Futuristic Prediction of Missing Value Imputation Methods Using Extended ANN

Missing data is universal complexity for most part of the research fields which introduces the part of uncertainty into data analysis. We can take place due to many types of motives such as samples mishandling, unable to collect an observation, measurement errors, aberrant value deleted, or merely be short of study. The nourishment area is not an exemption to the difficulty of data missing. Most frequently, this difficulty is determined by manipulative means or medians from the existing datasets which need improvements. The paper proposed hybrid schemes of MICE and ANN known as extended ANN to search and analyze the missing values and perform imputations in the given dataset. The proposed mechanism is efficiently able to analyze the blank entries and fill them with proper examining their neighboring records in order to improve the accuracy of the dataset. In order to validate the proposed scheme, the extended ANN is further compared against various recent algorithms or mechanisms to analyze the efficiency as well as the accuracy of the results.

Current Advances in Mechanisms and Treatment of Dry Eye Disease: Toward Anti-inflammatory and Immunomodulatory Therapy and Traditional Chinese Medicine

Dry eye is currently one of the most common ocular surface disease. It can lead to ocular discomfort and even cause visual impairment, which greatly affects the work and quality of life of patients. With the increasing incidence of dry eye disease (DED) in recent years, the disease is receiving more and more attention, and has become one of the hot research fields in ophthalmology research. Recently, with the in-depth research on the etiology, pathogenesis and treatment of DED, it has been shown that defects in immune regulation is one of the main pathological mechanisms of DED. Since the non-specific and specific immune response of the ocular surface are jointly regulated, a variety of immune cells and inflammatory factors are involved in the development of DED. The conventional treatment of DED is the application of artificial tears for lubricating the ocular surface. However, for moderate-to-severe DED, treatment with anti-inflammatory drugs is necessary. In this review, the immunomodulatory mechanisms of DED and the latest research progress of its related treatments including Chinese medicine will be discussed.

The pH Value Control of Morphology and Luminescence Properties of Gd2O2S: Tb3+ Phosphors

Developing rare-earth doped oxysulfide phosphors with diverse morphologies has significant value in many research fields such as in displays, medical diagnosis, and information storage. All of the time, phosphors with spherical morphology have been developed in most of the related literatures. Herein, by simply adjusting the pH values of the reaction solution, Gd2O2S:Tb3+ phosphors with various morphologies (sphere-like, sheet-like, cuboid-like, flat square-like, rod-like) were synthesized. The XRD patterns showed that phosphors with all morphologies are pure hexagonal phase of Gd2O2S. The atomic resolution structural analysis by transmission electron microscopy revealed the crystal growth model of the phosphors with different morphology. With the morphological change, the band gap energy of Gd2O2S:Tb3+ crystal changed from 3.76 eV to 4.28 eV, followed by different luminescence performance. The samples with sphere-like and cuboid-like microstructures exhibit stronger cathodoluminescence intensity than commercial product by comparison. Moreover, luminescence of Gd2O2S:Tb3+ phosphors have different emission performance excited by UV light radiation and an electron beam, which when excited by UV light is biased towards yellow, and while excited by an electron beam is biased towards cyan. This finding provides a simple but effective method to achieve rare-earth doped oxysulfide phosphors with diversified and tunable luminescence properties through morphology control.

A Recursive Algorithm for the Forward Kinematic Analysis of Robotic Systems Using Euler Angles

Forward kinematics is one of the main research fields in robotics, where the goal is to obtain the position of a robot’s end-effector from its joint parameters. This work presents a method for achieving this using a recursive algorithm that builds a 3D computational model from the configuration of a robotic system. The orientation of the robot’s links is determined from the joint angles using Euler Angles and rotation matrices. Kinematic links are modeled sequentially, the properties of each link are defined by its geometry, the geometry of its predecessor in the kinematic chain, and the configuration of the joint between them. This makes this method ideal for tackling serial kinematic chains. The proposed method is advantageous due to its theoretical increase in computational efficiency, ease of implementation, and simple interpretation of the geometric operations. This method is tested and validated by modeling a human-inspired robotic mobile manipulator (CHARMIE) in Python.

A Family of Generalized Legendre-Based Apostol-Type Polynomials

Numerous polynomials, their extensions, and variations have been thoroughly explored, owing to their potential applications in a wide variety of research fields. The purpose of this work is to provide a unified family of Legendre-based generalized Apostol-Bernoulli, Apostol-Euler, and Apostol-Genocchi polynomials, with appropriate constraints for the Maclaurin series. Then we look at the formulae and identities that are involved, including an integral formula, differential formulas, addition formulas, implicit summation formulas, and general symmetry identities. We also provide an explicit representation for these new polynomials. Due to the generality of the findings given here, various formulae and identities for relatively simple polynomials and numbers, such as generalized Bernoulli, Euler, and Genocchi numbers and polynomials, are indicated to be deducible. Furthermore, we employ the umbral calculus theory to offer some additional formulae for these new polynomials.

The Contribution of Innovative Changes to the Strategic Development of Universities in the Context of Achieving Sustainability

This paper constitutes a scientific approach, where there have been highlighted the most important innovative changes that influence the development of a higher education institution and shape the activity of universities towards permanent adaptation to achieve their sustainability. The topicality of the research topic stems from the need to identify effective measures to achieve sustainability in higher education institutions. The aim of the research is to identify the innovative changes that have a positive impact on the strategic development of the universities. The problem of the research lies in highlighting the most important innovative changes that can affect the universities in achieving their sustainability. In this context, we can reiterate that innovative changes have been defined and analysed from the perspective of the need of university change in the direction of their assimilation within institutions. In addition, the innovative potential of the Republic of Moldova was evaluated from the perspective of the analysis of the categories of scientific researchers, of the research fields, of the expenses undertaken by the state for the development of scientific researches. The research methodology focused on the use of several methods: analysis, synthesis, induction, abduction, deduction, qualitative research through documentation, scientific abstraction. In conclusion, we can reiterate that the Republic of Moldova has a valuable innovation potential, which can lead the country to achieve sustainability. The best solution would be the efficient capitalization of the innovative potential of the country but also its direction towards ensuring an ecological-economic-social balance, in the context of applying a management of university sustainability within higher education institutions.

Analyzing the generalizability of the network-based topic emergence identification method

Topic evolution helps the understanding of current research topics and their histories by automatically modeling and detecting the set of shared research fields in academic publications as topics. This paper provides a generalized analysis of the topic evolution method for predicting the emergence of new topics, which can operate on any dataset where the topics are defined as the relationships of their neighborhoods in the past by extrapolating to the future topics. Twenty sample topic networks were built with various fields-of-study keywords as seeds, covering domains such as business, materials, diseases, and computer science from the Microsoft Academic Graph dataset. The binary classifier was trained for each topic network using 15 structural features of emerging and existing topics and consistently resulted in accuracy and F1 over 0.91 for all twenty datasets over the periods of 2000 to 2019. Feature selection showed that the models retained most of the performance with only one-third of the tested features. Incremental learning was tested within the same topic over time and between different topics, which resulted in slight performance improvements in both cases. This indicates there is an underlying pattern to the neighbors of new topics common to research domains, likely beyond the sample topics used in the experiment. The result showed that network-based new topic prediction can be applied to various research domains with different research patterns.

Fixation classification: how to merge and select fixation candidates

Eye trackers are applied in many research fields (e.g., cognitive science, medicine, marketing research). To give meaning to the eye-tracking data, researchers have a broad choice of classification methods to extract various behaviors (e.g., saccade, blink, fixation) from the gaze signal. There is extensive literature about the different classification algorithms. Surprisingly, not much is known about the effect of fixation and saccade selection rules that are usually (implicitly) applied. We want to answer the following question: What is the impact of the selection-rule parameters (minimal saccade amplitude and minimal fixation duration) on the distribution of fixation durations? To answer this question, we used eye-tracking data with high and low quality and seven different classification algorithms. We conclude that selection rules play an important role in merging and selecting fixation candidates. For eye-tracking data with good-to-moderate precision (RMSD < 0.5∘), the classification algorithm of choice does not matter too much as long as it is sensitive enough and is followed by a rule that selects saccades with amplitudes larger than 1.0∘ and a rule that selects fixations with duration longer than 60 ms. Because of the importance of selection, researchers should always report whether they performed selection and the values of their parameters.

Ecological Momentary Assessment: A Meta-Analysis on Designs, Samples, and Compliance Across Research Fields

Ecological Momentary Assessments (i.e., EMA, repeated assessments in daily life) are widespread in many fields of psychology and related disciplines. Yet, little knowledge exists on how differences in study designs and samples predict study compliance and dropout—two central parameters of data quality in (micro-)longitudinal research. The current meta-analysis included k = 477 articles (496 samples, total N = 677,536). For each article, we coded the design, sample characteristics, compliance, and dropout rate. The results showed that on average EMA studies scheduled six assessments per day, lasted for 7 days, and obtained a compliance of 79%. Studies with more assessments per day scheduled fewer assessment days, yet, the number of assessments did not predict compliance or dropout rates. Compliance was significantly higher in studies providing financial incentives. Otherwise, design or sample characteristics had little effects. We discuss the implications of the findings for planning, reporting, and reviewing EMA studies.

Forward-looking insights in laser-generated ultra-intense γ-ray and neutron sources for nuclear application and science

Ultra-intense MeV photon and neutron beams are indispensable tools in many research fields such as nuclear, atomic and material science as well as in medical and biophysical applications. For applications in laboratory nuclear astrophysics, neutron fluxes in excess of 1021 n/(cm2 s) are required. Such ultra-high fluxes are unattainable with existing conventional reactor- and accelerator-based facilities. Currently discussed concepts for generating high-flux neutron beams are based on ultra-high power multi-petawatt lasers operating around 1023 W/cm2 intensities. Here, we present an efficient concept for generating γ and neutron beams based on enhanced production of direct laser-accelerated electrons in relativistic laser interactions with a long-scale near critical density plasma at 1019 W/cm2 intensity. Experimental insights in the laser-driven generation of ultra-intense, well-directed multi-MeV beams of photons more than 1012 ph/sr and an ultra-high intense neutron source with greater than 6 × 1010 neutrons per shot are presented. More than 1.4% laser-to-gamma conversion efficiency above 10 MeV and 0.05% laser-to-neutron conversion efficiency were recorded, already at moderate relativistic laser intensities and ps pulse duration. This approach promises a strong boost of the diagnostic potential of existing kJ PW laser systems used for Inertial Confinement Fusion (ICF) research.