Six-Year Demographic Study of the Terrestrial Orchid, Crepidium acuminatum: Implications for Conservation

Studies on population dynamics are helpful for understanding the factors determining population development and predicting the effects of disturbances, such as harvesting of plant species. In an investigation of the demography of a terrestrial medicinal orchid known as Crepidium acuminatum, the effects of harvesting on its population dynamics were recorded. Data on recruitment, growth and survival were collected in three populations of C. acuminatum over a 6-year period (2012–2017) in central Nepal. A matrix modeling method was used to determine the effect of different harvesting regimes on the population growth and survival of this species. Population growth rates (λ) of unharvested populations were relatively similar and stable in different years of the study. Harvesting significantly reduced λ. The results of this study indicate that the sustainable survival of a population that is subject to harvesting can only occur when it is either selective (only flowering individuals or only small amounts of vegetative individuals) or rotational (once every 3–5 or more years). This study demonstrates the necessity of using a sustainable method when harvesting natural populations. Our results are useful for developing efficient management strategies for this species. As each species has a different biology, similar studies are needed for other rare and/or economically important species in the Himalayan region and in other understudied parts of the world.

DeepProg: an ensemble of deep-learning and machine-learning models for prognosis prediction using multi-omics data

Multi-omics data are good resources for prognosis and survival prediction; however, these are difficult to integrate computationally. We introduce DeepProg, a novel ensemble framework of deep-learning and machine-learning approaches that robustly predicts patient survival subtypes using multi-omics data. It identifies two optimal survival subtypes in most cancers and yields significantly better risk-stratification than other multi-omics integration methods. DeepProg is highly predictive, exemplified by two liver cancer (C-index 0.73–0.80) and five breast cancer datasets (C-index 0.68–0.73). Pan-cancer analysis associates common genomic signatures in poor survival subtypes with extracellular matrix modeling, immune deregulation, and mitosis processes. DeepProg is freely available at https://github.com/lanagarmire/DeepProg

Research on Bidding Game and Its Application Based on Copetition Scenario

Bidding decision is not only a science, an art, but also a game. The more intense the competition, the more important the game. In practice, there is the possibility of collaboration between bidders and even hidden competing behaviors such as bidding rigging. In this study, the optimized low-price bid winning method was discussed, and the characteristics and application of the bidding game under the copetition scenarios were studied. The results show the following: (1) Under the copetition scenario, the rational bidding behavior of bidders will deviate according to the different information advantages, and there is a game of making bidding strategy decisions according to the competitive scenario. (2) There is a close functional relationship between the winning bid result and the evaluation elimination factor, the number of bidders, and the number of bidders who operate bidding rigging. (3) Based on the quotation strategy matrix modeling, it enables the quantitative decision making bid amount, offer score, and deviation risk. This study enriches the theory of quota decision in copetition scenarios and is enlightening for similar business behavior game decisions.

Intermittent Arc Fault Detection And Location For Aircraft Power Distribution System

This thesis addresses a non-destructive diagnostic method for intermittent arc fault detection and location. Intermittent arc faults appear in aircraft power systems in unpredictable manners when the degraded wires are wet, vibrating against metal structures, or under mechanical stresses, etc. They could evolve into serious faults that may cause on-board fires, power interruptions, system damage and catastrophic incidents, and thus have raised much concern in recent years. Recent trends in solid state power controllers (SSPCs) motivated the development of non-destructive diagnostic methods for health monitoring of aircraft wiring. In this thesis, the ABCD matrix (or transmission matrix) modeling method is introduced to derive normal and faulty load circuit models with better accuracy and reduced complexity compared to the conventional differential equation approach, and an intermittent arc fault detection method is proposed based on temporary deviations of load circuit model coefficients and wiring parameters. Furthermore, based on the faulty wiring model, a genetic algorithm (GA) is proposed to estimate the fault-related wiring parameters, such as intermittent arc location and average intermittent arc resistance. The proposed method can be applied to both the alternating current (AC) power distribution system (PDS) and direct current (DC) PDS. Simulations and experiments using a DC power source have been conducted, and the results have demonstrated effectiveness of the proposed method by estimating the fault location with an accuracy of +/- 0.5 meters on 24.6 meters wire. Unlike the existing techniques which generally requires special devices, the proposed method only needs circuit voltage and current measurement at the source end as inputs, and is thus suitable for SSPC-based aircraft PDS.

Intermittent Arc Fault Detection And Location For Aircraft Power Distribution System

This thesis addresses a non-destructive diagnostic method for intermittent arc fault detection and location. Intermittent arc faults appear in aircraft power systems in unpredictable manners when the degraded wires are wet, vibrating against metal structures, or under mechanical stresses, etc. They could evolve into serious faults that may cause on-board fires, power interruptions, system damage and catastrophic incidents, and thus have raised much concern in recent years. Recent trends in solid state power controllers (SSPCs) motivated the development of non-destructive diagnostic methods for health monitoring of aircraft wiring. In this thesis, the ABCD matrix (or transmission matrix) modeling method is introduced to derive normal and faulty load circuit models with better accuracy and reduced complexity compared to the conventional differential equation approach, and an intermittent arc fault detection method is proposed based on temporary deviations of load circuit model coefficients and wiring parameters. Furthermore, based on the faulty wiring model, a genetic algorithm (GA) is proposed to estimate the fault-related wiring parameters, such as intermittent arc location and average intermittent arc resistance. The proposed method can be applied to both the alternating current (AC) power distribution system (PDS) and direct current (DC) PDS. Simulations and experiments using a DC power source have been conducted, and the results have demonstrated effectiveness of the proposed method by estimating the fault location with an accuracy of +/- 0.5 meters on 24.6 meters wire. Unlike the existing techniques which generally requires special devices, the proposed method only needs circuit voltage and current measurement at the source end as inputs, and is thus suitable for SSPC-based aircraft PDS.

Hydrologic soil group based curve number matrix modeling for Enset-Based land use system in Meki River Watershed, Western Lake Ziway Sub-Basin, Central Rift Valley of Ethiopia

Background Enset-Based land use system (EBLUS) exhibits good carbon stock and infiltration rate equivalent to forest covered areas, which enhances infiltration and water holding capacity and it can reduce the curve number (CN) of the watersheds but it was not considered in former studies. Therefore, this study is planned to model the hydrologic soil group (HSG) based CN matrix of EBLUS relative to other LUSs with established hydrological characteristics in the Meki river watershed. The soil data is used to determine the HSG of the watershed collected from Ministry of Water, Irrigation and Energy (MOWIE) and verified by Harmonized World Soil Database (HWSD). A Model is developed for CN of EBLUS relative to other LUSs (Alemu’s formula). The model considers both infiltration rate measured using Amozi-meter and carbon stoke of soil weighed as 85% and 15% respectively. HEC-GEO-HMS model is used to consider the CN of EBLUS as a separate LUS to verify the developed CN matrix model to generate CN of the sub-watersheds. Result The field measurement results show that an infiltration rate of 12.9675,11.1875,10.375,7.065 and 12.8125mm hr -1 for Natural Forest, Grassland and plantation, cultivated, built-up and EBLUS respectively. The model is: and the resulting CN matrix of EBLUS is 39,51.5,58.3 and 61.6 for HSG of A,B,C and D respectively. Conclusion Significant reduction in mean CN of the watershed that shows the role of EBLUS in managing the water resources and flood is high. Therefore, escalating EBLUS will reduce the CN of the watershed which reduces runoff volume in the watershed and it ensures the sustainability of Lake Ziway by reducing sedimentation.