bluuebunny/crossref_metadata_2025 huggingface dataset:
Understanding Persuasion Cascades in Online Product Rating Systems: Modeling, Analysis, and Inference
Authors: Xie Hong , Zhong Mingze , Li Yongkun , Lui John C. S.
Published: April 2021

Online product rating systems have become an indispensable component for numerous web services such as Amazon, eBay, Google Play Store, and TripAdvisor. One functionality of such systems is to uncover the product quality via product ratings (or reviews) contributed by consumers. However, a well-known psychological phenomenon called “
message-based persuasion
” lead to “
biased
” product ratings in a cascading manner (we call this the
persuasion cascade
). This article investigates:
(1) How does the persuasion cascade influence the product quality estimation accuracy? (2) Given a real-world product rating dataset, how to infer the persuasion cascade and analyze it to draw practical insights?
We first develop a mathematical model to capture key factors of a persuasion cascade. We formulate a high-order Markov chain to characterize the opinion dynamics of a persuasion cascade and prove the convergence of opinions. We further bound the product quality estimation error for a class of rating aggregation rules including the averaging scoring rule, via the matrix perturbation theory and the Chernoff bound. We also design a maximum likelihood algorithm to infer parameters of the persuasion cascade. We conduct experiments on both synthetic data and real-world data from Amazon and TripAdvisor. Experiment results show that our inference algorithm has a high accuracy. Furthermore, persuasion cascades notably exist, but the average scoring rule has a small product quality estimation error under practical scenarios.
Reducing Cumulative Errors of Incremental CP Decomposition in Dynamic Online Social Networks
Authors: Wang Jingjing , Jiang Wenjun , Li Kenli , Li Keqin
Published: April 2021

CANDECOMP/PARAFAC (CP) decomposition is widely used in various online social network (OSN) applications. However, it is inefficient when dealing with massive and incremental data. Some incremental CP decomposition (ICP) methods have been proposed to improve the efficiency and process evolving data, by updating decomposition results according to the newly added data. The ICP methods are efficient, but inaccurate because of serious error accumulation caused by approximation in the incremental updating. To promote the wide use of ICP, we strive to reduce its cumulative errors while keeping high efficiency. We first differentiate all possible errors in ICP into two types: the cumulative reconstruction error and the prediction error. Next, we formulate two optimization problems for reducing the two errors. Then, we propose several restarting strategies to address the two problems. Finally, we test the effectiveness in three typical dynamic OSN applications. To the best of our knowledge, this is the first work on reducing the cumulative errors of the ICP methods in dynamic OSNs.
Scholar2vec: Vector Representation of Scholars for Lifetime Collaborator Prediction
Authors: Wang Wei , Xia Feng , Wu Jian , Gong Zhiguo , Tong Hanghang , Davison Brian D.
Published: April 2021

While scientific collaboration is critical for a scholar, some collaborators can be more significant than others, e.g., lifetime collaborators. It has been shown that lifetime collaborators are more influential on a scholar’s academic performance. However, little research has been done on investigating predicting such special relationships in academic networks. To this end, we propose Scholar2vec, a novel neural network embedding for representing scholar profiles. First, our approach creates scholars’ research interest vector from textual information, such as demographics, research, and influence. After bridging research interests with a collaboration network, vector representations of scholars can be gained with graph learning. Meanwhile, since scholars are occupied with various attributes, we propose to incorporate four types of scholar attributes for learning scholar vectors. Finally, the early-stage similarity sequence based on Scholar2vec is used to predict lifetime collaborators with machine learning methods. Extensive experiments on two real-world datasets show that Scholar2vec outperforms state-of-the-art methods in lifetime collaborator prediction. Our work presents a new way to measure the similarity between two scholars by vector representation, which tackles the knowledge between network embedding and academic relationship mining.
Neural Networks for Entity Matching: A Survey
Authors: Barlaug Nils , Gulla Jon Atle
Published: April 2021

Entity matching is the problem of identifying which records refer to the same real-world entity. It has been actively researched for decades, and a variety of different approaches have been developed. Even today, it remains a challenging problem, and there is still generous room for improvement. In recent years, we have seen new methods based upon deep learning techniques for natural language processing emerge.

In this survey, we present how neural networks have been used for entity matching. Specifically, we identify which steps of the entity matching process existing work have targeted using neural networks, and provide an overview of the different techniques used at each step. We also discuss contributions from deep learning in entity matching compared to traditional methods, and propose a taxonomy of deep neural networks for entity matching.
Knowledge Transfer with Weighted Adversarial Network for Cold-Start Store Site Recommendation
Authors: Liu Yan , Guo Bin , Zhang Daqing , Zeghlache Djamal , Chen Jingmin , Hu Ke , Zhang Sizhe , Zhou Dan , Yu Zhiwen
Published: April 2021

Store site recommendation aims to predict the value of the store at candidate locations and then recommend the optimal location to the company for placing a new brick-and-mortar store. Most existing studies focus on learning machine learning or deep learning models based on large-scale training data of existing chain stores in the same city. However, the expansion of chain enterprises in new cities suffers from data scarcity issues, and these models do not work in the new city where no chain store has been placed (i.e., cold-start problem). In this article, we propose a unified approach for cold-start store site recommendation, Weighted Adversarial Network with Transferability weighting scheme (WANT), to transfer knowledge learned from a data-rich source city to a target city with no labeled data. In particular, to promote positive transfer, we develop a discriminator to diminish distribution discrepancy between source city and target city with different data distributions, which plays the minimax game with the feature extractor to learn transferable representations across cities by adversarial learning. In addition, to further reduce the risk of negative transfer, we design a transferability weighting scheme to quantify the transferability of examples in source city and reweight the contribution of relevant source examples to transfer useful knowledge. We validate WANT using a real-world dataset, and experimental results demonstrate the effectiveness of our proposed model over several state-of-the-art baseline models.
Probability Ordinal-Preserving Semantic Hashing for Large-Scale Image Retrieval
Authors: Zhang Zheng , Zhu Xiaofeng , Lu Guangming , Zhang Yudong
Published: April 2021

Semantic hashing enables computation and memory-efficient image retrieval through learning similarity-preserving binary representations. Most existing hashing methods mainly focus on preserving the piecewise class information or pairwise correlations of samples into the learned binary codes while failing to capture the mutual triplet-level ordinal structure in similarity preservation. In this article, we propose a novel Probability Ordinal-preserving Semantic Hashing (POSH) framework, which for the first time defines the ordinal-preserving hashing concept under a non-parametric Bayesian theory. Specifically, we derive the whole learning framework of the ordinal similarity-preserving hashing based on the maximum
posteriori
estimation, where the probabilistic ordinal similarity preservation, probabilistic quantization function, and probabilistic semantic-preserving function are jointly considered into one unified learning framework. In particular, the proposed triplet-ordering correlation preservation scheme can effectively improve the interpretation of the learned hash codes under an economical anchor-induced asymmetric graph learning model. Moreover, the sparsity-guided selective quantization function is designed to minimize the loss of space transformation, and the regressive semantic function is explored to promote the flexibility of the formulated semantics in hash code learning. The final joint learning objective is formulated to concurrently preserve the ordinal locality of original data and explore potentials of semantics for producing discriminative hash codes. Importantly, an efficient alternating optimization algorithm with the strictly proof convergence guarantee is developed to solve the resulting objective problem. Extensive experiments on several large-scale datasets validate the superiority of the proposed method against state-of-the-art hashing-based retrieval methods.
Parallel Greedy Algorithm to Multiple Influence Maximization in Social Network
Authors: Wu Guanhao , Gao Xiaofeng , Yan Ge , Chen Guihai
Published: April 2021

Influence Maximization (IM) problem is to select

influential users to maximize the influence spread, which plays an important role in many real-world applications such as product recommendation, epidemic control, and network monitoring. Nowadays multiple kinds of information can propagate in online social networks simultaneously, but current literature seldom discuss about this phenomenon. Accordingly, in this article, we propose Multiple Influence Maximization (MIM) problem where multiple information can propagate in a single network with different propagation probabilities. The goal of MIM problems is to maximize the overall accumulative influence spreads of different information with the limit of seed budget

. To solve MIM problems, we first propose a greedy framework to solve MIM problems which maintains an

-approximate ratio. We further propose parallel algorithms based on semaphores, an inter-thread communication mechanism, which significantly improves our algorithms efficiency. Then we conduct experiments for our framework using complex social network datasets with 12k, 154k, 317k, and 1.1m nodes, and the experimental results show that our greedy framework outperforms other heuristic algorithms greatly for large influence spread and parallelization of algorithms reduces running time observably with acceptable memory overhead.
TPmod: A Tendency-Guided Prediction Model for Temporal Knowledge Graph Completion
Authors: Bai Luyi , Ma Xiangnan , Zhang Mingcheng , Yu Wenting
Published: April 2021

Temporal knowledge graphs (TKGs) have become useful resources for numerous Artificial Intelligence applications, but they are far from completeness. Inferring missing events in temporal knowledge graphs is a fundamental and challenging task. However, most existing methods solely focus on entity features or consider the entities and relations in a disjoint manner. They do not integrate the features of entities and relations in their modeling process. In this paper, we propose TPmod, a tendency-guided prediction model, to predict the missing events for TKGs (extrapolation). Differing from existing works, we propose two definitions for TKGs: the
Goodness
of relations and the
Closeness
of entity pairs. More importantly, inspired by the attention mechanism, we propose a novel tendency strategy to guide our aggregated process. It integrates the features of entities and relations, and assigns varying weights to different past events. What is more, we select the Gate Recurrent Unit (GRU) as our sequential encoder to model the temporal dependency in TKGs. Besides, the Softmax function is employed to generate the final decreasing group of candidate entities. We evaluate our model on two TKG datasets: GDELT-5 and ICEWS-250. Experimental results show that our method has a significant and consistent improvement compared to state-of-the-art baselines.
Comparative Perspectives on Racial Discrimination in Hiring: The Rise of Field Experiments
Authors: Quillian Lincoln , Midtbøen Arnfinn H.
Published: April 2021

This article reviews studies of hiring discrimination against racial and ethnic minority groups in cross-national perspective. We focus on field experimental studies of hiring discrimination: studies that use fictitious applications from members of different racial and ethnic groups to apply for actual jobs. There are more than 140 field experimental studies of hiring discrimination against ethno-racial minority groups in 30 countries. We outline seventeen empirical findings from this body of studies. We also discuss individual and contextual theories of hiring discrimination, the relative strengths and weaknesses of field experiments to assess discrimination, and the history of such field experiments. The comparative scope of this body of research helps to move beyond micromodels of employer decision-making to better understand the roles of history, social context, institutional rules, and racist ideologies in producing discrimination. These studies show that racial and ethnic discrimination is a pervasive international phenomenon that has hardly declined over time, although levels vary significantly over countries. Evidence indicates that institutional rules regarding race and ethnicity in hiring can have an important influence on levels of discrimination. Suggestions for future research on discrimination are discussed.
The regulation of polyamine pathway proteins in models of skeletal muscle hypertrophy and atrophy: a potential role for mTORC1
Authors: Tabbaa Michael , Ruz Gomez Tania , Campelj Dean G. , Gregorevic Paul , Hayes Alan , Goodman Craig A.
Published: April 2021

Polyamines have been shown to be absolutely required for protein synthesis and cell growth. The serine/threonine kinase, the mechanistic target of rapamycin complex 1 (mTORC1), also plays a fundamental role in the regulation of protein turnover and cell size, including in skeletal muscle, where mTORC1 is sufficient to increase protein synthesis and muscle fiber size, and is necessary for mechanical overload-induced muscle hypertrophy. Recent evidence suggests that mTORC1 may regulate the polyamine metabolic pathway, however, there is currently no evidence in skeletal muscle. This study examined changes in polyamine pathway proteins during muscle hypertrophy induced by mechanical overload (7 days), with and without the mTORC1 inhibitor, rapamycin, and during muscle atrophy induced by food deprivation (48 h) and denervation (7 days) in mice. Mechanical overload induced an increase in mTORC1 signaling, protein synthesis and muscle mass, and these were associated with rapamycin-sensitive increases in adenosylmethione decarboxylase 1 (Amd1), spermidine synthase (SpdSyn), and c-Myc. Food deprivation decreased mTORC1 signaling, protein synthesis, and muscle mass, accompanied by a decrease in spermidine/spermine acetyltransferase 1 (Sat1). Denervation, resulted increased mTORC1 signaling and protein synthesis, and decreased muscle mass, which was associated with an increase in SpdSyn, spermine synthase (SpmSyn), and c-Myc. Combined, these data show that polyamine pathway enzymes are differentially regulated in models of altered mechanical and metabolic stress, and that Amd1 and SpdSyn are, in part, regulated in a mTORC1-dependent manner. Furthermore, these data suggest that polyamines may play a role in the adaptive response to stressors in skeletal muscle.
Dietary fat and low fiber in purified diets differently impact the gut-liver axis to promote obesity-linked metabolic impairments
Authors: Daniel Noëmie , Rossi Perazza Laίs , Varin Thibault V. , Trottier Jocelyn , Marcotte Bruno , St-Pierre Philippe , Barbier Olivier , Chassaing Benoit , Marette André
Published: April 2021

Selecting the most relevant control diet is of critical importance for metabolic and intestinal studies in animal models. Chow and LF-purified diet differentially impact metabolic and gut microbiome outcomes resulting in major changes in intestinal integrity in LF-fed animals which contributes to altering metabolic homeostasis. Dietary fat and low fiber both contribute to the deleterious metabolic effect of purified HF diets through both selective and overlapping mechanisms.
Abrogation of mesenchyme-specific TGF-β signaling results in lung malformation with prenatal pulmonary cysts in mice
Authors: Miao Qing , Chen Hui , Luo Yongfeng , Chiu Joanne , Chu Ling , Thornton Matthew E. , Grubbs Brendan H. , Kolb Martin , Lou Jianlin , Shi Wei
Published: April 2021

The TGF-β signaling pathway plays a pivotal role in controlling organogenesis during fetal development. Although the role of TGF-β signaling in promoting lung alveolar epithelial growth has been determined, mesenchymal TGF-β signaling in regulating lung development has not been studied in vivo due to a lack of genetic tools for specifically manipulating gene expression in lung mesenchymal cells. Therefore, the integral roles of TGF-β signaling in regulating lung development and congenital lung diseases are not completely understood. Using a Tbx4 lung enhancer-driven Tet-On inducible Cre transgenic mouse system, we have developed a mouse model in which lung mesenchyme-specific deletion of TGF-β receptor 2 gene ( Tgfbr2) is achieved. Reduced airway branching accompanied by defective airway smooth muscle growth and later peripheral cystic lesions occurred when lung mesenchymal Tgfbr2 was deleted from embryonic day 13.5 to 15.5, resulting in postnatal death due to respiratory insufficiency. Although cell proliferation in both lung epithelium and mesenchyme was reduced, epithelial differentiation was not significantly affected. Tgfbr2 downstream Smad-independent ERK1/2 may mediate these mesenchymal effects of TGF-β signaling through the GSK3β-β-catenin-Wnt canonical pathway in fetal mouse lung. Our study suggests that Tgfbr2-mediated TGF-β signaling in prenatal lung mesenchyme is essential for lung development and maturation, and defective TGF-β signaling in lung mesenchyme may be related to abnormal airway branching morphogenesis and congenital airway cystic lesions.
Transient effects of forebrain ischemia on fetal heart rate variability in fetal sheep
Authors: Maeda Yoshiki , Lear Christopher A. , Beacom Michael J. , Davidson Joanne O. , Zhou Kelly Q. , Gunning Mark , Ikeda Tomoaki , Gunn Alistair J. , Bennet Laura
Published: April 2021

Fetal heart rate variability (FHRV) is a key index of antenatal and intrapartum fetal well-being. FHRV is well established to be mediated by both arms of the autonomic nervous system, but it remains unknown whether higher centers in the forebrain contribute to FHRV. We tested the hypothesis that selective forebrain ischemia would impair the generation of FHRV. Sixteen chronically instrumented near-term fetal sheep were subjected to either forebrain ischemia induced by bilateral carotid occlusion or sham-ischemia for 30 min. Time, frequency, and nonlinear measures of FHRV were assessed during and for seven days after ischemia. Ischemia was associated with profound suppression of electroencephalographic (EEG) power, which remained suppressed throughout the recovery period ( P < 0.001). During the first 5 min of ischemia, multiple time and frequency domain measures were increased (all P < 0.05) before returning back to sham levels. A delayed increase in sample entropy was observed during ischemia ( P < 0.05). For the first 3 h after ischemia, there was moderate suppression of two measures of FHRV (very-low frequency power and the standard deviation of RR-intervals, both P < 0.05) and increased sample entropy ( P < 0.05). Thereafter, all measures of FHRV returned to control levels. In conclusion, profound forebrain ischemia sufficient to lead to severe neural injury had only transient effect on multiple measures of FHRV. These findings suggest that the forebrain makes a limited contribution to FHRV. FHRV therefore primarily originates in the hindbrain and is unlikely to provide meaningful information on forebrain neurodevelopment or metabolism.
Liver circadian clock disruption alters perivascular adipose tissue gene expression and aortic function in mice
Authors: Pati Paramita , Valcin Jennifer A. , Zhang Dingguo , Neder Thomas H. , Millender-Swain Telisha , Allan John Miller , Sedaka Randee , Jin Chunhua , Becker Bryan K. , Pollock David M. , Bailey Shannon M. , Pollock Jennifer S.
Published: April 2021

The liver plays a central role that influences cardiovascular disease outcomes through regulation of glucose and lipid metabolism. It is recognized that the local liver molecular clock regulates some liver-derived metabolites. However, it is unknown whether the liver clock may impact cardiovascular function. Perivascular adipose tissue (PVAT) is a specialized type of adipose tissue surrounding blood vessels. Importantly, cross talk between the endothelium and PVAT via vasoactive factors is critical for vascular function. Therefore, we designed studies to test the hypothesis that cardiovascular function, including PVAT function, is impaired in mice with liver-specific circadian clock disruption. Bmal1 is a core circadian clock gene, thus studies were undertaken in male hepatocyte-specific Bmal1 knockout (HBK) mice and littermate controls (i.e., flox mice). HBK mice showed significantly elevated plasma levels of β-hydroxybutyrate, nonesterified fatty acids/free fatty acids, triglycerides, and insulin-like growth factor 1 compared with flox mice. Thoracic aorta PVAT in HBK mice had increased mRNA expression of several key regulatory and metabolic genes, Ppargc1a, Pparg, Adipoq, Lpl, and Ucp1, suggesting altered PVAT energy metabolism and thermogenesis. Sensitivity to acetylcholine-induced vasorelaxation was significantly decreased in the aortae of HBK mice with PVAT attached compared with aortae of HBK mice with PVAT removed, however, aortic vasorelaxation in flox mice showed no differences with or without attached PVAT. HBK mice had a significantly lower systolic blood pressure during the inactive period of the day. These new findings establish a novel role of the liver circadian clock in regulating PVAT metabolic gene expression and PVAT-mediated aortic vascular function.
The Protective Effect of Aspirin against Myocardial Hypertrophy in Rats
Authors: Wu Xiaolong , Wei Minghui , Zhang Haifeng , Fan Xiaomei , Ma Xiaochen , Liu Jiaming , Xue Mingming
Published: April 2021

The protective effect of aspirin against myocardial hypertrophy (MH) was studied. Model rats of pressure overload MH were prepared by abdominal aortic coarctation. Rats were randomly divided into the sham group (n = 9), MH model group (n = 9), and MH+aspirin group (n = 9), which was, respectively, divided into the 4‐week group and 8‐week group according to the time of intragastric administration. Arterial blood pressure and left ventricular mass index (LVMI) were measured. Changes in myocardial tissue structure were observed by HE staining, Masson staining, and reticular fiber staining. Cardiomyocyte apoptosis was detected by TUNEL assay. The levels of TNF‐α, IL‐10, TXA2, and PGI2 in myocardium and plasma were detected by ELISA. The arterial blood pressure in the MH model group was significantly higher than that in the 4‐ and 8‐week sham groups, but that in the MH+aspirin group was significantly lower than that in the MH model group. At 4 and 8 weeks, the LVWI in the MH model group was significantly higher than that in the sham group, but it was significantly reduced after aspirin treatment. The myocardial cell hypertrophy was obvious, collagen fibers were proliferated, and reticular fibers were reduced in the 4‐ and 8‐week MH model groups. Compared with the MH model groups, myocardial cells in the MH+aspirin groups were significantly reduced, the collagen fiber content was significantly reduced, and the reticular fiber content was increased. The apoptotic cardiomyocytes in the 4‐ and 8‐week MH model groups were obviously increased. The apoptosis of myocardial cells in the MH+aspirin groups was obviously decreased. The TNF‐α levels in the myocardial tissue of the 4‐ and 8‐week MH model groups were significantly increased, while those of the MH+aspirin groups were significantly decreased. There was no significant change in the IL‐10 level or PGI2 level at 4 weeks. At 8 weeks, the PGI2 level was significantly decreased in the MH model group while significantly increased in the MH+aspirin group. The TXA2 levels were significantly increased in the 4‐ and 8‐week MH model groups and those in the 4‐ and 8‐week MH+aspirin groups were significantly lower. Aspirin has an anti‐inflammatory effect, can effectively reduce the expression of inflammatory factors, inhibit myocardial apoptosis, and has a certain protective effect against MH.
All Inside Intraepiphyseal ACL Reconstruction Using Flexible Curved Instrumentation and Intraoperative Fluoroscopy in a Skeletally Immature Patient
Authors: Puzzitiello Richard N. , Agarwalla Avinesh , Garcia Grant , Forlenza Enrico M. , Forsythe Brian
Published: April 2021

Case. A 13-year-old skeletally immature female presenting with an anterior cruciate ligament (ACL) rupture after a noncontact injury was treated with an intraepiphyseal ACL reconstruction. Flexible instrumentation was utilized to drill a femoral tunnel with an anatomic starting point, with a trajectory that curved inferolaterally away from the physis. At three years postoperatively, she had returned to her preinjury functioning and did not display any lower limb length growth abnormalities. Conclusions. The novel application of curved guides and flexible instruments, with intraoperative fluoroscopy, facilitated growth plate avoidance and a successful outcome of ACL reconstruction in a skeletally immature patient.
Research on Aerodynamic Optimization Method of Multistage Axial Compressor under Multiple Working Conditions Based on Phased Parameterization Strategy
Authors: Cheng Jinxin , Dong Zhaohui , Zhao Shengfeng , Xiang Hang
Published: April 2021

Multistage axial compressor is the key component of aeroengine and gas turbine to realize energy conversion. In order to avoid the “curse of dimensionality” problem in the global optimization process of AL-31F four-stage low-pressure compressor under multiple working conditions, an optimization method based on phased parameterization strategy is proposed. The method uses the idea of “exploration before exploitation” for reference and divides the optimization process into two phases. In the first phase, the traditional parametric modification method based on stacking line is adopted; in the second phase, the full-blade surface parametric modification method with significant low-dimensional characteristics is adopted. Based on the improved artificial bee colony algorithm, a multitask concurrent optimization system is built on the supercomputing platform, and the engineering optimization solution is obtained within 91 hours. The optimization results are as follows: under the condition of meeting the constraints, the adiabatic efficiency is increased by 0.3% and the surge margin is 4.0% at the design speed; the adiabatic efficiency is increased by 0.8% and the surge margin is 2.3% at the off-design speed. These results verify the usefulness and reliability of the optimization method in the field of aerodynamic optimization of a multistage axial flow compressor.
[Retracted] Music Classification and Detection of Location Factors of Feature Words in Complex Noise Environment
Authors: Xu Yulan , Li Qiaowei
Published: April 2021

In order to solve the problem of the influence of feature word position in lyrics on music emotion classification, this paper designs a music classification and detection model in complex noise environment. Firstly, an intelligent detection algorithm for electronic music signals under complex noise scenes is proposed, which can solve the limitations existing in the current electronic music signal detection process. At the same time, denoising technology is introduced to eliminate the noise and extract the features from the signal. Secondly, from the perspective of audio and lyrics of song sentiment analysis and the unique characteristics of lyrics text, a lyric sentiment analysis method based on text title and position weight is proposed. Finally, considering the influence of the weight of feature words in different positions on the classification of lyrics, the analytic hierarchy process is used to calculate the weight of feature words in different positions of text title and lyrics before, in, and after the text. The results show that in the complex noise environment, the accuracy of music classification and detection of the proposed model is more than 90%, which is far beyond the control range of the actual application of music processing. The effect of music classification and detection is better than that of the contrast model, which has a certain practical application value.
Minerals Filling in Anhydrite Dissolution Pores and Their Origins in the Ordovician Majiagou Formation of the Southeastern Ordos Basin, China
Authors: Liu Lihong , Wang Chunlian , Du Zhili , Gong Jianghua
Published: April 2021

Mold pore cementation is the key factor constraining the reservoir property in the study area. The anhydrite dissolution pores in the Ordovician Majiagou Formation of southeastern Ordos Basin are commonly filled by minerals such as dolomite, calcite, pyrite, and quartz accounting for more than 90% of the total molds resulting in significant porosity volume reduction. The anhydrite dissolution pores in the Jingbian Gas Field in the middle east of the basin, however, are rarely filled by minerals with more than 30% molds, remaining open to become good reservoir space. Studies reveal that the calcite filling in anhydrite dissolution pores has a relatively negative δ18O value (-15.58‰~-8.96‰ VPDB) and negative δ13C value (-7.56‰~0.26‰ VPDB), which is interpreted to be caused by thermochemical sulfate reduction (TSR). The higher homogenization temperatures (140-234°C) and high salinity (19.13-23.18 wt.% NaCl equivalent) of the primary inclusions in calcite confirm the above interpretation. Dolomite is the second most abundant carbonate formed as by-product of TSR, which is promoted by the precipitation of calcite and resulted enriched in Mg2+/Ca2+ ratio in the pore water. Pyrite forms by the reaction of H2S released from TSR with the Fe2+ in the horizon, which is supported by its cubic habit and relatively high δ34S value (10.50‰~24.00‰VCDT). Quartz with relatively high homogenization temperature (113-154°C) is considered to precipitate in low-pH solution from calcite and pyrite precipitation after TSR. The southeastern Ordos Basin is much lower than the Jingbian Gas Field in paleogeographic location, which is submerged in the sea water of marine phreatic environments for a long time when sea water flooded from the southeastern direction. TSR occurs due to calcium sulfate enriched in pore water resulting in the minerals of dolomite, calcite, pyrite, and quartz filling in the molds leading to the low porosity and permeability of the study area.
Design of English Intelligent Simulated Paper Marking System
Authors: Yang Lina , Liu Wei
Published: April 2021

In this paper, we analyze the intelligent subdesign of the simulated marking system through an in‐depth study of it. This paper proposes a correlation analysis‐based quantification of N‐element sense values and a rationality enhancement‐based scoring fitting algorithm for English essays. This paper also extracts word features, sentence features, and chapter structure features in essays to fit English composition scores. Since not all students can complete the essays according to the topic requirements, a triage scoring model is used to separate the normal essays from the low‐scoring essays. Statistically, it was found that the essay scores also showed a certain normal distribution. The standard support vector regression algorithm is prone to data skewing problems, so this paper addresses this problem by using a rationality enhancement method that gives a corresponding penalty factor according to the distribution of the dataset. The results show that the English essay scoring fitting algorithm proposed in this paper can well improve the prediction accuracy of some data and solve the problem of skewed data where the scores show a normal distribution. This paper designs and implements an online mock examination system that incorporates an intelligent scoring system for essays, enabling it to meet the needs of teachers and students for online examinations and intelligent scoring.
Using ANN to Estimate the Critical Buckling Load of Y Shaped Cross-Section Steel Columns
Authors: Nguyen Thuy-Anh , Ly Hai-Bang , Mai Hai-Van Thi , Tran Van Quan
Published: April 2021

Accurate measurement of the critical buckling stress is crucial in the entire field of structural engineering. In this paper, the critical buckling load of Y-shaped cross-section steel columns was predicted by the Artificial Neural Network (ANN) using the Levenberg-Marquardt algorithm. The results of 57 buckling tests were used to generate the training and testing datasets. Seven input variables were considered, including the column length, column width, steel equal angles thickness, the width and thickness of the welded steel plate, and the total deviations following the Ox and Oy directions. The output was the critical buckling load of the columns. The accuracy assessment criteria used to evaluate the model were the correlation coefficient (R), root mean square error (RMSE), and mean absolute error (MAE). The selection of an appropriate structure of ANN was first addressed, followed by two investigations on the highest accuracy models. The first one consisted of the ANN model that gave the lowest values of MAE = 40.0835 and RMSE = 30.6669, whereas the second one gave the highest value of R = 0.98488. The results revealed that taking MAE and RMSE for model assessment was more accurate and reasonable than taking the R criterion. The RMSE and MAE criteria should be used in priority, compared with the correlation coefficient.
A Review of Smart Contracts Applications in Various Industries: A Procurement Perspective
Authors: Xu Yongshun , Chong Heap-Yih , Chi Ming
Published: April 2021

Smart contracts have been well‐received by researchers and practitioners for the unique features of automatic execution, transparency, and nontampering in a blockchain environment. However, little is known about the current development status of knowledge and practice regarding the application of smart contracts in various industries, especially from the procurement perspective. Thus, this paper aims to address the gap with a mixed method of bibliometric analysis and systematic literature review. Based on the evaluation of 174 filtered publications, the review has analyzed the current development status of this research area with its distributions in years and journals, cooperation networks between authors, institutions, and countries, keywords cooccurrence network, and classifications of the application of smart contracts. The results show the application of smart contracts has attracted global attention since 2016 with the Ethereum and Hyperledger fabric as the main platforms in various industries, especially in information communication technology (ICT), public management, supply chain, energy, finance, and healthcare. Various functions and benefits of smart contracts, as well as their potential advantages, have been identified and articulated from the procurement perspective. A research framework has also been developed to highlight future procurement needs in business operations across the industries via an integrated procurement approach of smart contracts.
A Data Preservation Method Based on Blockchain and Multidimensional Hash for Digital Forensics
Authors: Liu Gongzheng , He Jingsha , Xuan Xinggang
Published: April 2021

Since digital forensics becomes more and more popular, more and more attention has been paid to the originality and validity of data, and data preservation technology emerges as the times require. However, the current data preservation models and technologies are only the combination of cryptography technology, and there is a risk of being attacked and cracked. And in the process of data preservation, human participation is also needed, which may lead to data tampering. To solve problems given, this paper presents a data preservation model based on blockchain and multidimensional hash. With the decentralization and smart contract characteristics of blockchain, data can be automatically preserved without human participation to form a branch chain of custody in the unit of case, and blockchain has good antiattack performance, which is the so‐called 51% attack. Meanwhile, in order to solve the problem of data confusion and hard to query caused by the excessive number of cases, hash, cryptography, and timestamps are used to form a serialized main chain of custody. Because of the confliction problem of hash and judicial trial needs to absolutely guarantee the authenticity and validity of data, multidimensional hash is used to replace regular hash. In this way, the data preservation becomes an automatic, nonhuman‐interventional process. Experiments have been carried out to show the security and effectiveness of the proposed model.
Completion Rates of Advance Directives in a Trauma Emergency Room: Association with Age
Authors: Graw Jan A. , Burchard René
Published: April 2021

Introduction An advance directive (AD) is a written legal document in which a person can express wishes and preferences for medical treatment for the moment when that person is no longer able to make medical decisions because of a serious illness or injury. While ADs have emerged in public, it is unclear, how many adults in Germany have completed an AD, and frequencies differ among different patient cohorts and medical settings. The aim of this study was to evaluate how many patients visiting a trauma emergency room (ER) in an academic teaching hospital had completed an AD. Furthermore, patient characteristics were compared between patients who had completed an AD and those who had not completed an AD. Methods. Patients with a traumatic injury or disease who attended the ER of an academic teaching hospital in the period from October 2015 to March 2016 (n = 499) were surveyed for completion rates of ADs. Results. Prior to their visit to the ER, 12.8% of the included patients possessed a completed AD. Patients with a completed AD had a higher age (median age: 54 (IQR: 34–66) vs. 35 (IQR: 25–50)

p
<
0.001

) and were less often living in an urban residential location (UR) (UR: 23.5% vs. 39.4%,

p
=
0.029

). Groups did not differ between sex (

p
=
0.115

), frequencies of high school graduates (

p
=
0.482

), and possession of a private health insurance (

p
=
0.072

), disability insurance (

p
=
0.291

), or an accident insurance (

p
=
0.790

). Conclusion. Completion rates of ADs remain low among patients visiting an ER of an academic teaching hospital in Germany. Increasing age but not factors such as sex, educational background, or insurance status were associated with a higher frequency of completed ADs.
Statistical Physics Modeling of Sorption Isotherms of Aluminum, Iron, and Indium on Tetraphenylporphyrin (H2TPP) and Tetrakis(4-tolylphenyl)porphyrin (H2TTPP): Phenomenological Investigation of Metalloporphyrins at the Molecular Level
Authors: Elqahtani Zainab , Ben Yahia Mohamed , Alfryyan Nada , Aldaghfag Shatha , Aouaini Fatma
Published: April 2021

A quartz crystal adsorbent functionalized with two promising porphyrins (the 5,10,15,20-tetrakis(4-tolylphenyl)porphyrin and the 5,10,15,20-tetraphenylporphyrin) was applied for the investigation of the adsorption phenomenon of aluminum chloride, iron chloride, and indium chloride. The aim is to prove new insights about the appropriate adsorption materials for metalloporphyrin fabrication. The equilibrium isotherms were measured at five adsorption temperatures (from 290 to 330 K) through the microbalance (QCM) method. The discussion of the experimental observations indicated that the adsorption of the aluminum chloride and the iron chloride was performed via a monolayer process. On contrary, the participation of the chloride ions in the double-layer adsorption of the indium chloride was explained by the layer-by-layer process. Overall, the statistical physics modeling of the experimental curves indicated that the number of ions per adsorbent site [Formula: see text] was found inferior to 1 for all the adsorption systems (multi-interaction process for the three ions). Interestingly, the physicochemical investigation of the three adopted models showed that the complexation mechanism of the tested porphyrins was an endothermic process since the two steric parameters ([Formula: see text] and [Formula: see text]) increased with the rise of the temperature. The FeCl3 curves were discussed via a monolayer adsorption model which includes the parameters [Formula: see text] and [Formula: see text] (lateral interaction description), indicating the lowest stability of the formed iron-porphyrin complex. The energetic study showed that the adsorption energies [Formula: see text] of AlCl3 on H2TTPP and H2TPP are superior to 40 kJ/mol (chemical adsorption mechanism), whereas the adsorption mechanisms of FeCl3 and InCl3 took place via a physical process since they presented adsorption energy values lower than 40 kJ/mol.
New Bernstein Type Results in Weighted Warped Products
Authors: Zhang Ning
Published: April 2021

In this paper, we obtain new parametric uniqueness results for complete constant weighted mean curvature hypersurfaces under suitable geometric assumptions in weighted warped products. Furthermore, we also prove very general Bernstein type results for the constant mean curvature equation for entire graphs in these ambient spaces.
Trend of Bacterial Uropathogens and Their Susceptibility Pattern: Study of Single Academic High-Volume Center in Italy (2015–2019)
Authors: Serretiello Enrica , Folliero Veronica , Santella Biagio , Giordano Giuseppina , Santoro E. , De Caro Francesco , Pagliano Pasquale , Ferro Matteo , Aliberti Silvana M. , Capunzo Mario , Galdiero Massimiliano , Franci Gianluigi , Boccia Giovanni
Published: April 2021

Urinary tract infections (UTIs) are a very widespread infection that can occur in disparate age range, in both sexes and in pregnancy/menopause state. Treatment of UTIs is difficult due to the emergence of antibiotic-resistant bacterial strains. The present study shows five years of data collected on patients admitted at the University Hospital “San Giovann di Dio e Ruggi d’Aragona” in Salerno, Italy. The investigation exhibits the incidence of the infection, of the gender, and of the age group affected, identifying the most representative bacteria involved, drawing their profile of antimicrobial resistance. Bacterial identification and antibiotic susceptibility testing were performed using the VITEK 2 system. Among the 46382 studied patients, 9896 (21.34%) and 36486 (78.66%) were positive and negative for microorganism growth, respectively. Of 9896 positive patients, 6158 (62.23%) females and 3738 (37.77%) males were identified. The highest incidence of positive subjects (56.66%) was recorded in the elderly (>61 years). 8431 (85.20%) uropathogens were Gram-negative, 1367 (13.81%) were Gram-positive, and 98 (0.99%) were Candida species (Candida spp.). Escherichia coli (E. coli) and Enterococcus faecalis (E. faecalis) were the most representative Gram-negative and Gram-positive strains, respectively. The Gram-negative bacteria most representative were highly resistant to ampicillin, whereas among the Gram-positive bacteria, E. faecalis was highly resistant to gentamicin and streptomycin high level synergy, and Enterococcus faecium (E. faecium) to ampicillin, ampicillin/sulbactam, and imipenem. This retrospective work investigates the local epidemiological trend in our university hospital in order to induce an increasingly targeted empirical therapeutic approach for the treatment of UTIs.
Denosumab as the Treatment of Recalcitrant Tuberculous Pleural Effusion-Associated Hypercalcemia
Authors: Abd Jalil Afdhal Afiq , Wan Muhamad Hatta Sharifah Faradila , Mohamad Aimi Fadilah , Abdul Rani Mohammed Fauzi
Published: April 2021

Denosumab is a human monoclonal antibody that binds to RANKL (receptor activator of nuclear factor-kappa B ligand). It has mainly been used in the treatment of osteoporosis for a variety of causes especially in situations refractory to bisphosphonates or when kidney function is impaired. It is also used in cases of malignancy-associated hypercalcemia. There are many causes of hypercalcemia, but only rarely it is associated with granulomatous diseases such as tuberculous pleural effusion. We report a case of hypercalcemia from tuberculous pleural effusion that was initially admitted with left medium abundance pleural effusion and a serum corrected calcium level of 3.48 mmol/L. The calcium level was successfully normalized within 72 hours of subcutaneous denosumab administration after other interventions have failed.
Impact of Patent Infringement Compensation Rules on Patent Quality Problems
Authors: Dong Liang , Pan Helin
Published: April 2021

Compensation rules for patent infringement greatly affect patent quality, which is closely related to R&D investments. In this study, A duopoly game model was developed to analyze innovative factories’ R&D investment and patent licensing behavior, as well as the strategic choices of potential infringers under different compensation rules for patent infringement. Furthermore, a comparative analysis was conducted to analyze the patent quality under different scenarios, ultimately finalizing an optimal sequence rule for patent infringement compensation. The results show that patent quality is influenced by the invention height of patent and R&D efficiency, while the amount of patent infringement compensation has a great effect on potential infringement behavior. Patent quality can be effectively improved if the court adopts a proper sequence rule for patent infringement compensation according to the market circumstances.
Artificial Intelligence‐Based Diagnosis of Diabetes Mellitus: Combining Fundus Photography with Traditional Chinese Medicine Diagnostic Methodology
Authors: Xiang Yang , Shujin Lai , Hongfang Chang , Yinping Wen , Dawei Yu , Zhou Dong , Zhiqing Li
Published: April 2021

In this study, we propose a technique for diagnosing both type 1 and type 2 diabetes in a quick, noninvasive way by using equipment that is easy to transport. Diabetes mellitus is a chronic disease that affects public health globally. Although diabetes mellitus can be accurately diagnosed using conventional methods, these methods require the collection of data in a clinical setting and are unlikely to be feasible in areas with few medical resources. This technique combines an analysis of fundus photography of the physical and physiological features of the patient, namely, the tongue and the pulse, which are used in Traditional Chinese Medicine. A random forest algorithm was used to analyze the data, and the accuracy, precision, recall, and F1 scores for the correct classification of diabetes were 0.85, 0.89, 0.67, and 0.76, respectively. The proposed technique for diabetes diagnosis offers a new approach to the diagnosis of diabetes, in that it may be convenient in regions that lack medical resources, where the early detection of diabetes is difficult to achieve.