In Vivo Human-Like Robotic Phenotyping of Leaf and Stem Traits in Maize and Sorghum in Greenhouse

In plant phenotyping, the measurement of morphological, physiological and chemical traits of leaves and stems is needed to investigate and monitor the condition of plants. The manual measurement of these properties is time consuming, tedious, error prone, and laborious. The use of robots is a new approach to accomplish such endeavors, which enables automatic monitoring with minimal human intervention. In this study, two plant phenotyping robotic systems were developed to realize automated measurement of plant leaf properties and stem diameter which could reduce the tediousness of data collection compare to manual measurements. The robotic systems comprised of a four degree of freedom (DOF) robotic manipulator and a Time-of-Flight (TOF) camera. Robotic grippers were developed to integrate an optical fiber cable (coupled to a portable spectrometer) for leaf spectral reflectance measurement, a thermistor for leaf temperature measurement, and a linear potentiometer for stem diameter measurement. An Image processing technique and deep learning method were used to identify grasping points on leaves and stems, respectively. The systems were tested in a greenhouse using maize and sorghum plants. The results from the leaf phenotyping robot experiment showed that leaf temperature measurements by the phenotyping robot were correlated with those measured manually by a human researcher (R2 = 0.58 for maize and 0.63 for sorghum). The leaf spectral measurements by the phenotyping robot predicted leaf chlorophyll, water content and potassium with moderate success (R2 ranged from 0.52 to 0.61), whereas the prediction for leaf nitrogen and phosphorus were poor. The total execution time to grasp and take measurements from one leaf was 35.5±4.4 s for maize and 38.5±5.7 s for sorghum. Furthermore, the test showed that the grasping success rate was 78% for maize and 48% for sorghum. The experimental results from the stem phenotyping robot demonstrated a high correlation between the manual and automated stem diameter measurements (R2 \u3e 0.98). The execution time for stem diameter measurement was 45.3 s. The system could successfully detect and localize, and also grasp the stem for all plants during the experiment. Both robots could decrease the tediousness of collecting phenotypes compare to manual measurements. The phenotyping robots can be useful to complement the traditional image-based high-throughput plant phenotyping in greenhouses by collecting in vivo morphological, physiological, and biochemical trait measurements for plant leaves and stems. Advisors: Yufeng Ge, Santosh Pitl

アフガニスタンにおける自然災害把握のためのリモートセンシング技術の応用に関する研究

広島大学(Hiroshima University)博士(工学)Doctor of Engineeringdoctora

Traffic behavior of local area network based on M/M/1 queuing model using Poisson and exponential distribution

Nowadays, Local Area Networks (LAN) are one of the most popular networks, and the LAN performance is very important for operators. The LAN method has been applied as an essential infrastructure of numerous companies and organizations for a long time. This study aims to evaluate the M/M/1 queuing model in LAN Based on Poisson and Exponential Distribution and compare the traffic behavior of these Distributions in terms of some essential parameters. Moreover, it also aims to design and implement a model to perform the M/M/1 queuing model with different metrics and finally analyze the results to evaluate traffic behavior of M/M/1 queuing model for Poisson and Exponential Distribution in LAN

Multiporphyrin coordination arrays based on complexation of magnesium(II) porphyrins with porphyrinylphosphine oxides

Di- and triporphyrin arrays consisting of 5,15-diphenylporphyrinatomagnesium(II) (MgDPP) coordinated to free- base and Ni( II) porphyrinyl mono- and bis-phosphine oxides, as well as the self-coordinating diphenyl[10,20-diphenylporphyrinatomagnesium(II)-5-yl]phosphine oxide [MgDPP(Ph2PO)], were synthesised in excellent yields and characterised by various spectroscopic techniques. Phosphine oxides stabilise Mg(II) coordination to porphyrins and the resulting complexes have convenient solubilities, while the Ni(II) complexes exhibit interesting intramolecular fluorescence quenching behaviour. The binding constant of MgDPP to triphenylphosphine oxide (5.3 +/- 0.1 x 10(5) M-1) and the very high self- association constant of [MgDPP(Ph2PO)] (5.5 +/- 0.5 x 10(8) M-1) demonstrate the strong affinity of phosphine oxides towards Mg(II) porphyrins. These complexes are the first strongly bound synthetic Mg(II) multiporphyrin complexes and could potentially mimic the "special pair" in the photosynthetic reaction centre

Impact of Accounting for Polygenic Effects on the Accuracy of Genomic Evaluations in Livestock Breeding

To investigate the accuracy of genomic breeding values, different scenarios were defined by accounting for polygenic effects, a different number of quantitative trait loci (30, 90, 150), and three levels of heritability (0.15, 0.25, and 0.4). The Bayes B method was used to estimate marker effects. A historical population was simulated stochastically, which consisted of 100 animals at first 100 generations, then the population size gradually increased to 1000 animals during the next 100 generations. The animals in generation 201 with known genotypic and phenotypic records were assigned as the reference population, and animals of generation 202 were considered as the validation population. The genome was comprised of one chromosome with 100 cM length and 500 markers that were distributed through the genome randomly. Picking up the information that was not captured by linkage disequilibrium (LD), including polygenic effects in the predictions increased the accuracy of genomic evaluations. As the trait heritability went from 0.15 to 0.40, the average genomic accuracy increased from 0.48 to 0.64. An increment in the number of quantitative trait loci (NQTL) declined the accuracy of the Bayes B method. This study suggests that the highest accuracy (0.74) was achieved when additive genotypic effects were coded by a few quantitative trait loci and a lot of small effects included in the prediction of genomic breeding values

Solution of a practical Vehicle Routing Problem for monitoring Water Distribution Networks

In this work, we introduce a generalisation of the Vehicle Routing Problem for a specific application in the monitoring of a Water Distribution Network (WDN). In this problem, multiple technicians must visit a sequence of nodes in the WDN and perform a series of tests to check the quality of water. Some special nodes (i.e., wells) require technicians to first collect a key from a key centre. The key must then be returned to the same key centre after the test has been performed, thus introducing precedence constraints and multiple visits in the routes. To solve the problem, a Mixed Integer Linear Programming model and an Iterated Local Search have been implemented. The efficiency of the proposed methods is demonstrated by means of extensive computational tests on randomly created and real-world instances

Principled Data-Driven Decision Support for Cyber-Forensic Investigations

In the wake of a cybersecurity incident, it is crucial to promptly discover how the threat actors breached security in order to assess the impact of the incident and to develop and deploy countermeasures that can protect against further attacks. To this end, defenders can launch a cyber-forensic investigation, which discovers the techniques that the threat actors used in the incident. A fundamental challenge in such an investigation is prioritizing the investigation of particular techniques since the investigation of each technique requires time and effort, but forensic analysts cannot know which ones were actually used before investigating them. To ensure prompt discovery, it is imperative to provide decision support that can help forensic analysts with this prioritization. A recent study demonstrated that data-driven decision support, based on a dataset of prior incidents, can provide state-of-the-art prioritization. However, this data-driven approach, called DISCLOSE, is based on a heuristic that utilizes only a subset of the available information and does not approximate optimal decisions. To improve upon this heuristic, we introduce a principled approach for data-driven decision support for cyber-forensic investigations. We formulate the decision-support problem using a Markov decision process, whose states represent the states of a forensic investigation. To solve the decision problem, we propose a Monte Carlo tree search based method, which relies on a k-NN regression over prior incidents to estimate state-transition probabilities. We evaluate our proposed approach on multiple versions of the MITRE ATT&CK dataset, which is a knowledge base of adversarial techniques and tactics based on real-world cyber incidents, and demonstrate that our approach outperforms DISCLOSE in terms of techniques discovered per effort spent