bluuebunny/crossref_metadata_2025 huggingface dataset:
Messaging about very low nicotine cigarettes (VLNCs) to influence policy attitudes, harm perceptions and smoking motivations: a discrete choice experiment
Authors: Reynolds Reed M , Popova Lucy , Ashley David L , Henderson Katherine C , Ntansah Charity A , Yang Bo , Hackworth Emily E , Hardin James , Thrasher James
Published: September 2022

Background

To reduce smoking and the harms it causes, countries, including the USA, are considering policies to reduce nicotine in combustible tobacco to minimally addictive levels. Effective messages about very low nicotine cigarettes (VLNCs) and this policy are crucial in combating misperceptions threatening the policy’s effectiveness.

Data and methods

A discrete choice experiment assessed messages about VLNCs. Participants were 590 adults who smoked exclusively, 379 adults who both smoked and used e-cigarettes, 443 adults who formerly smoked and 351 young adults who never smoked (total n=1763). Seven message attributes were varied systematically (source, harm, chemicals, nicotine, satisfaction, addictiveness and quitting efficacy). Outcomes were selection of messages that generated the most positive attitude towards reduced nicotine policy, the greatest perceived harmfulness of VLNCs, and most strongly motivated quitting and initiating behaviour for VLNCs.

Results

Information about specific harms and chemicals of VLNCs had the largest effects on selection of messages as eliciting more negative attitudes towards VLNCs policy, increasing perceived VLNC harmfulness, increasing motivation to quit VLNCs and decreasing motivation to try VLNCs. Messages with information about quitting efficacy were selected as more motivating to quit among those who smoke, but also more motivating to try VLNCs among those who do not smoke.

Conclusion

Harm and chemical information can be prioritised to ensure VLNCs are not misperceived as less harmful than regular cigarettes. Messages about increased quitting efficacy and reduced addictiveness associated with VLNCs may backfire if presented to those who do not smoke.
Consequences of “natural” disasters on aquatic life and habitats
Authors: Cooke Steven J. , Galassi Diana M.P. , Gillanders Bronwyn M. , Landsman Sean J. , Hammerschlag Neil , Gallagher Austin J. , Eliason Erika J. , Kraft Clifford E. , Taylor Mark K. , Crisafulli Charlie M. , Shugar Dan H. , Lennox Robert J.
Published: September 2022

“Natural” disasters (also known as geophysical disasters) involve physical processes that have a direct or indirect impact on humans. These events occur rapidly and may have severe consequences for resident flora and fauna as their habitat undergoes dramatic and sudden change. Although most studies have focused on the impact of natural disasters on humans and terrestrial systems, geophysical disasters can also impact aquatic ecosystems. Here, we provide a synthesis on the effects of the most common and destructive geophysical disasters on aquatic systems (life and habitat). Our approach spanned realms (i.e., freshwater, estuarine, and marine) and taxa (i.e., plants, vertebrates, invertebrates, and microbes) and included floods, droughts, wildfires, hurricanes/cyclones/typhoons, tornadoes, dust storms, ice storms, avalanches (snow), landslides, volcanic eruptions, earthquakes (including limnic eruptions), tsunamis, and cosmic events. Many geophysical disasters have dramatic effects on aquatic systems. The evidence base is somewhat limited for some natural disasters because transient events (e.g., tornadoes and floods) are difficult to study. Most natural disaster studies focus on geology/geomorphology and hazard assessment for humans and infrastructure. However, the destruction of aquatic systems can impact humans indirectly through loss of food security, cultural services, or livelihoods. Many geophysical disasters interact in complex ways (e.g., wildfires often lead to landslides and flooding) and can be magnified or otherwise mediated by human activities. Our synthesis reveals that geophysical events influence aquatic ecosystems, often in negative ways, yet systems can be resilient provided that effects are not compounded by anthropogenic stressors. It is difficult to predict or prevent geophysical disasters but understanding how aquatic ecosystems are influenced by geophysical events is important given the inherent connection between peoples and aquatic ecosystems.
Estimating scattering potentials in inverse problems with Volterra series and neural networks
Authors: Balassa Gábor
Published: September 2022

Inverse problems often occur in nuclear physics, when an unknown potential has to be determined from the measured cross sections, phase shifts or other observables. In this paper, a data-driven numerical method is proposed to estimate the scattering potentials, using data, that can be measured in scattering experiments. The inversion method is based on the Volterra series representation, and is extended by a neural network structure to describe problems, which require a more robust estimation. The Volterra series method is first used to describe the one-dimensional scattering problem, where the transmission coefficients, and the phase shifts are used as inputs to determine the unknown potentials in the Fourier domain. In the second example the scattering process described by the radial Schrödinger equation is used to estimate the scattering potentials from the energy dependence of the phase shifts, where neural networks are used to describe the scattering problem. At the end, to show the capabilities of the proposed models, real-life data is used to estimate the $${}_{}^3 S_1$$

3

S
1

NN potential with the neural network approach from measured phase shifts, where a few percent relative match is obtained between the measured values and the model calculations.
A Precise Computational Method for Hippocampus Segmentation from MRI of Brain to Assist Physicians in the Diagnosis of Alzheimer’s Disease
Authors: Genish T. , Kavitha S. , Vijayalakshmi S.
Published: September 2022

Hippocampus segmentation on magnetic resonance imaging is more significant for diagnosis, treatment and analyzing of neuropsychiatric disorders. Automatic segmentation is an active research field. Previous state-of-the-art hippocampus segmentation methods train their methods on healthy or Alzheimer’s disease patients from public datasets. It arises the question whether these methods are capable for recognizing the hippocampus in a different domain. Therefore, this study proposes a precise computational method for hippocampus segmentation from MRI of brain to assist physicians in the diagnosis of Alzheimer’s disease (HCS-MRI-DAD-LBP). Initially, the input images are pre-processed by Trimmed mean filter for image quality enhancement. Then the pre-processed images are given to ROI detection, ROI detection utilizes Weber’s law which determines the luminance factor of the image. In the region extraction process, Chan–Vese active contour model (ACM) and level sets are used (UACM). Finally, local binary pattern (LBP) is utilized to remove the erroneous pixel that maximizes the segmentation accuracy. The proposed model is implemented in MATLAB, and its performance is analyzed with performance metrics, like precision, recall, mean, variance, standard deviation and disc similarity coefficient. The proposed HCS-MRI-DAD-LBP method attains in OASIS dataset provides high disc similarity coefficient of 12.64%, 10.11% and 1.03% compared with the existing methods, like HCS-DAS-MLT, HCS-DAS-RNN and HCS-DAS-GMM and in ADNI dataset provides high precision of 20%, 9.09% and 1.05% compared with existing methods like HCS-MRI-DAD-CNN-ADNI, HCS-MRI-DAD-MCNN-ADNI and HCS-MRI-DAD-CNN-RNN-ADNI, respectively.
Multiple Mobile Robot Task and Motion Planning: A Survey
Authors: Antonyshyn Luke , Silveira Jefferson , Givigi Sidney , Marshall Joshua
Published: September 2022

With recent advances in mobile robotics, autonomous systems, and artificial intelligence, there is a growing expectation that robots are able to solve complex problems. Many of these problems require multiple robots working cooperatively in a multi-robot system. Complex tasks may also include the interconnection of task-level specifications with robot motion-level constraints. Many recent works in the literature use multiple mobile robots to solve these complex tasks by integrating task and motion planning. We survey recent contributions to the field of combined task and motion planning for multiple mobile robots by categorizing works based on their underlying problem representations, and we identify possible directions for future research. We propose a taxonomy for task and motion planning based on system capabilities, applicable to multi-robot and single-robot systems.
Animal Communication in Linguistic and Cognitive Perspective
Authors: Scott-Phillips Thom , Heintz Christophe
Published: September 2022

Detailed comparative studies have revealed many surface similarities between linguistic communication and the communication of nonhumans. How should we interpret these discoveries in linguistic and cognitive perspective? We review the literature with a specific focus on analogy (similar features and function but not shared ancestry) and homology (shared ancestry). We conclude that combinatorial features of animal communication are analogous but not homologous to natural language. Homologies are found instead in cognitive capacities of attention manipulation, which are enriched in humans, making possible many distinctive forms of communication, including language use. We therefore present a new, graded taxonomy of means of attention manipulation, including a new class we call Ladyginian, which is related to but slightly broader than the more familiar class of Gricean interaction. Only in the latter do actors have the goal of revealing specifically informative intentions. Great ape interaction may be best characterized as Ladyginian but not Gricean.
Meta-Analysis on the Anesthetic Effects of Remifentanil plus Dexmedetomidine versus Remifentanil Alone in Cardiac Surgery
Authors: Zeng Xiao-Lin , Liao Jun-Hua , Zhong Hui-Sheng , Yin Xin-Hua , Wang Rui , Liang Jiu-Ping , Zhang Zheng-Sheng
Published: September 2022

In this study, we performed a meta-analysis to investigate the anesthesia effects of remifentanil plus dexmedetomidine versus remifentanil alone in cardiac surgery. Literature search was performed on PubMed, Web of Science, Embase, China Knowledge Infrastructure, Wanfang Data, and other databases for relevant literature published in English or Chinese before October 2021. A total of 17 studies, consisting of 1350 patients, were included in this study. Of these, 10 studies showed that remifentanil plus dexmedetomidine had a good anesthesia effect in cardiac surgery (

OR
=
3.61

, 95% CI: 1.73, 7.52,

P
<
0.001

), and 8 studies showed that the Ramsay score test of anesthesia (

SMD
=
0.88

; 95% CI: -0.77, 2.53;

P
<
0.001

) in the experimental group was better than that in the control group. In addition, changes in the hemodynamic heart rate (

SMD
=
−
0.74

; 95% CI: -1.41, -0.07;

P
<
0.001

) and mean arterial pressure (

SMD
=
−
0.18

; 95% CI: -0.72, 0.36;

P
<
0.001

) of the two groups of anesthesia were counted in 17 studies, which also showed that the anesthesia effect of remifentanil plus dexmedetomidine was good. Thus, remifentanil plus dexmedetomidine may be a more promising option for cardiac surgery anesthesia than remifentanil alone.
A Novel Feature Selection with Hybrid Deep Learning Based Heart Disease Detection and Classification in the e-Healthcare Environment
Authors: B. Dwarakanath , M. Latha , R. Annamalai , Kallimani Jagadish S. , Walia Ranjan , Belete Birhanu
Published: September 2022

With the advancements in data mining, wearables, and cloud computing, online disease diagnosis services have been widely employed in the e-healthcare environment and improved the quality of the services. The e-healthcare services help to reduce the death rate by the earlier identification of the diseases. Simultaneously, heart disease (HD) is a deadly disorder, and patient survival depends on early diagnosis of HD. Early HD diagnosis and categorization play a key role in the analysis of clinical data. In the context of e-healthcare, we provide a novel feature selection with hybrid deep learning-based heart disease detection and classification (FSHDL-HDDC) model. The two primary preprocessing processes of the FSHDL-HDDC approach are data normalisation and the replacement of missing values. The FSHDL-HDDC method also necessitates the development of a feature selection method based on the elite opposition-based squirrel searchalgorithm (EO-SSA) in order to determine the optimal subset of features. Moreover, an attention-based convolutional neural network (ACNN) with long short-term memory (LSTM), called (ACNN-LSTM) model, is utilized for the detection of HD by using medical data. An extensive experimental study is performed to ensure the improved classification performance of the FSHDL-HDDC technique. A detailed comparison study reported the betterment of the FSHDL-HDDC method on existing techniques interms of different performance measures. The suggested system, the FSHDL-HDDC, has reached its maximum level of accuracy, which is 0.9772.
[Retracted] KDOAM‐25 Overcomes Resistance to MEK Inhibitors by Targeting KDM5B in Uveal Melanoma
Authors: Zhang Hongjun , Liu Xiangnan , Chen Yong’an , Xu Rui , He Shengli
Published: September 2022

Background. Uveal Melanoma (UM) is a potentially lethal cancer, and epigenetics may participate in the regulation of MEK resistance. This study is aimed at targeting the epigenetic kinase to overcome the resistance to MEK inhibitor. Method. We developed the 92.1 and OMM1 MEK‐inhibitor resistant cell lines by culturing them in the trametinib (Tra) mixed medium. We utilized CCK8 analysis for detecting the viability of the cell. Western blot was used to determine the ERK1/2 and Akt phosphorylation. Small compound library screening assays were carried out by CCK8 analysis. To test the apoptosis, we employed flow cytometric analysis with Annexin‐V/PI. Western blot and CCK8 were used to explore the epigenetic regulation of KDM5B in MEK‐resistance cell lines. To knock out the expression level of KDM5B, we used the CRISPR/Cas9 by lentivirus delivering well‐validated shRNAs in pLKO.1 vector. The directly binding affinity of KDOAM‐25 to KDM5B was determined by drug affinity responsive target stability (DARTS) and microscale thermophoresis (MST). Results. The phosphorylation of ERK1/2 and Akt (T308) was inhibited in OMM1 cell lines. However, inhibition of Tra was abolished in OMM1‐R cell lines. From a compound screening assay, we identified that KDOAM‐25 robustly inhibited the viability and colony formation of MEK‐resistance cell lines. Furthermore, KDOAM‐25 significantly promoted cell death in OMM1‐R cells. H3K4me3 (tri‐methylation of lysine 4 on histone H3) and H3K27ac (acetyl of lysine 27 on histone H3) were both upregulated in OMM1‐R cells. Tra significantly inhibited the expression of KDM5B in OMM1‐P cells. However, the effect on KDM5B was abolished in OMM1‐R cells. Knockdown of KDM5B robustly suppressed the cell viability in OMM1‐R cells. KDOAM‐25 directly interacted with KDM5B. Conclusion. KDOAM‐25 inhibited the viability and colony formation and promoted cell death of MEK‐resistance cell lines through H3K4me3 and H3K27ac, indicating that KDOAM‐25 may be a potential therapeutic agent for MEK resistance in UM patients.
Using of Single‐Layer Porcine Small Intestinal Submucosa in Urethroplasty on a Beagle Model
Authors: Qin Daorui , Wang Xuejun , Mao Yu , Tang Yunman
Published: September 2022

Purpose. To evaluate the role of porcine small intestinal submucosa (SIS) in reducing fistula during urethroplasty and to observe its degradation process in beagle models. Methods. 22 male beagles were divided into the SIS group and control group. All animals received surgical operation to establish the hypospadias model. Urethroplasty was followed. In the SIS group, the urethra was covered with a single‐layer SIS material while no SIS material covered in the control group. At the 2nd, 4th, and 12th weeks after the operation, there were 3, 3, and 5 animals in each group, respectively, sacrificed for surgical site histological examinations. The inflammation reaction and collagen hyperplasia levels were assessed. The fistula was identified by retrograde cystourethrography at the 4th and 12th weeks after the operation. Results. the incidence of urethral fistula was 25% (2/8) in the SIS group and 75% (6/8) in the control group. The inflammation reaction of SIS and control groups had no significant difference (U = 52.50, P = 0.58). The collagen fiber increased in both groups; however, the SIS group had a much more gentle increase compared to the control group (U = −0.00, P < 0.001). In the SIS group, the SIS material was roughly complete on the specimens 2 w after surgery but became loose and discontinuous 4 w after surgery and could not be found 12 w after surgery. Conclusion. The material can decrease the incidence of urethral fistula in the animal models, when used as a coverage layer. The SIS degradation process started 2 w–4 w after the operation and finished before 12 w in the animal model.
[Retracted] A Model for Evaluating the Effectiveness of Precise Governance of Social Assistance from a Management Perspective and Environment of Public Health
Authors: Li Juan
Published: September 2022

The research study describes the assessment model and its effect of precise governance of social assistance related to management’s perspective. This research study was conducted in Chinese organizations which are related to the precise governance of social assistance. This research study is based on primary data analysis for gathering the data using specific questions related to social assistance and management. In this research study, the precise governance of social assistance is considered an independent variable, and management’s perspective is the dependent variable. The planning, designing, leading, the growth of the organization process, and system control are all subparts of management’s perspective. To measure the data, this study used 100 plus respondent persons who know social assistance related to governance and management. To measure the data, smart PLS software, and AMOS software and run informative results included growth curve model, the smart algorithm model, the regression weighted analysis, the correlation, covariance, minimization history, and assessment of normality. The overall result found that precise governance of social assistance shows positive and significant effects in the management perspectives.
Effects of Genetic Variants in the Nicotine Metabolism Pathway on Smoking Cessation
Authors: Li Huijie , Wang Qiang , Li Suyun , Jia Chongqi
Published: September 2022

Background. We aimed to investigate the associations of various genetic variants in the nicotine metabolism pathway with smoking cessation (SC) in the Chinese Han population. Method. A case-control study was conducted where 363 successful smoking quitters were referred to as cases, and 345 failed smoking quitters were referred to as controls. A total of 42 genetic variants in 10 genes were selectedand genotyped. The weighted gene score was applied to analyze the whole gene effect. Logistic regression was used to explore associations of each genetic variant and gene score with smoking cessation. Results. Our study found that the variants CYP2A6

∗

4, rs11726322, rs12233719, and rs3100 were associated with a higher probability of quitting smoking, while rs3760657 was associated with a lower probability of quitting smoking. Moreover, the gene scores of CYP2D6, FMO3, UGT2B10, UGT1A9, UGT2B7, and UGT2B15 were shown to exert a positive effect, while the gene score of CYP2B6 was detected to exert a negative effect on successful smoking cessation. Conclusion. This study revealed that genetic variants in the nicotine metabolic pathway were associated with smoking cessation in the Chinese Han population.
An Extensive Study on Graph Colourings and Dominator Chromatic Number of Sugeno-Type Fuzzy Graphs
Authors: Sangeetha Devi A.
Published: September 2022

The concept of graph colouring has become a very active field of research that enhances many practical applications and theoretical challenges. An accurate (vertex) colouring through the condition that each vertex is whichever unaccompanied in its colour class or adjoining to all vertices of at least one colour class is known as dominator colouring. A graph is dominator colouring is a suitable colouring in which at least one vertex dominates each colour class. Many difficult and global issues are solved using fuzzy graph colouring approaches. In this paper, we introduce a new Sugeno-Type Fuzzy graph of groups, which is found using several fuzzy graph operations such as dominant path-colouring number, cycle, union, join, and products. A number that is representative based on those vertices in the formations of all paths with those vertices as their starts and ends to compare with other paths is the minimum number of shared edges based on those vertices in the formations of all paths with those vertices as their starts and ends to compare with other paths. The sugeno dominant path-colouring number is the minimum Sugeno number of shared edges among the Sugeno cardinality of all sets of shared edges, which allows for various techniques. These results are used in studying various and recently introduced chromatic numbers of graphs.
Giant Partially Thrombosed Coronary Aneurysm in Multisystem Inflammatory Syndrome Associated with SARS-CoV-2 in Children
Authors: Manchola Narváez Karen Daniela , Ortíz Natalia del Pilar Delgado , Ardila Gómez Iván José , López Pilar Pérez , Rivera Ortíz Martín Fernando
Published: September 2022

Multisystem inflammatory syndrome in children (MIS-C) is a postinfectious condition which usually develops 4 to 6 weeks after SARS-CoV-2 infection in a genetically predisposed individual. Clinical features are heterogeneous and include fever, respiratory compromise, mucocutaneous involvement with conjunctival abnormalities and erythematous exanthem, abdominal pain, and diarrhea. Neurologic and cardiovascular symptoms can also develop, including coronary artery dilatation. Some cases involve 2 or more organs and require critical admission. Echocardiography is the mainstay of cardiac evaluation in the acute setting as well as on outpatient follow-up. We present the case of a 4-month-old female with no past medical or surgical history who presented with a prolonged febrile syndrome associated with severe respiratory illness, gastrointestinal symptoms, and mucocutaneous abnormalities. Diagnosis of MIS-C was established based on clinical findings, persistently elevated markers of systemic inflammation and positive SARS-CoV-2 molecular test and evidence of prior SARS-CoV-2 infection with SARS-CoV-2 IgG positive. Echocardiogram evidenced myopericarditis and coronary aneurysms and patient was deemed candidate for immunomodulatory therapy with intravenous immunoglobulin (IVIg), resulting in favorable clinical and paraclinical outcomes. Few cases of giant coronary aneurysms have been reported in children. There are no existing literature reports about coronary thrombosis or thrombus formation resulting from vascular aneurysmal dilations in this population. As such, the prognosis and natural history of coronary artery aneurysms in the setting of MIS-C remain largely unknown.
Teaching Improvement Strategies of University English Audiovisual or Speaking Course under the Background of Hadoop
Authors: Han Jing
Published: September 2022

Under the background of “Internet plus,” the social application and penetration of cloud computing and big data have produced far-reaching impacts in many aspects in the field of education. Taking college English audio-visual teaching at present. However, there are still many problems in classroom teaching practice. Focusing on college English listening and speaking teaching, this study explores the teaching strategies of college English listening and speaking to improve college pupils’ comprehensive listening and speaking ability. With the rapid development of information technology, relying on information technology and using modern technologies such as multimedia to conduct three-dimensional audio-visual teaching can effectively improve the efficiency of audio-visual classrooms by 16%. Based on the background of Internet plus, this study tries to reconstruct the evaluation model of college English audio-visual courses by using multivariate and multidimensional evaluation models and combining quantitative and qualitative evaluation to improve the teaching quality and effect of college English audio-visual course.
SCOTT: Scheduling of Comprehensive Objectives for Tasks with Multitargets in Computing Networks
Authors: Zhang Guowei , Liu Zening , Wang Kunlun , Zang Xiaodong , Zuo Yong , Yang Yang
Published: September 2022

Local and customized services are realized with new type computing architecture by utilizing the spare resources distributed on the helper nodes (HNs) throughout the network. The heterogeneity of mobile edge and fog computing networks makes them natural to support multitarget tasks, and efficient task scheduling is always a fundamental and hot issue in multitask multihelper (MTMH) computing networks. Unlike most of the researches concentrating on the optimization of a single or limited service metrics, this article proposes a service framework for multitarget tasks, which is more universal for future 6G networks supporting customized services. The comprehensive quality of service (CQoS) is constructed to indicate the comprehensive objectives of the task nodes (TNs) with multiple targets. By formulating and transforming the CQoS maximal problem into two one-variable form subproblems, an algorithm named scheduling of comprehensive objectives for tasks with multitargets (SCOTT) is proposed. The SCOTT algorithm achieves the optimal offloading service solutions considering service metrics including delay, energy consumption, and economic cost. Extensive numerical simulations are carried out, which indicate that the proposed SCOTT algorithm can effectively achieve the optimal offloading solutions including node selection, task division, and transmission power for TNs with various service targets. Moreover, the universal applicability of the SCOTT algorithm is verified with case studies and numerical results.
Clinical Application of Multi-Index Combined Risk Assessment in Early Pregnancy for Screening of Preeclampsia
Authors: Xu Xiaohong , Yan Guoxiu , Liu Jijun , Li Xuelei , Zhang Bin , Meng Xianglian , Chen Hongbo , Han Baoliang , Shao Kun , Zhao Xuefen , Liu Jing , Yan Yan
Published: September 2022

Objective. To explore the predictive value of single-index screening or multi-index combined screening for preeclampsia. Methods. From January 1, 2019, to December 31, 2021, pregnant women with a singleton pregnancy who had been regularly checked in each center since the first trimester (between 11 and 14 weeks of gestation) were retrieved from multiple participating centers. The risk calculation software LifeCycle 7.0 was used to calculate the risk values before 32 weeks, 34 weeks, and 37 weeks of gestation, and through a receiver operating characteristic (ROC) curve analysis, the predictive values of pregnancy-associated protein A (PAPP-A), the placental growth factor (PLGF), the mean arterial pressure (MAP), the uterine artery pulsatility index (UTPI), or a combined multi-index were calculated for preeclampsia. Results. Finally, 22 pregnant women developed preeclampsia, and the area under the ROC curve of the PAPP-A + PLGF + MAP + UTPI combined screening program was greater than that of other screening programs before 37 weeks of gestation (AUC = 0.975, 0.946, or 0.840 for <32 weeks, <34 weeks, or <37 weeks, respectively). At 32 weeks, the Youden index was at its maximum. Conclusion. PAPP-A + PLGF + MAP + UTPI combined screening is the optimal screening mode for preeclampsia screening before 37 weeks of gestation, and the combined prediction using multiple indicators in early pregnancy is more suitable for predicting the risk of early-onset preeclampsia.
Transferable Adversarial Attacks against Automatic Modulation Classifier in Wireless Communications
Authors: Hu Lin , Jiang Han , Li Wen , Han Hao , Yang Yang , Jiao Yutao , Wang Haichao , Xu Yuhua
Published: September 2022

Deep neural network-based automatic modulation recognition (AMR) technology has become an increasingly important area due to the advantages of self-extraction of features and high identification accuracy. Based on the view of security threats to machine learning classifiers, we investigate the influence of adversarial samples on the AMR model in this paper. The traditional method is based on label gradient attack without taking advantage of the feature-level transferability, resulting in the attack effect that is not perfect. So, we exploit the feature-level transferability property that could be met to fulfill realistic imperceptibility and transfer needs. In this paper, firstly, we proposed an AMR scheme with high recognition accuracy as our attack model. Secondly, we proposed a transferable attack method based on a feature gradient-based, which increases perturbation to clean signal based on features space. Finally, we introduce a new attack strategy, in which we select two original and one adversarial target signal sample as the input of triplet loss to achieve higher attack strength and high transferability. Meanwhile, this paper proposes indicators of signal characteristics to test the effectiveness of our proposed attack method. Based on experimental results, our proposed feature gradient-based adversarial attack method outperforms the currently labeled gradient attack methods regarding attack effectiveness and transferability.
Nonintrusive Load Identification for Industrial Users Integrated with LSQR and Sequential Leader Clustering
Authors: Yi Shuhui , Diao Yinglong , Liu Junjie , Fang Tian , Yin Xiaodong
Published: September 2022

Nonintrusive load identification for industrial users can accurately acquire the operation of each load. However, it is a major challenge in the demand-side response due to the hardship of collection data for modelling, and high precision measuring equipment is required. Aiming at this situation, a nonintrusive load identification method is proposed, combining the least square QR (LSQR) with the sequential leader clustering algorithm. Firstly, regarding accurate depiction of industrial loads, some appropriate load feature indices of steady-state and transient processes are extracted, respectively. For steady-state processes, the active power, the reactive power, and the root mean square (RMS) current value are selected as the feature indices. In the case of transient processes, ten feature indices of three stages are employed: before, during, and after transient events, consisting of the duration of transient events, the RMS current value before and after transient events, the average value of active power before and after transient events, the average value of reactive power before and after transient events, the maximum RMS current value of transient events, etc. On this base, the LSQR algorithm is proposed to decompose unknown composite power to access the operation of various loads at steady-state. The sequential leader clustering algorithm is propounded to classify transient events of typical industrial loads and further identify which kind of loads had switched. Finally, to validate the effectiveness of the presented model, data of industrial loads from a concrete plant are collected, including blender, cement screw, sewage dump, and inclined belt conveyor, and simulation analysis is fulfilled. The results indicate that the model proposed can effectively achieve the nonintrusive industrial load identification, and least unified residue (LUR) is about 10−16, which is much better than the factorial hidden Markov model (FHMM) and the artificial neural network (ANN) model.
Correlation of Presacral Tumour Recurrence with Tumour Metastasis and Long-Term Tumour Recurrence Risk in Patients with Rectal Cancer
Authors: Wang Lei , Wu Hongxia , Wang Rongrong , Zhang Hongyan , Chen Jie
Published: September 2022

Objective. To explore the risk factors that affect long-term presacral tumour recurrence in patients with rectal cancer (RC) after radical rectal cancer resection. Methods. In our study, a total of 50 patients with presacral tumour recurrence after radical resection of RC in our hospital between May 2017 and May 2018 were enrolled in the observation group, and the other 50 patients without presacral tumour recurrence after the resection over the same span were enrolled in the control group. The two groups were compared in distant metastatic rate and long-term recurrence, and corresponding K-M curves were drawn. Additionally, the quality of life of the two groups was also compared. Patients in both groups were assigned to a long-term recurrence group or a non-long-term recurrence group based on their long-term recurrence, and a multivariate logistic regression analysis was carried out for analysis of risk factors of long-term recurrence. Results. The two groups were not greatly different in clinical data (

P
>
0.05

). The observation group was higher than the control group in terms of distant metastasis and long-term recurrence (

P
<
0.05

). In addition, the MOS 36-Item Short-Form Health Survey (SF-36) scores of the observation group were all lower than those of the control group in the eight dimensions (

P
<
0.05

). Moreover, tumour diameter (OR: 0.315, 95% CI: 0.118–0.835), differentiation (OR: 2.652, 95% CI: 1.086–6.852), and presacral recurrence (OR: 2.370, 95% CI: 1.263–4.447) were all independent risk factors for long-term recurrence of patients undergoing radical resection of RC. Conclusions. Patients undergoing radical resection of RC face greatly higher risks of presacral tumour distant metastasis and long-term tumour recurrence, and tumour diameter ≥ 5 cm, low-differentiation degree, and presacral recurrence are independent risk factors for long-term recurrence of patients undergoing radical resection of RC. In the future, when performing radical resection of rectal cancer, it is necessary to pay attention to the changes in the above indicators in patients so as to prevent tumour recurrence.
Spatio-Temporal Rainfall Variability and Concentration over Sri Lanka
Authors: Pawar Uttam , Karunathilaka Pasindu , Rathnayake Upaka
Published: September 2022

Changes in precipitation patterns significantly affect flood and drought hazard management and water resources at local to regional scales. Therefore, the main motivation behind this paper is to examine the spatial and temporal rainfall variability over Sri Lanka by Standardized Rainfall Anomaly Index (SRAI) and Precipitation Concentration Index (PCI) from 1990 to 2019. The Mann–Kendall (MK) trend test and Sen’s slope (SS) were utilized to assess the trend in the precipitation concentration based on PCI. The Inverse Distance Weighting (IDW) interpolation method was incorporated to measure spatial distribution. Precipitation variability analysis showed that seasonal variations are more than those of annual variations. In addition, wet, normal, and dry years were identified over Sri Lanka using SRAI. The maximum SRAI (2.27) was observed for the year 2014 for the last 30 years (1990–2019), which shows the extremely wet year of Sri Lanka. The annual and seasonal PCI analysis showed moderate to irregular rainfall distribution except for the Jaffna and Ratnapura areas (annual scale-positive changes in Katugastota for 21.39% and Wellawaya for 17.6%; seasonal scale-Vavuniya for 33.64%, Trincomalee for 31.26%, and Batticaloa for 18.79% in SWMS). The MK test, SS-test, and percent change analyses reveal that rainfall distribution and concentration change do not show a significant positive or negative change in rainfall pattern in Sri Lanka, despite a few areas which experienced significant positive changes. Therefore, this study suggests that the rainfall in Sri Lanka follows the normal trend of precipitation with variations observed both annually and seasonally.
College Students’ Mental Health Education Consulting Management System Design Based on Big Data Algorithms
Authors: Song Xuezhen
Published: September 2022

In view of the progress and development of society and the accelerated pace of life, the demand for personnel is increasing. Students’ psychology is not yet fully mature, so students are often unable to withstand these pressures, resulting in many psychological discomfort or problems. The normal life, learning, and growth of students are affected. Considering the increasing number of psychological problems among students, education on students’ mental health has become particularly important, which has become an important part of students’ work. Looking at the actual case of higher education, the development of students’ mental health education is very unbalanced, and there are vague understandings and deviations in practice. Many people believe that the main purpose of student mental health education is to focus on counseling and therapy for some students with mental health problems. These people have no concept of mental illness prevention and mental health education development. The result is that mental health education has not been well publicized and promoted, and mental health education has not done a good job. For the current situation, this paper analyzed college students’ mental health education through a research experiment on big data algorithms. The experimental data showed that there were 40 students who thought that the psychological education course after the optimization of big data was very helpful in life, accounting for 40.6% of the total; there were 30 people, accounting for 30.7%, who thought that there was some help; 19 people, accounting for 18.81%, who thought that there was little help; and 10 people, accounting for 9.9%, who thought that there was no help. Compared with before optimization, the number of students who thought it was very helpful increased by 35, and the number of students who thought it was not helpful decreased by 20. From these data, it could be seen that mental health education played an important role in promoting the harmonious development of students’ body and mind, improving mental health, and helping students survive in the rapidly developing social competition in the future.
The Application of Computer Augmented Reality Technology in Ancient Murals
Authors: Zhang Mingming
Published: September 2022

In recent years, the country has given the green light to the development of augmented reality technology. Various preferential policies have sprung up such as mushrooms after rain, and augmented reality technology has gradually been applied to all aspects of our lives. In terms of cultural relic protection, the research and protection of ancient murals are undoubtedly not the most difficult. In terms of protection, murals are very fragile, and there are many factors that need to be paid attention to, such as air humidity, temperature, microorganisms, and the shedding of mud layers. In terms of research, the study of frescoes requires a lot of careful observation, but exhaled breath or body temperature that researchers get too close to can have unforeseen consequences. Based on this, the article expounds the composition of the augmented reality technology system. Through image recognition, three-dimensional tracking registration, virtual-real combination, and other technologies, the image obtained by the camera is recognized, and then the environment where the mural is located is a virtual scene through three-dimensional tracking technology. We build and finally use the combination of virtual and real technology to make the ancient murals vividly presented in front of us for our researchers to study more conveniently. The use of Gaussian function, SURF algorithm, FREAK algorithm, RANSAC algorithm, and so on to improve the augmented reality technology system makes the presentation of ancient murals more detailed and realistic. After experiments, we came to the conclusion that the ancient murals processed by augmented reality technology are far more detailed and more complete than the pictures taken by ordinary cameras. Processing ancient frescoes is also far less time-consuming than the existing image processors.
Potential Molecular Mechanism of Upregulated Aryl Hydrocarbon Receptor Nuclear Translocator 2 in Nasopharyngeal Carcinoma
Authors: Huang Si-Wei , Chen Gang , Li Jian-Di , Qin Li-Ting , Huang Zhi-Guang , Huang Su-Ning , Lu Wei , Zeng Jiang-Hui , Mo Bin-Yu , Dang Yi-Wu , Wei Zhu-Xin , Luo Jia-Yuan
Published: September 2022

Background. Currently, the benefits of nasopharyngeal carcinoma (NPC) therapy are limited, and it is necessary to further explore possible therapeutic targets. Aryl hydrocarbon receptor nuclear translocator 2 (ARNT2) has been extensively studied in other cancer species, but little has been explored in NPC. The aim of this study was to verify the expression level of ARNT2 and its underlying mechanism in NPC. Methods. Datasets containing ARNT2 mRNA expression levels were retrieved and collected from various databases to explore the expression status of ARNT2 in NPC. ARNT2-related coexpressed genes, differential expressed genes, and target genes were obtained for functional enrichment analysis. The potential target gene of ARNT2 and their regulatory relationship were studied through ChIP-seq data. CIBERSORTx was used to assess the immune infiltration of NPC, and the association with ARNT2 expression was calculated through correlation analysis. Results. ARNT2 was upregulated and possessed an excellent discriminatory capability in NPC samples. ARNT2 positively correlated target genes were clustered in pathways in cancer, while negatively correlated target genes were enriched in immune-related pathway. The ChIP-seq information of ARNT2 and histone showed that prostaglandin-endoperoxide synthase 2 (PTGS2) was a potential target gene of ARNT2. CIBERSORTx revealed the immunity status in NPC, and ARNT2 expression was correlated with infiltration of five immune cells. Conclusions. ARNT2 is overexpressed in NPC and may regulate PTGS2 to participate in the cancer process. ARNT2 serves as a key oncogenic target in NPC patients.
Internet of Things Information System and Clothing Computer Renderings Digital Art
Authors: Sun Ningning , Kong Rui
Published: September 2022

The Internet of Things (IoT) is developing rapidly and is integrated into all aspects of life. Clothing is an indispensable part of meeting the basic needs of the human body, and its traditional functions include warmth, health care, decoration, and beauty. While as a special type of clothing that integrates multidisciplinary technology, smart clothing has a wider scope of action. With the update and iteration of science and technology, many technologies that originally belonged to nonclothing disciplines have also been applied to the clothing field. Accordingly, for the special clothing category of smart clothing based on embedded system, a set of design process that can be used for this category of smart clothing was proposed. Using this design process, an intelligent fire suit that can be used to protect the personal safety of firefighters and assist firefighters to cooperate was designed and implemented. Starting from the daily working environment and working characteristics of firefighters, this firefighting obedience analyzed the design points from the perspectives of clothing comfort, warning, toxic and harmful gas monitoring, and firefighters’ cooperation. After testing, the test results of the outer layer fabric, waterproof and moisture-permeable layer fabric, and thermal insulation and comfort layer fabric of the fire suit all met the corresponding national standards; the monitoring sensitivity of harmful gases was high; it could achieve a “good” warning effect in a dark environment. Compared with ordinary firefighting suits, it was more comfortable under the subjective and objective test and scored 0.669 higher under the seven-point scale; its interactive performance met the actual needs. The clothing has complete functions and a complete feedback mechanism, which has a positive effect on ensuring the personal safety of firefighters.
Sustained Aurora Kinase B Expression Confers Resistance to PI3K Inhibition in Head and Neck Squamous Cell Carcinoma
Authors: Shah Pooja A. , Sambandam Vaishnavi , Fernandez Anne M. , Zhao Hongyun , Mazumdar Tuhina , Shen Li , Wang Qi , Ahmed Kazi M. , Ghosh Soma , Frederick Mitchell J. , Wang Jing , Johnson Faye M.
Published: September 2022

Tumor suppressor mutations in head and neck squamous cell carcinoma (HNSCC) dominate the genomic landscape, hindering the development of effective targeted therapies. Truncating and missense mutations in NOTCH1 are frequent in HNSCC, and inhibition of PI3K can selectively target NOTCH1 mutant (NOTCH1MUT) HNSCC cells. In this study, we identify several proteins that are differentially regulated in HNSCC cells after PI3K inhibition based on NOTCH1MUT status. Expression of Aurora kinase B (Aurora B), AKT, and PDK1 following PI3K inhibition was significantly lower in NOTCH1MUT cell lines than in wild-type NOTCH1 (NOTCH1WT) cells or NOTCH1MUT cells with acquired resistance to PI3K inhibition. Combined inhibition of PI3K and Aurora B was synergistic, enhancing apoptosis in vitro and leading to durable tumor regression in vivo. Overexpression of Aurora B in NOTCH1MUT HNSCC cells led to resistance to PI3K inhibition, while Aurora B knockdown increased sensitivity of NOTCH1WT cells. In addition, overexpression of Aurora B in NOTCH1MUT HNSCC cells increased total protein levels of AKT and PDK1. AKT depletion in NOTCH1WT cells and overexpression in NOTCH1MUT cells similarly altered sensitivity to PI3K inhibition, and manipulation of AKT levels affected PDK1 but not Aurora B levels. These data define a novel pathway in which Aurora B upregulates AKT that subsequently increases PDK1 selectively in NOTCH1MUT cells to mediate HNSCC survival in response to PI3K inhibition. These findings may lead to an effective therapeutic approach for HNSCC with NOTCH1MUT while sparing normal cells.

Significance:

Aurora B signaling facilitates resistance to PI3K inhibition in head and neck squamous cell carcinoma, suggesting that combined inhibition of PI3K and Aurora kinase is a rational therapeutic strategy to overcome resistance.