±π/32 and symbol synchronization deviation stabilizes within ±0.5 sampling point in a real-time system of 1.5Gsa/s.