Miracle-dz/UniFAR-FTUs huggingface dataset:
Androgenic responses of winter wheat (Triticum aestivum L.) combinations in in vitro anther culture

The androgenic parameters were investigated by in vitro anther culture (AC) on nine F2-5 breeding combinations of winter wheat (Triticum aestivium L.). Each combination produced embryo-like structures (ELS), green plantlets, albino plantlets, transplanted plantlets and acclimatized plantlets, with respect to the number of anthers in AC. The number of AC-derived ELS was between 11.73 and 52.76 ELS/100 anthers with the mean of 26.22 ELS/100 anthers, out of which the number of regenerated green plantlets varied from 3.20 to 26.40 green plantlets/100 anthers and the mean was 9.76 green plantlets/100 anthers, while the number of transplanted plantlets ranged from 2.16 to 21.77 transplanted plantlets/100 anthers. Furthermore, the number of albinos/100 anthers was mitigated and varied between 0.72 and 6.20 albinos/100 anthers. We also studied the number of green and albino plantlets per 100 ELS. The rate of green plantlets per 100 ELS ranged between 14.81% and 64.01%, with the overall mean 33.59%, while the rate of albinos per 100 ELS ranged from 4.82% to 20.70% with the overall mean 11.93%. In our experiment, the rate of acclimatized plantlets (70.15-91.57%) depended mostly on the combination. This study asserted the importance of AC method in wheat for in vitro production of green plants. Although albinism was found in each combination, it did not hinder the production of green plantlets. The satisfying results were achieved in green plantlets production compared to the previously published data, but further improvement will be needed continuously, experiment by experiment. The generated acclimatized plantlets will be used in the wheat breeding program as doubled haploid (DH) lines.
Effect of Nanosilver and Antibiotics Separately and in Combination on Pseudomonas aeruginosa Clinical Isolates

Pseudomonas aeruginosa is especially problematic for seriously ill patients in ICUs. From 1992 to 1997, data from the National Nosocomial Infections Surveillance System showed that P. aeruginosa was responsible for 21% of pneumonias, 10% of urinary tract The present study have been carried out to determine the Effect of Nanosilver and Antibiotics Separately and in Combination on Pseudomonas aeruginosa Clinical Isolates. There were Forty Pseudomonas aeruginosa were isolated from clinical specimens. The MicroScan WalkAway-96 SI System was used in laboratory Identification and antimicrobial susceptibility testing was performed. Also, the well diffusion method was used to evaluate the antibacterial activity of silver nanoparticles in combination with antibiotics. All isolates were susceptible to more than MIC of nanosilver compared to a high resistance to multiple classes of antibiotics. The Average of inhibition zone diameters of different concentrations of nanosilver dilutions. The combined effect of the silver nanoparticles with antibiotics was assessed in comparison to antibiotics, with well diffusion method. It was observed that the collective effect of silver nanoparticles with antibiotics was additive. The activity of the three antibiotics: gentamycine. ampicillin, and tetracycline with nanoparticles, against Pseudomonas aeruginosa was shown as percentage enhancement in antibacterial effect. Silver nano particles may provide safe and effective treatment for MDR Pseudomonas aeruginosa infections when combined with antibiotics. More further studies are required for understand synergistic effect of nanosilver combining, Assessment of safety and efficacy of new antibioticnanosilver combination. K e y w o r d s
Impact of biotic and abiotic factors on lac production and peoples’ livelihood improvement in India-An overview

In India, Madhya Pradesh is the third largest lac producing state after Chhattisgarh and Jharkhand, as they contribute 12, 16 and 60%, respectively in total countryâ€™s lac production. Lac is produced in ten out of eleven agro-climatic zones of Madhya Pradesh. In total lac production of the state, Baisakhi (Butea monosperma, inoculated in October-November) crop contributes 76% followed by 19% by Katki (Butea monosperma, inoculated in June-July), 3% by Aghani (Schleichera oleosa, inoculated in June-July) and 2% by Jethwi (Schleichera oleosa, inoculated in January-February). Ranchi was leading lac producing district followed by Simdega, Gumla, Khunti of Jharkhand and Seoni district of Madhya Pradesh. Increasing summer temperature up to 45 â°C, the countryâ€™s lac production was declined from 20,050 tons in 2003-04 to 16978 tons in 2014-15. In case of biotic factors, Tachardiaephagus tachardiae and Tetrastichus purpureus are the most abundant lac associated parasites and Eublemma amabilis and Pseudohypatopa pulverea are the most destructive key predators of lac insects. By combating these hindrances, lac cultivation generated an employment for 16-160 man days. Lac cultivation produces maximum gross return (Rs. 9,77,600) from 100 Kusum host plants, and the highest Benefit-Cost (6.80) ratio was recorded for Ber-kusumi (Ziziphus mauritiana- Schleichera oleosa) crop in Ranchi, Jharkhand. In this context, the study finds out obstacles in lac production of India and suggests some control measures to improve lac producing peoplesâ€™ livelihood.
Personalized Adaptive Cruise Control Based on Online Driving Style Recognition Technology and Model Predictive Control

This paper proposes a personalized adaptive cruise control (ACC) system based on driving style recognition and model predictive control (MPC) to meet different driving styles while ensuring car-following, comfort and fuel-economy performances. To obtain the controller parameters corresponding to different driving styles, a set of real vehicle experiments are conducted to collect driving data of 66 randomly recruited drivers, then the experimental data is clustered through unsupervised machine learning method. On the basis of it, a driving style classifier is designed by supervised machine learning method, which can be used to recognize the driving style of drivers online. Then, the control problem of the personalized ACC system is described as a multi-objective optimization problem which is solved by MPC method. The simulation results show that the proposed personalized ACC system can meet the requirements of different driving styles and guarantee various performances.
Rainfall and temperature influence expression of foliar symptoms of grapevine leaf stripe disease (esca complex) in vineyards

Two vineyards in the Abruzzo Region, central Italy, affected by grapevine leaf stripe disease (GLSD), were monitored for incidence and severity of foliar symptoms of the disease for 21 consecutive years (1994 to 2014). Each year, rainfall and temperature were recorded. Correlations between symptom incidence or severity and rainfall or temperature of different periods of the year or single months were assessed. July rainfall and temperature were highly correlated with incidence and severity of leaf symptoms. The vineyards were characterized by high longevity, vigour and yields, and a particular dynamic of GLSD. In the period following 2005, the year of peak vine mortality, there was a decrease of the expression of foliar symptoms, which was not correlated with both of the climate parameters. Greater mortality occurred for vines showing severe symptoms at the first appearance of GLSD than for vines with low severity.
Measuring the Acceleration of the Social Construction of Time using the BOE (Boletin Oficial del Estado)

The Practice of Conceptual History, by Reinhart Koselleck, explores the idea that there is a direct relationship between technological advancements and an acceleration in the social construction of time. This paper will quantify this theory by measuring information density and information variety of narratives in a BOE (Boletín Oficial del Estado) dataset of thirty years (1988-2018). Using Quantitative Narrative Analysis, we will define a narrative unit as a triplet of Subject, Verb, Object (SVO), and we will define information density (ID) as the ratio of narrative units per words per year. Afterwards, we will quantify the different contexts of narratives to measure information variety (IV) by constructing a network of semantic closeness from trained word embeddings. This paper will present an increased IV and ID over the observation time, indicating more and more facts being reported. The results will show evidence of an acceleration of the social construction of time.
Dynamic Buffer Sizing and Pacing as Enablers of 5G Low-Latency Services

3GPP standards organization is performing an impressive effort trying to reach sub-millisecond latencies for 5G. However, such efforts may become fruitless if exogenously generated delays at transport layer are not considered. Nowadays, Radio Access Networks (RANs) are deployed with large buffers to achieve full utilization and avoid squandering wireless resources. Unfortunately, and since the data path’s bottleneck resides on the radio link, RAN’s buffers are bloated by TCP’s congestion control algorithm. Thus, a flow with low-latency requirements that encounters a bloated buffer, suffers from inevitable large sojourn times associated with the buffer depletion time, severely downgrading its Quality of Service (QoS). This paper presents different solutions for efficiently multiplexing distinct traffic patterns that share buffers on the 5G stack. Bufferbloat is extensively studied within the actual 5G QoS scenario, which presents multiple challenges inherited from the dynamic radio link nature and the presence of multiple queues at different entities. We propose and extensively emulate different algorithms in order to avoid the exogenous delay caused by the bufferbloat phenomena. We use real cellular network traces with realistic delay-sensitive and background traffic patterns in different scenarios. The outcome presents valuable insights in the algorithms that will enable low-latency services to be delivered through the 5G network stack satisfying restrictive envisioned constraints.
Genetic Analysis Workshop 14: Introduction to Workshop Summaries

Genetic Analysis Workshop 14 (GAW14) was held September 7–10, 2005, in Noordwijkerhout, The Netherlands. The overarching theme was the comparison of microsatellite and single‐nucleotide polymorphism (SNP) markers for genome‐wide scans, and the statistical methods that can best exploit the information provided in such scans for linkage and association studies. The 183 contributions submitted to GAW14 were organized into 18 presentation groups of 7–15 papers each. GAW14 participants had the choice of two data sets to analyze. COGA generously donated extensive family data on alcoholism, related phenotypes, pertinent covariates, and a set of previously analyzed genomescan microsatellite marker genotypes. The simulated data were designed to have similarities to the real data set. Extensive new SNP genotyping was performed on DNA provided by COGA for the previously genotyped families. Illumina, Affymetrix, and the Center for Inherited Disease Research performed this work and donated these data to GAW14 and to COGA. The group summary papers collected in this issue present the major findings from GAW14. Genet. Epidemiol. 29(Suppl. 1):S1–S6, 2005. Published 2005 Wiley‐Liss, Inc.
Development of Microsatellite Markers for the Hermatypic Coral Porites lutea

By mining the EST and genomic microsatellite sequence resources of Porites species in GenBank, and using interspecific amplification and resequencing, we developed 20 microsatellite markers for the stony coral Porites lutea, meanwhile, validated and excluded 43 microsatellite markers of its symbiotic zooxanthella. These markers of P. lutea were genotyped in one wild population (n=16) from the southwest of Hainan Island fringing reefs. Nineteen of the 20 markers showed polymorphism. The number of alleles was 2~7 (mean 3.5±1.8), and the observed heterozygosity (Ho) and expected heterozygosity (He) were 0.06~0.94 (mean 0.34±0.29) and 0.18~0.83 (mean 0.57±0.20), respectively. Four markers remained deviated from the Hardy-Weinberg Equilibrium (HWE) after multiple comparison correction for Bonferroin (PHWE<0.05). This is the first set of microsatellite markers of P. lutea. They should be useful for the research on the genetic diversity and connectivity of this species and the conservation of endangered coral reef ecosystems and reef-building coral communities in the Indo-Pacific region.
Summary of Changes: I/O-Efficient Algorithms for Degeneracy Computation on Massive Networks

Degeneracy is an important concept to measure the sparsity of a graph which has been widely used in many network analysis applications. Many network analysis algorithms, such as clique enumeration and truss decomposition, perform very well in graphs having small degeneracies. In this paper, we propose an I/O-efficient algorithm to compute the degeneracy of the massive graph that cannot be fully kept in the main memory. The proposed algorithm only uses O(n) memory, where n denotes the number of nodes of the graph. We also develop an I/O-efficient algorithm to incrementally maintain the degeneracy on dynamic graphs. Extensive experiments show that our algorithms significantly outperform the state-of-the-art degeneracy computation algorithms in terms of both running time and I/O costs. The results also demonstrate high scalability of the proposed algorithms. For example, in a real-world web graph with 930 million nodes and 13.3 billion edges, the proposed algorithm takes only 633 seconds and uses less than 4.5GB memory to compute the degeneracy.
Reinforcement Learning in Self Organizing Cellular Networks

Self-organization is a key feature as cellular networks densify and become more heterogeneous, through the additional small cells such as pico and femtocells. Selforganizing networks (SONs) can perform self-configuration, self-optimization, and self-healing. These operations can cover basic tasks such as the configuration of a newly installed base station, resource management, and fault management in the network. In other words, SONs attempt to minimize human intervention where they use measurements from the network to minimize the cost of installation, configuration, and maintenance of the network. In fact, SONs aim to bring two main factors in play: intelligence and autonomous adaptability. One of the main requirements for achieving such goals is to learn from sensory data and signal measurements in networks. Therefore, machine learning techniques can play a major role in processing underutilized sensory data to enhance the performance of SONs. In the first part of this dissertation, we focus on reinforcement learning as a viable approach for learning from signal measurements. We develop a general framework in heterogeneous cellular networks agnostic to the learning approach. We design multiple reward functions and study different effects of the reward function, Markov state model, learning rate, and cooperation methods on the performance of reinforcement learning in cellular networks. Further, we look into the optimality of reinforcement learning solutions and provide insights into how to achieve optimal solutions. In the second part of the dissertation, we propose a novel architecture based on vii spatial indexing for system-evaluation of heterogeneous 5G cellular networks. We develop an open-source platform based on the proposed architecture that can be used to study large scale directional cellular networks. The proposed platform is used for generating training data sets of accurate signal-to-interference-plus-noise-ratio (SINR) values in millimeter-wave communications for machine learning purposes. Then, with taking advantage of the developed platform, we look into dense millimeter-wave networks as one of the key technologies in 5G cellular networks. We focus on topology management of millimeter-wave backhaul networks and study and provide multiple insights on the evaluation and selection of proper performance metrics in dense millimeter-wave networks. Finally, we finish this part by proposing a self-organizing solution to achieve k-connectivity via reinforcement learning in the topology management of wireless networks.
Diversity-Multiplexing Tradeoff of Multi-Layer Scattering MIMO Channels

Multi-layer (or multi-cluster) scattering Multiple-Input Multiple-Output (MIMO) channels are considered in the framework of the diversity-multiplexing tradeoff (DMT). This MIMO channel model finds application in indoor networks, typical of 5G architectures, in which the signal propagates from the transmitter to the receiver through the walls and floors of a building (represented by scattering layers). These results extend the seminal work by Zheng and Tse from the independent identically distributed (iid) Rayleigh fading MIMO channel to a channel matrix which is the product of iid Rayleigh fading matrix components. It is worth noting that the resulting product channel matrix elements are not independent. It is shown that the presence of multiple scattering layers eventually degrades the DMT performance of a MIMO channel by an amount depending only on the least three dimensions of the matrices characterizing the product channel matrix.
User-Centered Design of a Scenario-Based Serious Game: Game-Based Teaching of Future Healthcare

A scenario-based serious game for teaching about healthcare solutions was developed and tested in multiple iterations. The serious game captures the complexity of a real-world healthcare scenario, projecting it into a “scenario system” that can be accessed by the game participants to explore and experience the playthrough of a critical healthcare situation. The aim is to teach about existing health technology, and its uses and interactions between healthcare professionals, technologies, and patients. The platform for the serious game is mobile, such that different locations can be chosen for different scenarios. Actions of the participants trigger further actions, tasks, and storyline progress in the scenario so that a self-paced experience with defined dynamics can be created for the participants. The scenario-based serious game was tested with high school students and senior citizens in multiple iterations.
Growth Engineering And Characterization Of Vanadium Dioxide Films For Ultraviolet Detection

There is a need for efficient ultraviolet (UV) detectors in many fields, such as aerospace, automotive manufacturing, biology, environmental science, and defense, due to photomultiplier tubes (the currently available technology) often not meeting application constraints in weight, robustness, and power consumption. In my thesis, I demonstrate that high quality vanadium dioxide (VO2) thin films, epitaxially grown on niobium doped titanium dioxide substrates (TiO2:Nb), display a strong photoconductive response in the UV spectral range, making them promising candidates for photomultiplier-free UV photodetection. By adjusting the characteristics of the substrate and VO2 film, the samples achieve external quantum efficiency exceeding 100% (reaching beyond 1,000% for optimized samples) superior to that of current wide band gap UV detectors at room temperature. The mechanism for photocurrent production in VO2/TiO2:Nb heterostructure is a space-charge region, engineered in the heterojunction, yielding favorable conditions for hole tunneling from TiO2:Nb into VO2. Improving upon the heterostructure, I demonstrate up to an order of magnitude improvement in parameters such as responsivity, external quantum efficiency, detectivity, and dark current density by applying Au films to the VO2/TiO2:Nb heterostructure. Ultimately, my work proved that the VO2/TiO2:Nb heterostructure is a promising alternative technology for UV detection in high demand fields, with great potential for scalable device production.
Cannabis Chemovar Nomenclature Misrepresents Chemical and Genetic Diversity; Survey of Variations in Chemical Profiles and Genetic Markers in Nevada Medical Cannabis Samples

Introduction: Medical cannabis patients receive clinical benefits from the secondary metabolites of the plant, which contain a variety of cannabinoids and terpenoids in combinations that can be used to classify the chemovars. State-regulated medical cannabis programs rely on breeder-reported “strain” names both within diversion control systems and to describe the medical cannabis products that are sold to patients in medical cannabis dispensaries. In state-regulated medical cannabis programs, there is no conventional nomenclature system that correlates the breeder-reported names with their profiles of active ingredients, and these “strain” names are invalid as they refer to chemical differences properly referred to as to chemovars. Materials and Methods: To determine the actual levels of chemical diversity represented in 2662 samples of Cannabis flower collected between January 2016 and June of 2017 in Nevada, chemical profile data were measured from these samples by a state-qualified third-party testing laboratory. Principal component analysis (PCA) was used to define clusters in data sets representing both cannabinoids and terpenoids, cannabinoids only, or terpenoids only. Results: The PCA of the terpenoid only data set revealed three well-defined clusters. All three terpenoids only data clusters had high tetrahydrocannabinolic acid synthase, but the terpene profiles listed in reverse-order of abundance best defined these chemovars. The three chemovars in Nevada were labeled with 396 breeder-reported sample names, which overestimate the diversity and do not inform patients regarding chemical properties. Representative DNA samples were taken from each chemovar to determine whether the genetic diversity was greater than the chemical diversity. The limited genotyping experiment was based on DNA sequence polymorphisms. The genetic analysis revealed twelve distinct genetic clades, which still does not account for the entirety of the 396 reported sample names. The finite genotypes did not correlate with the chemotypes determined for the samples. This suggests that either the DNA-markers used were too narrowly restricted for factual separation or that environmental factors contributed more significantly to the chemical profiles of cannabis than genetics. Conclusion: The three chemovars and twelve genotypes reflect low medical diversity on the market in Nevada during its “medical use only” phase. Furthermore, the 396 breeder-reported sample names within this set imply a false sense of diversity of products in Nevada dispensaries.
A Throughput-Optimized Channel-Oriented Processing Element Array for Convolutional Neural Networks

Over the past decade, significant developments have taken place in the field of deep learning. State-of-the-art convolutional neural networks (CNNs), a branch of deep learning, have been increasingly applied in various fields such as image classification, speech recognition, and natural language processing. Due to the high computational complexity of CNNs, lots of works have proposed their CNN accelerators to address this issue. Besides, a processing element (PE) array has been recently further focused and discussed since it is responsible for the entire computations as the core of CNN accelerators. Therefore, the specialized design of a PE array becomes one of the main researches on CNN accelerators for energy efficiency and high throughput. In this brief, a throughput-optimized PE array for CNNs based on the channel-oriented data pattern is proposed. The proposed PE array features fully PE interconnection which achieves scalability. Besides, any sized convolution can be processed in the PE array while maximizing the utilization of PEs by exploiting the channel-oriented data pattern. Compared to previous works, this brief achieves <inline-formula> <tex-math notation="LaTeX">$1.22\times $ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$1.25\times $ </tex-math></inline-formula> improvement in the throughput density on AlexNet and VGG-16 respectively.