bluuebunny/crossref_metadata_2025 huggingface dataset:
Incentivizing Data Quality in Blockchain-Based Systems—The Case of the Digital Cardossier
Authors: Spychiger Florian , Tessone Claudio J. , Zavolokina Liudmila , Schwabe Gerhard
Published: September 2022

Inspired by an industry initiative to address the celebrated market for lemons (poor-quality used cars), we investigate how incentives for a permissioned blockchain-based system in the automobile ecosystem can be designed to ensure high-quality data storage and use by different stakeholders. The peer-to-peer distributed ledger platform connects organizations and car owners with disparate interests and hidden intentions. While previous literature has chiefly examined incentives for permissionless platforms, we leverage studies about crowdsensing applications to stimulate research on incentives in permissioned blockchains. This article uses the action design research approach to create an incentive system featuring a rating mechanism influenced by data correction measures. Furthermore, we propose relying on certain institutions capable of assessing data generated within the system. This combined approach of a decentralized data correction and an institutionalized data assessment is distinct from similar incentive systems suggested by literature. By using an agent-based model with strategy evolution, we evaluate the proposed incentive system. Our findings indicate that a rating-based revenue distribution leads to markedly higher data quality in the system. Additionally, the incentive system reveals hidden information of the agents and alleviates agency problems, contributing to an understanding of incentive design in inter-organizational blockchain-based data platforms. Furthermore, we explore incentive design in permissioned blockchains and discuss its latest implications.
Phase-based Cepstral features for Automatic Speech Emotion Recognition of Low Resource Indian languages
Authors: Chakraborty Chinmay , Dash* Tusar Kanti , Panda Ganapati , Solanki Sandeep Singh
Published: September 2022

Automatic speech emotion recognition (SER) is a crucial task in communication-based systems, where feature extraction plays an important role. Recently, a lot of SER models have been developed and implemented successfully in English and other western languages. However, the performance of the traditional Indian languages in SER is not up to the mark. This problem of SER in low-resource Indian languages mainly the Bengali language is dealt with in this paper. In the first step, the relevant phase-based information from the speech signal is extracted in the form of phase-based cepstral features (PBCC) using cepstral, and statistical analysis. Several pre-processing techniques are combined with features extraction and gradient boosting machine-based classifier in the proposed SER model. Finally, the evaluation and comparison of simulation results on speaker-dependent, speaker-independent tests are performed using multiple language datasets, and independent test sets. It is observed that the proposed PBCC features-based model is performing well with an average of 96% emotion recognition efficiency as compared to standard methods.
LMAC: Efficient Carrier-Sense Multiple Access for LoRa
Authors: Gamage Amalinda , Liando Jansen , Gu Chaojie , Tan Rui , Li Mo , Seller Olivier
Published: September 2022

Current LoRa networks including those following the LoRaWAN specification use the primitive ALOHA mechanism for media access control due to LoRa’s lack of carrier sense capability. From our extensive measurements, the channel activity detection feature that was recently introduced to LoRa for energy-efficiently detecting preamble chirps can also detect payload chirps reliably. This sheds light on an efficient carrier-sense multiple access protocol that we refer to as LMAC for LoRa networks. This article presents the designs of three advancing versions of LMAC that respectively implement carrier-sense multiple access, and balance the communication loads among the channels defined by frequencies and spreading factors based on the end nodes’ local information and then additionally the gateway’s global information. Experiments on a 50-node lab testbed and a 16-node university deployment show that, compared with ALOHA, LMAC brings up to 2.2× goodput improvement and 2.4× reduction of radio energy per successfully delivered frame. Thus, should LoRaWAN’s ALOHA be replaced with LMAC, network performance boosts can be realized.
Identification of Anomalies in Mammograms through Internet of Medical Things (IoMT) Diagnosis System
Authors: Khan Amjad Rehman , Saba Tanzila , Sadad Tariq , Nobanee Haitham , Bahaj Saeed Ali
Published: September 2022

Breast cancer is the primary health issue that women may face at some point in their lifetime. This may lead to death in severe cases. A mammography procedure is used for finding suspicious masses in the breast. Teleradiology is employed for online treatment and diagnostics processes due to the unavailability and shortage of trained radiologists in backward and remote areas. The availability of online radiologists is uncertain due to inadequate network coverage in rural areas. In such circumstances, the Computer-Aided Diagnosis (CAD) framework is useful for identifying breast abnormalities without expert radiologists. This research presents a decision-making system based on IoMT (Internet of Medical Things) to identify breast anomalies. The proposed technique encompasses the region growing algorithm to segment tumor that extracts suspicious part. Then, texture and shape-based features are employed to characterize breast lesions. The extracted features include first and second-order statistics, center-symmetric local binary pattern (CS-LBP), a histogram of oriented gradients (HOG), and shape-based techniques used to obtain various features from the mammograms. Finally, a fusion of machine learning algorithms including K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA are employed to classify breast cancer using composite feature vectors. The experimental results exhibit the proposed framework’s efficacy that separates the cancerous lesions from the benign ones using 10-fold cross-validations. The accuracy, sensitivity, and specificity attained are 96.3%, 94.1%, and 98.2%, respectively, through shape-based features from the MIAS database. Finally, this research contributes a model with the ability for earlier and improved accuracy of breast tumor detection.
Mitochondrial Targeted Antioxidant SKQ1 Ameliorates Acute Kidney Injury by Inhibiting Ferroptosis
Authors: Song Jiayu , Sheng Jingyi , Lei Juan , Gan Weihua , Yang Yunwen
Published: September 2022

Emerging evidence suggests that ferroptosis is highly correlated with the pathogenesis of acute kidney injury (AKI). Ferroptosis, an iron-dependent form of cell death, is manifested by a toxic accumulation of lipid peroxides and ultrastructural changes in mitochondria. We herein investigated the effect of Visomitin (SKQ1), a novel mitochondria-targeting antioxidant, on several AKI models in vivo and in vitro. Our results revealed that SKQ1 treatment greatly reversed renal outcomes in cisplatin, ischemia-reperfusion injury (IRI), or folic acid-induced AKI models. These effects were reflected in attenuated levels of renal injury biomarkers, histologic indices of tubular injury, and inflammatory infiltration in the SKQ1-treated groups. Transcriptomics analysis depicted ferroptosis signaling as the most pronounced pathway downregulated after SKQ1 treatment. Consequently, administration of SKQ1 significantly ameliorated lipid peroxide accumulation and inhibited ferroptosis in the kidneys of mice with AKI. In cultured human proximal tubule epithelial cells (HK2), SKQ1 treatment markedly mitigated cisplatin-induced mitochondrial reactive oxygen species (ROS) production, resulting in lower levels of lipid peroxidation and ferroptosis. In conclusion, SKQ1 treatment protected against ischemic- or nephrotoxic-induced AKI by inhibiting ferroptosis in vivo and in vitro. These results could facilitate a broader understanding of the interaction between mitochondrial antioxidants and ferroptotic defense mechanisms, providing a possible therapeutic strategy in AKI.
Effects of Slip and Inclined Magnetic Field on the Flow of Immiscible Fluids (Couple Stress Fluid and Jeffrey Fluid) in a Porous Channel
Authors: Bitla Punnamchandar , Sitotaw Fekadu Yemataw
Published: September 2022

In this paper, we study the flow of two immiscible fluids namely, couple stress fluid and Jeffrey fluid in a porous channel. Instead of the classical no-slip conditions on the boundaries, we used slip boundary conditions, which are more realistic and meaningful. In addition, we used inclined magnetic field effects on the fluid flow. The couple stress fluid and Jeffrey fluid are flowing adjacent to each other in the region I and in the region II, respectively, of the horizontal porous channel. The nondimensionalized governing equations are solved analytically by using slip conditions at the lower and upper boundaries and interface conditions at the fluid-fluid interface. The analytical expressions for the velocity components in both regions are obtained in closed form. The effects of slip parameter, Hartmann number, couple stress parameter, Jeffrey parameter, angle of inclination, and Darcy number on velocity components in both regions are investigated. In the absence of slip, couple stress parameter, and Jeffrey parameters, limiting cases are obtained and discussed.
Intelligent Detection and Diagnosis of Power Failure Relying on BP Neural Network Algorithm
Authors: Liu Linna
Published: September 2022

The development of economy and the needs of urban planning have led to the rapid growth of power applications and the corresponding frequent occurrence of power failures, which many times lead to a series of economic losses due to failure to repair in time. To address these needs and shortcomings, this paper introduces a BP neural network algorithm to determine the neural network structure and parameters for fault diagnosis of power electronic inverter circuits with improved hazard. By optimizing the weights and thresholds of neural networks, the learning and generalization ability of neural network fault diagnosis systems can be improved. It can effectively extract fault features for training, sort out the business logic of power supply intelligent detection, analyze the potential hazards of power supply, and effectively perform circuit intelligent control to achieve effective fault detection of power supply circuits. It can provide timely feedback and hints to improve the fault identification ability and the corresponding diagnosis accuracy. Simulation results show that the method can eventually determine the threshold value for intelligent power fault detection and diagnosis by analyzing the convergence of long-term relevant indicators, avoiding the blindness of subjective experience and providing a theoretical basis for intelligent detection and diagnosis.
Promotion Path and Application of Enterprises Sustainable Competitive Advantage: Perspective of TMT Behavior Integration
Authors: Xi Lei , Liu Caichao , Sun Yu
Published: September 2022

The role of the sustainable competitive advantage of enterprises is becoming increasingly prominent. Taking TMT behavior integration, enterprise sustainable competitive advantage, absorptive capacity, and critical reflection as independent, dependent, intermediary, and regulatory variables, respectively, and 223 enterprises as an empirical research sample, through theoretical analysis and empirical research, this paper reveals the influence of TMT behavior integration on sustainable competitive advantage of enterprises and intermediary role of absorptive capacity and the regulatory role of critical reflection. The empirical results show that the behavior integration of top management team can promote the sustainable competitive advantage of enterprises. The potential absorptive capacity plays a partial intermediary role between TMT behavior integration and enterprise financial performance and a complete intermediary role between TMT behavior integration and enterprise strategic performance. Realistic absorptive capacity can partially mediate the behavioral integration of TMT and the sustainable competitive advantage of enterprises. Critical reflection plays a positive regulatory role between TMT behavior integration and enterprise strategic performance. The research shows that in order to improve enterprises sustainable competitive advantage, enterprises should take the following measures: ① Enterprise executives must attach importance to TMT behavior integration. ② Enterprise executives should focus on critical thinking. ③ Enterprise executives should take cultivation of absorptive capacity seriously and give full play to the intermediary effect of absorptive capacity. Research model provides a new path to build sustainable competitive advantage, and can be applied to enhance enterprises sustainable competitive advantage from the perspective of TMT behavioral integration.
Mechanical Behaviors and AE Characteristics of Brittle Coal Specimens with Different Fissure Angles
Authors: Gu Xuebin , Guo Weiyao , Zhang Chengguo , Guo Chuanqing , Wang Chao , Wang Fangfang , Zhao Tongbin , Chen Yang
Published: September 2022

Uniaxial compression tests were performed on coal specimens with five fissure angles to study the mechanical behaviors and acoustic emission (AE) characteristics of fractured coals. AE and video monitoring techniques were used to examine crack propagation in the fractured specimens. The stress–strain curves, mechanical properties, and cracking processes at different fissure angles were analysed. The AE counts and dominant frequency characteristics, during the failure processes of the specimens, were investigated. In addition, five types of AE signals were classified according to the AE spectral frequency analysis, and low-frequency–high-energy signals were used to accurately predict the brittle fracture processes of the fractured specimens. Finally, a comparison with sandstone specimens revealed the influence of primary cracks on the strength of brittle coal specimens containing preexisting fissures under uniaxial compression. The test results are helpful to elucidate the mechanical behavior and failure mechanism in underground engineering, such as hydraulic slotting in mines.
Antidyslipidemic, Antioxidant, and Anti-inflammatory Effects of Jelly Drink Containing Polyphenol-Rich Roselle Calyces Extract and Passion Fruit Juice with Pulp in Adults with Dyslipidemia: A Randomized, Double-Blind, Placebo-Controlled Trial
Authors: Khongrum Jurairat , Yingthongchai Pratoomporn , Boonyapranai Kongsak , Wongtanasarasin Wachira , Donrung Nowwapan , Sukketsiri Wanida , Prachansuwan Aree , Chonpathompikunlert Pennapa
Published: September 2022

Oxidative stress and inflammation play key roles in the pathophysiology in the pathophysiology of dyslipidemia, which are positive risks that increase atherosclerosis leading to important healthcare problems. Therefore, we aimed to study the antioxidant, anti-inflammatory, and lipid-lowering effects of jelly drink containing polyphenol-rich roselle calyces extract and passion fruit juice with pulp concentrate (RP jelly drink) in comparison to a placebo jelly drink for 8 weeks. Forty-three adults with dyslipidemia were randomly assigned into two groups: the RP jelly drink group and the placebo group. Glucose, total cholesterol (TC) triglyceride (TG), low-density lipoprotein-cholesterol (LDL-C), high-density lipoprotein-cholesterol (HDL-C), oxidative stress biomarkers, inflammatory parameters, and monocyte chemotactic protein-1 (MCP-1) were measured with fasting blood samples at baseline, 4 weeks and 8 weeks of intervention. Results showed a significant decrease in LDL-C and TG, respectively, after 8 weeks of RP jelly drink consumption (LDL-C:

107.63
±
22.98
 
m
g
/
d
L

; TG:

109.79
±
38.83
 
m
g
/
d
L

) compared to baseline measurements (LDL-C:

128.43
±
32.74
 
m
g
/
d
L

; TG:

132.33
±
75.11
 
m
g
/
d
L

). These may be possible due to reduced inflammation and improvements in oxidative stress, as demonstrated by the reduction of tumor necrosis factor- (TNF-) α and malondialdehyde (MDA), and the enhancement of glutathione (GSH) after consuming the RP jelly drink for 8 weeks. However, no significant differences of treatment on glucose, total cholesterol, MCP-1, interleukin-6, and interleukin-10 were observed. In conclusion, daily consumption of RP jelly drink for 8 weeks resulted in significant improvement in lipid profiles in subjects with dyslipidemia. However, more research is needed to assess its nutritional and functional potential.
Modulation Instability Analysis, Solitary Wave Solutions, Dark Soliton Solutions, and Complexitons for the (3 + 1)‐Dimensional Nonlinear Schrödinger Equation
Authors: Qin Chun-Yan
Published: September 2022

This paper addresses the (3 + 1)‐dimensional nonlinear Schrödinger equation, which could be utilized to express many physical media the envelope of the wave amplitude. With the help of He’s semi‐inverse method, the solitary wave solutions are explicitly constructed to the equation. The dark soliton solutions of the equation are also strictly constructed by making use of the solitary ansatz method; in order to guarantee the existence of solitons, some conditions are given. Furthermore, by employing the tanh  method, we also present complexitons of the equation. Finally, with the aid of linear stability analysis, an effective and straightforward method is presented to analyze modulation instability of the equation.
Effects of Drug-Coated Balloon Therapy on CT Imaging Results and Levels of Vascular Inflammatory Cytokines in Patients with Arteriosclerosis Obliterans Lesions
Authors: Yang Yanlin
Published: September 2022

Objective. The aim of the study is to explore the effects of drug-coated balloon (DCB) therapy on computed tomography (CT) imaging results and levels of vascular inflammatory cytokines in patients with arteriosclerosis obliterans (ASO) lesions. Methods. A total of 200 patients with ASO lesions admitted to our hospital from January 2021 to December 2021 were enrolled. According to the random number table method, they were divided into observation groups (n = 100) and control groups (n = 100). The observation group was treated with DCB, while the control group was treated with ordinary balloon. At 1 month after surgery, the clinical curative effect was evaluated by ankle-brachial index (ABI). The image quality was compared and vascular patency was evaluated by arterial ultrasound and CT angiography. The minimum luminal diameter (MLD) and late luminal loss (LLL) were recorded. Before and at 1 month after surgery, the severity of disease was assessed by Rutherford grading of lower limb ischemia. Before, at 7 d and 1 month after surgery, inflammatory factors [serum matrix metalloproteinase-9 (MMP-9), monocyte chemoattractant protein-1 (MCP-1), high sensitivity C-reactive protein (hs-CRP), interleukin-4 (IL-4), interleukin-6 (IL-6)] were compared between the two groups. The occurrence of postoperative complications was recorded. Results. The clinical response rate in the observation group was significantly higher than that in the control group (97.00% vs 89.00%) (

P
<
0.05

). The restenosis rate in the observation group was significantly lower than that in the control group (1.00% vs 7.00%) (

P
<
0.05

). The MLD in the observation group was significantly longer than that in the control group, and the LLL was significantly lower than that in the control group (

P
<
0.05

). There was no significant difference in image quality between the two groups (

P
>
0.05

). After surgery, disease severity in both groups was relieved, which was milder in the observation group than in the control group (

P
<
0.05

). At 7 d and 1 month after surgery, levels of MMP-9, MCP-1, hs-CRP, IL-4, and IL-6 in both groups were decreased, which were lower in the observation group than in the control group (

P
<
0.05

). There was no significant difference in the incidence of complications between the two groups (6.00% vs 7.00%) (

P
>
0.05

). Conclusion. The curative effect of DCB is good on patients with ASO lesions, which can reduce the restenosis rate, control luminal loss, relieve inflammatory response, and improve disease severity.
Flexible Polymer Solar Cells with High Efficiency and Good Mechanical Stability
Authors: Kathir I. , Shinde Santaji Krishna , Parswajinan C. , Hanumanthakari Sudheer , Loganathan K. , Madhavarao S. , Seikh A. H. , Siddique M. H. , Ganesan Manikandan
Published: September 2022

Single-junction polymer solar cells have demonstrated exceptional power conversion efficiency. Interlayer adhesion will be critical in building flexible polymer solar cells since inorganic conveyance layers would surely break. Aluminium-doped zinc oxide modified by polydopamine has emerged as a viable electron transportation layer in polymer solar cells, enhancing mechanical qualities by offering a high degree of flexibility and adhesion to the active layer. Power conversion efficiency of 12.7% is achieved in nonfullerene polymer solar cells built on PBDB-T2F:IT-4F with aluminium-doped zinc oxide 1.5% polydopamine electron transporting layer. Furthermore, the device based on Ag-mesh wire-wound electrodes has a power conversion efficiency of 11.5% and retains more than 90% of original power conversion efficiency afterward 1500 cycles of bending. For implantable and adaptable polymer solar cells for wide areas, roll-to-roll fabrication of inorganic electron transport layers is advantageous because of their mechanical resilience and thickness insensitivity.
Fecal Microbiota Transplantation Combined with a Low FODMAP Diet for the Treatment of Irritable Bowel Syndrome with Predominant Diarrhea
Authors: Huang Hong-Li , Zhu Jia-Qi , Yang Liu-Si , Wu Qiong , Shou Di-Wen , Chen Hui-Ting , Ma Jun , Li Yong-Qiang , Xu Hao-Ming , Zhou Yong-Jian
Published: September 2022

Background. Fecal microbiota transplantation (FMT) has been found to be effective in irritable bowel syndrome with predominant diarrhea (IBS-D). We conducted this study to determine the impact of a low FODMAP diet (LFD) on the gut microbiota and the efficacy of FMT in the treatment of IBS-D. Methods. A retrospective analysis of a single-arm open-label prospective study was conducted to investigate the impact of FMT alone (

n
=
40

) and FMT+LFD (

n
=
40

) in refractory IBS-D. The IBS-quality of life (QOL), IBS-severity scoring system (SSS), gastrointestinal symptom rating scale (GSRS), Hamilton anxiety scale (HAMA), and Hamilton depression scale (HAMD) were used to evaluate the efficacy, and partial 16S rDNA amplicon sequencing was used to profile the microbiota. Results. The response rates were higher in the FMT+LFD group than in the FMT group (1 mo, 3 mo, 6 mo: 70.0% vs. 55.0%, 67.5% vs. 57.5%, 62.5% vs. 27.5%, respectively). The FMT+LFD group showed significantly better improvement in IBS-QOL at 1, 3, and 6 months; IBS-SSS at 6 months; and GSRS at 1 month compared to FMT alone. Changes in HAMA and HAMD were similar in the two groups. The LFD significantly upregulated the FMT-induced microbial diversity (OTUs: 666 vs. 574, Adonis:

P
=
0.02

) and significantly strengthened the upregulation of Bacteroides, Alistipes, and Ruminococcaceae_UCG-002 and the downregulation of Bifidobacterium. Conclusion. An LFD enhanced the efficacy of FMT, increased the gut microbial diversity after FMT, and strengthened the inhibitory effect of FMT on conditional pathogens.
Codes over Lattice‐Valued Intuitionistic Fuzzy Set Type‐3 with Application to the Complex DNA Analysis
Authors: Riaz Asra , Kousar Sajida , Kausar Nasreen , Pamucar Dragan , Addis Gezahagne Mulat
Published: September 2022

In this article codes over lattice valued intuitionistic fuzzy set type‐3 (LIFS‐3) are defined. Binary block codes and linear codes are constructed over LIFS‐3. Hamming distance and related properties of these newly established codes are examined. The research findings are applied to genetic codes. The set L of sixty‐four codons is converted into a lattice and then codes are created over the set S of twenty amino acids by defining membership and nonmembership functions from the set of twenty amino acids to the sixty‐four codon set. Comparison of codes over L‐fuzzy set and LIFS‐3 conducted in terms of hamming distance for codon system that ensures the efficiency of newly established codes.
[Retracted] Vasoactive Drug Therapy and Clinical Nursing of Patients with Cardiovascular Disease
Authors: Li Menglin , Xu Meixiang , Feng Minfang , Ren Lingyan
Published: September 2022

Cardiovascular disease (CVD) is a common human disease with a large number of patients. Vasoactive drugs have a good effect on the contraction and expansion of blood vessels, which can provide certain help for the management of cardiovascular diseases. However, the clinical care of cardiovascular disease has always been based on the superficial resistance level of body fat, weight, and so on, which is unfavorable for the real recovery of patients with cardiovascular disease. This article aims to quantitatively evaluate the effects of clinical care based on vasoactive drug therapy and intrinsic factors of cardiovascular disease. For the treatment of vasoactive drugs, this paper selects the principle of action of the adrenal hormone to judge and analyze the expansion and contraction of the cardiovascular disease. For the clinical nursing of patients with cardiovascular disease, based on multivariate logistic regression, this paper selects internal factors such as age and blood pressure to model the nursing effect. Experiments have shown that the logistic regression model established in this paper can evaluate well the recovery effect of patients with cardiovascular disease. The AUC value of the model reached around 0.9. This showed that the clinical care of patients with cardiovascular disease can not only rationally judge the recovery effect through the model but also adjust the physical and mental conditions of patients with cardiovascular disease according to the coefficients of the model to achieve the best recovery effect.
Deep Learning Based Tongue Prickles Detection in Traditional Chinese Medicine
Authors: Wang Xinzhou , Luo Siyan , Tian Guihua , Rao Xiangrong , He Bin , Sun Fuchun
Published: September 2022

Tongue diagnosis is a convenient and noninvasive clinical practice of traditional Chinese medicine (TCM), having existed for thousands of years. Prickle, as an essential indicator in TCM, appears as a large number of red thorns protruding from the tongue. The term “prickly tongue” has been used to describe the flow of qi and blood in TCM and assess the conditions of disease as well as the health status of subhealthy people. Different location and density of prickles indicate different symptoms. As proved by modern medical research, the prickles originate in the fungiform papillae, which are enlarged and protrude to form spikes like awn. Prickle recognition, however, is subjective, burdensome, and susceptible to external factors. To solve this issue, an end-to-end prickle detection workflow based on deep learning is proposed. First, raw tongue images are fed into the Swin Transformer to remove interference information. Then, segmented tongues are partitioned into four areas: root, center, tip, and margin. We manually labeled the prickles on 224 tongue images with the assistance of an OpenCV spot detector. After training on the labeled dataset, the super-resolutionfaster-RCNN extracts advanced tongue features and predicts the bounding box of each single prickle. We show the synergy of deep learning and TCM by achieving a 92.42% recall, which is 2.52% higher than the previous work. This work provides a quantitative perspective for symptoms and disease diagnosis according to tongue characteristics. Furthermore, it is convenient to transfer this portable model to detect petechiae or tooth-marks on tongue images.
A Multilevel Temporal Context Network for Sleep Stage Classification
Authors: Lv Xingfeng , Li Jinbao , Xu Qian
Published: September 2022

Sleep stage classification is essential in diagnosing and treating sleep disorders. Many deep learning models have been proposed to classify sleep stages by automatic learning features and temporal context information. These temporal context features come from the intra-epoch temporal features, which represent the overall morphology of an epoch, and temporal features of adjacent epochs and long epochs, which represent the influence between epochs. However, most existing methods do not fully use the complementarity of the three-level temporal features, resulting in incomplete extracted temporal features. To solve this problem, we propose a multilevel temporal context network (MLTCN) to learn the temporal features from intra-epoch, adjacent epochs, and long epochs, which utilizes the complete temporal features to improve classification accuracy. We evaluate the performance of the proposed model on the Sleep-EDF datasets published in 2013 and 2018. The experimental results show that our MLTCN can achieve an overall accuracy of 84.2% and a kappa coefficient of 0.78 on the Sleep-EDF-2013 dataset. On the larger Sleep-EDF-2018 dataset, the overall accuracy is 81.0%, and a kappa coefficient is 0.74. Our model can better assist sleep experts in diagnosing sleep disorders.
Systematic Analysis of Tumor Microenvironment Patterns and Oxidative Stress Characteristics of Endometrial Carcinoma Mediated by 5-Methylcytosine Regulators
Authors: Dong Chunli , Dang Ling , Gao Xiaocui , Xu Renyan , Zhang Hui , Zhang Xin
Published: September 2022

As a widely distributed RNA methylation modification, m5C is involved in the regulation of tumorigenesis. Nevertheless, its fundamental process is not clear. This research sought to examine the genetic properties of the 5-methylcytosine (m5C) regulator in endometrial carcinoma, as well as the prognostic significance and impact of m5C regulators on oxidative stress. Therefore, the TCGA-UCEC data set was used to explore the characteristics of 17 RNAm5C-related genes in the transcriptome, genome, and regulatory network. The subtypes of RNAm5C in UCEC were identified based on the expression levels of 17 RNAm5C-related genes. The prognosis of RNAm5C-2 was significantly better than that of RNAm5C-1. Then, we examined the differences (variations) across various subtypes in terms of immune cell infiltration (ICI) as well as the expression of immune-related signal markers. The findings demonstrated that there were distinct variations in the infiltration level of immune cells in each subtype, which may be the reason for the differences in the prognosis of each subtype. In addition, the differentially expressed genes (DEGs) among RNAm5C subtypes of different UCEC tumors were identified, and the DEGs significant for survival were screened. After obtaining 34 prognostic genes, the dimensionality was reduced to construct an RNA methylation score (RS). As per the findings, RS is a more accurate marker for determining the prognosis for patients with endometrial cancer. The RS was used to categorize UCEC tumor samples, and these results led to the formation of high-score and low-score groups. The patients in the group with a high-RNA methylation score exhibited a survival time that was considerably longer in contrast with those in the group with a low-RNA methylation score. The capacity of RS to predict whether or not immunotherapy would be beneficial was explored further. In the group with a high-RNA methylation score, the objective response rate to the anti-PD-L1 therapy was substantially greater compared to that observed in the subgroup with a low-RNA methylation score. Additionally, there were variations across various RS groups in terms of clinical features, tumor mutation burden, and the infiltration level of immune cells. After binary tree analysis and PCR verification of 34 prognostic genes, it is finally found that the six genes of MAGOH3P, TRBJ2_3, YTHDF1P1, RP11_323D18.5, RP11_405M12.2, and ADAM30 are significantly overexpressed in cancer tissues. These genes can be used as potential biomarkers of endometrial cancer and provide data support for precise immunotherapy in UCEC tumors.
The Evaluation on the Credit Risk of Enterprises with the CNN-LSTM-ATT Model
Authors: Zhang Lei
Published: September 2022

Credit evaluation is a difficult problem in the process of financing and loan for small and medium-sized enterprises. Due to the high dimension and nonlinearity of enterprise behavior data, traditional logistic regression (LR), random forest (RF), and other methods, when the feature space is very large, it is easy to show low accuracy and lack of robustness. However, recurrent neural network (RNN) will have a serious gradient disappearance problem under long sequence training. This paper proposes a compound neural network model based on the attention mechanism to meet the needs of enterprise credit evaluation. The convolutional neural network (CNN) and the long short-term memory (LSTM) network were used to establish the model, using soft attention, the gradient propagates back to other parts of the model through the attention mechanism module. In the multimodel comparison experiment and three different enterprise data experiments, the CNN-LSTM-ATT model proposed in this paper is superior to the traditional models LR, RF, CNN, LSTM, and CNN-LSTM in most cases. The experimental results under multimodel comparison reflect the higher accuracy of the model, and the group test reflects the higher robustness of the model.
Unilateral Biportal Endoscopic Discectomy versus Microendoscopic Discectomy for the Treatment of Lumbar Spinal Stenosis: A Systematic Review and Meta-Analysis
Authors: Niu Yufei , Shen Zhen , Li Haoyang
Published: September 2022

Objective. In minimally invasive spinal surgery, the treatment of lumbar spinal stenosis with microendoscopic discectomy (MED) or unilateral biportal endoscopic discectomy (UBED) shows effective results, but which is more effective is controversial. Our study aimed to evaluate the efficacy and safety of UBED versus MED in the treatment of lumbar spinal stenosis by a systematic review and meta-analysis, so as to provide reference for the promotion of UBED in clinical practice. Methods. The multiple databases like PubMed, EMBASE, Web of Science, Cochrane Library, Chinese National Knowledge Databases, Chinese BioMedical Database, and Wanfang Database were used to search for the relevant studies. Review Manager 5.4 was adopted to estimate the effects of the results among selected articles. Odds ratio (OR) and mean difference (MD) with 95% confidence intervals (CIs) were used to estimate the overall pooled effect. Subgroup analysis, forest plots, funnel plots and Egger’s test for the articles included were also conducted. Results. Three randomized clinical trials and seven cohort studies were finally retrieved, these studies included 685 and 829 patients in the UBED and MED groups, respectively. There were no differences in terms of operation time (MD = -0.92, P =0.72), estimated blood loss (MD = -26.31, P =0.08), complications (MD =0.81, P =0.38) and Oswestry Disability Index (ODI) score (P >0.05 in four subgroup) between the two groups. The visual analog scale (VAS) score of back pain in the UBED group was better than MED group only at 6 months (MD = -0.23, P =0.006) after operation, the VAS score of leg pain in the UBED group was better than that of MED group at 3 mouths (MD = -0.22, P =0.002) and 6 months (MD = -0.24, P =0.006) after operation, the UBED group had a less postoperative length of stay than the MED group (MD = -1.85, P <0.001). The bias analysis showed that there was no potential publication bias in the included literature. Conclusion. This study showed that compared with MED, UBED has the advantages of short hospital stay and good short-term curative effect, but there is no significant difference in long-term efficacy and safety, they can be replaced by each other in clinical application.
Promoting Sustainable Green Growth through the Use of Political Institutions: The Role of the Law
Authors: Zou Caixia
Published: September 2022

In the era of global development, due to the destruction of the environment by traditional production, traditional industries have been forced to open the road of green growth transformation. What kind of methods that can effectively promote sustainable green growth has become one of the current research topics that has attracted much attention. Addressing this issue is very important for the green growth sector. With the in‐depth research on green growth, the research on the promotion of green growth by political institutions has gradually been carried out, and its policy advantages are of great significance to solve the problem of green growth transformation. The purpose of this paper is to examine the role of law in the promotion of sustainable green growth by political institutions. Through the analysis and research of sustainable green growth, law, and logistic regression model, it can improve the level of sustainable green growth of enterprises and solve the problem that the current level of sustainable green growth of enterprises is not high. This paper analyzes sustainable green growth, the role of law, and logistic regression models and uses relevant formulas to explain them. The experimental results show that the green growth index of group A production enterprises is higher than that of group B. Legal means have played a very important role in improving the level of sustainable green growth in the promotion of sustainable green growth by political institutions. It can meet the requirements of green growth under the current SDG concept, and its sustainability has been greatly improved.
Visual Planarization in Oil Painting Techniques in Digital Information Age
Authors: Zhang Yiping
Published: September 2022

The arrival of the digital information age has realized the transformation of people’s production and lifestyle, which has also promoted the vigorous development of the art field. At present, the application of informatization in oil painting techniques is increasing. The purpose of this paper is to realize the research on the visual planarization of oil painting techniques in the digital information environment to promote the ecological development of the computer and art field. By using the advanced methods and technologies of its information display, this paper proposed the use of image processing algorithms in the digital information age to guide the integration of digital information technology and oil painting techniques. This helped to analyze and solve objective problems such as the profound meaning of the objective existence in the art field and the inability to understand the emotion in it. It can be seen from the analysis of the oil painting color model that, in the RGB model, when the wavelength is within a certain range, it can be used. But when the wavelength is between 444 nanometers and 526 nanometers, the red intensity is too low. That is to say, it can not effectively express its corresponding color in this wavelength range. This model is very challenging. In the XYZ color model, when the wavelength is in a certain region, the output of red light becomes a value less than 0. Any color in this space can be well represented by a linear combination of X, Y, and Z. Therefore, the study of visual flattening of oil painting is very important in the field of art.
Persistent Child Poverty and Mortality in a Cohort of Children with Cancer in Alabama
Authors: Hoppmann Anna L. , Dai Chen , Daves Marla , Imran Hamayun , Whelan Kimberly , Kenzik Kelly , Bhatia Smita
Published: September 2022

Background:

One-fifth of U.S. counties are designated persistent child poverty counties (≥20% of children in poverty since 1980). The association between a persistent child poverty environment and mortality in children with cancer is unknown.

Methods:

Our cohort includes 2,089 children with cancer (2000–2016) in Alabama. We used multivariable Cox proportional hazards modeling (adjusted for sociodemographics/clinical characteristics) to assess mortality by persistent child poverty designation at 1, 5, and 10 years from diagnosis. Distance to treatment was subsequently explored.

Results:

Forty-two percent of the cohort lived in a persistent child poverty county; they were more likely to be African American (P < 0.0001), have public/no insurance (P = 0.0009), and live >100 miles to treatment (P < 0.0001). Children in persistent child poverty counties were 30% more likely to die by 5 years [95% confidence interval (CI) = 1.06–1.59; P = 0.012]. Distance (per 20-mile increase) to treatment was associated with a 9% increased mortality risk (P < 0.0001). Children with both exposures (distance >100 miles and persistent child poverty) faced the highest mortality risk at 5 years (HR = 1.80; 95% CI = 1.39–2.33; P < 0.0001). In subanalysis, children exposed to persistent child poverty were at higher risk for cancer-related mortality. However, the risk of health-related mortality did not differ.

Conclusions:

Among children with cancer from the Deep South, persistent child poverty was a prevalent exposure associated with inferior overall survival. Distance to treatment was independently associated with inferior survival. Children with both exposures had the highest risk of mortality.

Impact:

Persistent child poverty is associated with inferior survival among children with cancer; mechanisms underlying this disparity warrant investigation.

See related commentary by Orjuela-Grimm and Beauchemin, p. 295
Efficacy and Safety of the Anti–PD-L1 mAb Socazolimab for Recurrent or Metastatic Cervical Cancer: a Phase I Dose-Escalation and Expansion Study
Authors: An Jusheng , Tang Jie , Li Benjamin X. , Xiong Huihua , Qiu Hui , Luo Lin , Wang Li , Wang Danbo , Zhou Qi , Xu Qin , Song Honglin , Zhang Yunyan , Zhang Hongping , Li Yujie , Yu Xiaohui , Zhang Jing , Ng Rachel , Zhao Wayne , Wong Michael , Dai Xiangrong , Li Guiling , Wu Lingying
Published: September 2022

Purpose:

This study (ClinicalTrials.gov identifier, NCT03676959) is an open, phase I dose-escalation and expansion study investigating the safety and efficacy of the recombinant, fully human anti–programmed death ligand 1 (PD-L1) mAb socazolimab in patients diagnosed with recurrent or metastatic cervical cancer.

Patients and Methods:

Patients received socazolimab every 2 weeks until disease progression. The study was divided into a dose-escalation phase and a dose-expansion phase. Safety and tolerability were primary endpoints of the dose-escalation phase. The primary endpoints of the dose-expansion phase were safety and the objective response rate (ORR) of the 5 mg/kg dose. Efficacy was assessed by the third-party independent review committee (IRC) using the Response Evaluation Criteria in Solid Tumors version 1.1 (RECIST 1.1).

Results:

104 patients were successfully enrolled into the study. Twelve patients were included in the dose-escalation phase, with one complete response and two partial responses in the 5 mg/kg treatment group. Ninety-two patients (5 mg/kg) were enrolled in the dose-expansion phase. Fifty-four patients (59.3%) had baseline PD-L1–positive tumor expression (combined positive score ≥1). ORR was 15.4% [95% confidence interval (CI), 8.7%–24.5%]. Median PFS was 4.44 months (95% CI, 2.37–5.75 months), and the median OS was 14.72 months (95% CI, 9.59–NE months). ORR of PD-L1–positive patients was 16.7%, and the ORR of PD-L1–negative patients was 17.9%. No treatment-related deaths occurred.

Conclusions:

Our study demonstrates that socazolimab has durable safety and efficacy for the treatment of recurrent or metastatic cervical cancer and exhibits a safety profile similar to other anti–PD-1/PD-L1 mAbs.
Personal Identity and Narrativity in Borderline Personality Disorder: A Phenomenological Reconfiguration
Authors: Bois Cassandre , Fazakas István , Salles Juliette , Gozé Tudi
Published: September 2022

Borderline personality disorder (BPD) is a complex condition marked by heterogeneity. People with BPD have a profusion of symptoms spread across various levels of lived experience, such as identity, affectivity, and interpersonal relationships. Researchers and clinicians have often resorted to the structuring concept of Self to organize the fragmentation of their experience at the identity level. Notably, using the concept of the narrative self, Fuchs proposed to interpret BPD as a fragmentation of narrative identity. This interpretation of BPD, widely shared, has been challenged by Gold and Kyratsous, who have proposed a complementary understanding of the self through the idea of agency, and to which Schmidt and Fuchs in turn have countered. This article proposes to contribute to this discussion from a phenomenological perspective. First, we will briefly review the discussions around narrative interpretation of BPD. From the problems left unresolved by the discussion, we will then justify the necessity to proceed with a stratification of the self from a phenomenology method. Third, from the thought of the Hungarian phenomenologist László Tengelyi, we will continue with an archaeology of the self, in three layers – self-institution, self-formation, and minimal self – integrating Schmidt and Fuchs’ concepts of self, in addition to those of Gold and Kyratsous, but also, to a lesser extent, those of Dan Zahavi. Finally, we will proceed with a phenomenological reconfiguration of the experiences and manifestations associated with the identity axis of BPD.
Application of artificial intelligence in early fault detection of transmission line-a case study in India
Authors: Mawle Prashant P. , Dhomane Gunwant A. , Burade Prakash G.
Published: September 2022

Reliable energy is ensured by the power quality, safety and security. For reliability and economic growth of transmission utilities, it is necessary to maintain continuity of supply, which is challenging under deregulated system. It is essential for utilities to conduct regular maintenance of transmission lines before supply interrupts. To protect line from fault, it is necessary to detect fault on line, its classification and location at the earliest. Various smart techniques along with application of artificial intelligence (AI) in power system are under investigation. This paper tries to find solution by identifying practical common faults occurred on transmission lines, and also suggests the suitable maintenance methodology. It uses the artificial neural network (ANN) method and live line maintenance technique (LLMT) for pre identification of a fault and subsequent predictive maintenance. Paper compares results of combination of ANN with LLMT and cold line maintenance technique (CLMT). Comparison of statistical analysis shows combine model of ANN and LLMT results in minimize outage time, failure rate which can improve system availability and increases revenue.
Maximum power point tracking based on improved spotted hyena optimizer for solar photovoltaic
Authors: Alvianandy Muhammad Farizky , Windarko Novie Ayub , Sumantri Bambang
Published: September 2022

The conventional maximum power point tracking (MPPT) method such as perturb and observe (P&O) under partial shading conditions with non-uniform irradiation, can get trapped on local maximum power point (LMPP) and cannot reach global maximum power point (GMPP). This study proposes a bio-inspired metaheuristic algorithm spotted hyena optimizer (SHO) and improved SHO as a new MPPT technique. The proposed SHO-MPPT and improved SHO-MPPT are used to extract GMPP from solar photovoltaic (PV) arrays operated under uniform irradiation and non-uniform irradiation. Simulation with Powersim (PSIM) and experimental with the emulated PV source were presented. Furthermore, to evaluate the performance of the proposed algorithm, SHO-MPPT is compared with P&O-MPPT and particle swarm optimization (PSO)-MPPT. The SHO-MPPT has an accuracy of 99% and has the good capability, but there are power fluctuations before reaching MPP. Therefore, improved SHO-MPPT was developed to get better results. The improved SHO-MPPT proved high accuracy of 99% and faster than SHO-MPPT and PSO-MPPT in tracking the maximum power point (MPP). Furthermore, there are minor power fluctuations.