bluuebunny/crossref_metadata_2025 huggingface dataset:
Chronostratigraphic correlation of the Upper Silurian Salina Group for the Michigan and Appalachian Basins through coupled (δ13Ccarb) chemostratigraphy and subsurface geophysical analyses
Authors: Oborny Stephan C. , Cramer Bradley D. , Brett Carlton E. , Bancroft Alyssa M.
Published: September 2022

The Upper Silurian Salina Group of eastern North America is well known for its thick evaporite successions and hydrocarbon resources. These strata have been assigned to numerous chronostratigraphic schemes within Ohio and Michigan and are currently identified by varying subsurface and outcrop nomenclatural schemes. These chronostratigraphic challenges have persisted for over 50 yr and dramatically inhibit the correlation of events recorded in the Silurian section of eastern North America with the global record of Silurian biogeochemical events. To help resolve the chronostratigraphic correlation of these units, we provide new high-resolution δ13Ccarb chemostratigraphic analyses of a core located in central Ohio for strata assigned to the Greenfield and Tymochtee Formations and integrate existing biostratigraphic, chemostratigraphic, and subsurface geophysical data in western, southern, and eastern Ohio. The new data presented here, integrated for the first time with basinwide subsurface geophysical data, demonstrate a mid-late Homerian Stage global sea-level lowstand, suggest a short interval of tectonic stability within the study area at the beginning of “Salina B–G” deposition, during which accommodation was occupied by the Greenfield Formation and laterally equivalent strata, and provide chronostratigraphic constraints for basin flexure and potential forebulge migration associated with renewed tectonic activity. The new chronostratigraphic correlation of these strata provides a broader picture of Silurian environmental change across the eastern half of the Laurentian paleocontinent.
Validity and reliability of the Spanish version of the ACE tool for assessing competencies in evidence-based medicine in medical students
Authors: Sgarbossa Nadia , Marino Sofia , Aletta Lucas , Vázquez Peña Fernando Ramón , Franco Juan Victor Ariel
Published: September 2022

Objective

To translate and culturally adapt the tool ‘Assessing Competency in evidence-based medicine (EBM)’ (ACE) to Spanish and to implement it in a cohort of medical students for the evaluation of the instrument’s psychometric properties.

Design

Bilingual translators produced a translation and backtranslation of the original instrument, with interim consensus in each stage with oversight and input by a group of experts. We then performed cognitive interviews to adapt the wording of the tool culturally. Finally, we implemented the final version in a cohort of medical students on a virtual general practice course with EBM modules.

Setting

Medical School in Buenos Aires, Argentina. Due to restrictions to in-person teaching during the COVID-19 pandemic, we conducted this study in the context of virtual learning.

Participants

We included 125 fourth and fifth-year medical students.

Main outcome measures

We measured internal consistency with the Kuder-Richardson coefficient (>0.6 as a threshold for reliability) and construct validity through a Pearson’s correlation between the examinations carried out with the translated instrument and the results of the regular examinations of EBM in the same students (expected values of at least 0.3 to 0.7). We also compared the total score of the instrument of fifth-year students to fourth-year students.

Results

As for measurements for internal consistency, the coefficient Kuder-Richardson resulted in a value of 0.268, below our prespecified threshold. For construct validity, the Pearson correlation between the sum of the items and regular examinations was 0.139, also below our prespecified threshold. However, fifth-year students averaged 0.94 points more than fourth-year students (95% CI 0.24 more to 1.65 more).

Conclusion

The translated and cross-culturally adapted version of the ACE tool into Spanish had low reliability and validity in an MBE course taught and evaluated in a virtual environment.

Trial registration

Not applicable.
‘It all stems from relationship’: effectiveness of a crowdsourcing contest to elicit community ideas on how to build trust between healthcare providers and community about COVID-19
Authors: Mathews Allison Christine , Wenhold Miranda , Caban-Holt Allison , Starks Takiyah , Richmond Alan , Vines Anissa , Byrd Goldie Smith
Published: September 2022

Objective

The COVID-19 pandemic requires a nimble approach to building trust between healthcare providers and community. Crowdsourcing is one community-engaged approach that may be effective at engaging marginalised communities to identify ways to build trust. This early-stage innovation report assesses the effectiveness of using a crowdsourcing contest to elicit community ideas on how to build trust between healthcare providers and community about COVID-19 and promote community engagement about vaccines.

Methods

This mixed-methods study conducted a qualitative assessment of crowdsourcing contest entries and evaluated online community engagement via social media analytics (reach, video views, engagement). Themes from contest entries informed the development of community leader video interviews. Qualitative data from contest entries were digitally transcribed and analysed using axial coding.

Results

Contest participants (n=19) were European Americans (n=10), African Americans (n=8, 87%) and American Indians (n=1), the majority of whom identified as women (n=18) and were 18–80 years old. Contest entry recommendations included: (1) partner with community stakeholders and providers, (2) improve access to credible information from trusted sources, (3) use multiple channels of communication, and (4) use clear and plain language.

Conclusion

Crowdsourcing contests coupled with public education are beneficial community engagement tools to identify new ways to promote trust between medical professionals and diverse community members about COVID-19. Crowdsourcing contests also provide opportunity for partnership and critical dialogue between healthcare professionals and community leaders.
Lymphoid aggregates in bone marrow: a diagnostic pitfall
Authors: Maccio Umberto , Rets Anton V
Published: September 2022

Lymphoid aggregates in bone marrow specimens are a relatively frequent finding that may pose a diagnostic challenge for a pathologist. The distinction between reactive and neoplastic aggregates has significant clinical relevance. Although many testing modalities such as immunohistochemistry, flow cytometry and molecular studies are currently available in clinical laboratories, the appropriate utilisation of these modalities and the awareness of their potential pitfalls are important. When a neoplastic process is ruled out, the significance of benign lymphoid aggregates in bone marrow is often unclear, as they may be associated with a broad spectrum of conditions including infections, autoimmune disorders, medications, or may even be idiopathic.

This review focuses on evidence-based criteria that can aid in making the distinction between benign and malignant lymphoid aggregates and discusses the advantages, disadvantages and limits of ancillary tests used for this purpose. Finally, the most common aetiologies of benign lymphoid aggregates and their associations with specific diseases are discussed.
Recurring germline mosaicism in a family due to reversion of an inherited derivative chromosome 8 from an 8;21 translocation with interstitial telomeric sequences
Authors: Bi Weimin , Yuan Bo , Liu Pengfei , Murry Jaclyn B , Qin Xiang , Xia Fan , Quach Thao , Cooper Lance M , Wiszniewska Joanna , Hixson Patricia , Peacock Sandra , Tonk Vijay S , Huff Robert W , Ortega Veronica , Lupski James R , Scherer Steven E , Littlejohn Rebecca Okashah , Velagaleti Gopalrao V N , Roeder Elizabeth R , Cheung Sau Wai
Published: September 2022

Background

Mosaicism for chromosomal structural abnormalities, other than marker or ring chromosomes, is rarely inherited.

Methods

We performed cytogenetics studies and breakpoint analyses on a family with transmission of mosaicism for a derivative chromosome 8 (der(8)), resulting from an unbalanced translocation between the long arms of chromosomes 8 and 21 over three generations.

Results

The proband and his maternal half-sister had mosaicism for a der(8) cell line leading to trisomy of the distal 21q, and both had Down syndrome phenotypic features. Mosaicism for a cell line with the der(8) and a normal cell line was also detected in a maternal half-cousin. The der(8) was inherited from the maternal grandmother who had four abnormal cell lines containing the der(8), in addition to a normal cell line. One maternal half-aunt had the der(8) and an isodicentric chromosome 21 (idic(21)). Sequencing studies revealed microhomologies at the junctures of the der(8) and idic(21) in the half-aunt, suggesting a replicative mechanism in the rearrangement formation. Furthermore, interstitial telomeric sequences (ITS) were identified in the juncture between chromosomes 8 and 21 in the der(8).

Conclusion

Mosaicism in the proband, his half-sister and half-cousin resulting from loss of chromosome 21 material from the der(8) appears to be a postzygotic event due to the genomic instability of ITS and associated with selective growth advantage of normal cells. The reversion of the inherited der(8) to a normal chromosome 8 in this family resembles revertant mosaicism of point mutations. We propose that ITS could mediate recurring revertant mosaicism for some constitutional chromosomal structural abnormalities.
Prevalence of Setaria equine microfilaraemia in horses in Hungary
Authors: Hornok S. , Genchi C. , Bazzocchi C. , Fok É , Farkas R
Published: September 2022

Peripheral blood samples were collected randomly from 195 horses in various parts of Hungary, and the presence of microfilariae was evaluated by the Knott technique. On the basis of morphological identification 18 of the horses (9·2 per cent) were infected with Setaria equina, and the infection was confirmed in 10 animals by pcr and sequencing. The level of microfilaraemia was between 1 and 1138 larvae in 2 ml of blood. There was no correlation between the time of sampling or the sex of the animals (stallions versus mares) and the prevalence of infection, but the prevalence decreased with age. There was a significant association between the prevalence of microfilaraemia and the presence of still waters; positive samples were collected either in the region of Lake Balaton, the largest lake in the country, or at places with nearby ponds.
Unusually thick freshwater ice and its impacts on aquatic resources in the National Petroleum Reserve in Alaska (NPR-A) during the winter of 2020–2021
Authors: Arp Christopher D. , Engram Melanie , Bondurant Allen C. , Drew Katie A.
Published: September 2022

Despite a long-term thinning trend in freshwater ice in northern Alaska, cold low-snow cover winters can still emerge to grow thick ice. In 2021, we observed abnormally thick ice by winter's end on lakes and rivers throughout the Fish Creek Watershed in the National Petroleum Reserve in Alaska. This recent and anomalous winter presented an opportunity to assess how such conditions, more typical of many decades previous, affected aquatic habitat and winter water supply. Observed maximum ice thickness in 2021 of 1.9 m closely matched low-snow ice growth simulations, whereas previous records averaged 1.5 m and more closely matched high-snow ice growth simulations. The resulting extent of bedfast lake ice from late winter synthetic aperture radar (SAR) analysis in 2021 was the highest on record since 1992. This SAR analysis suggests a 33% reduction in liquid water below ice by lake surface area compared with the recent thin-ice winter of 2018 (1.2 m). Together, these results help place the cold, low-snow winter of 2020–2021 in context of the long-term trend toward warmer, snowier winters that appear to becoming more common in Arctic Alaska.
Correlation analysis of four KRTAP gene polymorphisms and cashmere fiber diameters in two cashmere goat breeds
Authors: Wua Cuiling , Qin Chongkai , Fu Xuefeng , Zhao Bingru , Wu Yujiang , He Junmin , Mao Jingyi , Liu Jing , Huang Xixia , Tian Kechuan
Published: September 2022

Fiber diameter, a quantitative trait, is controlled by minor effect polygenes. Keratin-associated proteins (KRTAPs) are an important part of hair, and their rich polymorphisms facilitate the mining of cashmere trait molecular markers. In this study, Jiangnan and Tibetan cashmere goats were taken as the research object; multiplex PCR and exome sequencing technology were used to identify the exon regional polymorphisms of cashmere goats KRTAP15-1, KRTAP13.1, KRTAP27-1, and KRTAP24-1. The effects of mutation sites on the fiber diameter of cashmere were analyzed by least square method. The results showed that there were 28 mutation sites in the four KRTAP genes in Jiangnan cashmere goats and Tibetan cashmere goat populations. Among them, the KRTAP13.1, KRTAP27-1, and KRTAP24-1 gene polymorphisms were found to be significantly related to the fiber diameter of Jiangnan cashmere goats. The exploration of molecular markers in this study will help to improve the fiber diameter of the down, while the identification of gene polymorphisms will provide original data for the utilization and protection of germplasm resources of cashmere goats.
Image of Bonnor black dihole with a thin accretion disk and its polarization information
Authors: Zhang Zelin , Chen Songbai , Jing Jiliang
Published: September 2022

We have studied the image of Bonnor black dihole surrounded by a thin accretion disk where the electromagnetic emission is assumed to be dominated respectively by black body radiation and synchrotron radiation. Our results show that the intensity of Bonnor black dihole image increases with the magnetic parameter and the inclination angle in both radiation models. The image of Bonnor black dihole in the synchrotron radiation model is one order of magnitude brighter than that in the black body radiation model, but its intensity in the former decreases more rapidly with the radial coordinate. Especially, for the synchrotron radiation model, the intensity of the secondary image is stronger than that of the direct image at certain an inclination angle. We also present the polarization patterns for the images of Bonnor black dihole arising from the synchrotron radiation, which depend sharply on the magnetic parameter and inclination angle. Finally, we make a comparison between the polarimetric images of Bonnor black dihole and M87*. Our result further confirms that the image of black hole depends on the black hole’s properties itself, the matter around black hole and the corresponding radiation occurred in the accretion disk.
Sleep Staging Method for Imbalanced EEG Data Based on Differential Privacy Federated Learning
Authors: Yin Kangning , Ding Zhen , Yang Xiaoyu , Tan Zhuofu , Zhu Rui , Ji Xinhui , Wang Zhiguo , Yin Guangqiang
Published: September 2022

EEG is a personal privacy information, that is difficult to share data and not conducive to the study of sleep staging. In this paper, the Federated Learning (FL) method is introduced to solve the problem of data island in sleep staging EEG signals. To simulate the distribution of different user data, we introduce the K-means algorithm into EEG dataset partitioning and transform it into a non-independent and identically distributed unbalanced dataset. Then, based on the classic Multilayer Perceptron (MLP) network, we analyse the privacy leakage problem in FL, and Differential Privacy (DP) is used to protect the privacy of user EEG data. Next, we analyse the reason for the accuracy loss between FL and Centralized Learning (CL), and the effect of the imbalance of the data distribution of each client. We propose a self-adaptive batch method to adaptively balance the batch size of each client, and experiments show that the training results of imbalanced datasets can be improved. Finally, when the accuracy of the model does not reach the historical best value in the set epoch interval, the Mandatory Optimization Strategy (MOS) is used to further improve the accuracy and reduce the randomness of the model. The DP-FL based on the self-adaptive batch and MOS strategy is tested on our dataset and the Sleep-EDF Database Expanded dataset, which not only protects user privacy but also achieves the same accuracy as CL. The accuracy is 82.88% on our dataset and 91.81% on the subset of the Sleep-EDF Database Expanded dataset.
Defending against Poisoning Backdoor Attacks on Federated Meta-learning
Authors: Chen Chien-Lun , Babakniya Sara , Paolieri Marco , Golubchik Leana
Published: September 2022

Federated learning allows multiple users to collaboratively train a shared classification model while preserving data privacy. This approach, where model updates are aggregated by a central server, was shown to be vulnerable to
poisoning backdoor attacks
: a malicious user can alter the shared model to arbitrarily classify specific inputs from a given class. In this article, we analyze the effects of backdoor attacks on federated
meta-learning
, where users train a model that can be adapted to different sets of output classes using only a few examples. While the ability to adapt could, in principle, make federated learning frameworks more robust to backdoor attacks (when new training examples are benign), we find that even one-shot attacks can be very successful and persist after additional training. To address these vulnerabilities, we propose a defense mechanism inspired by
matching networks
, where the class of an input is predicted from the similarity of its features with a
support set
of labeled examples. By removing the decision logic from the model shared with the federation, the success and persistence of backdoor attacks are greatly reduced.
How Is the Stroke? Inferring Shot Influence in Badminton Matches via Long Short-term Dependencies
Authors: Wang Wei-Yao , Chan Teng-Fong , Peng Wen-Chih , Yang Hui-Kuo , Wang Chih-Chuan , Fan Yao-Chung
Published: September 2022

Identifying significant shots in a rally is important for evaluating players’ performance in badminton matches. While there are several studies that have quantified player performance in other sports, analyzing badminton data has remained untouched. In this article, we introduce a badminton language to fully describe the process of the shot, and we propose a deep-learning model composed of a novel short-term extractor and a long-term encoder for capturing a shot-by-shot sequence in a badminton rally by framing the problem as predicting a rally result. Our model incorporates an attention mechanism to enable the transparency between the action sequence and the rally result, which is essential for badminton experts to gain interpretable predictions. Experimental evaluation based on a real-world dataset demonstrates that our proposed model outperforms the strong baselines. We also conducted case studies to show the ability to enhance players’ decision-making confidence and to provide advanced insights for coaching, which benefits the badminton analysis community and bridges the gap between the field of badminton and computer science.
Molecular Reaction Imaging of a Surface Recombination Process Explains Performance Variation Among Smooth MoS2 Photoelectrodes
Authors: Erdewyk Michael Van , Sambur Justin B.
Published: September 2022

Transition metal dichalcogenides (TMD) such as WSe2 and MoS2 are highly efficient and stable light absorbers in TMD∣I−,I3
−∣Pt liquid junction solar cells. It is generally accepted that TMD crystals with a large fraction of exposed edge sites exhibit lower power conversion efficiencies (PCEs) than apparently smooth crystals. However, one open question is why does the PCE vary significantly from one crystal to another? Answering this critical question could lead to robust syntheses for high quality and uniform TMD samples. In this work, we apply nanoscale photoelectrochemical microscopy techniques to study n-type TMD nanoflake∣I−,I3
−∣Pt cells. Using a combination of near-diffraction-limited photocurrent mapping and molecular reaction imaging techniques, we reveal a previously hidden surface recombination process: photogenerated holes in hidden p-type domains travel micron-scale distances parallel to the solid/liquid interface and preferentially react with iodide at step-edges. The overall efficiency of the nanoflake, as evidenced from whole nanoflake-level photoelectrochemical measurements, is dictated by the size, efficiency, and location of n- and p-type domains. These results provide a unifying view of efficiency losses in smooth TMD photoelectrodes and open the possibility to design electrode architectures that leverage the long-range lateral charge transport property for photoelectrocatalysis.
Pharmacological nNOS inhibition modified small-conductance Ca2+-activated K+ channel without altering Ca2+ dynamics
Authors: Koya Taro , Watanabe Masaya , Natsui Hiroyuki , Kadosaka Takahide , Koizumi Takuya , Nakao Motoki , Hagiwara Hikaru , Kamada Rui , Temma Taro , Anzai Toshihisa
Published: September 2022

We demonstrated that pharmacological nNOS inhibition increased the atrial arrhythmia inducibility and a specific small-conductance Ca2+-activated K+ channel blocker, apamin, reversed the enhanced atrial arrhythmia inducibility. Apamin mitigated APD abbreviation without alteration of Ca2+ transient, implying an underlying mechanism of posttranslational modification of SK channels.
Interleukin 4/13 signaling in cardiac regeneration and repair
Authors: Bakhshian Nik Amirala , Alvarez-Argote Santiago , O’Meara Caitlin C.
Published: September 2022

Interleukin 4 (IL4) and interleukin 13 (IL13) are closely related cytokines that have been classically attributed to type II immunity, namely, differentiation of T-helper 2 (TH2) cells and alternative activation of macrophages. Although the role of IL4/13 has been well described in various contexts such as defense against helminth parasites, pathogenesis of allergic disease, and several models of wound healing, relatively little is known about the role of IL4/13 in the heart following injury. Emerging literature has identified various roles for IL4/13 in animal models of cardiac regeneration as well as in the adult mammalian heart following myocardial injury. Notably, although IL4 and IL13 signal to hematopoietic cell types following myocardial infarction (MI) to promote wound healing phenotypes, there is substantial evidence that these cytokines can signal directly to non-hematopoietic cell types in the heart during development, homeostasis, and following injury. Comprehensive understanding of the molecular and cellular actions of IL4/13 in the heart is still lacking, but overall evidence to date suggests that activation of these cytokines results in beneficial outcomes with respect to cardiac repair. Here, we aim to comprehensively review the role of IL4 and IL13 and their prospective mechanisms in cardiac regeneration and repair.
The Impact of Environmental Technology and Environmental Policy Strictness on China’s Green Growth and Analysis of Development Methods
Authors: Wu Yiming , Zhang Yichao
Published: September 2022

Environmental technology and the stringency of environmental policies have a certain effect on green growth in the country, and the sustainable development of the environment under the influence of green growth requires the effective application of the environmental technology based on such institutional policies related to the environment. In this paper, the mutual effect of green growth on the ecological environment is fully analyzed to identify an intrinsic link between them. Based on the intrinsic link, policies related to environment protection and environmental technology are conducive to facilitating green growth while at the same time playing a continuous role in the improvement of the environment. Driven by the factors of scientific and technological innovation, it can boost the harmonious development of the economy and the environment. The empirical evidence has demonstrated that the appropriate environmental policy and environmental technology model can be identified with full understanding of the environmental policy and environmental technology. At the same time, the industry drive and technological innovation can also be taken as a strong support for the environmental policy and the environmental technology in accordance with the market demand through research from the perspective of the government, the market, and the general public. The stringency of environmental policies has substantially increased the requirements of environmental technology, and the innovation of environmental technology, the structure of economic organizations, and the level of development have a great effect on the green growth in the country.
The Impact of Cognitive Heterogeneity on the Behavioral Integration of the R&D Team: The Perspective of Conflict Management
Authors: Xie Hongtao , Song Yun , Cai Chaoxun , Zheng Junwei
Published: September 2022

The impact of team heterogeneity on team effectiveness has received extensive attention. The paper aims to explore the mechanism of the effect of cognitive heterogeneity on behavioral integration in R&D teams. Based on the IPO theory, this study proposed six hypotheses about how cognitive heterogeneity directly and indirectly (via team conflict and conflict management) affect behavioral integration in R&D teams. Using data collected from 383 R&D teams in 326 high‐tech enterprises in China, we investigated whether the dimensions of team conflict mediate the relationship between cognitive heterogeneity and behavioral integration and whether the dimensions of conflict management regulate the impact of cognitive heterogeneity on behavioral integration. The results show the following: (1) cognitive heterogeneity has a significant negative impact on the behavioral integration of R&D teams. Task conflict and relationship conflict fully mediate the relationship between cognitive heterogeneity and behavioral integration. (2) Cooperative conflict management positively moderates the impact of cognitive heterogeneity on task conflict and the impact of task conflict on team behavioral integration. (3) Avoidance conflict management reversely adjusts the positive impact of cognitive heterogeneity on task conflict and relationship conflict and the negative impact of relationship conflict on team behavioral integration. The research results can provide theoretical guidance to improve the process management of cognitive heterogeneity R&D teams.
Digital and Geographical Feature Detection by Machine Learning Techniques Using Google Earth Engine for CPEC Traffic Management
Authors: Mazhar Sarah , Sun Guangmin , Bilal Anas , Li Yu , Farhan Muhammad , Awan Hamid Hussain
Published: September 2022

The center of human settlements is in the cities, which must have high-quality habitats for their inhabitants. Many megachallenges of urbanization, population development, global advancement, environmental destruction, traffic management, and climate change must be addressed. This study is aimed at understanding how to maintain balanced land development in rapidly urbanizing towns to solve this challenge and mobility issues. Climate and weather forecasts, land cover, environmental indices, nonoptical and optical wavelengths, water history, and air quality are only some of the datasets available on Google Earth Engine, a publicly usable data repository. Machine learning techniques, i.e., random forest (RF), support vector machine (SVM), and classification and regression tree (CART), are used to monitor spatial-temporal change regarding water, vegetation, and urbanization for Pakistan from 2013 to 2021 using Landsat 8. The detection of urban land suitability concerning multiple metrics such as ecological response variables, environmental tension, socio-economic development potential, and natural resource potential is also found. Dataset features were classified as bands in the Google Earth Engine. Moreover, for 2020 and 2021, classification results showing the change in water, vegetation, and urbanization are also represented concerning China Pakistan Economic Corridor (CPEC) highway and the railway track to monitor and control traffic and its management.
Effectiveness of Artificial Intelligence (AI) in Improving Pupils’ Deep Learning in Primary School Mathematics Teaching in Fujian Province
Authors: Qiu Yaping , Pan Junjie , Ishak Nor Asniza
Published: September 2022

Several primary school students in Fujian Province have perceived studying mathematics as challenging. To deal with this issue, computer technology advancements, specifically artificial intelligence (AI), present an opportunity to evaluate individual students’ learning challenges and give individualized support to optimize their success in mathematics classes. It is also possible to use virtual reality (VR) to assist learners in acquiring complex mathematical and logical ideas and to lessen learners’ mistakes. As a result, researchers, particularly beginners, are missing out on a complete perspective of the study of AI in teaching mathematics. That is why we are exploring the role of AI in math education by developing a “fuzzy-based tweakable convolution neural network with a long short-term memory (FT-CNN-LSTM-AM)” method. For this investigation, the students’ datasets are taken and educated by mathematical teaching via the application of AI. The proposed method is utilized to predict the students’ performance in mathematical education. A grey wolf optimizer is employed to boost the effectiveness of the proposed method. Furthermore, the performance of the proposed method is analyzed and compared with existing approaches to gain the highest reliability.
Development and Characterization of Carbon‐Based Adsorbents Derived from Agricultural Wastes and Their Effectiveness in Adsorption of Heavy Metals in Waste Water
Authors: Natrayan L. , Kaliappan S. , Dheeraj Kumar Reddy C. Naga , Karthick M. , Sivakumar N.S. , Patil Pravin P , Sekar S. , Thanappan Subash
Published: September 2022

The current work focuses on peanut shells and agricultural wastes richly in many nations subjected to pyrolysis treatment at various temperatures in the range of 500–800°C to determine the feasible physiochemical characteristics of the biochar. The biochars with the high surface area were employed to adsorb Pb2+ (lead) ions, the heaviest pollutants in the water bodies. The raw material, biochar, and pyrolyzed biochar were characterized by SEM, FTIR, partial and elemental analysis, and BET tests. The adsorption characteristics of the biochar, pre‐ and postpyrolysis treatment, were studied with the assistance of batch adsorption tests under varying test conditions. Adsorbing conditions were determined by evaluating the effects of adsorbing parameters like initial concentration of the lead in water, pH of the adsorbent, contact time, and mixing speed on the effective adsorption of Pb2+ ions from water. Langmuir, Freundlich, and Themkin isotherm expressions were employed to study the experimental results. The adsorption kinetics study showed that the synthesized biochars were chemically stable enough to adsorb the Pb ions onto the surface.
The Progressive Failure Mechanism of the Tunnel-Slope System under Rainfall: An Experimental Investigation
Authors: Jiao Qinglei , Wang Yingchao , Jiang Wen
Published: September 2022

Tunnel excavation has always been an important reason for the stability failure of the tunnel-slope system at the portal section. In case of rainfall, it is likely to cause serious disasters such as tunnel vault collapse, water inrush from the tunnel face, and slope slip. In this study, the Sunjiaya tunnel of the Daping slope group was taken as the engineering background, and the tunnel model experiment system under the condition of rainfall and groundwater seepage was designed independently to explore the failure laws of slope instability induced by tunnel excavation under the condition of rainfall. Meanwhile, a fiber grating monitoring system was also used to measure the displacement, water content, earth pressure, and seepage pressure at different positions of the tunnel-slope system in the process of tunnel excavation under the condition of rainfall. The results show that the slope instability caused by rainfall infiltration is gradual. At the beginning of rainfall, the rainfall infiltration has little effect on the stability of the tunnel-slope system and then gradually increases with the continuous rainfall. Finally, the slope surface is uneven, and the phenomenon of gully and surface flow is serious. The foot of slope moves back continuously, resulting in overall collapse. Moreover, during the process of tunnel excavation, the cracks on the tunnel vault of the unburied tunnel lining develop in quadratic along the tunnel excavation direction, and the closer to the excavation section, the larger the collapse range. Finally, there is an integral collapse of the vault of tunnel excavation section. In addition, variation laws of parameters in the tunnel-slope system also provide an important explanation for the hysteresis of the stability failure of the tunnel-slope system. The results have important guiding significance for the stability of the tunnel-slope system during construction.
Neighboring Algorithm for Visual Semantic Analysis toward GAN-Generated Pictures
Authors: Zhang Lu-Ming , Sheng Yichuan
Published: September 2022

Generative adversarial network (GAN)-guided visual quality evaluation means scoring GAN-propagated portraits to quantify the degree of visual distortions. In general, there are very few image- and character-evaluation algorithms generated by GAN, and the algorithm’s athletic ability is not capable. In this article, we proposed a novel image ranking algorithm based on the nearest neighbor algorithm. It can obtain automatic and extrinsic evaluation of GAN procreate images using an efficient evaluation technique. First, with the support of the artificial neural network, the boundaries of the variety images are extracted to form a homogeneous portrait candidate pool, based on which the comparison of product copies is restricted. Subsequently, with the support of the K-nearest neighbors algorithm, from the unified similarity candidate pool, we extract the most similar concept of K-Emperor to the generated portrait and calculate the portrait quality score accordingly. Finally, the property of generative similarity that produced by the GAN models are trained on a variety of classical datasets. Comprehensive experimental results have shown that our algorithm substantially improves the efficiency and accuracy of the natural evaluation of pictures generated by GAN. The calculated metric is only 1/9–1/28 compared to the other methods. Meanwhile, the objective evaluation of the GAN and human consistency has increased by more than 80% in line with human visual perception.
Prediction of the Coefficient of Pressure Fluctuations during the Hydraulic Jump Using ELM, GMDH, and M5MT
Authors: Chen Tzu-Chia , Theruvil Sayed Biju , Opulencia Maria Jade Catalan , Alfilh Raed H. C. , Abdulhasan Maki Mahdi , Sadat Sayed Hashmat
Published: September 2022

Pressure fluctuations are a critical phenomenon that can endanger the safety and stability of hydraulic structures, especially stilling basins. Hence, the accurate estimation of the dimensionless coefficient of pressure fluctuations () is critical for hydraulic engineers. This study proposed predictive soft computing models to estimate on sloping channels. Therefore, three robust soft computing methods, including extreme learning machine (ELM), group method data of handling (GMDH), and M5 model tree (M5MT), were used to estimate . The results revealed that ELM was more accurate than GMDH and M5MT methods when comparing statistical indices, including correlation coefficient (CC), root mean square error (RMSE), mean absolute error (MAE), scatter index (SI), index agreement (Ia), and BIAS values. The performance of ELM was found to be more accurate (CC = 0.9183, RMSE = 0.0067, MAE = 0.0051, SI = 11.88%, Ia = 0.9569) when compared with the results of GMDH (CC = 0.8818, RMSE = 0.0078, MAE = 0.0058, SI = 13.89%, Ia = 0.9361) and M5MT (CC = 0.6883, RMSE = 0.0120, MAE = 0.0090, SI = 21.28%, Ia = 0.7905) in the testing stage. In addition, the BIAS values revealed that ELM slightly overestimated the values of , especially at the peak point compared with GMDH and M5MT results. Overall, the suggested soft computing techniques worked well for predicting pressure fluctuation changes in the hydraulic jump.
Application of Data Mining Model in Smart Chemistry Education
Authors: Fei Shanshan
Published: September 2022

In order to solve the problem that the activities of teachers and students in traditional teaching methods are constrained by lesson plans, the teaching methods are aging, and they do not have the ability to expand students’ learning through big data; due to the problem of low self-learning and extracurricular communication ability of students after class, the author proposes the application of a data mining model in smart chemistry education. The author designs an educational wisdom platform based on big data, and its overall structure includes physical layer, virtual resource layer, logic layer, presentation layer, application layer, network layer, and user layer; the big data center module in the platform collects all business data through devices such as networks and sensors and stores the business data in mass data storage devices; the software design part uses the multi-feature fusion acquisition algorithm to collect student data and completes the early warning of student performance through the performance early warning algorithm based on correlation analysis technology. The results showed that only 5.15% and 4.49% of the students who designed the platform could not improve their after-class autonomous learning and extracurricular communication skills, and more than 95% of the students had good feedback after using the platform. Conclusion. It shows that the design platform can effectively improve the students’ ability of self-learning and extracurricular communication after class, and the application efficiency is high.
Hypercapnia from Physiology to Practice
Authors: Almanza-Hurtado Amilkar , Polanco Guerra Camilo , Martínez-Ávila María Cristina , Borré-Naranjo Diana , Rodríguez-Yanez Tomás , Dueñas-Castell Carmelo
Published: September 2022

Acute hypercapnic ventilatory failure is becoming more frequent in critically ill patients. Hypercapnia is the elevation in the partial pressure of carbon dioxide (PaCO2) above 45 mmHg in the bloodstream. The pathophysiological mechanisms of hypercapnia include the decrease in minute volume, an increase in dead space, or an increase in carbon dioxide (CO2) production per sec. They generate a compromise at the cardiovascular, cerebral, metabolic, and respiratory levels with a high burden of morbidity and mortality. It is essential to know the triggers to provide therapy directed at the primary cause and avoid possible complications.
[Retracted] The Role of Gender‐Related Immune Genes in Childhood Acute Myeloid Leukemia
Authors: Hao Lu , Chen Qiuyan , Chen Xi , Zhou Qing
Published: September 2022

The study of immune genes and immune cells is highly focused in recent years. To find immunological genes with prognostic value, the current study examines childhood acute myeloid leukemia according to gender. The TARGET database was used to gather the “mRNA expression profile data” and relevant clinical data of children with AML. To normalize processing and find differentially expressed genes (DEG) between male and female subgroups, the limma software package is utilized. We identified prognostic‐related genes and built models using LASSO, multivariate Cox, and univariate Cox analysis. The prognostic significance of prognostic genes was then examined through the processing of survival analysis and risk score (RS) calculation. We investigated the connections between immune cells and prognostic genes as well as the connections between prognostic genes and medications. Finally, five immune genes from the TARGET database have been identified. These immune genes are considerably correlated to the prognosis of male patients.
Analysis of Signs and Effects of Surgical Breast Cancer Patients Based on Big Data Technology
Authors: Hong Zhen , Xu Qin , Yan Xin , Zhang Ran , Ren Yuanfang , Tong Qian
Published: September 2022

Big data in health care has gained popularity in recent years for disease prediction. Breast cancer infections are the most common cancer in urban Indian women, as well as women internationally, and are impacted by many events across countries and regions. Breast malignant growth is a notable disease among Indian women. According to the WHO, it represents 14% of all malignant growth tumors in women. A couple of studies have been directed utilizing big data to foresee breast malignant growth. Big data is causing a transformation in healthcare, with better and more ideal results. Monstrous volumes of patient-level data are created by using EHR (Electronic Health Record) systems data because of fast mechanical upgrades. Big data applications in the healthcare business will assist with improving results. Conventional forecast models, then again, are less productive in terms of accuracy and error rate because the exact pace of a specific calculation relies upon different factors such as execution structure, datasets (little or enormous), and kinds of datasets utilized (trait-based or picture based). This audit article looks at complex information mining, AI, and profound learning models utilized for recognizing breast malignant growth. Since “early identification is the way to avoidance in any malignant growth,” the motivation behind this audit article is to support the choice of fitting breast disease expectation calculations, explicitly in the big information climate, to convey powerful and productive results. This survey article analyzes the precision paces of perplexing information mining, AI, and profound learning models utilized for distinguishing breast disease on the grounds that the exactness pace of a specific calculation relies upon different factors such as execution structure, datasets (little or enormous), and dataset types (quality based or picture based). The reason for this audit article is to aid the determination of suitable breast disease expectation calculations, explicitly in the big information climate, to convey successful and productive outcomes. Thus, “Early discovery is the way to counteraction in the event of any malignant growth.”
An Extensive Critique on Electric Vehicle Components and Charging Systems
Authors: Iqubal Mohamed , Sathiyan Paul , Stonier Albert Alexander , Peter Geno , Vanaja Dishore Shunmugham , Ganji Vivekananda
Published: September 2022

Nowadays the demand for electric vehicles has been increasing due to their dropping price range and zero emission of carbon. This article initially discusses the development of various electric vehicles from the past to the present. Since the electric motor plays a significant role in EVs, various motors suitable for EVs have been identified and surveyed in this work. The battery storage system is a critical component of EVs; hence, the article goes over all the different types of batteries, from lead acid to lithium ion. Additionally, the other vehicle components such as converters required for charging the batteries, intelligent controllers, electric vehicle charging process, power management, and battery energy management concepts that are available are discussed in detail. Moreover, we bring our work to a conclusion by outlining the research opportunities that remain for the academic and industrial groups. Therefore, the proposed work intends to assist as a state-of-the-art reference for researchers in the field of various electric vehicle configurations, storage systems, converter configurations, charging techniques, control methods, and modulation methods.
The Number of Intraoperative Intestinal Venous Circulating Tumor Cells Is a Prognostic Factor for Colorectal Cancer Patients
Authors: Liu Zhining , Gu Yimei , Yu Fulong , Zhou Lianbang , Cheng Xiaohu , Jiang Heng , Huang Yang , Zhang Yingfeng , Xu Tongtong , Qian Wenbao , Li Xianghua
Published: September 2022

Purpose. To assess the association between intestinal venous blood (IVB) circulating tumor cells (CTCs) and clinicopathological parameters in stage I-III colorectal cancer (CRC) patients. Methods. Participants were retrospectively retrieved, who were admitted to our hospital or took annual physical exams between December 1, 2015 and December 31, 2018. A negative enrichment-immunofluorescence in situ hybridization (NE-imFISH) technique was used to isolate and identify CTCs. Receiver operating characteristic (ROC) curves and Youden index values were used to determine the critical CTC cutoff value for the diagnosis of CRC. Kaplan-Meier and log-rank methods were used to conduct survival analyses, and multivariate Cox regression analyses were employed for multivariate corrections to comprehensively evaluate the value of CTCs in the diagnosis of CRC. Relationships between IVB CTCs, clinicopathological parameters, and prognosis were then analyzed based upon patient postoperative follow-up data. Results. In total, we retrieved 282 patients including 48 healthy controls, 72 patients with benign colorectal tumors, and 162 CRC patients. CRC patients exhibited significantly higher numbers of CTCs relative to control patients or those with benign disease. CTC numbers in CRC patient peripheral blood (PB) and IVB were closely associated with tumor node metastasis (TNM) staging (P < 0.01), carbohydrate antigen-125 (CA-125) levels (P < 0.001), and KRAS (Kirsten rat sarcoma virus oncogene) mutation status (P < 0.001). The disease-free survival (DFS) of patients in the CTC-negative group was significantly longer than that of patients in the CTC-positive group (24.60 ± 13.31 months vs. 18.70 ± 10.19 months, P < 0.05), with the same being true with respect to their overall survival (OS) (30.60 ± 12.44 months vs. 35.25 ± 11.57 months, P < 0.05). A multivariate analysis revealed that the detection ≥2 CTCs/3.2 ml was independently associated with poorer DFS and OS. CTC counts were independently predictive of CRC patients TNM staging, CA-125, and KRAS mutation status in both univariate and multivariate Cox proportional hazards regression analyses. Conclusion. CTCs are valuable biomarkers that can be monitored to predict CRC patient disease progression.
A Study on the Influence of Mining Depth on the Stress Distribution Characteristics of Stope Surrounding Rock
Authors: Li Shaobo , Wang Lei , Ren Qihan , Zhu Chuanqi , Liu Huaqiang , Liu Huaiqian , Chen Lipeng
Published: September 2022

The occurrence of severe mine pressure in stope is of high risk, and the mining depth is the main control factor which affecting the occurrence of mine pressure in stope. In order to study the influence law of mining depth on stope strata behavior, taking 11318E fully mechanized working face in Xieqiao Coal Mine of Huainan Mining Group as the engineering background, the stress distribution and evolution characteristics of stope surrounding rock under mining depth were studied by similar material simulation. And the evolution laws of mechanical characteristics of stope surrounding rock under different mining depths of 600 m and 1200 m were simulated. It is concluded that with the increase of mining depth, the damage range of surrounding rock on the mining field increases significantly. The initial collapse of the immediate roof above the working face is not affected by the mining depth. The periodic weighting caving step decreases with the increase of mining depth, and the height of two zones above the working face increases with the increase of mining depth. The stress transfer and evolution characteristics of the strata above the stope were revealed. The increase of front caving angle indicates that the pressure on the support of the working face is greater, indicating that the support of the working face should be strengthened with the increase of mining depth. The research results have a guiding role in the control of surrounding rock and disaster prevention of deep mine stope.