bluuebunny/crossref_metadata_2025_split huggingface dataset:
A review on reconstruction algorithms and hardware implementation in electrical impedance tomography
Authors: Jia Yuheng 
Published: November 2023

Abstract
The current medical imaging techniques can only be used in a few diagnostic scenarios after the development of qualitative lesions, and they frequently include the use of radiation, among other things. Electrical impedance tomography, in contrast, uses no radiation and is non-invasive. Electric impedance tomography (EIT), which has these benefits, is frequently utilized for the early stages of disease detection and treatment. This study discusses the research advancements, image reconstruction methods, hardware system design, and clinical applications of EIT in the treatment of lung disorders for the application of EIT in the treatment of lung lesions. The intricacy of EIT systems and their solutions is explained by looking at and introducing a few key components of EIT technology. This provides research ideas for future studies and confirms the technology’s extensive development prospects. The outcome demonstrates that EIT is still in a relatively early stage of development and that image reconstruction algorithms are now being utilized to improve imaging resolution. The accuracy of data collection and processing is increasing, and hardware technology is also advancing quickly. EIT is also employed in clinical settings for pathology in the bladder, brain, and lungs. Future uses of EIT in medicine have a lot of potential for real-time, long-term monitoring and early diagnosis.
Comparison of improved variational autoencoder models for human face generation
Authors: He Lijie 
Published: November 2023

Abstract
The variational autoencoder (VAE) model has evolved a number of VAE improved models in the past 10 years, including CVAE, WAE, NVAE, etc. These models have greatly improved the calculation speed of VAE and the resolution of generated images. The main goal of this paper is to compare the principles of these different models and the effect of generating images. The principle analysis method is mainly used to study the improvement direction of the VAE model in different papers. The main ideas for improving the VAE model include optimizing the loss function, optimizing the objective function, introducing other parameters, or improving code efficiency. Some models even add many algorithms in computer vision in for improving image effect. In the experiments in this paper, results show the image results processed by different models. In the current optimal model NVAE, this algorithm has solved most of the VAE image blur problems, and has achieved perfection in image details. The improvement of the VAE model in the future may require innovative ideas. Under the current principle and model structure, the room for improvement of the VAE model is relatively limited. The final experiment shows that to improve the quality of VAE generated images, it is required to optimize the objective function, optimize the algorithm and add image visual processing, and then the generated images will be significantly improved.
Optimization approach in quadratic assignment and graph isomorphism
Authors: Liu Ruyi 
Published: November 2023

Abstract
Graph matching problem, also known as quadratic assignment problem (QAP) in a practical context, aims at finding the best correspondence between two given graphs. If two nodes are connected in one graph, it would be ideal if their corresponding images are connected in the other. The optimal solution should maximize the number of matches between the two graphs, while minimizing the weight of mismatches. Graph isomorphism is a particular case where an exact correspondence with no mismatches could be found. The quadratic assignment problem itself is hard to solve due to its high computational complexity. In this paper, the researcher analyzes two optimization approaches in detail: convex relaxation and the Fast Approximate QAP algorithm, both trying to solve the problem with lower computational effort. The performances of these two methods are further compared through numerical experiments. This paper focuses on pairs of equal-sized undirected graphs. ρ correlated graphs are constructed to simulate the noise in inexact graph matching problems. After comparing two existing methods, we propose an even faster optimization approach with comparable accuracy. This new approach provides a particular way to approximate an ideal initialization for the traditional Fast Approximate QAP algorithm. The significant reduction in the runtime makes obtaining good correspondence between large graphs feasible.
Continuous-bag-of-words and Skip-gram for word vector training and text classification
Authors: Xia Haowen 
Published: November 2023

Abstract
Natural language processing is one of the most challenging parts in the study of artificial intelligence and is widely used in real-life applications. One of the basic questions is how to calculate the probability of a particular text sequence appearing in a certain context. Word2Vec is a powerful tool that provides a solution to the question for its ability to transform words into word vectors, and to train in high efficiency on large datasets and corpora. It has many models of which Continuous-Bag-Of-Words and Skip-gram are of great significance and also known to many people. Furthermore, some extended techniques related to the models are also proposed in order to simultaneously decrease required training time and increase the rate of accuracy for the training. Even though there are now a number of papers that describe these fundamental concepts, the quality vary greatly. To better understand the models and their extensions, and how well they behave when used for real tasks, different combinations of the models and techniques are made in this paper so as to compare their performance in processing large input data and the ability for correct prediction in the task of text classification. This is done as it could lead to more provision of details and understandings of the model for subsequent researches on this field of study.
Can machine recognize a long-missed old friend? A test to the FaceNet face recognition algorithm
Authors: Dong Yuxuan 
Published: November 2023

Abstract
Facial recognition receives heated discussion in recent years. Given the increasing demand for portable and easy-to-use face recognition programs, several coding libraries have been developed that includes the algorithm for identifying and classifying faces. One example of such libraries, the “face_recognition” library developed by Adam Geitgey allows fast execution for face recognition tasks and requires only 1 photo as training input. It adopts the Convolutional Neural Network (CNN) from OpenFace, which is built based on the FaceNet algorithm. Although the study claims that the algorithm has a high base on tests from common face dataset, it is not clear whether the algorithm can identify people consistently when they are in their different ages. In this paper, a face dataset is constructed by collecting publicly available photos from celebrities that can be traced to a certain date of taken. These photos are grouped by 5 pieces per person with each photo taken roughly 4 years apart from the previous one. Then, experiments are conducted to test the effectiveness of face_recognition library on identifying the person from these groups of photos. The results report an over 75% success rate for the algorithm on image identification tasks, indicating a good ability for the face encoding to resist the gap of time.
Numerical simulation of the mold filling process of the brake lever for an electric multiple unit
Authors: Cao Xia , Ma Yunan 
Published: November 2023

Abstract
The brake lever for an electric multiple unit (EMU) of a train is an important supporting component that directly affects the usability and safety of EMU. In this study, the mold filling, solidification, and shrinkage processes of the brake lever of the EMU were simulated using Anycasting software, and the distributions of the mold filling speed, mold filling temperature, and shrinkage defects were analyzed. The results show that the brake lever for the EMU exhibits casting defects such as shrinkage porosity and shrinkage cavities. To eliminate these casting defects, a chilled-iron process was developed; however, the appearance of cementite increased the brittleness of the castings, which deteriorated their strength and plastic toughness. Hence, a snap-chilling sheet process was designed to fabricate the brake lever for the EMU, and a numerical simulation of the process was performed using the Anycasting software. The shrinkage defects of the castings were effectively eliminated; simultaneously, the cementite disappeared, and the microstructure of the thick parts of the brake lever showed no obvious changes. The prepared brake lever satisfied the requirements of the EMU.
Detailed high quality surface-based mouse CAD model suitable for electromagnetic simulations
Authors: Serano Peter , Makaroff Sergey , Ackerman Jerome L , Nummenmaa Aapo , Noetscher Gregory M 
Published: November 2023

Abstract
Transcranial magnetic stimulation (TMS) studies with small animals can provide useful knowledge of activating regions and mechanisms. Along with this, functional magnetic resonance imaging (fMRI) in mice and rats is increasingly often used to draw important conclusions about brain connectivity and functionality. For cases of both low- and high-frequency TMS studies, a high-quality computational surface-based rodent model may be useful as a tool for performing supporting modeling and optimization tasks. This work presents the development and usage of an accurate CAD model of a mouse that has been optimized for use in computational electromagnetic modeling in any frequency range. It is based on the labeled atlas data of the Digimouse archive. The model includes a relatively accurate four-compartment brain representation (the ‘whole brain’ according to the original terminology, external cerebrum, cerebellum, and striatum [9]) and contains 21 distinct compartments in total. Four examples of low- and high frequency modeling have been considered to demonstrate the utility and applicability of the model.
Preliminary insight into the relationship between bioaerosols and urban environment obtained from the COVID-19 self-quarantine period in the Tokyo metropolitan area
Authors: Murata Kotaro , Okochi Hiroshi , Kamogawa Masashi 
Published: November 2023

Abstract
Anthropogenic activities and meteorological conditions influence the composition of urban bioaerosols. The COVID-19 epidemic drastically reduced anthropogenic activities in the metropolitan areas of Japan in 2020. This study examined inter-day fluctuations in airborne bacterial and fungal compositions in Tokyo, Japan, between April and June 2020, under exceptionally unusual conditions where the movement of people and economic activity had been greatly reduced. The result showed, as expected, that local meteorological factors, especially wind and precipitation, substantially impacted emissions and changes in bacterial and fungal aerosols. However, we found that variations in the composition of urban bioaerosols could potentially be affected by anthropogenic influences, such as the concentrations of nitrogen oxides, ozone, and particulate matter, and human density. Although such factors are not as influential as wind and precipitation, it was shown for the first time that drastic changes in human activities within an area can affect the dynamics of bioaerosols. This could be a finding that should be considered for public health issues related to air quality in changing urban areas such as fast-growing cities.
Computational investigation towards highly efficient Sb2Se3 based solar cell with a thin WSe2 BSF layer
Authors: Ebon Md Islahur Rahman , Ali Md Hasan , Haque Md Dulal , Touhidul Islam Abu Zafor Md 
Published: November 2023

Abstract
A novel structure of chalcogenide-based antimony selenide (Sb2Se3) which is the most promising absorber materials in the field of thin film solar cells with a tungsten diselenide (WSe2) back surface field (BSF) has been proposed in this paper. Antimony selenide (Sb2Se3) with a BSF has been observed by one-dimensional solar cell capacitance simulator (SCAPS-1D). Its recognizable properties make it one of the most usable for non-toxic solar cell absorbers. This paper demonstrated the planning and modeling of Al/FTO/n-TiO2/p-Sb2Se3/Ni (without BSF) and Al/FTO/n-TiO2/p-Sb2Se3/p+-WSe2/Ni (with BSF) structures. The capability of Al/FTO/n-TiO2/p-Sb2Se3/p+-WSe2/Ni heterojunction solar cell structure shows the promising performances. We have numerically simulated and observed the performance parameters such as power conversion efficiency (PCE), open circuit voltage (VOC), short circuit current density (JSC) and fill factor (FF) by tuning the different parameters such as thickness, doping concentration, defect density, the interface defect density, surface recombination velocity, series and shunt resistances along with temperature. The PCE of 20.61% with an absorber’s thickness of 800 nm for without BSF has been achieved. By inserting the WSe2 BSF with thickness of 100 nm, highly improving efficiency of 32.35% has been demonstrated. Our simulation and numerical analysis also provide valuable and important information that is very effective for further implementation and achieving high efficiency of thin film solar cell.
On varimax asymptotics in network models and spectral methods for dimensionality reduction
Authors: Cape J 
Published: November 2023

Abstract
Varimax factor rotations, while popular among practitioners in psychology and statistics since being introduced by Kaiser (1958), have historically been viewed with skepticism and suspicion by some theoreticians and mathematical statisticians. Now, work by Rohe & Zeng (2023) provides new, fundamental insight: varimax rotations provably perform statistical estimation in certain classes of latent variable models when paired with spectral-based matrix truncations for dimensionality reduction. We build on this new-found understanding of varimax rotations by developing further connections to network analysis and spectral methods rooted in entrywise matrix perturbation analysis. Concretely, this paper establishes the asymptotic multivariate normality of vectors in varimax-transformed Euclidean point clouds that represent low-dimensional node embeddings in certain latent space random graph models. We address related concepts including network sparsity, data denoising and the role of matrix rank in latent variable parameterizations. Collectively, these findings, at the confluence of classical and contemporary multivariate analysis, reinforce methodology and inference procedures grounded in matrix factorization-based techniques. Numerical examples illustrate our findings and supplement our discussion.
Global benchmarks in primary robotic bariatric surgery redefine quality standards for Roux-en-Y gastric bypass and sleeve gastrectomy
Authors: Giudicelli Guillaume , Gero Daniel , Romulo Lind , Chirumamilla Vasu , Iranmanesh Pouya , Owen Christopher K , Bauerle Wayne , Garcia Amador , Lucas Lisa , Mehdorn Anne-Sophie , Pandey Dhananjay , Almuttawa Abdullah , Cabral Francisco , Tiwari Abhishek , Lambert Virginia , Pascotto Beniamino , De Meyere Celine , Yahyaoui Marouan , Haist Thomas , Scheffel Oliver , Robert Maud , Nuytens Frederiek , Azagra Santiago , Kow Lilian , Prasad Arun , Vaz Carlos , Vix Michel , Bindal Vivek , Beckmann Jan H , Soussi David , Vilallonga Ramon , El Chaar Maher , Wilson Erik B , Ahmad Arif , Teixeira Andre , Hagen Monika E , Toso Christian , Clavien Pierre-Alain , Puhan Milo , Bueter Marco , Jung Minoa K 
Published: November 2023

Abstract

Background
Whether the benefits of the robotic platform in bariatric surgery translate into superior surgical outcomes remains unclear. The aim of this retrospective study was to establish the ‘best possible’ outcomes for robotic bariatric surgery and compare them with the established laparoscopic benchmarks.

Methods
Benchmark cut-offs were established for consecutive primary robotic bariatric surgery patients of 17 centres across four continents (13 expert centres and 4 learning phase centres) using the 75th percentile of the median outcome values until 90 days after surgery. The benchmark patients had no previous laparotomy, diabetes, sleep apnoea, cardiopathy, renal insufficiency, inflammatory bowel disease, immunosuppression, history of thromboembolic events, BMI greater than 50 kg/m2, or age greater than 65 years.

Results
A total of 9097 patients were included, who were mainly female (75.5%) and who had a mean(s.d.) age of 44.7(11.5) years and a mean(s.d.) baseline BMI of 44.6(7.7) kg/m2. In expert centres, 13.74% of the 3020 patients who underwent primary robotic Roux-en-Y gastric bypass and 5.9% of the 4078 patients who underwent primary robotic sleeve gastrectomy presented with greater than or equal to one complication within 90 postoperative days. No patient died and 1.1% of patients had adverse events related to the robotic platform. When compared with laparoscopic benchmarks, robotic Roux-en-Y gastric bypass had lower benchmark cut-offs for hospital stay, postoperative bleeding, and marginal ulceration, but the duration of the operation was 42 min longer. For most surgical outcomes, robotic sleeve gastrectomy outperformed laparoscopic sleeve gastrectomy with a comparable duration of the operation. In robotic learning phase centres, outcomes were within the established benchmarks only for low-risk robotic Roux-en-Y gastric bypass.

Conclusion
The newly established benchmarks suggest that robotic bariatric surgery may enhance surgical safety compared with laparoscopic bariatric surgery; however, the duration of the operation for robotic Roux-en-Y gastric bypass is longer.
First successful implementation of subintimal transcatheter withdrawal technique in intravascular ultrasound–guided tip detection antegrade dissection and reentry: a case report
Authors: Kitani Shunsuke , Tsuchikane Etsuo , Yamaki Masaru , Igarashi Yasumi 
Published: November 2023

Abstract

Background
Antegrade dissection and reentry (ADR) is an effective technique for wire passage in chronic total occlusion (CTO), and in recent years, the effectiveness of intravascular ultrasound (IVUS)–guided tip detection (TD)-ADR has been reported. However, the expansion of the subintimal space serves as a significant obstacle to the success of ADR, posing a limitation to the procedure.

Case summary
We present the first case of using IVUS-guided TD-ADR with the subintimal transcatheter withdrawal (STRAW) technique. The patient was a 68-year-old Asian female with effort angina pectoris and a CTO in the middle section of the right coronary artery (RCA). Two previous attempts at percutaneous coronary intervention (PCI) for the RCA at another hospital were unsuccessful. During the third attempt PCI, the antegrade wire migrated into the subintimal space. To address this, we performed IVUS-guided TD-ADR using the Conquest Pro 12 Sharpened Tip (CP12ST; Asahi Intecc, Aichi, Japan) wire. However, due to the expansion of the subintimal space, we were unable to puncture the true lumen. To reduce the subintimal space, we employed the STRAW technique, which allowed successful puncture of the true lumen using the CP12ST wire. Finally, stenting was performed, resulting in satisfactory antegrade blood flow.

Discussion
Intravascular ultrasound–guided TD provides accurate guidance for puncturing in ADR procedures, but the expansion of the subintimal space remains a significant challenge. The STRAW technique offers a solution by reducing the subintimal space and enabling successful puncture of the true lumen during IVUS-guided TD-ADR.
Decolonial Gazing and Hermeneutic Resistance: Black German Challenges to White German Cultural Hegemony in the Museum
Authors: Gallagher Maureen O 
Published: November 2023

Abstract
In this article, I highlight the ways that Black Europeans challenge universalizing notions of cultural heritage to emphasize decolonial possibilities. Focusing on the German context, I show how Black artists, intellectuals and activists interrogate the collection, display and spectatorship of museum objects in majority-white contexts using the strategies of decolonial gazing and hermeneutic resistance. I use the Berlin Ethnological Museum in its former and current iterations as a representative example of debates about collecting and looking at museums, showing how scholars like Fatima El-Tayeb and initiatives like No Humboldt21! challenge universalizing discourses and reflect the gaze back on whiteness. Finally, I offer a reading of a literary challenge to white hegemony within and beyond the museum in Sharon Dodua Otoo’s 2021 novel Adas Raum [Ada’s Realm].
MetGENE: gene-centric metabolomics information retrieval tool
Authors: Srinivasan Sumana , Maurya Mano R , Ramachandran Srinivasan , Fahy Eoin , Subramaniam Shankar 
Published: November 2023

Abstract

Background
Biomedical research often involves contextual integration of multimodal and multiomic data in search of mechanisms for improved diagnosis, treatment, and monitoring. Researchers need to access information from diverse sources, comprising data in various and sometimes incongruent formats. The downstream processing of the data to decipher mechanisms by reconstructing networks and developing quantitative models warrants considerable effort.

Results
MetGENE is a knowledge-based, gene-centric data aggregator that hierarchically retrieves information about the gene(s), their related pathway(s), reaction(s), metabolite(s), and metabolomic studies from standard data repositories under one dashboard to enable ease of access through centralization of relevant information. We note that MetGENE focuses only on those genes that encode for proteins directly associated with metabolites. All other gene–metabolite associations are beyond the current scope of MetGENE. Further, the information can be contextualized by filtering by species, anatomy (tissue), and condition (disease or phenotype).

Conclusions
MetGENE is an open-source tool that aggregates metabolite information for a given gene(s) and presents them in different computable formats (e.g., JSON) for further integration with other omics studies. MetGENE is available at https://bdcw.org/MetGENE/index.php.
Variability analysis of LC-MS experimental factors and their impact on machine learning
Authors: Rehfeldt Tobias Greisager , Krawczyk Konrad , Echers Simon Gregersen , Marcatili Paolo , Palczynski Pawel , Röttger Richard , Schwämmle Veit 
Published: November 2023

Abstract

Background
Machine learning (ML) technologies, especially deep learning (DL), have gained increasing attention in predictive mass spectrometry (MS) for enhancing the data-processing pipeline from raw data analysis to end-user predictions and rescoring. ML models need large-scale datasets for training and repurposing, which can be obtained from a range of public data repositories. However, applying ML to public MS datasets on larger scales is challenging, as they vary widely in terms of data acquisition methods, biological systems, and experimental designs.

Results
We aim to facilitate ML efforts in MS data by conducting a systematic analysis of the potential sources of variability in public MS repositories. We also examine how these factors affect ML performance and perform a comprehensive transfer learning to evaluate the benefits of current best practice methods in the field for transfer learning.

Conclusions
Our findings show significantly higher levels of homogeneity within a project than between projects, which indicates that it is important to construct datasets most closely resembling future test cases, as transferability is severely limited for unseen datasets. We also found that transfer learning, although it did increase model performance, did not increase model performance compared to a non-pretrained model.