bluuebunny/crossref_metadata_2025 huggingface dataset:
Twenty-year recovery of managed stand, in structure and composition, in boreal mixedwood stands of northwestern Quebec
Authors: Noualhaguet Marion , Work Timothy T. , Soubeyrand Maxence , Fenton Nicole J.
Published: February 2023

The natural disturbance-based management (NDBM) aims to maintain specific structural and compositional attributes of natural forests in managed stands. Operationally, NDBM relies on diversifying and adapting silvicultural practices, including partial harvesting (PC), to expand the range of options beyond that of simply clearcuts (CC). Established in 1998, the Sylviculture et Aménagement Forestier Écosystémique (SAFE) project evaluates this potential in hardwood, mixedwood, and coniferous stands in northwestern Québec, Canada. Our results confirmed a part of the NDBM objectives, i.e., PC allowed the maintenance of stands with mixed structure and composition, constituting an interesting complement to CC, which reset stand regeneration. However, PC did not accelerate the stand transition to later stages with less intensive harvesting or to earlier stages with more intensive harvesting. We essentially had an initial impact, delaying or stopping the stand evolution that dissipates over time and more quickly with less intensive harvesting. Furthermore, our results did not support the ability of PC to enhance the development of old-growth attributes like deadwood. Despite the 20-year horizon of this study, further field surveys will be required in the future to better understand the impact of different silvicultural treatments on forest productivity and biodiversity preservation throughout a forest rotation.
If this title is funny, will you cite me? Citation impacts of humour and other features of article titles in ecology and evolution
Authors: Heard Stephen B. , Cull Chloe A. , White Easton R.
Published: February 2023

Titles of scientific papers play a key role in their discovery, and “good” titles engage and recruit readers. Humour is a particularly interesting aspect of title construction, but little is known about whether funny titles boost or limit paper impact. We used a panel of scorers to assess title humour for 2439 papers in ecology and evolution, and measured associations between humour and subsequent citation (self-citation and citation by others). Papers with funnier titles were cited less, but this appears to reflect confounding with paper importance: self-citation data suggest that authors give funnier titles to papers they consider less important. After correction for this, papers with funny titles have significantly higher citation rates ( P < 2.2 × 10−16; roughly doubling from lowest to highest humour score)—suggesting that humour recruits readers. We also examined associations between citation rates and other features of titles. Inclusion of acronyms and taxonomic names was associated with lower citation rates, while assertive-statement phrasing and presence of colons, question marks, and political regions were associated with somewhat higher citation rates. Title length had no effect on citation. Our results suggest that scientists can use creativity with titles without having their work condemned to obscurity.
A new family of holomorphic homogeneous regular domains and some questions on the squeezing function
Authors: Bharali Gautam
Published: February 2023

We revisit the phenomenon where, for certain domains [Formula: see text], if the squeezing function [Formula: see text] extends continuously to a point [Formula: see text] with value [Formula: see text], then [Formula: see text] is strongly pseudoconvex around [Formula: see text]. In [Formula: see text], we present weaker conditions under which the latter conclusion is obtained. In another direction, we show that there are bounded domains [Formula: see text], [Formula: see text], that admit large [Formula: see text]-open subsets [Formula: see text] such that [Formula: see text] approaching any point in [Formula: see text]. This is impossible for planar domains. We pose a few questions related to these phenomena. But the core result of this paper identifies a new family of holomorphic homogeneous regular domains. We show via a family of examples how abundant domains satisfying the conditions of this result are.
Convergence and Riemannian bounds on Lagrangian submanifolds
Authors: Chassé Jean-Philippe
Published: February 2023

We consider collections of Lagrangian submanifolds of a given symplectic manifold which respect uniform bounds of curvature type coming from an auxiliary Riemannian metric. We prove that, for a large class of metrics on these collections, convergence to an embedded Lagrangian submanifold implies convergence to it in the Hausdorff metric. This class of metrics includes well-known metrics such as the Lagrangian Hofer metric, the spectral metric and the shadow metrics introduced by Biran et al. [Lagrangian shadows and triangulated categories, Astérisque 426 (2021) 1–128]. The proof relies on a version of the monotonicity lemma, applied on a carefully-chosen metric ball.
ECONOMIC DEVELOPMENT AND RURAL ENERGY POVERTY: EVIDENCE FROM CHINA
Authors: WANG YI , WANG JIANHE , WANG LEI , ZHANG LI , XIANG ZIMAN
Published: February 2023

Sustainable development places a premium on recognizing people at risk of energy poverty, defined as the incapability to get a sufficient level of residential energy services. Therefore, this study analyses the relationship between economic development and rural energy poverty in Chinese regions. Using statistics from the China Families Panel Studies, it is observed that the Great Chinese Drought increased the risk of living in poverty. We conclude that there is wide variation in China’s regions regarding economic liberalization and the country’s energy deprivation. In provinces with different degrees of poverty, there is an inverse U-shaped association between economic growth and the country’s energy poverty. The country’s energy poverty reduced or even eliminated by advancing economic development to a specific degree. Many socioeconomic indicators at the home level are connected with energy poverty in various ways depending on the dimension, implying that individualized criteria are required to classify vulnerable families in each size and country-level considerations.
Role of magnetic field in three-component antiferromagnetic dipolar Bose gases
Authors: Zhao Qiang
Published: February 2023

In this paper, we study the dynamics properties of three-component antiferromagnetic dipolar Bose–Einstein condensates with external magnetic field. Our results are obtained by numerical simulations of the spinor Gross–Pitaevskii equation. We find that the population of the spin components is seriously influenced by magnetic field. The dynamical evolution process is accelerated in the presence of magnetic field. As a result, the periodic oscillation of spin exchange dynamics emerges and the components [Formula: see text] have the same amplitude. In addition, the density profile displays that the influences of dipolar interaction are masked by magnetic field. Moreover, we introduce an order parameter to characterize the disorder-order crossover, where it is seen that the system develops toward the disordered state with strong dipole strength when magnetic field is included.
AACO: Aquila Anti-Coronavirus Optimization-Based Deep LSTM Network for Road Accident and Severity Detection
Authors: Kanchanamala Pendela , Lakshmanan Ramanathan , Muthu Kumar B. , Maram Balajee
Published: February 2023

Globally, traffic accidents are of main concern because of more death rates and economic losses every year. Thus, road accident severity is the most important issue of concern, mainly in the undeveloped countries. Generally, traffic accidents result in severe human fatalities and large economic losses in real-world circumstances. Moreover, appropriate, precise prediction of traffic accidents has a high probability with regard to safeguarding public security as well as decreasing economic losses. Hence, the conventional accident prediction techniques are usually devised with statistical evaluations, which identify and evaluate the fundamental relationships among human variability, environmental aspects, traffic accidents and road geometry. However, the conventional approaches have major restrictions based on the assumptions regarding function kind and data distribution. In this paper, Aquila Anti-Coronavirus Optimization-based Deep Long Short-Term Memory (AACO-based Deep LSTM) is developed for road accident severity detection. Spearman’s rank correlation coefficient and Deep Recurrent Neural Network (DRNN) are utilized for the feature fusion process. Data augmentation method is carried out to improve the detection performance. Deep LSTM detects the road accident and its severity, where Deep LSTM is trained by the designed AACO algorithm for better performance. The developed AACO-based Deep LSTM model outperformed other existing methods with the Mean Square Error (MSE), Root-Mean-Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) of 0.0145, 0.1204 and 0.075%, respectively.
Video Action Recognition by Combining Spatial-Temporal Cues with Graph Convolutional Networks
Authors: Li Tao , Xiong Wenjun , Zhang Zheng , Pei Lishen
Published: February 2023

Video action recognition relies heavily on the way spatio-temporal cues are combined in order to enhance recognition accuracy. This issue can be addressed with explicit modeling of interactions among objects within or between videos, such as the graph neural network, which has been shown to accurately model and represent complicated spatial- temporal object relations for video action classification. However, the visual objects in the video are diversified, whereas the nodes in the graphs are fixed. This may result in information overload or loss if the visual objects are too redundant or insufficient for graph construction. Segment level graph convolutional networks (SLGCNs) are proposed as a method for recognizing actions in videos. The SLGCN consists of a segment-level spatial graph and a segment-level temporal graph, both of which are capable of simultaneously processing spatial and temporal information. Specifically, the segment-level spatial graph and the segment-level temporal graph are constructed using 2D and 3D CNNs to extract appearance and motion features from video segments. Graph convolutions are applied in order to obtain informative segment-level spatial-temporal features. A variety of challenging video datasets, such as EPIC-Kitchens, FCVID, HMDB51 and UCF101, are used to evaluate our method. In experiments, it is demonstrated that the SLGCN can achieve performance comparable to the state-of-the-art models in terms of obtaining spatial-temporal features.
Person Event Detection Method in Computer Discipline Domain Based on BiGRU and CNN in Series
Authors: Zhang Xiaoming , Yang Xin , Wang Huiyong
Published: February 2023

The knowledge graph of computer discipline domain plays a critical role in computer education, and the person event is an important part of the discipline knowledge graph. Adding person events to the graph will make the discipline knowledge graph richer and more interesting, and enhance enthusiasm of students for learning. The most crucial step in building the person event knowledge graph is the extraction of trigger words. Therefore, this paper proposes a method based on the serial fusion of gated recurrent neural network and convolutional neural network (SC-BiGRU-CNN) for person event detection in the computer discipline domain. We extract the global features of the text from the person event sentences through the BiGRU model, and input the extracted global features into the CNN model to further extract the fine-grained features of the text. And then the extracted features are used to classify the event trigger words. In addition, a dataset (CD-PED) for person event detection in the computer discipline domain is constructed to obtain trigger words and their types. We perform experiments on the public dataset MAVEN and the domain dataset CD-PED, respectively. The experimental results show that our approach has significantly improved the [Formula: see text] value compared with the baseline model on the domain dataset CD-PED.
Dibit-based OR and NOR gates using reflective semiconductor optical amplifier
Authors: Bosu Surajit , Bhattacharjee Baibaswata
Published: February 2023

In the all-optical domain, the reflective semiconductor optical amplifier (RSOA) creates due attention in the research community because the RSOA has high gain, low-power consumption, high-speed signal processing ability as well as low noise performance. In this work, we have proposed dibit-based OR and NOR gates using RSOA. Dibit-based logic is utilized in this design to get the reduced bit-error problems and high degree of parallelism. To verify the functionality of the aforementioned designs, we have used MATLAB software and also the quality factor (Q), extinction ratio (ER), contrast ratio (CR), and bit-error-rate (BER) have been calculated.
Determination of nonlinear refractive index of large area monolayer MoS2 at telecommunication wavelength using time-resolved Z-scan technique
Authors: Chew Jing Wen , Chong Wu Yi , Muniandy Sithi Vinayakam , Yap Yuen Kiat , Ahmad Harith
Published: February 2023

The nonlinear refractive index of large area monolayer molybdenum disulfide (MoS2) thin film at the 1,550[Formula: see text]nm wavelength band is determined using time-resolved z-scan technique. MoS2 exhibits positive nonlinear phase shift with a large nonlinear refractive index, [Formula: see text] value of [Formula: see text]. The obtained value is similar to the [Formula: see text] value of monolayer MoS2 film measured at 2.0[Formula: see text][Formula: see text]m, and is approximately five and seven orders of magnitude larger than silicon and common bulk dielectrics. The large [Formula: see text] indicates that MoS2 can be used as nonlinear refractive 2D material for photonics applications operating in the 1,550[Formula: see text]nm optical telecommunications wavelength band.
Finite groups with minimal weakly BNA-subgroups
Authors: He Xuanli , Huang Muhong , Wang Jing
Published: February 2023

Let [Formula: see text] be a finite group. A subgroup [Formula: see text] of [Formula: see text] is said to be a BNA-subgroup of [Formula: see text] if either [Formula: see text] or [Formula: see text] for all [Formula: see text]. A subgroup [Formula: see text] of [Formula: see text] is said to be a weakly BNA-subgroup of [Formula: see text] if there exists a normal subgroup [Formula: see text] of [Formula: see text] such that [Formula: see text] and [Formula: see text] is a BNA-subgroup of [Formula: see text]. In this paper, we investigate the structure of a finite group [Formula: see text] under the assumption that every minimal subgroup of [Formula: see text] not having a supersolvable supplement in [Formula: see text] is a weakly BNA-subgroup of [Formula: see text].
Nielsen equivalence in triangle groups
Authors: Dutra Ederson R. F.
Published: February 2023

We show that any non-standard generating pair of a hyperbolic triangle group is represented by a special almost orbifold covering with a good marking.
Synthesizing human brain CT images from MRI with deep learning
Authors: Zhou Longfei , Pu Chenghan , Lu Yuyang , Zhang Ying , Han Ke , Jin YiRui , Zhang Shengquan , Liu Kaihua
Published: February 2023

Rapid development of artificial intelligence techniques significantly promotes important medical imaging applications such as disease diagnosis, medical image analysis and cross-modality synthesis. It is valuable to synthesize Computed Tomography (CT) scans of human brains from Magnetic Resonance Imaging (MRI) scans because CT images contain particular information for diseases such as malignant tumors, cerebrovascular malformations and aneurysms. Although there has been many studies trying to apply machine learning methods (e.g., fully convolutional neural networks and generative adversarial networks) to predict CT images based on MRI, there are still opportunities to increase the quality of synthesized CT images using the latest machine learning algorithms. In this study, we aim to synthesize high-credibility human brain CT images from MRI images using machine learning approaches. We build a dataset of both CT and MRI images of 41 patients from The Cancer Imaging Archive. A data pre-processing pipeline is designed to guarantee that the processed images are suitable as inputs to machine learning algorithms. The pipeline includes (1) pairing MRI and CT scans according to a certain time interval between CT and MRI exams of the same patient, (2) registering all MRI images to a standard MRI template, (3) registering all CT images to paired MRI images, (4) intensity normalization and (5) extracting 2D slices from 3D CT and MRI volumes. Three deep neural networks with encoder–decoder structure are selected and implemented including U-Net, U-Net++ and pix2pix to synthesize human brain CT images from MRI images. Experimental results illustrate that U-Net++ shows better performance than U-Net and pix2pix in synthesizing CT images from MRI by achieving a Mean Squared Error of 0.025, a Mean Absolute Error of 0.082 and a Peak Signal-to-Noise Ratio of 67.90, respectively. The trained machine learning models from this work can be applied in future human brain CT image prediction where CT images are absent and MRI images are available for specific patients, thereby supporting disease diagnosis based on CT images.
Learning Equilibria in Matching Markets with Bandit Feedback
Authors: Jagadeesan Meena , Wei Alexander , Wang Yixin , Jordan Michael I. , Steinhardt Jacob
Published: February 2023

Large-scale, two-sided matching platforms must find market outcomes that align with user preferences while simultaneously learning these preferences from data. Classical notions of stability (Gale and Shapley, 1962; Shapley and Shubik, 1971) are, unfortunately, of limited value in the learning setting, given that preferences are inherently uncertain and destabilizing while they are being learned. To bridge this gap, we develop a framework and algorithms for learning stable market outcomes under uncertainty. Our primary setting is matching with transferable utilities, where the platform both matches agents and sets monetary transfers between them. We design an incentive-aware learning objective that captures the distance of a market outcome from equilibrium. Using this objective, we analyze the complexity of learning as a function of preference structure, casting learning as a stochastic multi-armed bandit problem. Algorithmically, we show that “optimism in the face of uncertainty,” the principle underlying many bandit algorithms, applies to a primal-dual formulation of matching with transfers and leads to near-optimal regret bounds. Our work takes a first step toward elucidating when and how stable matchings arise in large, data-driven marketplaces.
Estimating and Evaluating the Uncertainty of Rating Predictions and Top-n Recommendations in Recommender Systems
Authors: Coscrato Victor , Bridge Derek
Published: February 2023

Uncertainty is a characteristic of every data-driven application, including recommender systems. The quantification of uncertainty can be key to increasing user trust in recommendations or choosing which recommendations should be accompanied by an explanation; uncertainty estimates can be used to accomplish recommender tasks such as active learning and co-training. Many uncertainty estimators are available, but to date, the literature has lacked a comprehensive survey and a detailed comparison. In this article, we fulfill these needs. We review the existing methods for uncertainty estimation and metrics for evaluating uncertainty estimates, while also proposing some estimation methods and evaluation metrics of our own. Using two datasets, we compare the methods using the evaluation metrics that we describe, and we discuss their strengths and potential issues. The goal of this work is to provide a foundation to the field of uncertainty estimation in recommender systems, on which further research can be built.
Learning Multi-turn Response Selection in Grounded Dialogues with Reinforced Knowledge and Context Distillation
Authors: Feng Jiazhan , Tao Chongyang , Zhao Xueliang , Zhao Dongyan
Published: February 2023

Recently, knowledge-grounded dialogue systems have gained increasing attention. Great efforts have been made to build response matching models where all dialogue content and knowledge sentences are leveraged. However, knowledge redundancy and distraction of irrelevant dialogue content often exist in knowledge-grounded conversations, which may affect the matching process and lead to inferior performance. In addition, irrelevant dialogue history and excessive knowledge also hinder the exploitation of popular pre-trained language models (PLMs) due to the limitation of input length. To address these challenges, we propose a new knowledge-grounded dialogue model based on PLMs, where a knowledge selector and a context selector are designed for filtering out irrelevant knowledge sentences and redundant dialogue history, respectively. Considering the lack of labeled data for the learning of two selectors, we pre-train them with weakly-supervised tasks and then jointly conduct the optimization of knowledge and context selection and fine-tuning of PLMs for response ranking with reinforcement learning (RL). By this means, the dialogue model can distill more accurate and concise knowledge and dialogue content for subsequent response ranking module, and the overall model can converge and perform better. We conduct experiments on two benchmarks and evaluation results indicate that our model can significantly outperform the state-of-the-art methods.
A Time-Adaptive Order Reduction Technique for the Doyle-Fuller-Newman Electrochemical Model of Lithium-Ion Batteries
Authors: Jané Eduardo , Medeiros Ruth , Varas Fernando , Higuera María
Published: February 2023

A time-adaptive reduced order model (ROM) is developed for the electrochemical model for lithium-ion cells derived by Doyle, Fuller, and Newman (DFN) [M. Doyle, T. F. Fuller and J. Newman, J. Electrochem. Soc., 140 1526 (1993)]. The main advantage of a time-adaptive strategy is that it does not require a set of full order model simulations to be generated beforehand and, thus, it is the most cost-effective alternative when no databases are available. However, the reduction of this electrochemical problem exhibits special features that require ad hoc solutions, preventing the application of generic strategies. This complexity is carefully analysed, focusing on mode selection, treatment of non-linearities and error estimation. Despite of all this analysis being done for a pseudo-two-dimensional DFN model, we show that such complexity is intrinsic to the physics of the electrochemical problem, making the analysis applicable to a pseudo-four-dimensional DFN model, where results prove that the benefits of a reduction in the number of degrees of freedom are more self-evident. The efficiency, robustness and accuracy of our method are remarkable, as shown by the macroscopic (cell voltage) and internal (variable distributions) results obtained from the simulation of two different electrochemical cells under several charge/discharge C-rates.
On the Error of Li-ion Battery Parameter Estimation Subject to System Uncertainties
Authors: Fogelquist Jackson , Lai Qingzhi , Lin Xinfan
Published: February 2023

Emerging lithium-ion battery systems require high-fidelity electrochemical models for advanced control, diagnostics, and design. Accordingly, battery parameter estimation is an active research domain where novel algorithms are being developed to calibrate complex models from input-output data. Amidst these efforts, little focus has been placed on the fundamental mechanisms governing estimation accuracy, spurring the question, why is an estimate accurate or inaccurate? In response, we derive a generalized estimation error equation under the commonly adopted least-squares objective function, which reveals that the error can be represented as a combination of system uncertainties (i.e., in model, measurement, and parameter) and uncertainty-propagating sensitivity structures in the data. We then relate the error equation to conventional error analysis criteria, such as the Fisher information matrix, Cramér-Rao bound, and parameter sensitivity, to assess the benefits and limitations of each. The error equation is validated through several uni- and bivariate estimations of lithium-ion battery electrochemical parameters using experimental data. These results are also analyzed with the error equation to study the error compositions and parameter identifiability under different data. Finally, we show that adding target parameters to the estimation without increasing the amount of data intrinsically reduces the robustness of the results to system uncertainties.
Effect of Cetyltrimethylammonium Bromide Additives on Etched Tunnel Formation of Aluminum Foils
Authors: Xiao Yuan , Zhao Anning , Luan Hong , Peng Ning , Wen Yuqing , Liang Libo
Published: February 2023

In this study, cetyltrimethylammonium bromide (CTAB) additive was introduced to adjust the tunnel etching behavior of aluminum foil. The distribution and growth of etched tunnels in solutions with and without CTAB additive of various concentrations were thoroughly investigated. Compared to the etched tunnels obtained in solutions without CTAB, those obtained in solutions with 1 wt% CTAB had obviously improved distribution and increased length, resulting in an enhancement of the specific surface area. These effects can be attributed to the improvement of wettability between aluminum surface and solution, which is helpful to increase the contact area and facilitate the mass transfer within tunnels. This work exhibits a facile way to improve the tunnel etching behavior of aluminum foil, showing a potential engineering value in industrial production.
Selecting the Regularization Parameter in the Distribution of Relaxation Times
Authors: Maradesa Adeleke , Py Baptiste , Wan Ting Hei , Effat Mohammed B. , Ciucci Francesco
Published: February 2023

Electrochemical impedance spectroscopy (EIS) is used widely in electrochemistry. Obtaining EIS data is simple with modern electrochemical workstations. Yet, analyzing EIS spectra is still a considerable quandary. The distribution of relaxation times (DRT) has emerged as a solution to this challenge. However, DRT deconvolution underlies an ill-posed optimization problem, often solved by ridge regression, whose accuracy strongly depends on the regularization level

λ
.

This article studies the selection of

λ

using several cross-validation (CV) methods and the L-curve approach. A hierarchical Bayesian DRT (hyper-

λ

) deconvolution method is also analyzed, whereby

λ

0

,

a parameter analogous to

λ
,

is obtained through CV. The analysis of a synthetic dataset suggests that the values of

λ

selected by generalized and modified generalized CV are the most accurate among those studied. Furthermore, the analysis of synthetic EIS spectra indicates that the hyper-

λ

approach outperforms optimal ridge regression. Due to its broad scope, this research will foster additional research on the vital topics of hyperparameter selection for DRT deconvolution. This article also provides, through pyDRTtools, an implementation, which will serve as a starting point for future research.
Fluoride Ion Conductivity of Rb Doped KSbF4
Authors: Kawahara Kazuaki , Ishikawa Ryo , Shibata Naoya , Ikuhara Yuichi
Published: February 2023

All solid-state fluoride ion batteries are considered to be high energy density and safe energy storage devices. However, the F– ion conductivities of solid electrolytes at room temperature are low (< 10-6 S cm-1), which requires the higher operating temperature of fluoride ion battery such as 420 K or higher. Therefore, the faster F– ion conductors is strongly desired to operate fluoride ion battery at lower or even room temperature. KSbF4 is one of the promising candidates to have a faster F– ion conductivity. To expand F– ion conduction pathways, we doped Rb in KSbF4 and further investigated the compositional dependence of ionic conductivity in K1-xRbxSbF4 system. We found that ionic conductivity strongly depends on the chemical composition and conductivity was maximized at x = 0.15. At room temperature, the F– ion conductivity in the bulk and at the grain boundary of K0.85Rb0.15SbF4 were σbulk = 1.01 × 10-4 S cm-1 and σGB = 4.80 × 10-5 S cm-1, respectively.
Perception of Dental House Officers regarding Endodontic File Separation during Endodontic Treatment
Authors: Maqbool Manahil , Tirmazi Syeda Sobia Masood , Shakoor Asma , Akram Zunaira , Nazir Rutaba , Chohan Arham Nawaz , Karobari Mohmed Isaqali , Noorani Tahir Yusuf
Published: February 2023

Background. Despite of having improved endodontic file designs as well as the reinforced metal alloy file structure, intracanal endodontic file separation (EFS) is still a very problematic and worrisome dental incident, which usually occurs without any visible signs of permanent deformation. Further, there have been conflicting reports regarding the clinical significance of leaving separated files within root canals. Aims. The aim of this study was to look into the current perceptions and awareness about file separation during endodontic treatment among the dental house officers (DHOs). Materials and Methods. A novel validated questionnaire comprising of 15 close‐ended questions was distributed anonymously via Google Forms through email to 1100 DHOs across Pakistan. The questionnaire consisted of two parts: the first component (Section I) collected demographic data and the second component (Section II) investigated the causes of EFS during root canal treatment. Following the completion of socioeconomic information, including age and gender, the DHOs were asked to answer a few questions about the various reasons for endodontic instrument fracture. Results. A total of 800 responses were recorded, with an effective rate of 72.8%. The majority of the DHOs (p value < 0.001) perceived that endodontic instrument fracture occurred in the posterior (61.5%) and apical third of the canal (50.5%) and in older permanent dentition (67.3%), possibly due to patient anxiety (62%). Better choice of instrument (61.15%), operators’ experience (95.3%), knowledge (87.5%), and proper root canal cleaning (91.1%) are believed to be the vital steps in reducing endodontic file separation/fracture. Furthermore, majority of them (p value < 0.001) perceived that stainless steel was a superior alloy for filing instruments. Manual files tend to be more prone to fractures due to repeated use than rotary files. Conclusion. This study demonstrated that young DHOs had adequate knowledge and awareness regarding the potential predisposing factors and handling techniques for EFS. This study thereby provides an evaluating tool to access the insights of the current perceptions and awareness of DHOs concerning EFS.
Using ORRT Models for Mean Estimation under Nonresponse and Measurement Errors in Stratified Successive Sampling
Authors: Choudhary Monica , Kour Sanam Preet , Kumar Sunil , Bouza Carlos N. , Santiago Agustín
Published: February 2023

In the context of a sample survey, the collection of information on a sensitive variable is difficult, which may cause nonresponse and measurement errors. Due to this, the estimates can be biased and the variation may increase. To overcome this difficulty, we propose an estimator for the estimation of a sensitive variable by using auxiliary information in the presence of nonresponse and measurement errors simultaneously. The properties of the proposed estimators have been studied, and the results have been compared with those of the usual complete response estimator. Theoretical results have been verified through a simulation study using an artificial population and two real-life applications. With the outcomes of the proposed estimator, a suitable recommendation has been made to the survey statisticians for their real-life application.
Galangin-Loaded Gold Nanoparticles: Molecular Mechanisms of Antiangiogenesis Properties in Breast Cancer
Authors: Qaddoori Malik H. , Al-Shmgani Hanady S.
Published: February 2023

Angiogenesis is important for tissue during normal physiological processes as well as in a number of diseases, including cancer. Drug resistance is one of the largest difficulties to antiangiogenesis therapy. Due to their lower cytotoxicity and stronger pharmacological advantage, phytochemical anticancer medications have a number of advantages over chemical chemotherapeutic drugs. In the current study, the effectiveness of AuNPs, AuNPs-GAL, and free galangin as an antiangiogenesis agent was evaluated. Different physicochemical and molecular approaches have been used including the characterization, cytotoxicity, scratch wound healing assay, and gene expression of VEGF and ERKI in MCF-7 and MDA-MB-231 human breast cancer cell line. Results obtained from MTT assay show cell growth reduction in a time- and dose-dependent aspect; also, in comparison to individual treatment, a synergistic impact was indicated. CAM assay results demonstrated galangin-gold nanoparticle capacity to suppress angiogenesis in chick embryo. Additionally, altering VEGF and ERKI gene expression was recorded. Taken together, all the results can conclude that galangin-conjugated gold nanoparticles can be a promising antiangiogenesis supplemental drug in breast cancer treatment.
Factors Influencing Blood Concentration of Voriconazole and Therapeutic Drug Monitoring in Patients with Child–Pugh Class C Cirrhosis
Authors: Zhang Ying , Wu Rongrong , Liu Fangfang , Wang Yonggang , Zhang Junchang , Ji Chengcheng , Lu Xianghong , Chang Dan , Mu Jinsong
Published: February 2023

What Is Known and Objective. CYP2C19 is an important influencing factor for voriconazole trough plasma concentration (Cmin); however, it is not verified in Child–Pugh C (CP-C) cirrhosis patients, and no voriconazole dosage regimen is recommended for these patients in the package insert. This retrospective study identified CYP2C19 and other factors influencing voriconazole Cmin for CP-C cirrhosis, and obtained an appropriate method of application of voriconazole for them. Methods. A total of 66 patients with CP-C cirrhosis who accepted voriconazole therapy were involved. The voriconazole Cmin, clinical characteristics, CYP2C19 genotype, and adverse effects (AEs) were recorded and analyzed. Results. Unlike other research studies, voriconazole Cmin was not different among normal metabolizers (NMs), intermediate metabolizers (IMs), and poor metabolizers (PMs) of the CYP2C19 enzyme in CP-C cirrhosis (

P

 > 0.05). The maintenance dose regimen for voriconazole was the only independent influencing factor for Cmin (

P

 = 0.045; OR = 3.753; 95% CI, 1.029–13.694). At about 1/3 of the recommended maintenance dose, only 16.7% (8/48) had Cmin >5.5 μg/mL, 4.5% (3/48) had Cmin <1 μg/mL, and only one AE happened. There were four voriconazole-related AEs that happened in this study, and three AEs occurred (3/4, 75%) when the maintenance dose was not adjusted with therapeutic drug monitoring (TDM). What Is New and Conclusion. Voriconazole Cmin did not significantly vary according to CYP2C19 enzyme metabolization status (being an NM, IM, or PM) in CP-C cirrhosis. Reducing the maintenance dose of voriconazole to approximately 1/3 the standard maintenance dose and administering in combination with TDM in patients with CP-C cirrhosis are recommended.
Identify Key Genes Correlated to Ischemia-Reperfusion Injury in Aging Livers
Authors: Yan Xijing , Liang Jinliang , Li Xuejiao , Hu Zhongying , Chen Jiahao , Zheng Jun , Li Rong
Published: February 2023

Background. With the intensification of population aging, the proportion of aging livers in the donor pool is increasing rapidly. Compared with young livers, aging livers are more susceptible to ischemia-reperfusion injury (IRI) during liver transplantation, which greatly affects the utilization rate of aging livers. The potential risk factors associated with IRI in aging livers have not been fully elucidated. Methods. In this work, five human liver tissue expression profiling datasets (GSE61260, GSE107037, GSE89632, GSE133815, and GSE151648) and a total of 28 young and aging liver tissues of human (

N
=
20

) and mouse (

N
=
8

) were used to screen and verify the potential risk factors associated with aging livers being more prone to IRI. DrugBank Online was used to screen drugs with potential to alleviate IRI in aging livers. Results. The gene expression profile and immune cell composition between young and aging livers had significant differences. Among the differentially expressed genes, aryl hydrocarbon receptor nuclear translocator-like (ARNTL), BTG antiproliferation factor 2 (BTG2), C-X-C motif chemokine ligand 10 (CXCL10), chitinase 3-like 1 (CHI3L1), immediate early response 3 (IER3), Fos proto-oncogene, AP-1 transcription factor subunit (FOS), and peroxisome proliferative activated receptor, gamma, coactivator 1 alpha (PPARGC1A), mainly involved in the regulation of cell proliferation, metabolism, and inflammation, were also dysregulated in liver tissues suffered from IRI and could form a FOS-centered interaction network. Nadroparin was screened out with the potential to target FOS in DrugBank Online. In addition, the proportion of dendritic cells (DCs) was significantly upregulated in aging livers. Conclusions. We combined the expression profiling datasets of liver tissues and samples collected in our hospital for the first time to reveal that the changes in the expression of ARNTL, BTG2, CXCL10, CHI3L1, IER3, FOS, and PPARGC1A and the proportion of dendritic cells may be associated with aging livers being more prone to IRI. Nadroparin may be used to mitigate IRI in aging livers by targeting FOS, and regulation of DC activity may also reduce IRI.
Pretreatment Carcinoembryonic Antigen Level Serves as a Potential Biomarker to Guide Adjuvant Radiotherapy in pT4N+ Colon Cancer Patients
Authors: Luo Dakui , Zhang Ruoxin , Yang Yufei , Li Qingguo , Li Xinxiang
Published: February 2023

The survival benefit of adjuvant radiotherapy in T4 colon cancer (CC) remains controversial, with conflicting results reported in the literature. This study aimed to explore the relationship between pretreatment carcinoembryonic antigen (CEA) level and overall survival (OS) of pT4N+ CC patients treated with adjuvant radiotherapy. Data of pT4N+ CC patients who received curative surgery between 2004 and 2015 were identified from the Surveillance, Epidemiology, and End Results (SEER) database. The primary outcome was OS, and subgroup analysis was conducted according to pretreatment CEA level. A total of 8763 patients were eligible for our study. In the CEA-normal group, 151 patients received adjuvant radiotherapy, while 3932 patients did not. In the CEA-elevated group, 212 patients received adjuvant radiotherapy, while 4468 patients did not. In general, adjuvant radiotherapy was associated with better OS in pT4N+ CC patients (HR = 0.846, 95% CI = 0.733–0.976,

P
=
0.022

). Intriguingly, only patients with an elevated pretreatment CEA level gained a survival benefit from adjuvant radiotherapy (HR = 0.782; 95% CI = 0.651–0.939;

P
=
0.008

) while those with a normal pretreatment CEA level did not (HR = 0.907; 95% CI = 0.721–1.141;

P
=
0.403

). Multivariable Cox regression analysis demonstrated that adjuvant radiotherapy was an independent protective factor in pT4N+ CC patients with an elevated pretreatment CEA level. Pretreatment CEA levels could serve as a potential biomarker to screen pT4N+ CC patients who would benefit from adjuvant radiotherapy.
Association of LAMA1 Single-Nucleotide Polymorphisms with Risk of Esophageal Squamous Cell Carcinoma among the Eastern Chinese Population
Authors: Zhang Shaoyuan , Fang Yong , Su Feng , Jiang Tian , Yu Jinjie , Lin Siyun , Lv Lu , Long Tao , Pan Huiwen , Qi Junqing , Zhou Qiang , Tang Weifeng , Ding Guowen , Wang Liming , Tan Lijie , Yin Jun
Published: February 2023

Introduction. LAMA1, also known as laminin subunit α1, is a member of the laminin family, which is widely reported to be a key basement membrane molecule that affects various biological activities and is associated with many kinds of diseases. We aimed to investigate the association between LAMA1single-nucleotide polymorphisms and the occurrence and progression of esophageal squamous cell carcinoma in the Chinese population. Method. 2,186 participants were collected retrospectively between October 2008 and January 2017, including 1,043 ESCC patients and 1,143 noncancer patients. A 2 mL blood sample was obtained intravenously for the LDR for SNP analysis. The 6 SNP loci of LAMA1 were selected and examined. We analyzed the association of several genetic models of 6 LAMA1 SNP loci, sex, age, smoking and drinking status, and the occurrence of esophageal squamous cell carcinoma. Results. In the rs62081531 G > A locus, genotype GA was a protective factor for ESCC compared with GG (OR: 0.830,

P
=
0.046

), especially among the younger and nondrinkers. At rs607230 T > C, genotype TC was linked with a lower risk of ESCC compared with TT. (OR: 0.613,

P
=
0.034

). Haplotype Frequencies revealed that Ars62081531Grs621993Ars539713Trs566655Ars73938538Crs607230 (OR: 0.803,

P
=
0.028

) and Grs62081531Grs621993Ars539713Trs566655Crs73938538Crs607230 (OR: 0.679,

P
=
0.010

) were strongly associated with lower susceptibility of ESCC. Conclusion. The LAMA1 rs62081531, rs539713, rs566655, and rs607230 polymorphisms were demonstrated to be related to susceptibility to ESCC in the Chinese population. LAMA1 SNPs may have a significant impact on the occurrence of esophageal cancer and may serve as potential diagnostic biomarkers.
Exosome-Encapsulated miR-31, miR-192, and miR-375 Serve as Clinical Biomarkers of Gastric Cancer
Authors: He Jing , Wu Jing , Dong Shiyang , Xu Jing , Wang Jian , Zhou Xin , Rao Zhuqing , Gao Wen
Published: February 2023

In recent years, microRNAs (miRNAs) derived from exosomes have been attracting attention as novel clinical biomarkers in a variety of cancers. In this study, plasma samples from 60 gastric cancer (GC) patients and 63 healthy individuals were collected, and the exosomal microRNAs (ex-miRNAs) were isolated. We determined the specific ex-miRNAs through miRNA microarray and a database of differentially expressed miRNAs called dbDEMC. Then, the expression levels of exosomal miR-31, miR-192, and miR-375 were analyzed by quantitative polymerase chain reaction (qRT-PCR). Compared to the matched controls, exosomal miR-31, miR-375, and miR-192 were significantly upregulated in GC patients. Also, they were found to be associated with gender, with miR-192 being significantly upregulated in male GC patients. Kaplan–Meier analysis indicated that high expressions of exosomal miR-31, miR-375, and miR-192 were positively correlated with poor clinical outcomes of GC patients. Cox univariate and multivariate analysis found that ex-miR-375 expression and TNM stage were independent prognostic factors of overall survival (OS). Our findings revealed that exosomal miR-31, miR-192, and miR-375 might serve as noninvasive, sensitive, and specific biomarkers for the diagnosis and prognosis of GC patients.
Attitudes and Practices of the Use of Third-Generation Cephalosporins among Medical Doctors Practicing in Cameroon
Authors: Menkem Elisabeth Zeuko’o , Labo Nanfah Astride , Takang Tiku , Ryan Awah Lesley , Awah Achua Kenneth , Ekane Akume Stanley , Fekam Boyom Fabrice
Published: February 2023

Background. Third-generation cephalosporins (3GC) are among the most prescribed antibiotics worldwide. Antibiotic resistance, usually due to misuse and overuse, is a feared complication of public health concern. However, there are limited data in Cameroon concerning the knowledge and use of 3GC in our health services. The aim of this study was to assess the knowledge and use of 3GC among medical doctors in Cameroon and to generate baseline information for a wider scale research and policy implementation. Methods. This study was a cross-sectional study conducted among medical doctors practicing in Cameroon in general. Convenience sampling was used and the data were collected from both the online questionnaire and the review of files of patients admitted and discharged within the month of April 2021 and analysed with the use of IBM SPSS v25. Results and Discussion. A total of 52 respondents from the online questionnaire and 31 reviewed files were retained. Of the respondents, 27% were female and 73% were male. The mean age and years of experience were 29.6 ± 2.9 and 3.6 ± 2.1 years, respectively. Only 32.7% had correct knowledge of the number of generations of cephalosporins, and 48.1% had knowledge of the antimicrobial target. All medical doctors (MD) identified ceftriaxone as a 3GC, and it was the most commonly prescribed 3GC (71%). Most of the MD considered 3GC to be an efficient antibiotic. Just over half (54.7%) knew the correct posology of ceftriaxone. Only 17% and 9.4% knew the right posology for cefotaxime and ceftazidime, respectively, for the management of early-onset neonatal infection (EONNI). The misuse of 3GC was mostly attributed to nurses, MD, and poor institutional policies. Conclusion. There is average knowledge on 3GC among MD, with ceftriaxone being the most widely known and prescribed. Misuse is common among nurses and doctors. Poor institutional policies and limited laboratory capacities are to be blamed.
Resource Allocation for D2D Communication Underlaying Cellular Networks: A Distance-Based Grouping Strategy
Authors: Gao Jing , Meng Xiao , Yang Chen , Zhang Bo , Yi Xin
Published: February 2023

Device-to-device (D2D) communication with direct terminal connection is a promising candidate for 5G communication, which increases the capacity of cellular networks and spectral efficiency. Introducing D2D communication to cellular users (CUs) will increase system capacity, and CUs will provide reusable channel resources for D2D users (DUs). However, the sharing of channel resources between CUs and DUs will lead to cofrequency interference and affect the communication quality of user terminals. As a means of improving spectrum utilization and solving cofrequency interference problems, a one-to-many D2D communication system model is established in cellular networks. Through model analysis, the interference between CUs and DUs is correlated with their distance from one another. Considering the different interference of CUs to DUs at different distances, an algorithm for resource allocation based on distance grouping is proposed. With this algorithm, DUs will reuse channel resources of CUs within a reasonable distance in the group, and interference between DUs and CUs will be minimized. The improved particle swarm optimization algorithm is used to solve the optimal power, to achieve the maximum transmission rate of the system. Simulated results show that the algorithm will significantly improve system throughput and performance while also lowering the computational complexity of the algorithm, enabling the whole system to have better communication quality.