bluuebunny/crossref_metadata_2025 huggingface dataset:
Effect of aneuploidy of a non‐essential chromosome on gene expression in maize
Authors: Shi Xiaowen , Yang Hua , Chen Chen , Hou Jie , Ji Tieming , Cheng Jianlin , Birchler James A.
Published: January 2022

The non‐essential supernumerary maize (Zea mays) B chromosome (B) has recently been shown to contain active genes and to be capable of impacting gene expression of the A chromosomes. However, the effect of the B chromosome on gene expression is still unclear. In addition, it is unknown whether the accumulation of the B chromosome has a cumulative effect on gene expression. To examine these questions, the global expression of genes, microRNAs (miRNAs), and transposable elements (TEs) of leaf tissue of maize W22 plants with 0–7 copies of the B chromosome was studied. All experimental genotypes with B chromosomes displayed a trend of upregulated gene expression for a subset of A‐located genes compared to the control. Over 3000 A‐located genes are significantly differentially expressed in all experimental genotypes with the B chromosome relative to the control. Modulations of these genes are largely determined by the presence rather than the copy number of the B chromosome. By contrast, the expression of most B‐located genes is positively correlated with B copy number, showing a proportional gene dosage effect. The B chromosome also causes increased expression of A‐located miRNAs. Differentially expressed miRNAs potentially regulate their targets in a cascade of effects. Furthermore, the varied copy number of the B chromosome leads to the differential expression of A‐located and B‐located TEs. The findings provide novel insights into the function and properties of the B chromosome.
Prediction of fetal blood group antigens from maternal plasma using Ion AmpliSeq HD technology
Authors: Orzińska Agnieszka , Kluska Anna , Balabas Aneta , Piatkowska Magdalena , Kulecka Maria , Ostrowski Jerzy , Mikula Michal , Dębska Marzena , Uhrynowska Małgorzata , Guz Katarzyna
Published: January 2022

Background

Fetal blood group (BG) and platelet (HPA) antigens may trigger maternal immunization, causing a fetal disease. Noninvasive prenatal diagnostics (NIPT) predicts fetal genotype, identifying pregnancies with no risk. All current techniques detect fetal antigen alleles with unspecific background and without estimation of fetal fraction, thus new protocols for detection of fetal BG/HPA alleles with ultrahigh sensitivity still need to be tested to improve NIPT.

Aim

To design NIPT of clinically important antigens using Ion AmpliSeq HD technology.

Methods

Plasma DNA from 36 pregnant women (9–33 week of gestation, 24 immunized with anti‐HPA‐1a,‐3b,‐15a, ‐K, or ‐D+C+S), with known BG/HPA genotypes of their neonates/partners, was tested on Ion S5 System using the Ion AmpliSeq HD designer custom gene panel. NGS contained 25 rs‐targets encoding relevant BG/HPA antigens and 10 markers.

Results

Using the NGS protocol, 76 out of 85 differences in fetal/maternal BG/HPA genotypes were determined in concentration above 2% fetal paternally inherited allele chimerism. The level of unspecific reads for BG/HPA alleles was below 0.87%. In 24 immunized women NGS revealed feto‐maternal incompatibility in 11 cases (from 2.44% to 7.41%) and excluded in 10 (<0.05%), three cases had inconclusive results (1.79%, 0.19%, 0.11%). The presence of fetal DNA was confirmed in each case by detecting markers with at least 2% chimerism.

Conclusion

The use of Ion AmpliSeq HD technology improves the prediction of feto‐maternal incompatibility, increasing the sensitivity of BG/HPA NIPT and serving confirmation of the fetal DNA at the same workflow.
Autotransfusion of vaginally shed blood as a novel therapy in obstetric hemorrhage: A case series
Authors: Phillips Jaclyn M. , Tamura Takahiro , Waters Jonathan H. , Larkin Jacob , Sakamoto Sara
Published: January 2022

Objective

To report maternal outcomes in a cohort of women who received autotransfusion of vaginally shed blood and to describe the feasibility of blood collection and cell salvage processing at the time of vaginal hemorrhage.

Study Design and Methods

We conducted a retrospective case series of patients who received autotransfusion of vaginally shed blood at the time of obstetric hemorrhage from January 2014 to August 2020. Maternal data and cell salvage utilization characteristics were abstracted from the electronic medical record.

Results

Sixty‐four cases were identified in which autotransfusion of vaginally shed blood occurred during an obstetric hemorrhage. Median quantitative blood loss was 2175 ml (interquartile range 1500–2250 ml) with 89% of cases having a blood loss greater than 1000 ml. Patients on average received approximately 1.3 units of autologous blood product (384 ml, interquartile range 244–520 ml) and no direct adverse events were observed during transfusion. We observed heterogeneity in autologous blood volume across all values of quantitative blood loss. The need for allogenic blood transfusion was common and occurred in 72% of all cases (N = 46). There were no documented cases of maternal sepsis or severe infectious morbidity.

Conclusion

In 64 cases where autotransfusion of vaginally shed blood occurred, autotransfusion was well tolerated. Heterogeneity in autologous blood volume collection likely represents the lack of standardized protocols for blood collection in the delivery room. Autotransfusion of vaginally shed blood is a feasible and reasonable technique to employ during severe obstetric hemorrhage.
Algorithmic estimation of adrenal volume based on body weight and CT landmarks in dogs
Authors: Swepson Ryan , Hosgood Giselle , Stander Nerissa
Published: January 2022

Computed tomographic (CT) methods for quantifying adrenal size are helpful for identifying and directing management of adrenal disease in dogs. This retrospective, observational study aimed to use CT to determine anatomic landmarks associated with adrenal gland volume in dogs, and subsequently develop an algorithm to estimate adrenal volume for individual subjects. Abdominal CT images from 150 dogs without adrenal disease were assessed. The volume of the left and right adrenals, as well as dimensions from anatomic structures, were measured. Predictive models for adrenal volume were examined with cross validation. A further 29 dogs were used for external validation and model fit. Dogs in the test cohort weighed from 3.1 to 67.5 kg (mean 24.4 kg) with left (VLA) and right (VRA) adrenal volume ranging from 0.23 to 2.84 cm3(mean 1.22 cm3) and 0.14 to 2.97 cm3(mean 1.11 cm3), respectively. A predictive model for each adrenal based on weight (W) and L1 height (HL1) best explained the adrenal volume, with external validation confirming model fit. The final models, adjusted for bias, were:VLA = 0.154 + (0.02 × W) + (0.07 × HL1) (adjR2 = 0.62); andVRA = −0.116 + (0.02 × W) + (0.06 × HL1) (adjR2 = 0.64). In dogs undergoing CT, these algorithms can be used to estimate expected adrenal volume and allow comparison with the actual volume measured on CT, facilitating interpretation of CT findings, and clinical decision‐making.
Submucosal resection via a transanal approach for treatment of epithelial rectal tumors – a multicenter study
Authors: Cantatore Matteo , Jimeno Sandoval Juan Carlos , Das Smita , Sesana Alberto , Charlesworth Tim , Ryan Tony , Morello Emanuela M. , Gobbetti Matteo , Cinti Filippo , Rossanese Matteo
Published: January 2022

Objective

To report complications and long‐term outcomes after submucosal resections of benign and malignant epithelial rectal masses.

Study design

Retrospective multicentric study.

Sample population

Medical records of 93 dogs at 7 referral hospitals.

Methods

Records were reviewed for surgical time, diagnosis, margins, complications, and recurrences. Survival of dogs was evaluated based on tumor types, categorized as benign, carcinoma in situ, and carcinoma. The Kaplan‐Meier survival curve and Cox proportional hazards analysis were used to determine the association of a range of variables with recurrence and survival time.

Results

Duration of follow up was 708 days (range, 25‐4383). Twenty‐seven dogs (29%) developed complications. Recurrence was identified in 20/93 (21%), with 12/20 recurrent masses treated with repeat submucosal resection. Median survival was not reached in any group. The 1‐,2‐, 5‐year survival rates for carcinomas were 95%, 89%, and 73% respectively. However, overall survival was longer for benign tumors than carcinomas (P = .001). Recurrence was more likely when complications (P = .032) or incomplete margins (P = .023) were present. Recurrence was associated with an increased risk of death (P = .046).

Conclusion

Submucosal resection of both benign and malignant rectal masses was associated with a low rate of severe complications and prolonged survival in the 93 dogs described here.

Clinical significance

Submucosal resection is a suitable technique for resection of selected rectal masses.
Effects of a Mindfulness-Based Intervention on Sustained and Selective Attention in Young Top-Level Athletes in a School Training Setting: A Randomized Control Trial Study
Authors: Kittler Christoph , Arnold Manuel , Jekauc Darko
Published: January 2022

Attention is a key success factor in elite sports. Mindfulness training is suspected to be a determinant of attention. The present study was a wait-list controlled investigation of the effects of a mindfulness-based sport psychology program on sustained and selective attention in young elite athletes (n = 137) and the effects of mindfulness training dosage on improving attention scores. In addition, long-term effects were examined. Selective and sustained attention were assessed in a pre–post design using the Frankfurter Aufmerksamkeits-Inventar 2, a go/no-go task. The results of this study indicate that the Berlin Mindfulness-Based Training for Athletes improved both sustained and selective attention in young athletes and that more training in the same amount of time resulted in higher scores in the assessment. The data also indicate that students who continued to practice independently after the intervention had higher scores in the long-term measure.
“Keep the Pace! You’ve Got This!”: The Content and Meaning of Impactful Crowd Encouragement at Mass Running Events
Authors: Gibbs-Nicholls Sophie , McCormick Alister , Coyle Melissa
Published: January 2022

This study identified helpful and unhelpful encouragement at mass participation running events and explored the meaning that runners found in encouragement. First, 10 k and half-marathon postevent surveys differentiated instructional and motivational components of helpful and unhelpful support. Second, an inductive, reflexive thematic analysis of 14 interviews highlighted the reciprocal relationship between the crowd and runners, whereby quality of support was reflected in runners’ emotions and behavior. Participants drew pride in participation and belief from the crowd, and they wanted to “give back” through doing their best. Personal and authentic support was particularly valued. Although support was widely appreciated, at times it created a pressure to “perform.” As a novel intervention based on our combined findings, we recommend that crowds, event organizers, and psyching teams give encouragement “with IMPACT” (Instructional; Motivational; Personalized; Authentic; Confidence-building; Tailored to the distance). Crowds should also demonstrate the “core conditions” of authenticity, empathy, and being nonjudgmental within their encouragement.
Origin of Symphyotrichum anticostense (Asteraceae, Astereae), an endemic, high polyploid species of the Gulf of St. Lawrence region, based on morphological and nrDNA evidence
Authors: Vaezi Jamil , Brouillet Luc
Published: January 2022

Symphyotrichum anticostense (Fernald) G.L.Nesom (Asteraceae, Astereae), a rare endemic of the Gulf of St. Lawrence region, is a high allopolyploid (2n = 10x = 80). It has been hypothesized to be derived from the hybrid between tetraploid (2n = 4x = 32) individuals of Symphyotrichum boreale (Torr. & A.Gray) Á.Löve & D.Löve and the hexaploid (2n = 6x = 48) Symphyotrichum novi-belgii (L.) G.L.Nesom. We investigated this hypothesis using morphological and molecular internal transcribed spacer sequence data, and we tried to determine the potentially geographic origins of the taxon. Univariate morphological analyses show that 67.5% of the S. anticostense characters are parent-like, 43.5% from S. novi-belgii and 13% from S. boreale, the remainder not differing statistically from either parent; 23.5% are intermediate and 9% transgressive. Multivariate analyses show that S. anticostense is intermediate between its putative parents. The molecular results support the morphological data, but due to the insufficient resolution among ribotypes on the tree, a more rapidly evolving marker will be needed to more reliably ascertain the origin of S. anticostense. Besides the hypothesis of genetic drift and allele fixation following long-distance dispersals, at least three independent geographic origins may be suggested for S. anticostense; Anticosti Island, Lake St. John, and Gaspé Peninsula-New Brunswick-Maine.
No-go theorem for inflation in an extended Ricci-inverse gravity model
Authors: Do Tuan Q.
Published: January 2022

In this paper, we propose an extension of the Ricci-inverse gravity, which has been proposed recently as a very novel type of fourth-order gravity, by introducing a second order term of the so-called anticurvature scalar as a correction. The main purpose of this paper is that we would like to see whether the extended Ricci-inverse gravity model admits the homogeneous and isotropic Friedmann–Lemaitre–Robertson–Walker metric as its stable inflationary solution. However, a no-go theorem for inflation in this extended Ricci-inverse gravity is shown to appear through a stability analysis based on the dynamical system method. As a result, this no-go theorem implies that it is impossible to have such stable inflation in this extended Ricci-inverse gravity model.
Adequacy of Effective Born for electroweak effects and TauSpinner algorithms for high energy physics simulated samples
Authors: Richter-Was E. , Was Z.
Published: January 2022

Matching and comparing the measurements of past and future experiments call for consistency checks of electroweak (EW) calculations used for their interpretation. On the other hand, new calculation schemes of the field theory can be beneficial for precision, even if they may obscure comparisons with earlier results. Over the years, concepts ofImproved Born,Effective Born, as well as of effective couplings, in particular of$$\sin ^2\theta _W^{{\textit{eff}}}$$sin2θWeffmixing angle for EW interactions, have evolved. In our discussion, we use four versions of EW library for phenomenology of practically all HEP accelerator experiments over the last 30 years. We rely on the codes published and archived with the Monte Carlo program for$$e^+e^- \rightarrow f {\bar{f}} n(\gamma )$$e+e-→ff¯n(γ)and available for the as well. re-weighs generated events for introduction of EW effects. To this end, is first invoked, and its results are stored in data file and later used. Documentation of upgrade, to version 2.1.0, and that of its new arrangement for semi-automated benchmark plots are provided. In our paper, focus is placed on the numerical results, on the different approximations introduced in Improved Born to obtain Effective Born, which is simpler for applications of strong or QED processes inppor$$e^+e^-$$e+e-colliders. The$$\tau $$τlepton polarization$$P_{\tau }$$Pτ, forward–backward asymmetry$$A_{{\textit{FB}}}$$AFBand parton-level total cross section$$\sigma ^{{\textit{tot}}}$$σtotare used to monitor the size of EW effects and effective$$\sin ^2\theta _W^{{\textit{eff}}}$$sin2θWeffpicture limitations for precision physics. Collected results include: (i)Effective Bornapproximations and$$\sin ^2\theta _W^{{\textit{eff}}}$$sin2θWeff, (ii) differences between versions of EW libraries and (iii) parametric uncertainties due to, for example,$$m_t$$mtor$$\Delta \alpha _h^{(5)}(s)$$Δαh(5)(s). These results can be considered as benchmarks and also allow to evaluate the adequacy ofEffective Bornwith respect toImproved Born. Definitions are addressed too.
Supported Diagnosis of Attention Deficit and Hyperactivity Disorder from EEG Based on Interpretable Kernels for Hidden Markov Models
Authors: Maya-Piedrahita M. C. , Herrera-Gomez P. M. , Berrío-Mesa L. , Cárdenas-Peña D. A. , Orozco-Gutierrez A. A.
Published: January 2022

As a neurodevelopmental pathology, Attention Deficit Hyperactivity Disorder (ADHD) mainly arises during childhood. Persistent patterns of generalized inattention, impulsivity, or hyperactivity characterize ADHD that may persist into adulthood. The conventional diagnosis relies on clinical observational processes yielding high rates of overdiagnosis due to varying interpretations among specialists or missing information. Although several studies have designed objective behavioral features to overcome such an issue, they lack significance. Despite electroencephalography (EEG) analyses extracting alternative biomarkers using signal processing techniques, the nonlinearity and nonstationarity of EEG signals restrain performance and generalization of hand-crafted features. This work proposes a methodology to support ADHD diagnosis by characterizing EEG signals from hidden Markov models (HMM), classifying subjects based on similarity measures for probability functions, and spatially interpreting the results using graphic embeddings of stochastic dynamic models. The methodology learns a single HMM for EEG signal from each patient, so favoring the inter-subject variability. Then, the Probability Product Kernel, specifically developed for assessing the similarity between HMMs, fed a support vector machine that classifies subjects according to their stochastic dynamics. Lastly, the kernel variant of Principal Component Analysis provided a means to visualize the EEG transitions in a two-dimensional space, evidencing dynamic differences between ADHD and Healthy Control children. From the electrophysiological perspective, we recorded EEG under the Stop Signal Task modified with reward levels, which considers cognitive features of interest as insufficient motivational circuits recruitment. The methodology compares the supported diagnosis in two EEG channel setups (whole channel set and channels of interest in frontocentral area) and four frequency bands (Theta, Alpha, Beta rhythms, and a wideband). Results evidence an accuracy rate of 97.0% in the Beta band and in the channels where previous works found error-related negativity events. Such accuracy rate strongly supports the dual pathway hypothesis and motivational deficit concerning the pathophysiology of ADHD. It also demonstrates the utility of joining inhibitory and motivational paradigms with dynamic EEG analysis into a noninvasive and affordable diagnostic tool for ADHD patients.
Permutation entropy and statistical analysis of the historical evolution of the Mexican stock market index
Authors: Rodríguez-Achach M. , Suárez-Solís A. , Montoya A. R. Hernández , Escalante-Martínez J. E. , Calderón-Ramón C.
Published: January 2022

The objective of this work is to analyze the Indice de Precios y Cotizaciones (IPC), which is the Mexican stock market index, by using several statistical tools in order to study the tendencies that can shed light on the evolution of the IPC towards a more efficient market. The methodology used is to apply the statistical tools to the Mexican index and compare the results with a mature and well-known market index such as the Dow Jones Industrial Average (DJIA). We employ an autocorrelation analysis, and the volatility of the indexes, applied to the daily returns of the closing price on a moving time window during the studied period (1980–2018). Additionally, we perform an order three permutation entropy analysis, which can quantify the disorder present in the time series. Our results show that there is evidence that the IPC has become more mature since its creation and that it can be considered an efficient market since around year 2000. The behavior of the several techniques used shows a similar behavior to the DJIA which is not observed before that year. There are some limitations mainly because there is no high frequency data that would permit a more detailed analysis, specifically in the periods before and after a crisis is located. Our conclusion is that since around the year 2000, the Mexican stock index displays the typical behavior of other mature markets and can be considered as one.
Design and Performance Estimation of Mixed-Robotic Fulfillment System
Authors: Chi Cheng , Wu Shasha , Wang Luyao , Wu Yaohua
Published: January 2022

E-commerce retailers face the challenge to assemble a large number of time-critical picking orders. Common parts-to-picker autonomous intelligent warehouses such as automated vehicle storage and retrieval system and robotic mobile fulfillment system are often a little ill-suited for these prerequisites. A mixed-robotic fulfillment system is a hybrid robot picking system based on multi-device collaboration. It is a fusion innovation of traditional automated vehicle storage and retrieval system and robotic mobile fulfillment system. This paper comprehensively considers the characteristics of the system and customer demand, through the construction of a queuing network model to evaluate the performance of the system. A series of problems such as order service time, throughput capacity, and vehicle quantity configuration are analyzed experimentally. The validity of the model is verified by a simulation model.
The irreducible mass of Christodoulou–Ruffini–Hawking mass formula
Authors: Ha Yuan K.
Published: January 2022

We reveal three new discoveries in black hole physics previously unexplored in the Hawking era. These results are based on the remarkable 1971 discovery of the irreducible mass of the black hole by Christodoulou and Ruffini, and subsequently confirmed by Hawking.

(1) The Horizon Mass Theorem states that the mass at the event horizon of any black hole — neutral, charged, or rotating — is always twice its irreducible mass observed at infinity. (2) The External Energy Theorem asserts that the rotational energy of a Kerr black hole exists completely outside the horizon. This is due to the fact that the irreducible mass does not contain rotational energy. (3) The Moment of Inertia Theorem shows that every black hole has a moment of inertia. When the rotation stops, the irreducible mass of a Kerr black hole becomes the moment of inertia of a Schwarzschild black hole. This is recognized as the rotational equivalent of the rest mass of a moving body in relativity.

Thus after 50 years, the irreducible mass has gained a new and profound significance. No longer is it a limiting value in rotation, it determines black hole dynamics and structure. What is believed to be a black hole is a mechanical body with an extended structure. Astrophysical black holes are likely to be massive compact objects from which light cannot escape.
Traversable wormhole in logarithmic f(R) gravity by various shape and redshift functions
Authors: Sadeghi Jafar , Shokri Mehdi , Gashti Saeed Noori , Pourhassan Behnam , Rudra Prabir
Published: January 2022

In this paper, we study the traversable wormhole solutions for a logarithmic corrected [Formula: see text] model by considering two different statements of shape [Formula: see text] and redshift [Formula: see text] functions. We calculate the parameters of the model including energy density [Formula: see text], tangential pressure [Formula: see text] and radial pressure [Formula: see text] for the corresponding forms of the functions. Then, we investigate different energy conditions such as null energy condition, weak energy condition, dominant energy condition and strong energy condition for our considered cases. Finally, we explain the satisfactory conditions of energy of the models by related plots.
IMPROVEMENT IN HARDNESS AND ELECTROCHEMICAL CORROSION RESISTANCE OF AL-7075 ALLOY BY ARTIFICIAL AGEING
Authors: MUKHTAR RABIA , AFZAL NAVEED , RAFIQUE MOHSIN , FAROOQ AMEEQ
Published: January 2022

Artificial ageing of Al-7075 alloy was performed in a muffle furnace at different temperatures ranging from 120∘C to 190∘C for 3[Formula: see text]h. The formation of MgZn2 precipitates in the aged alloy was confirmed through the XRD data. The lattice parameter and crystallite size of aluminum were increased with the increase of the ageing temperature. The scanning electron microscopy results validated the precipitates of different shapes and sizes in the aged samples. The number density of the precipitates was found to be maximum at 170∘C. The Vickers hardness of Al-7075 alloy was increased from 125[Formula: see text]HV to 172[Formula: see text]HV with an increase of the ageing temperature from 120∘C to 170∘C and then decreased at 190∘C. The electrochemical tests of the un-aged and aged samples (in 3.5[Formula: see text]wt.% NaCl solution) showed a decrease in the corrosion rate (0.003[Formula: see text]mm/y) and an increase in the corrosion potential ([Formula: see text]137[Formula: see text]mV) of the alloy upon ageing up to 150∘C, indicating improvement in its corrosion resistance.
Raif-Semantics: A Robust Automated Interlinking Framework for Semantic Web Using MapReduce and Multi-node Data Processing
Authors: Aladakatti Shweta S. , Senthil Kumar S.
Published: January 2022

The era of the web has evolved and the industry strives to work better every day, the constant need for data to be accessible at a random moment is expanding, and with this expansion, the need to create a meaningful query technique in the web is a major concerns. To transmit meaningful data or rich semantics, machines/ projects need to have the ability to reach the correct information and make adequate connections, this problem is addressed after the emergence of Web 3.0, the semantic web is developing and being collected an immense. Information to prepare, this passes the giant data management test, to provide an ideal result at any time needed. Accordingly, in this article, we present an ideal system for managing huge information using MapReduce structures that internally help an engine bring information using the strength of fair preparation using smaller map occupations and connection disclosure measures. Calculations for similarity can be challenging, this work performs five similarity detection algorithms and determines the time it takes to address the patterns that has to be a better choice in the calculation decision. The proposed framework is created using the most recent and widespread information design, that is, the JSON design, the HIVE query language to obtain and process the information planned according to the customer’s needs and calculations for the disclosure of the interface. Finally, the results on a web page is made available that helps a user stack json information and make connections somewhere in the range of dataset 1 and dataset 2. The results are examined in 2 different sets, the results show that the proposed approach helps to interconnect significantly faster; Regardless of how large the information is, the time it takes is not radically extended. The results demonstrate the interlinking of the dataset 1 and dataset 2 is most notable using LD and JW, the time required is ideal in both calculations, this paper has mechanized the method involved with interconnecting via a web page, where customers can merge two sets of data that should be associated and used.
Optimal Control of Hybrid Energy Storage System of New Energy Power Generation System Based on Improved Particle Swarm Algorithm
Authors: Zhang Shangzhou
Published: January 2022

In order to ensure the stability and reliability of power supply and realize day and night power generation, wind and solar complementary power generation systems are built in areas with abundant solar and wind energy resources. However, the system investment cost is too high. Because of this, there are wind, light intermittent, and non-intermittent power generation systems. For issues such as stability, an energy storage system needs to be configured to stabilize power fluctuations. This paper aims to study the optimization control of hybrid energy storage system of new energy power generation system based on improved particle swarm algorithm. In this paper, the application of particle swarm algorithm to power system reactive power optimization has been researched in two aspects. Through optimization methods, reasonable adjustment of control variables, full use of equipment resources of the power grid, to improve voltage quality and reduce system operation network to ensure the stability of the voltage system. In addition, this paper selects the IEEE30 node test system and simulation data analysis, takes the hybrid energy storage system as the optimization object, and optimizes the reactive power of the newly improved particle swarm algorithm. The experiments in this paper show that the improved algorithm has a good effect in reactive power optimization, increasing the performance of the hybrid energy storage system by 27.02%. MPSO algorithm is also better than basic PSO algorithm. It can be seen from the figure that in the PSO algorithm, the algorithm basically tends to be stable after more than 40 iterations, and finally the algorithm converges to 0.089.
Accessibility Analysis Model of College Students’ Employment Education Resources Based on Mobile Search Algorithm
Authors: Zang Xiaoming
Published: January 2022

This paper introduces the mobile search algorithm into the research of employment accessibility measurement. It not only proves the effectiveness of the method, but also analyzes the characteristics and formation mechanism of College Students’ employment accessibility in the research case, and puts forward the corresponding control strategies. The results show that the employment accessibility of college students is not evenly distributed, but presents an obvious center periphery pattern. This paper puts forward the planning means of the accessibility of College Students’ employment education resources to promote the balanced distribution of employment and population, the reform of the employment structure and the reasonable improvement of policy.
Teaching Quality Analysis of College Ideological and Political Education Based on Deep Learning
Authors: Feng Liting , Dong Yulong
Published: January 2022

A social activity that uses certain ideas, concepts, political views, and moral values in a society or social group enriches students’ ideology and allows learners to form ideological and moral qualities that correspond to their social and political establishment. The continuous improvement of their complete quality and technical skills is at the heart of social and economic growth. In ideological and political education, risk factors are widely influenced, including the impact of educational purposes and education providers. In this paper, Deep Learning-Based Innovation Path Optimization Methodology (DL-IPOM) has been proposed to strengthen data awareness, improve the way of thinking in ideological and political education. The political instructional collaborative analysis is integrated with DL-IPOM to boost Ideological and political education excellence. The simulation analysis is conducted at (98.22%). The consistency of the proposed framework is demonstrated by efficiency, high accuracy (98.34%), overshoot index rate (94.2%), political thinking rate (93.6%), knowledge retention rate (80.2%), reliability rate (97.6%), performance (94.37%) when compared to other methods.
Finite generation of the cohomology rings for the extension algebras of monomial algebras
Authors: Shi Hongbo
Published: January 2022

We describe the cohomology ring of a monomial algebra in the language of dimension tree or minimal resolution graph and in this context we study the finite generation of the cohomology rings of the extension algebras, showing among others that the cohomology ring [Formula: see text] is finitely generated [Formula: see text] is [Formula: see text] is, where [Formula: see text] is the dual extension of a monomial algebra A and [Formula: see text] is the opposite algebra of A.
Theoretical investigation of five-coordinated manganese(III) oxophlorin with different axial ligands at various spin states using different DFT methods
Authors: Mirmarghabi Pezhman , Bahrami Homayoon
Published: January 2022

The Mn(III)-oxophlorin complexes with imidazole, pyridine and t-butylcyanide as axial ligands have been studied using B3LYP, Bv86p, and M06-2X methods. All of the possible optimized geometries are specified, while the M06-2X is employed. Results obtained show that the isomers of Mn(III)-oxophlorin with imidazole or pyridine are the most stable at quintet state, compared to singlet and triplet spin states. Besides, there are two and four [Formula: see text]-electrons on manganese in each of these complexes at triplet and quintet states, respectively. Also, Mn(III)-oxophlorin with t-butylcyanide as axial ligand is only stable at singlet state. Non-specific solvent effects show that dispersion and London forces have the basic role in stability of complexes in a solvent. Note that latter interactions can occur in medium with dielectric constant ([Formula: see text]) of [Formula: see text]8, such as [Formula: see text] for position of oxophlorin in heme oxygenase enzyme. NBO analysis show that there is no degeneracy between d orbitals of Mn in the five-coordinated Mn(III)-oxophlorin at singlet and triplet spin states, but two d orbitals of manganese are degenerated in latter complexes at quintet state. Such degeneracy of d orbitals is observed in a complex with square pyramid structure. Then five-coordinated Mn(III)-oxophlorin with imidazole or pyridine is the most stable at quintet spin state, because of its geometry corresponding to square pyramid configuration of atoms. Also, nonbounding interaction between Mn and the ring of oxophlorin or Mn and ligand are more effective in Mn(III)-oxophlorin with imidazole as axial ligand, compared to pyridine and t-butylcyanide.
MACROPRUDENTIAL SUPERVISION AND AGENTS’ INFORMATION: WHAT STRESS TESTS REALLY TELL THE MARKETS
Authors: PACICCO FAUSTO , VENA LUIGI , VENEGONI ANDREA
Published: January 2022

Central bank’s macroprudential supervisory activities have to fulfill three distinct tasks: (i) assessing the banking system’s vulnerability to exogenous adverse turbulence, (ii) evaluating the risk of systemic crisis originating from idiosyncratic shocks, and (iii) measuring financial market’s sensitivity to policy stimuli. Given that macroprudential stress tests are the centerpiece of this policy approach, it is important to establish whether they are up to the task. We study how the 2011–2018 European Banking Authority stress tests affected market risk perception and show that they provided agents with valuable information on the policy stances and the vulnerabilities of the banking system, carrying out the above tasks successfully, especially the second and third tasks.
Testing and Optimizing the Drude Polarizable Force Field for Blocked Amino Acids Based on High-Level Quantum-Mechanical Energy Surfaces
Authors: Chen Jinfeng , König Gerhard
Published: January 2022

The correct reproduction of conformational substates of amino acids was tested for the CHARMM Drude polarizable force field. This was achieved by evaluating the reorganization energies for all low lying energy minima occurring in all 15 neutral blocked amino acids on a quantum-mechanical (QM) energy surface at the MP2/cc-pVDZ level. The results indicate that the bonded parameters of the N-acetyl (ACE) and N-Methylamide (CT3) blocking groups lead to significant discrepancies. A reparametrization of five bond angles significantly improved the agreement with the QM energy surface. The corrected Drude force field exhibits almost the same average reorganization energies relative to the MP2 energy surface as the AM1 and PM3 semi-empirical methods.
TRACE: Travel Reinforcement Recommendation Based on Location-Aware Context Extraction
Authors: Fu Zhe , Yu Li , Niu Xi
Published: January 2022

As the popularity of online travel platforms increases, users tend to make ad-hoc decisions on places to visit rather than preparing the detailed tour plans in advance. Under the situation of timeliness and uncertainty of users’ demand, how to integrate real-time context into dynamic and personalized recommendations have become a key issue in travel recommender system. In this article, by integrating the users’ historical preferences and real-time context, a location-aware recommender system called TRACE (
T
ravel
R
einforcement Recommendations Based on Location-
A
ware
C
ontext
E
xtraction) is proposed. It captures users’ features based on location-aware context learning model, and makes dynamic recommendations based on reinforcement learning. Specifically, this research: (1) designs a travel reinforcing recommender system based on an Actor-Critic framework, which can dynamically track the user preference shifts and optimize the recommender system performance; (2) proposes a location-aware context learning model, which aims at extracting user context from real-time location and then calculating the impacts of nearby attractions on users’ preferences; and (3) conducts both offline and online experiments. Our proposed model achieves the best performance in both of the two experiments, which demonstrates that tracking the users’ preference shifts based on real-time location is valuable for improving the recommendation results.
Mixed Information Flow for Cross-Domain Sequential Recommendations
Authors: Ma Muyang , Ren Pengjie , Chen Zhumin , Ren Zhaochun , Zhao Lifan , Liu Peiyu , Ma Jun , de Rijke Maarten
Published: January 2022

Cross-domain sequential recommendation is the task of predict the next item that the user is most likely to interact with based on past sequential behavior from multiple domains. One of the key challenges in
cross-domain sequential recommendation
is to grasp and transfer the flow of information from multiple domains so as to promote recommendations in all domains. Previous studies have investigated the flow of
behavioral
information by exploring the connection between items from different domains. The flow of knowledge (i.e., the connection between knowledge from different domains) has so far been neglected. In this article, we propose a
mixed information flow network
for
cross-domain sequential recommendation
to consider both the flow of behavioral information and the flow of knowledge by incorporating a
behavior transfer unit
and a
knowledge transfer unit
. The proposed
mixed information flow network
is able to decide when cross-domain information should be used and, if so, which cross-domain information should be used to enrich the sequence representation according to users’ current preferences. Extensive experiments conducted on four e-commerce datasets demonstrate that the proposed
mixed information flow network
is able to improve recommendation performance in different domains by modeling mixed information flow. In this article, we focus on the application of
mixed information flow network
s to a scenario with two domains, but the method can easily be extended to multiple domains.
Causal Feature Selection with Missing Data
Authors: Yu Kui , Yang Yajing , Ding Wei
Published: January 2022

Causal feature selection aims at learning the Markov blanket (MB) of a class variable for feature selection. The MB of a class variable implies the local causal structure among the class variable and its MB and all other features are probabilistically independent of the class variable conditioning on its MB, this enables causal feature selection to identify potential causal features for feature selection for building robust and physically meaningful prediction models. Missing data, ubiquitous in many real-world applications, remain an open research problem in causal feature selection due to its technical complexity. In this article, we discuss a novel multiple imputation MB (MimMB) framework for causal feature selection with missing data. MimMB integrates Data Imputation with MB Learning in a unified framework to enable the two key components to engage with each other. MB Learning enables Data Imputation in a potentially causal feature space for achieving accurate data imputation, while accurate Data Imputation helps MB Learning identify a reliable MB of the class variable in turn. Then, we further design an enhanced kNN estimator for imputing missing values and instantiate the MimMB. In our comprehensively experimental evaluation, our new approach can effectively learn the MB of a given variable in a Bayesian network and outperforms other rival algorithms using synthetic and real-world datasets.
Forecasting the Market with Machine Learning Algorithms: An Application of NMC-BERT-LSTM-DQN-X Algorithm in Quantitative Trading
Authors: Liu Chang , Yan Jie , Guo Feiyue , Guo Min
Published: January 2022

Although machine learning (ML) algorithms have been widely used in forecasting the trend of stock market indices, they failed to consider the following crucial aspects for market forecasting: (1) that investors’ emotions and attitudes toward future market trends have material impacts on market trend forecasting (2) the length of past market data should be dynamically adjusted according to the market status and (3) the transition of market statutes should be considered when forecasting market trends. In this study, we proposed an innovative ML method to forecast China's stock market trends by addressing the three issues above. Specifically, sentimental factors (see Appendix [1] for full trans) were first collected to measure investors’ emotions and attitudes. Then, a non-stationary Markov chain (NMC) model was used to capture dynamic transitions of market statutes. We choose the state-of-the-art (SOTA) method, namely, Bidirectional Encoder Representations from Transformers (
BERT
), to predict the state of the market at time
t
, and a long short-term memory (
LSTM
) model was used to estimate the varying length of past market data in market trend prediction, where the input of
LSTM
(the state of the market at time
t
) was the output of
BERT
and probabilities for opening and closing of the gates in the
LSTM
model were based on outputs of the
NMC
model. Finally, the optimum parameters of the proposed algorithm were calculated using a reinforced learning-based deep Q-Network. Compared to existing forecasting methods, the proposed algorithm achieves better results with a forecasting accuracy of 61.77%, annualized return of 29.25%, and maximum losses of −8.29%. Furthermore, the proposed model achieved the lowest forecasting error: mean square error (0.095), root mean square error (0.0739), mean absolute error (0.104), and mean absolute percent error (15.1%). As a result, the proposed market forecasting model can help investors obtain more accurate market forecast information.
RHPTree—Risk Hierarchical Pattern Tree for Scalable Long Pattern Mining
Authors: Liu Danlu , Li Yu , Baskett William , Lin Dan , Shyu Chi-Ren
Published: January 2022

Risk patterns are crucial in biomedical research and have served as an important factor in precision health and disease prevention. Despite recent development in parallel and high-performance computing, existing risk pattern mining methods still struggle with problems caused by large-scale datasets, such as redundant candidate generation, inability to discover long significant patterns, and prolonged post pattern filtering. In this article, we propose a novel dynamic tree structure, Risk Hierarchical Pattern Tree (RHPTree), and a top-down search method, RHPSearch, which are capable of efficiently analyzing a large volume of data and overcoming the limitations of previous works. The dynamic nature of the RHPTree avoids costly tree reconstruction for the iterative search process and dataset updates. We also introduce two specialized search methods, the extended target search (RHPSearch-TS) and the parallel search approach (RHPSearch-SD), to further speed up the retrieval of certain items of interest. Experiments on both UCI machine learning datasets and sampled datasets of the Simons Foundation Autism Research Initiative (SFARI)—Simon’s Simplex Collection (SSC) datasets demonstrate that our method is not only faster but also more effective in identifying comprehensive long risk patterns than existing works. Moreover, the proposed new tree structure is generic and applicable to other pattern mining problems.
A Trajectory Evaluator by Sub-tracks for Detecting VOT-based Anomalous Trajectory
Authors: Gao Fei , Li Jiada , Ge Yisu , Shao Jianwen , Lu Shufang , Weng Libo
Published: January 2022

With the popularization of visual object tracking (VOT), more and more trajectory data are obtained and have begun to gain widespread attention in the fields of mobile robots, intelligent video surveillance, and the like. How to clean the anomalous trajectories hidden in the massive data has become one of the research hotspots. Anomalous trajectories should be detected and cleaned before the trajectory data can be effectively used. In this article, a Trajectory Evaluator by Sub-tracks (TES) for detecting VOT-based anomalous trajectory is proposed. Feature of Anomalousness is defined and described as the Eigenvector of classifier to filter Track Lets anomalous trajectory and IDentity Switch anomalous trajectory, which includes Feature of Anomalous Pose and Feature of Anomalous Sub-tracks (FAS). In the comparative experiments, TES achieves better results on different scenes than state-of-the-art methods. Moreover, FAS makes better performance than point flow, least square method fitting and Chebyshev Polynomial Fitting. It is verified that TES is more accurate and effective and is conducive to the sub-tracks trajectory data analysis.
A Self-Supervised Representation Learning of Sentence Structure for Authorship Attribution
Authors: Jafariakinabad Fereshteh , Hua Kien A.
Published: January 2022

The syntactic structure of sentences in a document substantially informs about its authorial writing style. Sentence representation learning has been widely explored in recent years and it has been shown that it improves the generalization of different downstream tasks across many domains. Even though utilizing probing methods in several studies suggests that these learned contextual representations implicitly encode some amount of syntax, explicit syntactic information further improves the performance of deep neural models in the domain of authorship attribution. These observations have motivated us to investigate the explicit representation learning of syntactic structure of sentences. In this article, we propose a self-supervised framework for learning structural representations of sentences. The self-supervised network contains two components; a lexical sub-network and a syntactic sub-network which take the sequence of words and their corresponding structural labels as the input, respectively. Due to the
n
-to-1 mapping of words to their structural labels, each word will be embedded into a vector representation which mainly carries structural information. We evaluate the learned structural representations of sentences using different probing tasks, and subsequently utilize them in the authorship attribution task. Our experimental results indicate that the structural embeddings significantly improve the classification tasks when concatenated with the existing pre-trained word embeddings.