bluuebunny/crossref_metadata_2025 huggingface dataset:
Performance of AI-Based Automated Classifications of Whole-Body FDG PET in Clinical Practice: The CLARITI Project
Authors: Berenbaum Arnaud , Delingette Hervé , Maire Aurélien , Poret Cécile , Hassen-Khodja Claire , Bréant Stéphane , Daniel Christel , Martel Patricia , Grimaldi Lamiae , Frank Marie , Durand Emmanuel , Besson Florent L.
Published: April 2023

Purpose: To assess the feasibility of a three-dimensional deep convolutional neural network (3D-CNN) for the general triage of whole-body FDG PET in daily clinical practice. Methods: An institutional clinical data warehouse working environment was devoted to this PET imaging purpose. Dedicated request procedures and data processing workflows were specifically developed within this infrastructure and applied retrospectively to a monocentric dataset as a proof of concept. A custom-made 3D-CNN was first trained and tested on an “unambiguous” well-balanced data sample, which included strictly normal and highly pathological scans. For the training phase, 90% of the data sample was used (learning set: 80%; validation set: 20%, 5-fold cross validation) and the remaining 10% constituted the test set. Finally, the model was applied to a “real-life” test set which included any scans taken. Text mining of the PET reports systematically combined with visual rechecking by an experienced reader served as the standard-of-truth for PET labeling. Results: From 8125 scans, 4963 PETs had processable cross-matched medical reports. For the “unambiguous” dataset (1084 PETs), the 3D-CNN’s overall results for sensitivity, specificity, positive and negative predictive values and likelihood ratios were 84%, 98%, 98%, 85%, 42.0 and 0.16, respectively (F1 score of 90%). When applied to the “real-life” dataset (4963 PETs), the sensitivity, NPV, LR+, LR− and F1 score substantially decreased (61%, 40%, 2.97, 0.49 and 73%, respectively), whereas the specificity and PPV remained high (79% and 90%). Conclusion: An AI-based triage of whole-body FDG PET is promising. Further studies are needed to overcome the challenges presented by the imperfection of real-life PET data.
Thalamus Segmentation Using Deep Learning with Diffusion MRI Data: An Open Benchmark
Authors: Pinheiro Gustavo Retuci , Brusini Lorenza , Carmo Diedre , Prôa Renata , Abreu Thays , Appenzeller Simone , Menegaz Gloria , Rittner Leticia
Published: April 2023

The thalamus is a subcortical brain structure linked to the motor system. Since certain changes within this structure are related to diseases, such as multiple sclerosis and Parkinson’s, the characterization of the thalamus—e.g., shape assessment—is a crucial step in relevant studies and applications, including medical research and surgical planning. A robust and reliable thalamus-segmentation method is therefore, required to meet these demands. Despite presenting low contrast for this particular structure, T1-weighted imaging is still the most common MRI sequence for thalamus segmentation. However, diffusion MRI (dMRI) captures different micro-structural details of the biological tissue and reveals more contrast of the thalamic borders, thereby serving as a better candidate for thalamus-segmentation methods. Accordingly, we propose a baseline multimodality thalamus-segmentation pipeline that combines dMRI and T1-weighted images within a CNN approach, achieving state-of-the-art levels of Dice overlap. Furthermore, we are hosting an open benchmark with a large, preprocessed, publicly available dataset that includes co-registered, T1-weighted, dMRI, manual thalamic masks; masks generated by three distinct automated methods; and a STAPLE consensus of the masks. The dataset, code, environment, and instructions for the benchmark leaderboard can be found on our GitHub and CodaLab.
Experimental Investigation on Structural Performance Enhancement of Brick Masonry Member by Internal Reinforcement
Authors: Jin Kiwoong , Choi Ho , Quan Chunri
Published: April 2023

This study focused on perforated bricks, and the structural performance enhancement of brick members by internal reinforcement was experimentally investigated. As a new reinforcing material, screw iron (SI) rods were selected for the internal reinforcement, and they were inserted into the perforated brick. To investigate the most-effective reinforcing method as well as to understand the fundamental structural behaviors, four specimens with different variables were fabricated, and three-point bending tests were carried out. From the experiments, it was found that the maximum strength of the specimen increased by more than two times with internal reinforcement. The internal reinforcement, fixed by nuts at both ends of the specimen to enhance integrity, increased the maximum strength by nine times. Moreover, the deformation capacities of the specimens were also greatly enhanced. The case where the internal SI rods were fixed by nuts without mortar also showed a similar structural performance to the case with mortar. The estimation methods of the maximum strength of the specimens were also discussed, and they showed reasonable agreement with the test results. It was proved that the proposed material and methods enabled effective utilization of the internal reinforcement, and they could contribute to the improvement of structural performances in masonry construction.
Development of an Accurate and Automated Quality Inspection System for Solder Joints on Aviation Plugs Using Fine-Tuned YOLOv5 Models
Authors: Sha Junwei , Wang Junpu , Hu Huanran , Ye Yongqiang , Xu Guili
Published: April 2023

The quality inspection of solder joints on aviation plugs is extremely important in modern manufacturing industries. However, this task is still mostly performed by skilled workers after welding operations, posing the problems of subjective judgment and low efficiency. To address these issues, an accurate and automated detection system using fine-tuned YOLOv5 models is developed in this paper. Firstly, we design an intelligent image acquisition system to obtain the high-resolution image of each solder joint automatically. Then, a two-phase approach is proposed for fast and accurate weld quality detection. In the first phase, a fine-tuned YOLOv5 model is applied to extract the region of interest (ROI), i.e., the row of solder joints to be inspected, within the whole image. With the sliding platform, the ROI is automatically moved to the center of the image to enhance its imaging clarity. Subsequently, another fine-tuned YOLOv5 model takes this adjusted ROI as input and realizes quality assessment. Finally, a concise and easy-to-use GUI has been designed and deployed in real production lines. Experimental results in the actual production line show that the proposed method can achieve a detection accuracy of more than 97.5% with a detection speed of about 0.1 s, which meets the needs of actual production
Evaluation of Prognostic Significance of the Expression of p53, Cyclin D1, EGFR in Advanced Oral Squamous Cell Carcinoma after Chemoradiation—A Systematic Review
Authors: Awawdeh Mohammed Adel , Sasikumar Rekha , Aboalela Ali Anwar , Siddeeqh Salman , Gopinathan Pillai Arun , Sawair Faleh , Khanagar Sanjeev B.
Published: April 2023

The majority of all head and neck tumours are squamous cell carcinomas (SCC). Recent advancements have assisted in producing a body of evidence for the genetic origins of these tumours. Identifying prognostic biomarkers in oral SCC would be of great importance in predicting tumour behaviour and in treatment planning. Many studies have proved that chemotherapy given with radiotherapy leads to better treatment outcomes and overall survival. Biomarkers like p53, cyclin D1, and EGFR, that regulate the cell cycle, have a pivotal role in tumour development and progression. This systematic review aims at analysing the prognostic significance of the expression of p53, cyclin D1, and EGFR in advanced oral SCC after chemoradiation therapy (CRT). A systematic search using predetermined keywords and inclusion/exclusion criteria was conducted in Medline/PubMed, Google Scholar, and other databases to identify relevant articles answering the research question “Does the expressions of p53, cyclin D1, and EGFR have a prognostic significance in recurrent oral SCC treated with chemoradiation?” Literature screening revealed eight articles that were considered for this study. The overexpression of p53, cyclin D1, EGFR was associated with the recurrence of the tumour, and pathologic response can be considered as a prognostic marker. However, the recurrence pattern not only depends on the biomarkers but also on the clinicopathologic factors that play a pivotal role in survival rates among oral cancer patients. The standard management of advanced head and neck SCC has been controversial. It has been concluded that concomitant therapy can result in better treatment outcomes. Clinicians need to scrutinize and evaluate p 53, cyclin D1 and EGFR as a diagnostic parameter for post tumour chemoradiation therapy. Personalised therapy strategies can be created for individual patients using biologically guided tumour characterization, which will enhance quality of life. Thus, the application of more sophisticated technology must be implemented for a better analysis of the tumour.
Classifying Microscopic Images of Reactive Lymphocytosis Using Two-Step Tandem AI Models
Authors: Nozaka Hiroyuki , Kushibiki Mihoko , Kamata Kosuke , Yamagata Kazufumi
Published: April 2023

The practical applications of automatic recognition and categorization technology for next-generation systems are desired in the clinical laboratory. We approached the identification of reactive lymphocytosis using artificial intelligence (AI) technology and studied its clinical usefulness for blood smear screening. This study created one- and two-step AI models for the identification of reactive lymphocytosis. The ResNet-101 model was applied for deep learning. The original image set for supervised AI training consisted of 5765 typical nucleated blood cell images. The subjects for clinical assessment were 25 healthy cases, 25 erythroblast cases, and 25 reactive lymphocytosis cases. The total accuracy (mean ± standard deviation) of the one- and two-step models were 0.971 ± 0.047 and 0.977 ± 0.024 in healthy, 0.938 ± 0.040 and 0.978 ± 0.018 in erythroblast, and 0.856 ± 0.056 and 0.863 ± 0.069 in reactive lymphocytosis cases, respectively. The two-step AI model showed a sensitivity of 0.960 and a specificity of 1.000 between healthy and reactive lymphocytosis cases. As our two-step tandem AI model showed high performance for identifying reactive lymphocytosis in blood smear screening, we plan to apply this method to the development of AI models to differentiate reactive and neoplastic lymphocytosis.
Study on Thermal Error Modeling for CNC Machine Tools Based on the Improved Radial Basis Function Neural Network
Authors: Feng Zhiming , Min Xinglong , Jiang Wei , Song Fan , Li Xueqin
Published: April 2023

The thermal error modeling technology of computer numerical control (CNC) machine tools is the core of thermal error compensation, and the machining accuracy of CNC machine tools can be improved effectively by the high-precision prediction model of thermal errors. This paper analyzes several methods related to thermal error modeling in the latest research applications, summarizes their deficiencies, and proposes a thermal error modeling method of CNC machine tool based on the improved particle swarm optimization (PSO) algorithm and radial basis function (RBF) neural network, named as IPSO-RBFNN. By introducing a compression factor to make the PSO algorithm balance between global and local search, the structure parameters of RBF neural network are optimized. Furthermore, in order to pick up the temperature-sensitive variables, an improved model, which combines the K-means clustering algorithm and correlation analysis method based on back propagation (BP) neural network is proposed. After the temperature-sensitive variables are selected, the IPSO-RBFNN method is adopted to establish the thermal error model for CNC machine tool. Based on the experimental data of the CNC machine tool under the name of DMG-DMU65, the predictive accuracy of the IPSO-RBFNN model in Z direction reaches 2.05 μm. Compared with other neural network method, it is improved by 10.48%, which indicates that it has better prediction ability. At last, the experiment verification for different thermal error terms at different velocities proves that this model has stronger robustness.
Study on Preparation and Rheological Properties of 3D Printed Pre-Foaming Concrete
Authors: Gao Yanan , Hua Sudong , Yue Hongfei
Published: April 2023

The high fluidity and low yield stress of fresh foam concrete affect the shape stability and buildability of foam concrete in the printing process, which is quite a challenge to its application in digital construction. Therefore, this article proposes the preparation and characteristics of 3D printed pre-foaming concrete (3DFC). The rheological properties proved that the addition of 0.1 wt.% Hydroxypropyl methyl cellulose (HPMC) to 3DFCs weakens the fluidity but increases the static yield stress and apparent viscosity, thus enhancing the buildability. More importantly, the influences of surfactant on the rheological property, compressive strength, pore structure and thermal conductivity of 3DFCs were evaluated. Analysis results show that the static yield stress of 3DFCs decreases from 1735 to 687 Pa with surfactant dosage from 0 to 2 wt.%. Moreover, the addition of surfactant significantly reduced the apparent viscosity of 3DFCs (especially at low shear rates), but its viscosity recovery rate was basically unchanged, which is good for buildability. Thanks to the increase of porosity, the volume density of 3DFCs decreased from 2211 to 1159 kg/m3, but the compressive strength of 3DFCs also decreased slightly. The thermal conductivity of 3DFCs shows good thermal insulation performance in the range of 0.2254–0.2879 W/m·K, which is also due to the increase in porosity of 3DFCs. Finally, in order to verify the practical application value of 3DFCs, an industrial printing product with more than 30 layers during the field application is displayed.
Learning Hierarchical Representations for Explainable Chemical Reaction Prediction
Authors: Hou Jingyi , Dong Zhen
Published: April 2023

This paper aims to propose an explainable and generalized chemical reaction representation method for accelerating the evaluation of the chemical processes in production. To this end, we designed an explainable coarse-fine level representation model that incorporates a small amount of easily available expert knowledge (i.e., coarse-level annotations) into the deep learning method to effectively improve the performances on reaction representation related tasks. We also developed a new probabilistic data augmentation strategy with contrastive learning to improve the generalization of our model. We conducted experiments on the Schneider 50k and the USPTO 1k TPL datasets for chemical reaction classification, as well as the USPTO yield dataset for yield prediction. The experimental results showed that our method outperforms the state of the art by just using a small-scale dataset annotated with both coarse-level and fine-level labels to pretrain the model.
Unsupervised Segmentation of Muscle Precursor Cell Images In Situ
Authors: Ruan Lihua , Yuan Yongchun , Zhang Tao
Published: April 2023

In vitro culture of muscle stem cells on a large scale could bring light to the treatment of muscle-related diseases. However, the current work related to muscle stem cell culture is still only performed in specialized biological laboratories that are very much limited by manual experience. There are still some difficulties to achieve an automated culture of complex morphological cells in terms of live cell observation and morphological analysis. In this paper, a set of bright-field cell in situ imaging devices is designed to perform non-contact and invasive imaging of muscle precursor cells in vitro, and a neural network structured lightweight unsupervised semantic segmentation algorithm is proposed for the acquired images to achieve online extraction of cell regions of interest without manual annotation and pre-training. The algorithm first uses a graph-based super-pixel segmentation to obtain a coarse segmentation, then aggregates the coarse segmentation results with the help of Laplace operators as a reference to a four-layer convolutional neural network (CNN). The CNN parameters learn to refine the boundaries of the cells which helps the final segmentation accuracy and mean intersection–merge ratio reach 88% and 77%, respectively.
Dependence of the Color Tunability on the H2Pc Thickness in DC-Voltage-Driven Organic Light-Emitting Diodes
Authors: Ahn Tae Jun , Choi Bum Ho , Yu Jae-Woong , Yu Yun Seop
Published: April 2023

Dependence of the color tunability on the metal free Phthalocyanine (H2Pc) layer thickness in DC-voltage-driven organic light-emitting diodes (OLEDs) was investigated. A H2Pc layer was employed as a blue/red emission layer, which was prepared on an Alq3 green emission layer. The thickness of the H2Pc layer varied from 5 to 30 nm, with a step of 5 nm. The fabricated color-tunable OLEDs (CTOLEDs) were subjected to a thermal treatment layer for 2 min at a temperature of 120 °C to improve the interface properties, especially between H2Pc and Alq3. The current density–voltage–luminance characteristics and Commission Internationale de L’Eclairage (CIE) coordinates of the CTOLEDs with and without thermal treatment were measured, and their energy band diagrams were analyzed with respect to the H2Pc thin film thicknesses. In addition, the recombination rates at the interfaces between the hole transport layer and Alq3 and the H2Pc/electron transport layer of the CTOLEDs with and without thermal treatment were theoretically investigated using a technology–computer-aided design (TCAD) program. The experimental and theoretical results showed that the emission color temperature from cool white to warm white at a low voltage can be controlled by adjusting the thickness of the H2Pc layer in the CTOLED. It was verified that the thermally treated H2Pc thin film layer acted as a barrier that prevented electrons from being transferred to the Alq3 at low applied voltages, resulting in white color emission with temperature tunability. The CTOLED with a 20 nm of H2Pc layer demonstrated the best stable interface state and stability, resulting in the lowest driving voltage, relatively high luminance, and optimal light emission uniformity, respectively.
GAN Data Augmentation Methods in Rock Classification
Authors: Zhao Gaochang , Cai Zhao , Wang Xin , Dang Xiaohu
Published: April 2023

In this paper, a data augmentation method Conditional Residual Deep Convolutional Generative Adversarial Network (CRDCGAN) based on Deep Convolutional Generative Adversarial Network (DCGAN) is proposed to address the problem that the accuracy of existing image classification techniques is too low when classifying small-scale rock images. Firstly, Wasserstein distance is introduced to change the loss function, which makes the training of the network more stable; secondly, conditional information is added, and the network has the ability to generate and discriminate image data with label information; finally, the residual module is added to improve the quality of generated images. The results demonstrate that by applying CRDCGAN to the augmented rock image dataset, the accuracy of the classification model trained on this dataset is as high as 96.38%, which is 13.39% higher than that of the classification model trained on the non-augmented dataset, and 8.56% and 6.27% higher than that of the traditional dataset augmented method and the DCGAN dataset augmentation method, respectively. CRDCGAN expands the rock image dataset, which makes the rock classification model accuracy effectively improved. The data augmentation method was found to be able to change the accuracy of the classification model to a greater extent.
Symbolic Parametric Representation of the Area and the Second Moments of Area of Periodic B-Spline Cross-Sections
Authors: Denk Martin , Jäger Michael , Wartzack Sandro
Published: April 2023

The calculation of moments of area is one of the most fundamental aspects of engineering mechanics for calculating the properties of beams or for the determination of invariants in different kind of geometries. While a variety of shapes, such as circles, rectangles, ellipses, or their combinations, can be described symbolically, such symbolic expressions are missing for freeform cross-sections. In particular, periodic B-spline cross-sections are suitable for an alternative beam cross-section, e.g., for the representation of topology optimization results. In this work, therefore, a symbolic description of the moments of area of various parametric representations of such B-splines is computed. The expressions found are then compared with alternative computational methods and checked for validity. The resulting equations show a simple method that can be used for the fast conceptual computation of such moments of area of periodic B-splines.
Suitability of Different Live Feed for First Feeding of Freshwater Fish Larvae
Authors: Lahnsteiner Franz , Lahnsteiner Elias , Duenser Anna
Published: April 2023

First feeding of many fish larvae depends on live feed. A comparative investigation on the effectiveness of different types of live feed is not available to our knowledge. Hence, we conducted a study to examine the effect of different types and combinations of live feed on the performance (survival rate, total length, body width, body mass, malformation rate) of pikeperch, Sander lucioperca, larvae. From day 0 (onset of exogenous feeding) to day 10, the saltwater rotifer Brachionus plicatilis, the freshwater rotifer Brachionus calyciflorus, the ciliate Paramecium bursaria, copepods (nauplii and copepodites) from a lake population, and Artemia nauplii were tested. Feeding with B. plicatilis, B. calyciflorus, and P. bursaria resulted in high survival rates of 80% and a homogenous and significant growth (increase in total length of 50% and in body width of 20%). As follow-up feed, copepod nauplii and Artemia nauplii were tested from day 11 to day 20. Copepod nauplii were superior to Artemia nauplii, as larvae fed with copepods showed higher survival rates (67–70% versus 38–47%) and a more homogeneous growth. A switch from seawater live feed to freshwater live feed or vice versa resulted in decreased survival rates. Therefore, a feeding regime consisting of B. calyciflorus or P. bursaria followed by copepods is considered optimal as first feed of pikeperch. The malformation rate was not affected by the tested feeding regimes. To investigate the wider applicability and transferability of these findings, complementary investigations were performed on burbot, Lota lota, and the freshwater whitefish Coregonus atterensis. The feeding regimes used for S. lucioperca larvae were also suitable for Lota lota. Moreover, L. lota could be fed with lake copepods from the onset of exogenous feeding. For C. atterensis, initial feeding with B. plicatilis, B. calyciflorus, or P. bursaria had no positive effects. Feeding with copepods from the onset of exogenous feeding was optimal considering survival rate and growth. Therefore, optimal first feeding regimes are very species specific and should be established for each new species.
From a Dinuclear System to Close Binary Cosmic Objects
Authors: Adamian G. G. , Antonenko N. V. , Lenske H. , Sargsyan V. V.
Published: April 2023

Applying the ideas from microscopic objects to macroscopic stellar and galactic systems, the evolution of compact di-stars and di-galaxies is studied in the mass asymmetry coordinate. The formation of stable binary systems is analyzed. The role of symmetrization of an initially asymmetric binary system is revealed in the transformation of gravitational energy into internal energy of stars or galaxies accompanied by the release of a huge amount of energy. For the contact binary stars, the change of the orbital period is explained by evolution to symmetry in mass asymmetry coordinates. The matter transfer in binary black holes is studied. The conditions for the merger of black holes in a binary system are analyzed regarding the radiation of gravitational waves. Using the model based on the Regge-like laws, the Darwin instability effect in binary systems is discussed. New analytical formulas are derived for the period of orbital rotation and the relative distance between the components of a binary system. The impossibility of the appearance of a binary cosmic object from a single cosmic object is revealed.
Use of Assimilation Analysis in 4D-Var Source Inversion: Observing System Simulation Experiments (OSSEs) with GOSAT Methane and Hemispheric CMAQ
Authors: Voshtani Sina , Ménard Richard , Walker Thomas W. , Hakami Amir
Published: April 2023

We previously introduced the parametric variance Kalman filter (PvKF) assimilation as a cost-efficient system to estimate the dynamics of methane analysis concentrations. As an extension of our development, this study demonstrates the linking of PvKF to a 4D-Var inversion aiming to improve on methane emissions estimation in comparison with the traditional 4D-Var. Using the proposed assimilation–inversion framework, we revisit fundamental assumptions of the perfect and already optimal model state that is typically made in the 4D-Var inversion algorithm. In addition, the new system objectively accounts for error correlations and the evolution of analysis error variances, which are non-trivial or computationally prohibitive to maintain otherwise. We perform observing system simulation experiments (OSSEs) aiming to isolate and explore various effects of the assimilation analysis on the source inversion. The effect of the initial field of analysis, forecast of analysis error covariance, and model error is examined through modified 4D-Var cost functions, while different types of perturbations of the prior emissions are considered. Our results show that using PvKF optimal analysis instead of the model forecast to initialize the inversion improves posterior emissions estimate (~35% reduction in the normalized mean bias, NMB) across the domain. The propagation of analysis error variance using the PvKF formulation also tends to retain the effect of background correlation structures within the observation space and, thus, results in a more reliable estimate of the posterior emissions in most cases (~50% reduction in the normalized mean error, NME). Our sectoral analysis of four main emission categories indicates how the additional information of assimilation analysis enhances the constraints of each emissions sector. Lastly, we found that adding the PvKF optimal analysis field to the cost function benefits the 4D-Var inversion by reducing its computational time (~65%), while including only the error covariance in the cost function has a negligible impact on the inversion time (10–20% reduction).
Big-Data-Driven Machine Learning for Enhancing Spatiotemporal Air Pollution Pattern Analysis
Authors: Zareba Mateusz , Dlugosz Hubert , Danek Tomasz , Weglinska Elzbieta
Published: April 2023

Air pollution is an important problem for public health. The spatiotemporal analysis is a crucial step for understanding the complex characteristics of air pollution. Using many sensors and high-resolution time-step observations makes this task a big data challenge. In this study, unsupervised machine learning algorithms were applied to analyze spatiotemporal patterns of air pollution. The analysis was conducted using PM10 big data collected from almost 100 sensors located in Krakow, over a period of one year, with data being recorded at 1-h intervals. The analysis results using K-means and SKATER clustering revealed distinct differences between average and maximum values of pollutant concentrations. The study found that the K-means algorithm with Dynamic Time Warping (DTW) was more accurate in identifying yearly patterns and clustering in rapidly and spatially varying data, compared to the SKATER algorithm. Moreover, the clustering analysis of data after kriging greatly facilitated the interpretation of the results. These findings highlight the potential of machine learning techniques and big data analysis for identifying hot-spots, cold-spots, and patterns of air pollution and informing policy decisions related to urban planning, traffic management, and public health interventions.
Impacts of Complex Terrain Features on Local Wind Field and PM2.5 Concentration
Authors: Song Yuqiang , Shao Min
Published: April 2023

Complex topography has nonnegligible effects on local meteorological conditions as well as the transportation of atmospheric pollutants, which deserves more extensive study. In this study, the impacts of complex terrain features (mountains and river valleys) on local wind field and PM2.5 concentration in a typically developed mega city along the Yangtze River were studied numerically using the WRFCALMET-CALPUFF system. The impacts of different model grid and terrain horizontal resolutions were firstly investigated against observations. Then, the impacts of terrain features, specifically the impacts of Mt. LS and the Yangtze River, on wind field and PM2.5 transportation were analyzed by “removing” Mt. LS and the Yangtze River from the meteorological diagnostic model and simulating the dispersion of PM2.5 from three virtual point sources in the chemical model. Results showed that: (i) higher terrain elevation and model horizontal resolutions, and updated land cover types, can effectively improve the prediction of wind direction where terrain features are complex; (ii) Mt. LS mainly acts as a barrier, and ridge wind is weakened after “removing” Mt. LS; (iii) after “removing” the Yangtze River, the transport of PM2.5 along the Yangtze River is weakened; (iv) the simulation of PM2.5 from virtual point sources showed that Mt. LS could have an effect of up to 55% on the PM2.5 concentration in Nanjing. This study showed that the local complex topographies have an obvious effect on the local wind field and the concentration of PM2.5. Therefore, it is important to consider the influence of local topographies and land cover types when predicting local wind field and air quality.
A Case Study on the Impact of East Asian Summer Monsoon on Surface O3 in China
Authors: Zhang Xin , Zhou Lihua , Zhang Xingying , Luo Yong , Sun Lei
Published: April 2023

The East Asian summer monsoon (EASM) was extremely strong in 2018, which substantially affected surface ozone (O3) in China. Taking 2018 and the average synthesis of 2003 and 2010 to represent the strong and weak EASM cases, respectively, GEOS-Chem with constant anthropogenic emission was employed to investigate the impact of the EASM on surface O3 in the east of China. Simulations show that surface O3 decreased in the northeast and the eastern coast of China and increased in most of the remaining regions during strong EASM. The difference in surface O3 between strong and weak EASM was around −15~7 ppbv. After analyzing relevant meteorological fields, it is found that the decrease in northeast China was mainly attributed to the large increase in vertical upward transport. The considerable decrease in the Huang-Huai-Hai region depended on the dilution and diffusion of eastward anomalous horizontal circulation. The increase in Hunan-Hubei-Guangdong Province was largely due to input from the north. In addition, the vast areas between the Yangtze River and the Yellow River were supported by higher temperatures and stronger shortwave solar radiation that promoted photochemical reactions. The reasons for changes in Shanxi-Sichuan-Yunnan Province were relatively more complex and thus require more in-depth exploration.
Fractional Step Scheme to Approximate a Non-Linear Second-Order Reaction–Diffusion Problem with Inhomogeneous Dynamic Boundary Conditions
Authors: Fetecău Constantin , Moroşanu Costică
Published: April 2023

Two main topics are addressed in the present paper, first, a rigorous qualitative study of a second-order reaction–diffusion problem with non-linear diffusion and cubic-type reactions, as well as inhomogeneous dynamic boundary conditions. Under certain assumptions about the input data: gd(t,x), gfr(t,x), U0(x) and ζ0(x), we prove the well-posedness (the existence, a priori estimates, regularity and uniqueness) of a solution in the space Wp1,2(Q)×Wp1,2(Σ). Here, we extend previous results, enabling new mathematical models to be more suitable to describe the complexity of a wide class of different physical phenomena of life sciences, including moving interface problems, material sciences, digital image processing, automatic vehicle detection and tracking, the spread of an epidemic infection, semantic image segmentation including U-Net neural networks, etc. The second goal is to develop an iterative splitting scheme, corresponding to the non-linear second-order reaction–diffusion problem. Results relating to the convergence of the approximation scheme and error estimation are also established. On the basis of the proposed numerical scheme, we formulate the algorithm alg-frac_sec-ord_dbc, which represents a delicate challenge for our future works. The benefit of such a method could simplify the process of numerical computation.
Granular Computing Approach to Evaluate Spatio-Temporal Events in Intuitionistic Fuzzy Sets Data through Formal Concept Analysis
Authors: Ali Imran , Li Yongming , Pedrycz Witold
Published: April 2023

Knowledge discovery through spatial and temporal aspects of data related to occurrences of events has many applications in digital forensics. Specifically, in electronic surveillance, it is helpful to construct a timeline to analyze information. The existing techniques only analyze the occurrence and co-occurrence of events; however, in general, there are three aspects of events: occurrences (and co-occurrences), nonoccurrences, and uncertainty of occurrences/non-occurrences with respect to spatial and temporal aspects of data. These three aspects of events have to be considered to better analyze periodicity and predict future events. This study focuses on the spatial and temporal aspects given in intuitionistic fuzzy (IF) datasets using the granular computing (GrC) paradigm; formal concept analysis (FCA) was used to understand the granularity of data. The originality of the proposed approach is to discover the periodicity of events data given in IF sets through FCA and the GrC paradigm that helps to predict future events. An experimental evaluation was also performed to understand the applicability of the proposed methodology.
On Two Intuitionistic Fuzzy Modal Topological Structures
Authors: Atanassov Krassimir , Angelova Nora , Pencheva Tania
Published: April 2023

The concept of an Intuitionistic Fuzzy Modal Topological Structure (IFMTS) was introduced previously, and some of its properties were studied. So far, there are two different IFMTSs based on the classical intuitionistic fuzzy operations: “union” (∪) and “intersection” (∩). In the present paper, two new IFMTSs are developed. They are based on new intuitionistic fuzzy topological operators from closure and interior types, introduced here for the first time, and on the two standard intuitionistic fuzzy modal operators □ and ◊. Some basic properties of the new IFMTSs are discussed. The newly presented IFMTSs could be considered as a basis for the next research on the IFMTSs. Some ideas for the future development of the IFMTS theory and open problems are formulated, related to the existence of other intuitionistic fuzzy operations that can generate new intuitionistic fuzzy topological operators and, respectively, new IFMTSs.
Lyapunov Functionals in Integral Equations
Authors: Raffoul Youssef N. , Raffoul Joseph
Published: April 2023

Lyapunov functions/functionals have found their footing in Volterra integro-differential equations. This is not the case for integral equations, and it is therefore further explored in this paper. In this manuscript, we utilize Lyapunov functionals combined with Laplace transform to qualitatively analyze the solutions of the integral equation In addition, we extend our method to nonlinear integral equations, integral equations with infinite delay, and integral equations with several kernels. We mention that Laplace transform has been used to solve integral equations of convolution types but has never been applied directly to integral equations that are not of the convolution type. In addition, our method allows us to find the upper estimates, and our necessary conditions are easy to verify.
Spanning k-Ended Tree in 2-Connected Graph
Authors: Lei Wanpeng , Yin Jun
Published: April 2023

Win proved a very famous conclusion that states the graph G with connectivity κ(G), independence number α(G) and α(G)≤κ(G)+k−1(k≥2) contains a spanning k-ended tree. This means that there exists a spanning tree with at most k leaves. In this paper, we strengthen the Win theorem to the following: Let G be a simple 2-connected graph such that |V(G)|≥2κ(G)+k, α(G)≤κ(G)+k(k≥2) and the number of maximum independent sets of cardinality κ+k is at most n−2κ−k+1. Then, either G contains a spanning k-ended tree or a subgraph of Kκ∨((k+κ−1)K1∪Kn−2κ−k+1).
A q-Analog of the Class of Completely Convex Functions and Lidstone Series
Authors: Al-Towailb Maryam , Mansour Zeinab S. I.
Published: April 2023

This paper introduces a q-analog of the class of completely convex functions. We prove specific properties, including that q-completely convex functions have convergent q-Lidstone series expansions. We also provide a sufficient and necessary condition for a real function to have an absolutely convergent q-Lidstone series expansion.
Reliability Class Testing and Hypothesis Specification: NBRULC−t∘ Characterizations with Applications for Medical and Engineering Data Modeling
Authors: Alqifari Hana , Eliwa Mohamed S. , Etman Walid B. H. , El-Morshedy Mahmoud , Al-Essa Laila A. , EL-Sagheer Rashad M.
Published: April 2023

Due to the complexity of the data being generated day in and day out in many practical domains, as a result of the development of scales for rating the success or failure of reliability, a new domain of reliability called the classes of life and determinant probability distributions has been presented. This article introduces novel statistical probability models for the reliability class of life test under different reliability processes in the age range t∘. Several probabilistic properties and features were derived and rigorously screened to test the new reliability class. According to the U-statistic, a novel hypothesis test was created to evaluate the exponentiality property. The comparative efficiency of the test according to Pitman’s asymptotic efficiency was examined and compared with other reliability classes. To prove the superiority of the new reliability class, some probability models were utilized, including the Weibull, Makeham, gamma, and linear failure rate models. Moreover, critical point simulations of the null Monte Carlo distribution and some applications of the censored and uncensored data were implemented to validate the class test listed by the reliability analysis.
First Entire Zagreb Index of Fuzzy Graph and Its Application
Authors: Jana Umapada , Ghorai Ganesh
Published: April 2023

The first entire Zagreb index (FEZI) is a graph parameter that has proven to be essential in various real-life scenarios, such as networking businesses and traffic management on roads. In this research paper, the FEZI was explored for a variety of fuzzy graphs, including star, firefly graph, cycle, path, fuzzy subgraph, vertex elimination, and edge elimination. This study presented several results, including determining the relationship between two isomorphic fuzzy graphs and between a path and cycle (connecting both end vertices of the path). This research also deals with the analysis of α-cut fuzzy graphs and establishes bounds for some fuzzy graphs. To apply these findings to modern life problems, the research team utilized the results to identify areas that require more development in internet systems. These results have practical implications for enhancing the efficiency and effectiveness of internet systems. The conclusion drawn from this research can be used to inform future research and aid in the development of more efficient and effective systems in various fields.
Thermal and Mechanical Safety Assessment of Type 21700 Lithium-Ion Batteries with NMC, NCA and LFP Cathodes–Investigation of Cell Abuse by Means of Accelerating Rate Calorimetry (ARC)
Authors: Ohneseit Sebastian , Finster Philipp , Floras Claire , Lubenau Niklas , Uhlmann Nils , Seifert Hans Jürgen , Ziebert Carlos
Published: April 2023

In this experimental investigation, we studied the safety and thermal runaway behavior of commercial lithium-ion batteries of type 21700. The different cathode materials NMC, NCA and LFP were compared, as well as high power and high energy cells. After characterization of all relevant components of the batteries to assure comparability, two abuse methods were applied: thermal abuse by the heat-wait-seek test and mechanical abuse by nail penetration, both in an accelerating rate calorimeter. Several critical temperatures and temperature rates, as well as exothermal data, were determined. Furthermore, the grade of destruction, mass loss and, for the thermal abuse scenario, activation energy and enthalpy, were calculated for critical points. It was found that NMC cells reacted first, but NCA cells went into thermal runaway a little earlier than NMC cells. LFP cells reacted, as expected, more slowly and at significantly higher temperatures, making the cell chemistry considerably safer. For mechanical abuse, no thermal runaway was observed for LFP cells, as well as at state of charge (SOC) zero for the other chemistries tested. For thermal abuse, at SOC 0 and SOC 30 for LFP cells and at SOC 0 for the other cell chemistries, no thermal runaway occurred until 350 °C. In this study, the experimental data are provided for further simulation approaches and system safety design.
Removal of Fe Impurity Ions from a Spent Vanadium Electrolyte Using Capacitive Deionization Based on Resin/Activated Carbon Composite Electrodes
Authors: Zhang Tianzhuang , Liu Tao , Zhang Yimin , Liu Hong
Published: April 2023

Capacitive deionization (CDI) based on LSC-957 resin/carbon composite electrodes was used to remove Fe impurity ions from a spent vanadium electrolyte, which enabled simple and efficient regeneration of the electrolyte. The experiments conducted in this study demonstrated that 3:1 was the optimal mass ratio of LSC-957 resin to activated carbon for the preparation of the composite electrodes, and the optimal operating voltage and operating time were 0.9 V and 6 h, respectively. After five stages of CDI tandem treatment, the adsorption rate of Fe impurity ions was 86.84% and the loss rate of V was only 3.8%. The energy efficiency of the regenerated electrolyte was 84.49%, and its performance was significantly improved compared to the spent vanadium electrolyte. The adsorption process of composite electrodes was analyzed by kinetic and isothermal models’ fit, SEM-EDS, and FTIR. This work has provided an effective and novel method for removing impurity ions from a spent electrolyte.
Comparison of CT- and MRI-Based Quantification of Tumor Heterogeneity and Vascularity for Correlations with Prognostic Biomarkers and Survival Outcomes: A Single-Center Prospective Cohort Study
Authors: Kim Hyo-Young , Bae Min-Sun , Seo Bo-Kyoung , Lee Ji-Young , Cho Kyu-Ran , Woo Ok-Hee , Song Sung-Eun , Cha Jaehyung
Published: April 2023

Background: Tumor heterogeneity and vascularity can be noninvasively quantified using histogram and perfusion analyses on computed tomography (CT) and magnetic resonance imaging (MRI). We compared the association of histogram and perfusion features with histological prognostic factors and progression-free survival (PFS) in breast cancer patients on low-dose CT and MRI. Methods: This prospective study enrolled 147 women diagnosed with invasive breast cancer who simultaneously underwent contrast-enhanced MRI and CT before treatment. We extracted histogram and perfusion parameters from each tumor on MRI and CT, assessed associations between imaging features and histological biomarkers, and estimated PFS using the Kaplan–Meier analysis. Results: Out of 54 histogram and perfusion parameters, entropy on T2- and postcontrast T1-weighted MRI and postcontrast CT, and perfusion (blood flow) on CT were significantly associated with the status of subtypes, hormone receptors, and human epidermal growth factor receptor 2 (p < 0.05). Patients with high entropy on postcontrast CT showed worse PFS than patients with low entropy (p = 0.053) and high entropy on postcontrast CT negatively affected PFS in the Ki67-positive group (p = 0.046). Conclusions: Low-dose CT histogram and perfusion analysis were comparable to MRI, and the entropy of postcontrast CT could be a feasible parameter to predict PFS in breast cancer patients.
Technologies for Vitrification Based Cryopreservation
Authors: Amini Mohammad , Benson James D.
Published: April 2023

Cryopreservation is a unique and practical method to facilitate extended access to biological materials. Because of this, cryopreservation of cells, tissues, and organs is essential to modern medical science, including cancer cell therapy, tissue engineering, transplantation, reproductive technologies, and bio-banking. Among diverse cryopreservation methods, significant focus has been placed on vitrification due to low cost and reduced protocol time. However, several factors, including the intracellular ice formation that is suppressed in the conventional cryopreservation method, restrict the achievement of this method. To enhance the viability and functionality of biological samples after storage, a large number of cryoprotocols and cryodevices have been developed and studied. Recently, new technologies have been investigated by considering the physical and thermodynamic aspects of cryopreservation in heat and mass transfer. In this review, we first present an overview of the physiochemical aspects of freezing in cryopreservation. Secondly, we present and catalog classical and novel approaches that seek to capitalize on these physicochemical effects. We conclude with the perspective that interdisciplinary studies provide pieces of the cryopreservation puzzle to achieve sustainability in the biospecimen supply chain.