MECHANIZATION DEVICES FOR HANDLING BRANCHES IN MODERN ORCHARDS

In most parts of China, pruned branches in orchards are still treated manually. In order to reduce the intensity and labor cost, this paper provides a mechanical solution for branches collection, which mainly includes the gathering device, picking device, and smashing device. A simulation platform with a human-computer interaction was developed in Matlab. It can set optimization goals based on human practical experience and optimize the main influencing parameters, and both the gathering device and the picking device achieve the design simulation on this platform. Furthermore, this method helps to quickly obtain the data of key components in the core device. Firstly, the shape of the Slide-way shell of the gathering device can be obtained quickly through different gathering ranges. Secondly, a group of angle and motion trajectory of the picking device with lower height was obtained from the simulation design. Furthermore, the performance of the improved picking device under four different laying conditions was tested. The results showed that, under R=38-42 r/min and V=0.80-0.95 m/s, the success rate of the picking device was 94.2%, 92.5%, 61.0% and 30.8%, respectively. The field test demonstrated that there are no significant differences between the simulated test results and practice test results.

Stay cool without fossil fuel. A passive eco-cooler for low-income population in informal settlements

With climate change severe events, more and more vulnerable populations suffer from extreme heat waves. This paper presents a hands-on experimental idea for testing vernacular passive cooling strategies using traditional Shisha clay funnels for the Egyptian hot dry climate. Several clay funnels were investigated in terms of shape, size and form. The clay funnels were measured and simulated for their efficiency in accelerating air flow inside residential units and ability to enhance the air velocity if used combined with cross ventilation strategies. Computational Fluid Dynamics (CFD) simulations were conducted in ANSYS Fluent to understand the airflow behaviour inside the simulated test shoe boxes resembling living rooms - using the standard k-∈ turbulence model - for single and multi-units’ configurations. Followed by experimental test cells application for the cooling system and monitoring for testing thermal performance. The simulation results showed significant enhancement in air flow and air speed inside the test room compared to conventional windows, while the test cells monitoring showed an average reduction in indoor temperature and humidity with 2 degrees and 15 % respectively. Further monitoring is needed for other alternations of the eco-cooler funnel design for better performance.

The New Volcanic Ash Satellite Retrieval VACOS Using MSG/SEVIRI and Artificial Neural Networks: 2. Validation

Volcanic ash clouds can damage aircrafts during flight and, thus, have the potential to disrupt air traffic on a large scale, making their detection and monitoring necessary. The new retrieval algorithm VACOS (Volcanic Ash Cloud properties Obtained from SEVIRI) using the geostationary instrument MSG/SEVIRI and artificial neural networks is introduced in a companion paper. It performs pixelwise classifications and retrieves (indirectly) the mass column concentration, the cloud top height and the effective particle radius. VACOS is comprehensively validated using simulated test data, CALIOP retrievals, lidar and in situ data from aircraft campaigns of the DLR and the FAAM, as well as volcanic ash transport and dispersion multi model multi source term ensemble predictions. Specifically, emissions of the eruptions of Eyjafjallajökull (2010) and Puyehue-Cordón Caulle (2011) are considered. For ash loads larger than 0.2g/□m and a mass column concentration-based detection procedure, the different evaluations give probabilities of detection between 70% and more than 90% at false alarm rates of the order of 0.3–3%. For the simulated test data, the retrieval of the mass load has a mean absolute percentage error of ~40% or less for ash layers with an optical thickness at 10.8m of 0.1 (i.e., a mass load of about 0.3– 0.7g/□m, depending on the ash type) or more, the ash cloud top height has an error of up to 10% for ash layers above 5km, and the effective radius has an error of up to 35% for radii of 0.6– 6m. The retrieval error increases with decreasing ash cloud thickness and top height. VACOS is applicable even for overlaying meteorological clouds, for example, the mean absolute percentage error of the optical depth at 10.8m increases by only up to ~30%. Viewing zenith angles > 60∘ increase the mean percentage error by up to ~20%. Desert surfaces are another source of error. Varying geometrical ash layer thicknesses and the occurrence of multiple layers can introduce an additional error of about 30% for the mass load and 5% for the cloud top height. For the CALIOP data, comparisons with its predecessor VADUGS (operationally used by the DWD) show that VACOS is more robust, with retrieval errors of mass load and ash cloud top height reduced by >10% and >50%, respectively. Using the model data indicates an increase in detection rate in the order of 30% and more. The reliability under a wide spectrum of atmospheric conditions and volcanic ash types make VACOS a suitable tool for scientific studies and air traffic applications related to volcanic ash clouds.

Integrally Bladed Rotor Mistuning Identification and Model Updating Using Geometric Mistuning Models

Non-uniform manufacturing variations and uneven usage wear and damage, referred to as mistuning, can drastically alter the dynamic response of Integrally Bladed Rotors (IBRs). Optical scanners, combined with Finite Element Model mesh metamorphosis algorithms, have provided capabilities to create analytical models that reduce the effect of geometrical uncertainties in numerical predictions. However, deviations in material properties cannot be obtained via optical scanning, so additional approaches are needed. A geometric mistuning Reduced-Order Model (ROM) is developed and modified to solve for unknown IBR sector eigenvalues that are linearly proportional to Elastic modulus. The developed approach accounts for both proportional and non-proportional mistuning and allows updating of the Elastic modulus for each sector in the ROM. Different tuned and mistuned modal reduction procedures are employed to understand the implications of each for identifying mistuning. Simulated test data with known inputs indicate the efficiency and accuracy of the method and improvements over using a traditional, tuned mode approach. The developed methods are then extended to bench-level traveling wave excitation data to discern how sector frequencies vary due to geometry and modulus mistuning.

Mesoscale draping simulation of carbon fiber mat for arbitrary weave architecture

We perform a mesoscale draping simulation using a woven carbon fiber mat model consisting of fiber bundles. The deformation of various types of fabrics during draping is predicted using fiber bundle data; no fabric experiments are necessary. First, bending experiments and simulations are performed to determine the material constants of the fiber bundle. To confirm the validity of the mesoscale model, we compare the experimental and simulation results of bias-extension tests. The simulated test shows that the simulation reproduces the deformation behavior of the carbon fiber mat under tensile force. The draping of a plain-weave or twill-weave carbon fiber mat on a hemispheric mold is simulated using the proposed mesoscale model and investigated experimentally. The results confirm that the simulation reproduces the deformation of the fabric using only the fiber bundle data.

Integrally Bladed Rotor Mistuning Identification and Model Updating Using Geometric Mistuning Models

Non-uniform manufacturing variations and uneven usage wear and damage, referred to as mistuning, can drastically alter the dynamic response of Integrally Blade Rotors (IBR)s. Optical scanners, combined with Finite Element Model (FEM) mesh metamorphosis algorithms, have provided capabilities to create analytical models that reduce the effect of geometrical uncertainties in numerical predictions. However, deviations in material properties cannot be obtained via optical scanning, so additional approaches are needed. A geometric mistuning Reduced-Order Model (ROM) is developed and modified to solve for unknown IBR sector eigenvalues that are linearly proportional to Elastic modulus. The developed approach accounts for both proportional and non-proportional mistuning and allows updating of the Elastic modulus for each sector in the ROM. Different tuned and mistuned modal reduction procedures are employed to understand the implications of each for identifying mistuning. Simulated test data with known inputs indicate the efficiency and accuracy of the method and improvements over using a traditional, tuned mode approach. The developed methods are then extended to bench-level traveling wave excitation data to discern how sector frequencies vary due to geometry and modulus mistuning.

NGPINT: A Next-generation protein-protein interaction software

ABSTRACTMapping protein-protein interactions at a proteome scale is critical to understanding how cellular signaling networks respond to stimuli. Since eukaryotic genomes encode thousands of proteins, testing their interactions one-by-one is a challenging prospect. High-throughput yeast-two hybrid (Y2H) assays that employ next-generation sequencing to interrogate cDNA libraries represent an alternative approach that optimizes scale, cost, and effort. We present NGPINT, a robust and scalable software to identify all putative interactors of a protein using Y2H in batch culture. NGPINT combines diverse tools to align sequence reads to target genomes, reconstruct prey fragments and compute gene enrichment under reporter selection. Central to this pipeline is the identification of fusion reads containing sequences derived from both the Y2H expression plasmid and the cDNA of interest. To reduce false positives, these fusion reads are evaluated as to whether the cDNA fragment forms an in-frame translational fusion with the Y2H transcription factor. NGPINT successfully recognized 95% of interactions in simulated test runs. As proof of concept, NGPINT was tested using published data sets and recognized all validated interactions. NGPINT can be used in any organism with an available reference, thus facilitating the discovery of protein-protein interactions in non-model organisms.

Predicting Long-term Evolution of COVID-19 by On-going Data using Bayesian Susceptible-Infected-Removed Model

In this study, we propose a novel statistical method to predict a long-term epidemic evolution based on a on-going data. We developed a Bayesian framework for the Susceptible-Infected-Removed model (Bayesian SIR), and estimated its underlying parameters based on day-by-day timeseries of the cumulative number of infectious individuals. The new Baysian framework extends the deterministic SIR model to a probabilistic form, which provides an accurate estimation of the underlying system by a short and noisy data. We applied it to the data reported on the Coronavirus Disease 2019 (COVID-19), and made a month long prediction on its evolution. Our simulated test using past timeseries to predict the current data gives a reasonable reliablity of the proposed method. Our analysis of the current data detected and warned a rising trend in the countries in Central Asia, Middle East, and South America, while United States or European countries, which have already experienced large numbers of infected cases, are predicted to slow down in the increase.

Coordinated control of fuel flow rate and air flow rate of a supersonic heat-airflow simulated test system

ABSTRACTThe gas temperature of the supersonic heat airflow simulated test system is mainly determined by the fuel and air flow rates which enter the system combustor. In order to realise a high-quality control of gas temperature, in addition to maintaining the optimum ratio of fuel and air flow rates, the dynamic characteristics of them in the combustion process are also required to be synchronised. Aiming at the coordinated control problem of fuel and air flow rates, the mathematical models of fuel and air supply subsystems are established, and the characteristics of the systems are analysed. According to the characteristics of the systems and the requirements of coordinated control, a fuzzy-PI cross-coupling coordinated control strategy based on neural sliding mode predictive control is proposed. On this basis, the proposed control algorithm is simulated and experimentally studied. The results show that the proposed control algorithm has good control performance. It cannot only realise the accurate control of fuel flow rate and air flow rate, but also realise the coordinated control of the two.