Miracle-dz/UniFAR-FTUs huggingface dataset:
Assessment of the Growth and Yield Characters of Some Promising Arabica Coffee Hybrids under Highland Environments in Southwestern Ethiopia

The economic value of Arabica coffee (Coffea arabica L.) is determined mainly by its yielding potential. However, being yield is a character of low heritability, the identification of superior genotype merely depends on consideration of other related and influencing characters. The objectives of this study were to: (i) estimate the broad-sense heritability of yield related characters (ii) determine the genetic and phenotypic correlations between yield and other important agronomic characters. (iii) to determine the performance of the eight promising F coffee hybrids under highland local environments based on growth and yield characters 1 relative to that of the existing commercial cultivars in Southwestern Ethiopia. The mean difference among genotypes across environments for all thirteen out of fourteen studied variable were significant (P 0.01), except number of stem nodes per tree. On this basis, the heritability estimates of the seven growth characters were moderate to high (0.33 to 75), while the other six including berry yield, exhibited lower heritability (below 0.24). Berry yield had positive genetic and phenotypic correlation with most characters; however the magnitude of genetic correlation were higher than phenotypic correlation. The characters that combined high heritability value (0.55 to 0.75) and high genetic correlation (r =0.51 to 0.91) includes girth, canopy diameter and plant G height followed by length of primary branches, internodes length of stem and internodes length of primary branches. These characters can be explored for indirect selection of higher yielding genotypes. Lack of genotype by environment interaction in present study permits direct selection of superior/comparable hybrids to best check-Ababuna in yield per se performance as well as that good for some of these particular character. The hybrid HC16 and HC18 had greater value for yield with resistance reaction to coffee berry disease(CBD), while HC8 had comparable yield and girth with moderately resistant reaction to CBD as compared to the best resistant check-74110.
Incorporating Resistance Into the Transition From Field Emission to Space Charge Limited Emission With Collisions

Field emitters and microplasmas often use series resistors to mitigate the rapid increase in current density that reduces device stability. This paper investigates the impact of external resistance on the transition of electron emission mechanism as a function of applied voltage <inline-formula> <tex-math notation="LaTeX">$V_{app}$ </tex-math></inline-formula>, gap distance <inline-formula> <tex-math notation="LaTeX">${D}$ </tex-math></inline-formula>, and electron mobility <inline-formula> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula>. For low <inline-formula> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula> (high gas pressure), the circuit transitions from Fowler–Nordheim (FN) to space charge limited emission by Mott–Gurney (MG) and Child–Langmuir (CL) before reaching Ohm’s law (OL). At higher <inline-formula> <tex-math notation="LaTeX">${\mu }$ </tex-math></inline-formula>, a triple point arises where the asymptotic solutions for FN, MG, and CL intersect. This triple point is uniquely defined by <inline-formula> <tex-math notation="LaTeX">${D}$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula>, or gap voltage <inline-formula> <tex-math notation="LaTeX">$V_{g}$ </tex-math></inline-formula> while also defining a specific gap impedance <inline-formula> <tex-math notation="LaTeX">$Z_{tp}$ </tex-math></inline-formula>. When <inline-formula> <tex-math notation="LaTeX">$R \leq Z_{tp}$ </tex-math></inline-formula>, the electron emission transitions from FN to MG to CL to OL with increasing <inline-formula> <tex-math notation="LaTeX">$V_{app}$ </tex-math></inline-formula> while MG and CL are bypassed at higher <inline-formula> <tex-math notation="LaTeX">${R}$ </tex-math></inline-formula>. For a given <inline-formula> <tex-math notation="LaTeX">${R}$ </tex-math></inline-formula>, increasing the applied voltage or emission current causes the gap to appear as a short.
The Index Predicting Power and Feedback Processing Characteristics in the WCST

Based on the performance of the Wisconsin Card Sorting Test (WCST), participants can be divided into high and low cognitive flexibility groups. The cognitive differences between the two groups need further study. However, studies have grouped participants according to different criteria of the WCST. Based on the classical WCST, the present study investigated two issues in college students: (1) the power of indexes for predicting performance, and (2) the feedback processing characteristics of high and low cognitive flexibility participants. The regression analysis showed TCF (trials to complete the first classification) and PR% (the percentage of perseverative response) were powerful predictors. We further divided participants into high and low cognitive flexibility groups according to the regression equation. Regarding the feedback processing characteristics, we classified all trials in rule-search phase as one of four types: correct-correct (coCO), correct-error (coER), error-error (erER), and error-correct (erCO), which were based on the relationship between the former feedback and the current response. The results revealed that compared with the low cognitive flexibility group, the high cognitive flexibility group could learn effectively from feedback. Differences in the feedback processing ability may be one of the reasons for the differential performance of college students on the WCST task.
Tree Species Identification Using 3D Spectral Data and 3D Convolutional Neural Network

In this study we apply 3D convolutional neural network (CNN) for tree species identification. Study includes the three most common Finnish tree species. Study uses a relatively large high-resolution spectral data set, which contains also a digital surface model for the trees. Data has been gathered using an unmanned aerial vehicle, a framing hyperspectral imager and a regular RGB camera. Achieved classification results are promising by with overall accuracy of 96.2 % for the classification of the validation data set.
A Semi-Supervised Learning Approach to IEEE 802.11 Network Anomaly Detection

With the remarkable development of Wi-Fi network, network security has become a key concern over the years. In order to face the increasing number of wireless network intrusion activities, an effective intrusion detection system is necessary. In this paper, a deep learning approach based on ladder network which self-learns the features necessary to detect network anomalies and perform attack classification accurately was proposed. And using focal loss as a loss function to enhance the discriminative ability of the model to classify difficult samples. In experiments on Aegean Wi-Fi Intrusion Dataset (AWID) public data-set, the network records was classified into 4 types: normal record, injection attack, impersonation attack, flooding attack. This paper achieved the classification accuracies of these four types of records are 99.77%, 82.79%, 89.32%, 73.41% respectively, and achieved an overall accuracy of 98.54%.
Codebook Based Precoding for Spatial Modulation

This paper investigates codebook-based precoding for spatial modulation (SM) systems to improve the performance, reduce the feedback overhead and dispense with Channel State Information at the Transmitter (CSIT). Phase-only codebook with systematic structure, that is independent of the CSIT, is proposed. This codebook could meet published performance of SM codebook precoding with same or lower feedback overhead and simpler implementation. When supplemented with the proposed gain-only codebook, the performance improves further at the cost of small additional feedback overhead.
Planar multilevel interconnection technology employing a polyimide

A planar multilevel interconnection technology, called planar metallization with polymer (PMP), has been developed, which utilizes a polyimide known as PIQ (polyimide isoindroquinazoline-dione) as an interlevel dielectric. The PIQ is highly resistant to heat and is mechanically flexible. Its low impurity concentrations also make it very stable in semiconductors. The PMP processing techniques have been refined to the stage where ICs can be fabricated commercially. A PIQ film etchant forms fine via-holes up to 3/spl times/3 /spl mu/m/SUP 2/, and chip size can be reduced by placing bonding pads on the active region of the device. Highly reliable linear and 256-bit bipolar memory ICs have been realized through this technology.
Model-based auxiliary input control: development and experimental analysis

A form of model-based control for robotic manipulators is proposed and experimentally evaluated. Algorithm development was motivated by experimental evaluation of an improved Lyapunov-based model reference adaptive control (LB-MRAC) technique. Those evaluations exposed the limitations of a decentralized LB-MRAC approach and the performance improvement potential of auxiliary inputs. Model-based auxiliary input control (MBAIC) is formed by augmenting the fixed-gain PD feedback loop of a normal model-based algorithm with the auxiliary input of the LB-MRAC. Feedback gain adaptation is not necessary. The tracking performance of several forms of LB-MRAC and MBAIC was evaluated over the standard PUMA test suite. Without feedforward compensation LB-MRAC tracking accuracy is inferior to that of a fixed-gain model-based controller. Addition of an auxiliary input significantly enhances model-based controller tracking accuracy and eliminates the large endpoint error previously associated with operation in uncertain payload configurations. <<ETX>>
Development of an Approach to a Biologically Inspired Self-Organizing Control Structure

In many fields of the industry, like biology, medicine, power generation, chemical engineering, etc., where dealing with complex systems, the design of the control structure is a key factor. Designing robust and fault-tolerant controllers for these sophisticated processes has always been a challenging task. This paper discusses the design of a fault tolerant multi-agent system using fractional order controllers for level control in a three-tank system, which is very close to the hydrodynamic evolution of the isotope separation columns cascade, the final goal of the research. The fractional order controllers are designed using the Particle Swarm Optimization (PSO) method. The proposed architecture is implemented in JADE (Java Agent Development Environment) . The control structure is tested with good results on the laboratory equipment simulating different faults in the system.
Power Control and Mode Selection Algorithm for D2D Communications

In this paper, we present a distance and interference based decision-making framework for device-to-device (D2D) communication mode selection (choosing one mode among dedicated mode, reuse mode, and cellular mode) and a power control approach for multiple D2D pairs underlaying cellular network. The proposed approach is based on the location of D2D users. If orthogonal resources are available and the D2D pair is close to each other, then the D2D dedicated mode is preferred. Otherwise, reuse (uplink or downlink) mode is utilized where interference is a prime concern. Reuse mode of D2D communication with allowable interference allows more users to be served with efficient spectrum utilization. Moreover, the power control approach is introduced to avoid the wastage of power and to reduce the impact of interference. Numerical results are provided to validate the significance of the proposed approach.
Designing and Developing a Chatbot Using Machine Learning

Deep Learning is a new area of Machine Learning and research, which was introduced with the aim of taking the Machine learning closer to one of its original and main goal which is: AI (Artificial intelligence). If we speak of automatic learning algorithm, they tend to be linear, the DL (Deep Learning) algorithms are configured to increase complexity and abstraction. To learn in depth, imagine a 5-year-old whose first word is cat. The child Keeps on learning what a cat is by showing objects and saying the word cat. The mother says: "Yes, it’s a cat" or "No, it’s not a cat". As the child continues to point objects, he becomes more aware of the characteristics and features of all cats. What the child does, without even Knowing what he is doing, it clarifies a very complex level of abstraction by constructing a scale in which each level of abstraction is created with the help of Knowledge acquired the layer which was before on the scale.The programs that run on the system which uses deep learning go through the same process. Each algorithm in the way that applies a non-linear approach on its input and it uses what it learns to make a statistical model from the given output. Repetition continues until the output has reached an acceptable point of correctness. The number of layers in which the process is passing at each stage is what gave the name a prefix as deep.
Clustering Based Resource Management Scheme for Latency and Sum Rate Optimization in V2X Networks

In this paper, a clustering based resource management scheme for latency and sum rate optimization in V2X networks is proposed to identify distinguished demands for different kinds of vehicular links, namely cellular-vehicle-to-vehicle (C-V2V) links and cellular-vehicle-to-infrastructure (C-V2I) links, and to improve the performance of cellular user in terms of sum rate, latency and throughput for C-V2I links while guaranteeing reliability for every C-V2V connection. To address the fast channel changes due to high mobility, we present a clustering based resource management model to achieve band sharing and efficient power management that depends on large scale fading. Besides, we have also considered the resource management problem of cellular V2X and VANETs users to reduce data transmission impacts. Firstly, the total average sum rate of each C-V2I links is used as an optimization goal to increase the throughput and to reduce latency of the entire C-V2I link. Secondly, cluster based optimum algorithms are proposed which give the optimum resource management. Simulation results reveal that the proposed scheme outperforms the existing scheme.
Collecting and Processing a Self-Driving Dataset in the UPB Campus

Although there is a diversity of publicly available datasets for autonomous driving, from small-scale to larger collections with thousands of miles of driving, we consider that the process of collecting and processing them is often overlooked in the literature. From a data-driven perspective, quality of a dataset has proven as important as quantity especially when evaluating self-driving technologies where safety is crucial. In this paper, we provide a guideline going through all the steps from configuring the hardware setup to obtaining a clean dataset. We describe the data collection scenario design, the hardware and software employed in the process, the challenges that must be considered, data filtering and validation stage. This work stems from our experience in collecting the UPB campus driving dataset released together with this work. It is our belief that having a clean and efficient process of collecting a small but meaningful dataset has the potential to improve benchmarking autonomous driving solutions, capturing local environment particularities.
Frequency and Phase Locking Experiments on a 2.45 GHz magnetron

This paper describes a study and experimental demonstration on a magnetron operating at 2.45 GHz. We checked the characteristics of the 1 kW magnetron. There were two noise sources of magnetron at frequencies of 0.5 Hz and 75 kHz. In order to suppress these noises, we used an external injection locking method. Measurement results showed that the frequency noise suppression was down up to 30 dBc and the phase fluctuation is reduced to 4 degrees when injection input power was 7 W. Power combining experiments were performed using two identical magnetrons. An initial result showed an output power of 2 kW, corresponding to combining efficiency $\pmb{ > 93\%}$.
More Capacity and Less Power: How 5G NR Can Reduce Network Energy Consumption

In this paper we compare the energy consumption of the 3GPP standards 5G New Radio (NR) and Long-Term Evolution (LTE). We show that the energy consumption in an idle network (without user plane traffic) can be up to 9 times lower for an NR stand-alone network compared to an LTE network. The network energy consumption in low or no traffic scenarios is a significant part of the operator OPEX in low traffic areas [1], such as in extreme rural scenarios, and here NR has a clear advantage compared to LTE. To evaluate the energy performance advantage of NR when we have very high traffic, such as in Super-Dense Urban and Urban scenarios, we perform system evaluations. We show that if we increase capacity in an existing LTE network with additional NR micro cells compared to using additional LTE cells, the reduction in network energy consumption is close to 50 percent. If we also upgrade the LTE cells in the macro coverage layer to NR a total energy reduction of up to 70 percent is achievable.
Clinical significance of serum IL-18 in diagnosing and predicting severe acute pancreatitis

Aim: To detect whether serum interleukin 18 (IL-18) serves as early predictor for diagnosis of severe acute pancreatitis (AP). Methods: AP patients were categorized into 2 groups as mild acute pancreatitis (MAP) and severe acute pancreatitis (SAP). All participants received assessment of IL-18, C-reactive protein (CRP) and chronic health evaluation (APACHE) II score. Results: IL-18 concentration in SAP participants in less than twenty-four hours subsequent to pain attack was elevated and remarkably higher than the concentration in MAP participants and healthy controls (P<0.01). The optimal cut off value of IL-18 was determined as 100 pg/mL in order to differentiate MAP from SAP. In terms of IL-18, specificity, sensitivity, positive predictive value (PPV), negative predictive value (NPV), as well as positive and negative likelihood ratio were determined as 76.35%, 63.35%, 0.6223, 0.6598, 3.2561 and 0.5268, respectively. It was proved by area under the curve (AUC) that prognostic accuracy of IL-18 (0.722) was similar to APACHE-II (0.729), which was higher than that of CRP (0.531) in differentiation of MAP from SAP. Conclusion: IL-18 serves as an early predictor and assists diagnosis of SAP in less than twenty-four hours subsequent to pain attack or admission, which was reliable, easy, and rapid. IL-18 is insufficient to serve as a stand-alone assay to diagnose AP and additional assays are necessary to diagnosis.
Scene text localization based on the ultimate opening

Database indexing and retrieval tools can enormously bene t from the automatic detection and processing of textual data in images. We present a new connected-component (CC) based method using morphological numerical residues for automatic localization of text in general image database. This technique tends to be robust to font, scale and slant changes and detects superimposed as well as scene text. The robustness of our approach is proven by the results in ImagEval evaluation campaign, which database included old postcards, graphic schemes, stamps, indoor and outdoor scene images and also images without any textual data. In spite of the wide variety of texts and images our approach obtains interesting results without parameter tuning for each image class.
L G ] 2 9 M ay 2 01 9 A Theoretical Analysis of Deep Q-Learning

Despite the great empirical success of deep reinforcement learning, its theoretical foundation is less well understood. In this work, we make the first attempt to theoretically understand the deep Qnetwork (DQN) algorithm (Mnih et al., 2015) from both algorithmic and statistical perspectives. In specific, we focus on a slight simplification of DQN that fully captures its key features. Under mild assumptions, we establish the algorithmic and statistical rates of convergence for the actionvalue functions of the iterative policy sequence obtained by DQN. In particular, the statistical error characterizes the bias and variance that arise from approximating the action-value function using deep neural network, while the algorithmic error converges to zero at a geometric rate. As a byproduct, our analysis provides justifications for the techniques of experience replay and target network, which are crucial to the empirical success of DQN. Furthermore, as a simple extension of DQN, we propose the Minimax-DQN algorithm for zero-sum Markov game with two players. Borrowing the analysis of DQN, we also quantify the difference between the policies obtained by Minimax-DQN and the Nash equilibrium of the Markov game in terms of both the algorithmic and statistical rates of convergence.
The Tomato

In his 1544 herbal, Matthiolus documents the existence of tomatoes in Italy and also reports that Italians ate them. Vernon Quinn proposes that the Spanish explorers brought it back to Spain from Mexico and that a Moor brought it to Tangiers and, from there, an Italian brought it to Italy where it was called Moor's apple, pomo dei mori, and a name with a similar sound, poma amoris, but a different meaning: love apple. Similarly, the French referred to it as love apple, pomme d'amour (Heiser 1969).
Helium two-phase flow in a thermosiphon open loop

The construction of high magnetic field superconducting coils requires the use of low temperature superconductors that must be cooled down to liquid helium temperature (4.2 K). Natural two-phase convection loops, i.e. thermosiphon loop, are used as cooling system for large superconducting magnets mainly because of its passive nature. The study present a thermohydraulics model realized with COMSOL Multiphysics of such a loop. The results of the computation are compared with experimental results obtained in a 2-m high loop.
Impact of Wenyang-Huoxue-Lishui formula on cytoskeletal structures and related proteins of immortalized podocytes in mice with puromycin aminonucleoside (PAN)-induced injury

In this study, we aimed to determine the impact of Wenyang-Huoxue-Lishui formula (WHL) on cytoskeletal structures and related proteins of immortalized podocytes in mice with puromycin aminonucleoside (PAN)-induced injury. Podocyte injury was induced by puromycin treatment, followed by intervention with traditional Chinese medicine (TCM)-containing serum. The mRNA and protein expression levels of cathepsin L (CatL) and synaptopodin were detected. The protein expression of membrane-associated guanylate kinase with inverted orientation (MAGI)-2 as well as Ras homolog gene family, member A (RhoA) was detected. The distribution of dendrin and changes in fluorescence distribution of cytoskeletal microfilaments and intermediate filaments were also observed. After the intervention, the mRNA and protein expression of cathepsin L were downregulated (P<0.01), while the mRNA expression of synaptopodin remained unchanged (P>0.05). The protein expression of MAGI-2, RhoA, and synaptopodin was upregulated (P<0.01). The ratio of nuclear translocation of dendrin decreased (P<0.01), but was not restored to the normal level (P<0.01). The cytoskeletal structures were restored in each treatment group. WHL reduced the nuclear translocation of dendrin and decreased cytoplasmic CatL levels after induction of podocyte injury, thereby reducing synaptopodin degradation, increasing RhoA expression, improving F-actin and intermediate filament remodeling, stabilizing cytoskeletal structure, and reducing foot process effacement, and subsequently reducing proteinuria.