Miracle-dz/UniFAR-FTUs huggingface dataset:
Who Enters Politics and Why? Basic Human Values in the UK Parliament James Weinberg, Bristol, Bristol University Press, 2020. 232 p. £ 75.00

Contrary to the prevailing narrative that all politicians are nothing more than privileged idlers, the decision to enter politics today is a very challenging, dare I say almost heroic, one. In fact, career politicians are obliged to operate in an anti-political climate that generates constant popular disdain and hostility towards political representatives and those holding institutional office. This being the case, then it is natural to wonder who those attracted to a political career are. What individual qualities do they possess, and what reasons do they have for undertaking such an uncertain, tough, and at the end of the day somewhat unrewarding, career? Political science and political sociology scholars who have investigated this topic have tended to focus their attention, however, on the socio-demographic characteristics and the political and institutional features of political representatives. What has been developed to a far lesser extent, on the other hand, is the exploration of the individual motivations and underlying values of those individuals who decide to embark on a career in politics and who attain significant positions of power within the political sphere. Any such studies have generally been of a biographical (or on occasion auto-biographical) nature, or in any case of a qualitative character. There is a lack of more systematic studies conducted using quantitative methods. In his work Who enters politics and why? Basic human values in the UK parliament, James Weinberg has tried to fill this gap. The most original aspect of Weinberg’s work, and one that I personally consider appreciable, is his contribution to political science and political sociology from a different perspective, that of political psychology. There are basically three research questions that the author tries to answer in this work, namely:
A Deep Residual Recurrent Neural Network Model-Augmented Attention With Physical Characteristics: Application to Turntable Servo System

Turntable servo systems are affected by working status changing in actual work, thus, bringing some uncertain factors. Having models that have accurate predictive capabilities can be valuable for control and decision-making purposes. This article proposes a deep residual recurrent neural network (DRRNN) model-augmented attention with physical characteristics, which is used to build an accurate speed prediction model for a turntable servo system. Integrating the known physical features into the design of the residual network model can increase the interpretability of the overall model. Meanwhile, the recurrent neural network-augmented attention model is used to represent the unknown nonlinear characteristics. The addition of the attention mechanism can effectively improve the accuracy of the nonlinear model. Finally, this article proposes an iterative identification method based on the linear and nonlinear model error with alternative compensation. Through alternating error compensation, the linear model’s accuracy can be continuously improved, then the overall and the nonlinear part of the model can be established accurately by retraining the whole model. Experimental results show that the DRRNN model designed in this article can predict the rotational speed in single-step or multistep well. Compared with the traditional mechanism modeling method, the modeling accuracy can be greatly improved.
THEORETICAL AND PRACTICAL ASPECTS OF MODERN PSYCHOLOGY PSYCHOLOGICAL PREDICTORS OF THE FORMATION OF HEALTH- PRESERVING COMPETENTNESS OF FUTURE SPECIALISTS

It is established that human behavior in each situation is determined by many factors: internal attitudes, motivation, skills, understanding of technology, knowledge and genetic predisposition The concept of competentness is revealed, which is an integral characteristic or criterion that describes the quality of human behavior in a particular activity. It is proved that the competentness approach is taken as the basis of the educational process of a modern specialist The assessment of students' attitude to personal health and healthy lifestyle was given by the method of questionnaires. As shown by the survey, students believe that the role of the formation of healthpreserving competentness in training is high. The results obtained made it possible to note that the necessary integrated approach of the educational process of a higher educational institution for the formation of health-preserving competentness in the training of future specialists allows creating conditions for enhancing the conscious attitude of students to future professional activities. It has also been proven that health-preserving competentness include physical, spiritual and intellectual self-development; emotional self-regulation and self-support; personal hygiene, taking care of one's own health, sexual literacy; internal ecological culture; ways to live safely. Also, a model was developed for the formation of health-preserving competentness of students of higher educational institutions, which includes the following principles: the principle of relevance; the principle of accessibility; the principle of consistency; the principle of consistency; principle of consciousness. Formed health-preserving competentness presuppose the presence of not only knowledge, skills and abilities to effectively manage the style of personal life, but also the development of such psychological predictors as empathy, tolerance, self-control, communication culture, adaptation to changing conditions of existence, the ability to navigate and self-determine in various areas. , adequate behavior in different life situations.
Adversarial Attack using Neural Image Modification

In order to help development into analyzing the characteristics of adversarial sample generation in artificial neural networks, this work proposes a framework for an adversarial attack that utilizes neural image modification to generate an adversarial sample. This method proves to be effective in reducing a target network’s accuracy in both untargeted and targeted attacks with good success rates. This method also shows some effectiveness against defensive distillation, but not transferrable between multiple models.
Topological Quantum Cathode Materials for Fast Charging Li‐Ion Battery Identified by Machine Learning and First Principles Calculation

Fast charging electrode materials require excellent ion conductivity as well as high and stable electrical conductivity. However, the main commercial cathode materials are semiconducting transition metal oxides suffering from low electrical conductivity, so cathode materials with good electrical conductivity are highly desirable. It is well‐known that topological quantum materials (TQMs) can exhibit robust electrical conductivity, thus providing a promising solution to this problem. The key question becomes which TQMs can have high ionic conductivity. Herein, such topological quantum cathode materials for fast charging Li‐ion battery are identified by using machine learning combined with first‐principle calculation, where the supervised regression models are trained for diffusion energy barrier prediction. Among 7385 TQMs, 20 materials are found to have a diffusion energy barrier less than 1.0 eV. Especially, LiMnAs exhibits a reversible capacity of 195.9 mAh g−1, higher than that of commercial cathodes while with comparable diffusion energy barrier. Furthermore, LiMnAs shows high interface stability with selected solid lithium metal oxides electrolytes. This study not only opens a new path to developing novel cathode materials, but also expands the applications of TQMs.
Precision Medicine for Hypertension Patients with Type 2 Diabetes via Reinforcement Learning

Precision medicine is a new approach to understanding health and disease based on patient-specific data such as medical diagnoses; clinical phenotype; biologic investigations such as laboratory studies and imaging; and environmental, demographic, and lifestyle factors. The importance of machine learning techniques in healthcare has expanded quickly in the last decade owing to the rising availability of vast multi-modality data and developed computational models and algorithms. Reinforcement learning is an appealing method for developing efficient policies in various healthcare areas where the decision-making process is typically defined by a long period or a sequential process. In our research, we leverage the power of reinforcement learning and electronic health records of South Koreans to dynamically recommend treatment prescriptions, which are personalized based on patient information of hypertension. Our proposed reinforcement learning-based treatment recommendation system decides whether to use mono, dual, or triple therapy according to the state of the hypertension patients. We evaluated the performance of our personalized treatment recommendation model by lowering the occurrence of hypertension-related complications and blood pressure levels of patients who followed our model’s recommendation. With our findings, we believe that our proposed hypertension treatment recommendation model could assist doctors in prescribing appropriate antihypertensive medications.
Genome-wide identification and expression analysis of the cucumber PYL gene family

Abscisic acid (ABA) is a very important hormone in plants. It regulates growth and development of plants and plays an important role in biotic and abiotic stresses. The Pyrabactin resistance 1-like (PYR/PYL) proteins play a central role in ABA signal transduction pathways. The working system of PYL genes in cucumber, an important economical vegetable (Cucumis sativus L.), has not been fully studied yet. Through bioinformatics, a total of 14 individual PYL genes were identified in Chinese long ‘9930’ cucumber. Fourteen PYL genes were distributed on six chromosomes of cucumber, and their encoded proteins predicted to be distributed in cytoplasm and nucleus. Based on the phylogenetic analysis, the PYL genes of cucumber, Arabidopsis, rice, apple, Brachypodium distachyon and soybeancould be classified into three groups. Genetic structures and conserved domains analysis revealed that CsPYL genes in the same group have similar exons and conserved domains. By predicting cis-elements in the promoters, we found that all CsPYL members contained hormone and stress-related elements. Additionally, the expression patterns of CsPYL genes were specific in tissues. Finally, we further examined the expression of 14 CsPYL genes under ABA, PEG, salt stress. The qRT-PCR results showed that most PYL gene expression levels were up-regulated. Furthermore, with different treatments about 3h, the relative expression of PYL8 was up-regulated and more than 20 times higher than 0h. It indicated that this gene may play an important role in abiotic stress.
Deviation from the Residue-Based Linear Free Energy Relationship Reveals a Non-Native Structure in Protein Folding

Multiprobe measurements, such as NMR and hydrogen exchange study, can provide the equilibrium constant K and kinetic rate constant k of the structural changes of a polypeptide on a per-residue basis. We previously found a linear relationship between residue-specific log K values and residuespecific log k values for the two-state topological isomerization of a 27-residue peptide. To test the general applicability of the residue-based linear free energy relationship (rbLEFR), we performed a literature search to collect residue-specific equilibrium and kinetic constants in various exchange processes, including protein folding, coupled folding and binding of intrinsically disordered peptides, and structural fluctuations of folded proteins. The good linearity in a substantial number of log-log plots proved that the rbLFER holds for the structural changes in a wide variety of protein-related phenomena. Protein molecules quickly fold into their native structures and change their conformations smoothly. Theoretical studies and molecular simulations advocate that the physicochemical basis is the consistency principle and the minimal frustration principle: Non-native structures/interactions are absent or minimized along the folding pathway. The linearity of the residue-based free energy relationship demonstrates experimentally the absence of non-native structures in transition states. In this context, the hydrogen exchange study of apomyoglobin folding intermediates is particularly interesting. We found that the residues that deviated from the linear relationship corresponded to the non-native structure, which had been identified by other experiments. The rbLFER provides a unique and practical method to probe the dynamic aspects of the transition states of protein molecules. Highlights A collection of equilibrium and kinetic constants of structural changes of proteins Residue-based linear free energy relationship widely holds between the two constants rbLFER indicates the absence of non-ground state structures in transition states rbLFER is an experiment proof of the consistency principle of protein folding Deviations from the linear relation suggest special structures in transition states
Synthesis Method of an Orthogonal Beamforming Network With Arbitrary Ports for Shaped Beams

This article proposes a synthesis method of orthogonal beamforming networks (BFNs) with arbitrary ports. The proposed method is based on matrix decomposition theory and breakthrough the synthesized BFN number to arbitrary. It has been theoretically demonstrated that the BFN with cascading 180° hybrid couplers can synthesize shaped beams. Therefore, a BFN synthesis method that combines the QR decomposition theory and the optimization algorithm is introduced. With the help of an optimization program, a specific power ratio of each 180° hybrid coupler in the BFN is determined. The proposed synthesis method is verified by experiments with an orthogonal $6\times2$ BFN with flat-topped radiation patterns. Through optimization, five 180° hybrid couplers with different power ratios synthesize the proposed BFN. Results show that two flat-topped beams with a beamwidth of (32° ± 0.3°), gain ripples less than 2.5 dB, and a sidelobe level less than 12 dB are achieved. The proposed method has the advantages of high accuracy and efficiency and a simple structure, which makes it convenient for multi-beam antenna arrays in multiple-input–multiple-output (MIMO) systems.
Production and Evaluation of Porous Photocatalyst Concrete Filter (deNOx PCF) for NOx Reduction

A porous photocatalyst concrete filter (deNOx PCF) is successfully manufactured to reduce NOx by mixing TiO2 photocatalyst with lightweight aerated concrete. From the results, 4% infusion rate of each foaming agent provided the smallest change of the height, and the optimal quality of the air bubbles can be produced by using foaming agent B with 4% of infusion rate. When 3% of TiO2 photocatalyst was mixed, less irregular and relatively homogeneous pores were formed on the surface with white color due to the proper amount of photocatalyst applied. For 3% of photocatalyst mixed with deNOx PCF, 1.03 μmol/hr of NO was reduced equivalent to 10.99% of NO reduction, suggesting that the TiO2 photocatalyst dispersed in the continuous and well-developed pores inside the specimen successfully performed the removal of NO flowing through deNOx PCF using synergistic effects of adsorption and photodegradation reaction. Finally, the specimen of porous deNOx PCF for reducing NOx developed in this study can be applied to various construction sites and the air quality can be solved by reducing NOx contributing to the formation of fine particles.
Based on Extended Real-valued Negative Selection Algorithm with Variable-sized Detectors

It is to generate detectors using random strings and then applying negative selection in intrusion detection system based on artificial immune system.The detection rate of the detector set is poor and the detector collection number is huge,by this method.This paper proposed one kind of improvement real-valued negative selection algorithm with variable-sized detectors,there is information about non-self space in non-self samples,so use gene library which is initialized by non-self samples to generate detectors,and apply gene library evolution strategy to update gene library,thus produces more effective detector sets.It is proved by experiments that this method is effective to improve the detection rate without affecting the rate of false alarm.
Growth Performance, Biochemical Composition and Nutrient Recovery Ability of Twelve Microalgae Consortia Isolated from Various Local Organic Wastes Grown on Nano-Filtered Pig Slurry

This paper demonstrated the growth ability of twelve algae-microbial consortia (AC) isolated from organic wastes when a pig slurry-derived wastewater (NFP) was used as growth substrate in autotrophic cultivation. Nutrient recovery, biochemical composition, fatty acid and amino acid profiles of algae consortia were evaluated and compared. Three algae-microbial consortia, i.e., a Chlorella-dominated consortium (AC_1), a Tetradesmus and Synechocystis co-dominated consortium (AC_10), and a Chlorella and Tetradesmus co-dominated consortium (AC_12) were found to have the best growth rates (µ of 0.55 ± 0.04, 0.52 ± 0.06, and 0.58 ± 0.03 d−1, respectively), which made them good candidates for further applications. The ACs showed high carbohydrates and lipid contents but low contents of both proteins and essential amino acids, probably because of the low N concentration of NFP. AC_1 and AC_12 showed optimal ω6:ω3 ratios of 3.1 and 3.6, which make them interesting from a nutritional point of view.
Lelliottia Rebaudianalis Sp. Nov. Isolated From Wild Stevia Rebaudiana Bertoni

A Gram-stain-negative, aerobic, chemoheterotrophic bacterium, characterized with rod shape and mobility, designated as LST-1T, was isolated from wild Stevia rebaudiana Bertoni and subjected to polyphasic taxonomic analysis. The LST-1T strain grew optimally at 37 °C and pH 6.0–7.0 in the presence of 0.5 % (w/v) NaCl. Phylogenetic sequence analysis based on 16S rDNA from LST-1 indicated that it is close to Lelliottia jeotgali (99.85%), Lelliottia nimipressuralis (98.82%), and Lelliottia amnigena (98.54%). Multi-locus sequence typing analysis of concatenated partial recA, atpD, and infB was performed to improve resolution, and clear distinctions between the closest related type strains were exhibited. Meanwhile, the results from average nucleotide identify analyses and DNA–DNA hybridization with four species (16S rDNA similarity > 98.65%) were less than 90% and 40% respectively, verifying the distinct characteristics from other species of Lelliottia, The cellular fatty acid profile of the strain consisted of C16:0, Summed Feature3, and Summed Feature8 (may be 16:1 w6c/16:1 w7c and 18:1 w6c) as major components. The major polar lipids included phosphatidylethanolamine, phosphatidylglycerol, aminophospholipid, three non-characteristic phospholipids, and a non-characteristic lipid. The genome of LST-1T is 4,611,055 bp, with a DNA G + C content of 55.02%. Combination of several phenotypic, chemotaxonomic, and genomic characteristics proved that the LST-1T strain does represent a novel genus, for which the name Lelliottia sp. LST-1 was proposed. The type strain is LST-1T (= CGMCC 1.19175T = JCM 34938T).
Social Media Use and Mental Health: A Global Analysis

Research indicates that excessive use of social media can be related to depression and anxiety. This study conducted a systematic review of social media and mental health, focusing on Facebook, Twitter, and Instagram. Based on inclusion criteria from the systematic review, a meta-analysis was conducted to explore and summarize studies from the empirical literature on the relationship between social media and mental health. Using PRISMA guidelines on PubMed and Google Scholar, a literature search from January 2010 to June 2020 was conducted to identify studies addressing the relationship between social media sites and mental health. Of the 39 studies identified, 20 were included in the meta-analysis. Results indicate that while social media can create a sense of community for the user, excessive and increased use of social media, particularly among those who are vulnerable, is correlated with depression and other mental health disorders.
Skeletal Muscle Depletion and Major Postoperative Complications in Locally-Advanced Head and Neck Cancer: A Comparison between Ultrasound of Rectus Femoris Muscle and Neck Cross-Sectional Imaging

Simple Summary Skeletal muscle mass (SMM) depletion is gaining popularity as independent predictor of postoperative complications in many surgical scenarios, even in the field of head and neck oncology. In this study, we demonstrate the value of ultrasound scans of the rectus femoris muscle together with the neck CT/MRI at C3 level in terms of estimation of SMM (through muscle cross sectional area), the identification of sarcopenic patients and as a predictor of major surgical morbidity in a cohort of locally-advanced head and neck cancer patients submitted to surgical treatment. This provides important tools for the on-going re-assessment of patients with regards to any pre-habilitation strategy aimed at reducing postoperative complications. Abstract Skeletal muscle mass (SMM) depletion has been validated in many surgical fields as independent predictor of complications through cross-sectional imaging. We evaluated SMM depletion in a stage III-IV head and neck cancer cohort, comparing the accuracy of CT/MRI at C3 level with ultrasound (US) of rectus femoris muscle (RF) in terms of prediction of major complications. Patients submitted to surgery were recruited from 2016 to 2021. SMM was estimated on CT/MRI by calculating the sum of the cross-sectional area (CSA) of the sternocleidomastoid and paravertebral muscles at C3 level and its height-indexed value (cervical skeletal muscle index, CSMI) and on US by computing the CSA of RF. Specific thresholds were defined for both US and CT/MRI according to ROC curve in terms of best prediction of 30-day major complications to detect sarcopenic subjects (40–53%). Sixty-five patients completed the study. At univariate analysis, major complications were associated to lower RF CSA, lower CSA at C3 level and lower CSMI, together with previous radiotherapy, higher ASA score and higher modified frailty index (mFI). At multivariate analysis RF CSA (OR 7.07, p = 0.004), CSA at C3 level (OR 6.74, p = 0.005) and CSMI (OR 4.02, p = 0.025) were confirmed as independent predictors in three different models including radiotherapy, ASA score and mFI. This analysis proved the value of SMM depletion as predictor of major complications in a head and neck cancer cohort, either defined on cross-sectional imaging at C3 or on US of RF.
Li-based MOF-derived multifunctional PEO polymer solid-state electrolyte for lithium energy storage

A three-dimensional (3D) metal-organic framework containing Li-oxygen clusters, namely {[Li2(IPA)]·DMF}n (1) (H2IPA = isophthalic acid), has been constructed under solvothermal conditions. The Li-based MOF can be applied to lithium energy.
Study of Mechanical Performances of Stepseals

A commercial available finite element software MARC/Mentat 320 was used in this paper to analyse the distribution of equivalent Von Mises stress on the axisymmetric profile of a stepseal under different operating conditions. The work was also made to analyse the radial force, the contact width and the powerloss of stepseals combined with O ring seals of three kind of hardness. The calculating results obtained in the current work are of remarkable significance for the design and application of stepseals.