bluuebunny/crossref_metadata_2025_split huggingface dataset:
Unified automatic construction method of multi-source language functional mock-up unit
Authors: Liu Xi , Wang Xi , Yu Bowei 
Published: November 2024

Abstract
Model simulation plays an important role in modern engineering design and system development. It simulates and validates complex systems through a virtual environment, reduces the cost and risk of physical tests, and improves the design efficiency and accuracy. Generating a functional mock-up unit (FMU) is the core of model simulation. Most FMU is generated by Modelica, but this generation method is not only very complicated but also requires experimentalists to be familiar with Modelica. Nowadays, physical models are becoming more and more complex. With the development of artificial intelligence, more and more intelligent models are involved in the simulation process. Therefore, it is essential to realize the simulation process of automatic and rapid generation of FMU by multi-source languages. In this paper, by combining Python and Modelica, two methods of automatic FMU generation are designed. Through simulation tests, it is verified that the FMU generated by these two methods can be applied to the simulation process, which brings significant efficiency improvement to the model design.
Partition-based outlier detection for high dimensional data
Authors: Zhang Jutian , Yang Xiaona , Wang Xin 
Published: November 2024

Abstract
Outlier detection has become increasingly important for high dimensional data (HDD) before data analysis. Although the multivariate outlier detection methods (ODMs) have been extensively studied in the research literature, there are few ODMs for ODMs when a correlation exists between data’s components. In this paper, we introduce a partition-based estimator of the minimum covariance determinant for HDD, along with a rapid algorithm designed to identify the clean subset. Additionally, an OD threshold is employed by examining the asymptotic distribution of the outlying measure distance to pinpoint outliers. Moreover, a one-step reweighting scheme is introduced to enhance the detection efficiency of the process. At last, simulation results demonstrate that the proposed RPMCD methods exhibit superior finite sample performance compared to other existing techniques.
Study on the change of reservoir seepage flow by microbial oil drive in the reservoir after polydynamic drive
Authors: Qu Changyue , Liu Zhenyu , Lv Anqi , Yang Yan 
Published: November 2024

Abstract
Microbial drive is an effective technical means to enhance low-permeability reservoirs. There is a large amount of residual oil in the reservoir after polydrive, so there is an urgent need to explore effective ways to improve the recovery rate. Combining the actual needs of the XK well area and the characteristics of microbial oil recovery technology, we carried out the numerical simulation study of microbial drive and evaluated the changes in seepage capacity of the reservoir and typical wells at different stages. The results show that in the first cycle of microbial injection, the seepage capacity around the injection wells is obviously improved, but the seepage capacity of each reservoir in the second cycle is not improved as well as that in the first cycle; the improvement of seepage capacity around the injection wells is related to the permeability of the reservoirs and the effective thickness; the higher the permeability and the thicker the effective thickness are, the faster the seepage capacity of the near-well zones is improved, and the bigger the improvement amplitude is, and the earlier the production wells nearby are able to see the effect. This study provides guidance for field tests and is of great significance for the improvement of reservoir recovery after polydynamic drive.
Mathematical analysis based on SAIQRS model
Authors: Ji Meng 
Published: November 2024

Abstract
Given the mobility of the population during epidemic transmission, the lethality of certain epidemics, and the fact that recovered individuals do not possess lifelong immunity, it is crucial to consider vaccination strategies to control epidemic spread. Vaccinating the population stimulates the production of antibodies, thereby reducing the disease transmission rate. Furthermore, individuals who have been infected with the same pathogen as the vaccine will generate a certain amount of antibodies, enhancing their resistance to future epidemics. In this paper, we develop a susceptible-antibodies-infected-isolated-removed-individuals (SAIQRS) model and derive the basic regeneration number R
0 for the deterministic SAIQRS model. Next, the effect of the stochastic factor is taken into account by changing the transmission rate β
1 → β
1 + σB(t), which extends the model to a stochastic SVIQRS model and gives the conditions for the extinction and persistence of the disease. Finally, the theorems of the deterministic and stochastic models are verified by numerical simulations.
Visual SLAM based on visual odometry feature extraction and real-time rendering of neural radiance fields
Authors: Xu Jun , Li Yuhang , Wu Xiaohua 
Published: November 2024

Abstract
In response to the limitation of traditional feature-based Visual SLAM, which focuses primarily on camera pose accuracy while neglecting the refinement of 3D map reconstruction, this paper proposes a Visual SLAM system called ON-SLAM (Simultaneous Localization and Mapping). The aim is to develop a Visual SLAM system that does not require pre-training, can quickly adapt to new and changing environments, and can generate dense point cloud maps in real time. Currently, both learning-based and non-learning-based methods fall short of these requirements due to their algorithmic limitations. The ON-SLAM framework integrates visual odometry technology with implicit neural representations to achieve real-time localization and high-precision map construction. This system is compatible with monocular cameras and relies solely on RGB inputs, demonstrating enhanced applicability in real-world scenarios. Experimental results show that ON-SLAM achieves state-of-the-art accuracy in both localization and map construction.
Approximate controllability of semilinear MFDEs with infinite delay
Authors: Cao Yueju 
Published: November 2024

Abstract
This paper develops the theory of approximate controllability for semilinear MFDEs (measure functional differential equations) with infinite delay within Hilbert spaces. The derivation of the criteria for the approximate controllability of these systems is achieved by constructing a suitable phase space framework and utilizing the theory of C
0 semigroup and fixed points. Finally, a comprehensive example is presented to elucidate our findings.
Robust model predictive bipartite consensus control for cooperation-competition MASs under DoS attacks
Authors: Zhang Jinnan , Hu Zhongrui , Zhang Songyuan , Dong Hongli 
Published: November 2024

Abstract
This paper addresses the bipartite consensus control problem for a class of discrete-time cooperation-competition multi-agent systems (MASs) subject to denial-of-service (DoS) attacks under the robust model predictive control (RMPC) framework. A signed graph is characterized to describe the cooperation-competition relationship, and the edges of the graph have either positive or negative connection weights. A DoS attacker accomplishes their objectives by obstructing the communication channel between the controller and the actuator. Meanwhile, with the goal of achieving accurate bipartite consensus, an observer-based RMPC algorithm is aimed to be designed in the context of the DoS attacks, which ensures the recursive feasibility and input-to-state stability (ISS) in the mean square sense. The effectiveness of the algorithm is verified by an example in the end.
Conformal prediction with censored data using Kaplan-Meier method
Authors: Sun Xiaolin , Wang Yanhua 
Published: November 2024

Abstract
In this paper, we introduce a prediction algorithm founded on conformal prediction, tailored for constructing prediction intervals in the context of censored survival data. Conformal prediction frameworks distinguish themselves from other prediction paradigms by their non-empirical evaluation, reliance on user-defined confidence intervals for modeling errors, and widespread adoption across regression and classification methodologies, inclusive of survival analysis, in recent years. Herein, we present a novel application wherein the Kaplan-Meier method is employed to compute empirical quantiles of nonconformal scores, specifically tailored for censored schematic variables. This novel approach facilitates the generation of well-calibrated prediction intervals for survival times, augmenting any existing survival prediction algorithm. Validation of its efficacy and computational efficiency is performed on both the real-world dataset ‘SUPPORT’ and the synthetic dataset ‘RRNLNPH.’
Research on overlapping community detection algorithm of label propagation based on node pair extension
Authors: Yan Huizi , Feng Yu , Zhang Weiqi , Xue Bowen , Li Jinrong 
Published: November 2024

Abstract
Community detection is a data analysis method used to reveal the aggregation behavior of the network. This paper improves the COPRA algorithm and proposes a PECOPRA algorithm with better performance to solve the problem. R-mcl similarity coefficient matrix is calculated in the pre-processing operation, the Pearson correlation matrix representing the node relationship is calculated, and the Pearson correlation matrix is filtered to obtain the result matrix. On this basis, the COPRA algorithm is used to calculate and map the community, and the extended modularity redivides the boundary nodes to improve the community partition accuracy. PECOPRA improves the accuracy of community detection quality and has better performance.
Generation of distribution network topology based on the LASSO algorithm
Authors: Li Guocheng , Hu Guorui , Cui Haichao , Lu Zeguang , Li Xiaoran , Li Zhimin , Lei Jiakun , Xu Linyu 
Published: November 2024

Abstract
A new method for intelligent generation of distribution network topology is proposed to address the challenge of monitoring the distribution network topology. Initially, the Least Absolute Shrinkage and Selection Operator (LASSO) technique is applied to calculate the correlation matrix among buses in the distribution grid. Through simulations, it has been shown that this method can effectively and precisely generate distribution network structures, both with and without loops, using time-series voltage data, even when prior knowledge of the network is not available.
An intelligent transformer enabled medium voltage AC network for loss minimization and node voltage improvement
Authors: Loaena Yalisho Girma 
Published: November 2024

Abstract
The research work presented here discusses about design and integration of intelligent transformer (IT) to the 15 kV/0.4 kV overloaded distribution feeder in Wolaita Sodo Town. The feeder contains 113 distribution transformers with different capacities ranging from 50 kVA to 1250 kVA. The proposed intelligent transformer is a three-stage modular type using phase shift multicarrier modulations for MMC front end converter. Its middle stage is dual active bridge having input series output parallel (DAB-ISOP) configuration using a phase shift modulation. The smart transformer’s back end uses an MMC inverter using interleaved phase shift modulation. The distribution feeder is modeled in MATLAB/Simulink and the designed intelligent transformer is allocated to it using voltage stability index (VSI). The peak load data, feeder data and transformer data of the distribution system under study are obtained from the utility office. The existing distribution feeder’s performance was evaluated by using backward-forward sweep load flow simulation for peak loading condition. The line flows and voltage profile at all nodes were observed and candidate nodes for enhancement were identified. Placement of modular intelligent transformer by using voltage stability index (VSI) was conducted to improve the voltage profiles, minimize reactive power flows and losses. Based on the simulation results for base case scenario, total power loss of 936.5 kW is obtained. The minimum node voltage of 0.81PU and maximum reactive power flow of 10.9MVAr were observed for the base case simulation. Placement of intelligent transformer at weak node 67 and node 111 improves the node voltages to have a minimum value 0.965 PU and maximum value of 1.05PU. The total power loss is reduced to 511.5 kW (54.5% improvement) after integration of intelligent transformer
Prologue
Authors: Insole Christopher J 
Published: November 2024

Abstract
The prologue outlines an understanding of ‘negative natural theology’. ‘Natural theology’ involves speaking about God without reference to revelation or tradition, using the resources of ‘reason alone’. ‘Negative theology’ is concerned with the ways in which some types of reasoning, thinking, and speaking, might run out, without this necessarily being an ending. Negative natural theology asks ‘what is at stake’ in the choice (if it is that) to talk about God/the divine, and different conceptions of the divine, or not to do so. What moves us, or persuades us, or blocks us, or enables us, to want to speak about God, or not, in different ways? Relevant factors might include reasons, evidence, and arguments, certainly, but also more intuitive and affective dimensions, including imagination and emotion. Also relevant are unconscious drives and factors. Concepts can convince, or fail to convince, but, also, they can attract and repel.
An Anatomical Approach to Radiofrequency-Assisted Facial Rejuvenation: Beyond the Treatment Gap
Authors: Stein Michael J , Vranis Neil M , Aston Sherrell J 
Published: November 2024

Abstract

Background
Radiofrequency-assisted (RF) facial rejuvenation has become a safe and reliable option for “treatment gap” patients, including (1) patients whose skin laxity is not severe enough to warrant a facelift, yet not mild enough to reliably treat with noninvasive procedures; (2) patients who have already undergone a face or neck lift and have recurrent laxity; and (3) patients who would benefit from a traditional face or neck lift but want to avoid surgery and are willing to accept a more modest improvement without extensive surgical scar burden and recovery.

Objectives
In this study we aimed to educate the reader about providing bipolar RF to various anatomic regions of the face.

Methods
A retrospective review of cases was conducted to assess the safety of zone-specific RF-assisted facial rejuvenation in S.A.'s practice.

Results
RF-microneedling with Morpheus8 was performed on 364 foreheads, 364 periorbita, 353 perioral areas, and 233 jawlines. RF-bipolar with AccuTite was performed on 43 nasolabial folds. There were no cases of hyperpigmentation or hypopigmentation, scarring, or prolonged erythema.

Conclusions
The result of patients treated with the combination of bipolar radiofrequency and fractional bipolar radiofrequency microneedling has expanded RF-assisted facial rejuvenation in our practice. We report on an anatomical approach to RF-assisted facial rejuvenation, as well as expanding indications beyond treatment gap patients.

Level of Evidence: 4 (Therapeutic)
Impact of the radiological morphology of the mesopancreas on the outcome after pancreatoduodenectomy for pancreatic ductal adenocarcinoma: retrospective study
Authors: Navez Julie , Pezzullo Martina , Bouchart Christelle , Arsenijevic Tatjana , Demetter Pieter , Closset Jean , Azurmendi Senar Oier , Racu Marie-Lucie , D’Haene Nicky , Devière Jacques , Verset Laurine , Bali Maria A , van Laethem Jean-Luc 
Published: November 2024

Abstract

Background
The most frequently invaded margins on pancreatoduodenectomy specimens for pancreatic ductal adenocarcinoma are vascular margins, particularly the superior mesenteric artery (or mesopancreatic) margin. Due to limited exploration of the radiological aspect of the mesopancreas, the aim of this study was to evaluate mesopancreatic infiltration through imaging of patients with pancreatic ductal adenocarcinoma who underwent pancreatoduodenectomy, to correlate these findings with histopathology and evaluate their impact on survival.

Methods
Data for all patients who underwent pancreatoduodenectomy for pancreatic ductal adenocarcinoma from 2015 to 2021 were reviewed, including review of surgical margin histopathology and blinded review of preoperative diagnostic imaging. According to qualitative radiological assessment, the mesopancreas was characterized as having normal fat, fat stranding, or solid infiltration. Survival data were analysed using Cox regression.

Results
A total of 149 patients were included. At baseline imaging, mesopancreatic fat stranding or solid infiltration was present in 47 patients (31.5%) and 20 patients (13.4%) respectively. Median overall survival and disease-free survival were significantly lower with mesopancreatic solid infiltration (17 and 8 months) compared with normal fat (30 and 14 months) and fat stranding (29 and 16 months) (P = 0.017 and 0.028 respectively). In multivariable analysis, pathological tumour size was an independent prognostic factor for overall survival, and tumour location in the uncinate process and pathological tumour size were independent prognostic factors for disease-free survival.

Conclusion
At diagnostic imaging, solid infiltration (but not fat stranding) of the mesopancreas is associated with a poor prognosis for pancreatic ductal adenocarcinoma patients who undergo pancreatoduodenectomy. Pathological tumour size significantly influences the prediction of overall survival, and tumour location in the uncinate process and pathological tumour size significantly influence the prediction of disease-free survival, suggesting further exploration of underlying mechanisms related to retroperitoneal tumoral invasion of vascular margins and the mesopancreas.
Effect of L-alanine exposure during early life stage on olfactory development, growth and survival in age-0 lake sturgeon Acipenser fulvescens
Authors: Edwards Tyler , Bouyoucos Ian A , Hasler Caleb T , Fry Mark , Anderson W Gary 
Published: November 2024

Abstract
Environmental factors play an important role in phenotypic development of fishes, which has implications for hatchery-reared fishes that are released into the wild where natural cues are present. There is interest in examining how early exposure to dietary odourants can affect development of olfaction. The aim of our study was to use behavioural, molecular and electro-physiological techniques to evaluate how introduction of the amino acid L-alanine to the rearing environment might influence the development of olfactory perception of dietary cues, growth and survival in lake sturgeon (Acipenser fulvescens), a species of conservation concern. We hypothesized that exposure to amino acids would influence the onset of feeding during dietary transitions from endogenous to exogenous feeding and predicted that the introduction of L-alanine during early development would promote growth and survival of age-0 lake sturgeon. Additionally, we hypothesized that olfaction in lake sturgeon is a developmentally plastic trait, predicting that the addition of L-alanine prior to exogenous feeding would influence mRNA transcript abundance of genes associated with detection of dietary cues. Our approach was to add L-alanine daily from 17 to 20 days post-fertilization (DPF) before the onset of exogenous feeding. We sampled individuals at 17, 21, 26, 31, 50, 65 and 80 DPF. Additionally, olfactory sensitivity to L-alanine was tested at ~1 year via electro-olfactogram (EOG). We observed no significant differences in mortality or EOG response between L-alanine and control treatments; however, significant differences were observed in morphometrics, behaviour and mRNA transcript abundance of all genes throughout development. Our results indicated the olfactory system exhibited developmental plasticity in response to L-alanine treatment until 50–65 DPF, suggesting that environmental odourants may influence early development of key olfactory processes. Our data could inform practises at conservation hatcheries that are used as part of enhancement programmes for lake sturgeon.
Diagnostic performance of approximal caries in bitewing radiographs from different monitors and room illuminances
Authors: Liu Xiao-Xuan , Li Gang 
Published: November 2024

Abstract

Objective
To compare the accuracy, duration, and certainty of diagnosing approximal caries in bitewing radiographs displayed in 3 monitors under 2 luminance conditions.

Methods
A total of 39 teeth without evident caries were selected from 11 patients undergoing partial jaw resection. Before the operation, 13 bitewing radiographs were captured by a digital imaging system. Eight observers evaluated the images under dark (9 lux) and bright (200 lux) conditions, using 2 medical-grade monitors and a commercial monitor. Using histological results as the gold standard, the areas under the receiver operating characteristic curves under different conditions were compared using the Z-test. Multivariate analysis of variance was conducted to assess the impact of various factors on diagnostic duration, while ordinal logistic regression was used to examine factors influencing diagnostic certainty level. It was considered significant when P &lt; .05.

Results
No significant difference was found in the diagnostic accuracy or duration for diagnosis of approximal caries under 2 luminance conditions with the 3 distinct monitors (P &gt; .05). Ambient light, clinical experience, and the pathological grade of approximal caries have influence on the degree of diagnostic confidence (P &lt; .05).

Conclusions
Different monitors and ambient luminance didn’t influence the diagnostic accuracy or evaluation duration. Ambient luminance, clinical experience, and the depth of caries affect the degree of diagnostic confidence.

Advances in knowledge
The study employing bitewing radiographs from real patients indicates that ambient luminance, clinical experience, and the depth of caries affect the degree of diagnostic confidence.
Evaluation of temporomandibular joint osteoarthritis using a new FRACTURE sequence of 3.0T MRI
Authors: Nozawa Michihito , Fukuda Motoki , Kotaki Shinya , Tomoda Daisuke , Morishita Ayaka , Akiyama Hironori , Ariji Yoshiko 
Published: November 2024

Abstract

Objectives
The purpose of this study was to determine the usefulness of a new MRI sequence, CT-like fast field echo with limited echo-spacing (FRACTURE), in diagnosing temporomandibular joint (TMJ) osteoarthritis compared with routine MRI TMJ sequences.

Methods
The study sample comprised 76 patients (152 joints) who underwent MRI and CT examinations to diagnose TMJ disorders. Two specialists in oral and maxillofacial radiology assessed the bony changes of the TMJ on FRACTURE, proton density-weighted (PDw), and fat-suppression T2-weighted (T2wFS) sequences. Receiver operating characteristic curves were plotted for each sequence, and the accuracy, sensitivity, specificity, and area under the curve (AUC) were calculated. Additionally, the interobserver agreement (Cohen’s kappa value) and sensitivity in assessing each osteoarthritis finding were calculated for each sequence.

Results
The FRACTURE sequence had the highest diagnostic performance, with an accuracy of 0.85, sensitivity of 0.85, specificity of 0.84, and AUC of 0.84. These values were 0.84, 0.72, 0.91, and 0.80, respectively, for the PDw sequence, and 0.83, 0.72, 0.91, and 0.79, respectively, for the T2wFS sequence. The AUC did not significantly differ between the FRACTURE and PDw sequences (Delong test, P &gt; .05), but did significantly differ between the FRACTURE and T2wFS sequences (P &lt; .05). For all osteoarthritis findings, the FRACTURE sequence had the highest kappa values and the highest sensitivity.

Conclusions
FRACTURE sequencing may be a promising tool for the diagnosis of TMJ osteoarthritis compared with other conventional sequences.