bluuebunny/crossref_metadata_2025_split huggingface dataset:
Gastrectomy reduces pancreatic secretory function via pancreatic atrophy
Authors: Satoi Shumpei , Kimura Yutaka , Shimizu Rie , Matsumoto Masataka , Kawaguchi Kohei , Yoshida Yuta , Murase Takaaki , Kamei Keiko , Matsumoto Ippei , Nakai Takuya , Takeyama Yoshifumi 
Published: April 2023

Abstract
Purpose
Although reports suggest that the pancreatic volume decreases after gastrectomy for gastric cancer, the relationship between the pancreatic volume and secretory function after gastrectomy remains unclear. In this study, we examined the relationship between the pancreatic volume and exocrine and endocrine functions after total gastrectomy.

Methods
The pancreatic volumes of 18 distal gastrectomy and 15 total gastrectomy patients were retrospectively measured using computed tomography volumetry up to 5 years postoperatively. Ten low anterior resection patients were selected as controls. In addition, the pancreatic volume and exocrine function evaluated by fecal elastase and the insulin secretory function evaluated by glucagon tolerance testing were prospectively examined before and one year after surgery in nine cases of total gastrectomy.

Results
After low anterior resection, the pancreatic volume did not change, but after distal and total gastrectomy, the pancreatic volume decreased continuously until the fifth year. After total gastrectomy, fecal elastase decreased significantly from 865.8 μg/g to 603.2 μg/g in the first year (p = 0.0316), and the insulin secretion capacity also decreased significantly from 3.83 ng/mL to 2.26 ng/mL (p = 0.0019).

Conclusions
The pancreatic volume decreases continuously after gastrectomy for gastric cancer, and the pancreatic exocrine and endocrine functions decrease along with pancreatic atrophy after total gastrectomy.
The impact of membrane perforation and L-PRF for vertical ridge augmentation with a xenogeneic block graft: an experimental study in a canine model
Authors: Eldabe Abdelrahman K. , Abdel-Ghaffar Khaled A. , Amr Ahmed E. , Abu-Seida Ashraf M. , Abdelhamid Ehab S. , Gamal Ahmed Y. 
Published: April 2023

Abstract
Objectives
This study evaluated clinically and histologically the efficacy of modified perforated collagen membrane (PCM) and/or leukocyte- and platelet-rich fibrin (L-PRF) in combination with xenogeneic block bone graft in the vertical alveolar ridge augmentation.

Materials and methods
Six adult mongrel dogs were enrolled in this randomized blinded study. After defect preparation, xenogeneic screw-fixed block graft was covered by an occlusive collagen membrane in group 1 that represented the control group (Block + CM). In group 2, L-PRF membrane was added first before top coverage by occlusive collagen membrane (Block + L-PRF + CM). Groups 3 (Block + PCM) and 4 (Block + L-PRF + PCM) were identical to the first two groups except that the occlusive collagen membrane was replaced by a perforated one. Following a healing period of 2 months, the dogs were submitted to the surgical reentry phase for clinical and histological evaluation.

Results
Clinically, no significant differences were found among all groups regarding vertical and horizontal ridge dimensions (p = 0.155, 0.492, respectively). Histomorphometric analysis revealed that the percentage of the total bone area and mature bone was significantly higher in group 4 (69.36 ± 2.72, 33.11 ± 5.18) compared to the control group (59.17 ± 4.27, 21.94 ± 2.86) (p = 0. 027, p = 0.029).

Conclusion
The use of xenogenic block grafts in combination with a double-layered perforated collagen L-PRF membrane in vertical ridge augmentation appeared to improve the inductive power of this challenging defect type.

Clinical relevance
Size and number of perforations may affect the mechanical and handling properties of the membrane.
The power of typed affine decision structures: a case study
Authors: Nolte Gerrit , Schlüter Maximilian , Murtovi Alnis , Steffen Bernhard 
Published: April 2023

AbstractTADS are a novel, concise white-box representation of neural networks. In this paper, we apply TADS to the problem of neural network verification, using them to generate either proofs or concise error characterizations for desirable neural network properties. In a case study, we consider the robustness of neural networks to adversarial attacks, i.e., small changes to an input that drastically change a neural networks perception, and show that TADS can be used to provide precise diagnostics on how and where robustness errors a occur. We achieve these results by introducing Precondition Projection, a technique that yields a TADS describing network behavior precisely on a given subset of its input space, and combining it with PCA, a traditional, well-understood dimensionality reduction technique. We show that PCA is easily compatible with TADS. All analyses can be implemented in a straightforward fashion using the rich algebraic properties of TADS, demonstrating the utility of the TADS framework for neural network explainability and verification. While TADS do not yet scale as efficiently as state-of-the-art neural network verifiers, we show that, using PCA-based simplifications, they can still scale to medium-sized problems and yield concise explanations for potential errors that can be used for other purposes such as debugging a network or generating new training samples.
Enhanced aerial vehicle system techniques for detection and tracking in fog, sandstorm, and snow conditions
Authors: Talaat Amira Samy , El-Sappagh Shaker 
Published: April 2023

AbstractUnmanned aerial vehicles are rapidly being utilized in surveillance and traffic monitoring because of their great mobility and capacity to cover regions at various elevations and positions. It is a challenging task to detect vehicles due to their various shapes, textures, and colors. One of the most difficult challenges is correctly detecting and counting aerial view vehicles in real time for traffic monitoring objectives using aerial images and videos. In this research, strategies are presented for improving the detection ability of self-driving vehicles in tough conditions, also for traffic monitoring, vehicle surveillance. We make classification, tracking trajectories, and movement calculation where fog, sandstorm (dust), and snow conditions are challenging. Initially, image enhancement methods are implemented to improve unclear images of roads. The improved images are then subjected to an object detection and classification algorithm to detect vehicles. Finally, new methods were evaluated (Corrected Optical flow/Corrected Kalman filter) to get the least error of trajectories. Also features like vehicle count, type, tracking trajectories by (Optical flow, Kalman Filter, Euclidean Distance) and relative movement calculation are extracted from the coordinates of the observed objects. These techniques aim to improve vehicle detection, tracking, and movement over aerial views of roads especially in bad weather. As a result, for aerial view vehicles in bad weather, our proposed method has an error of less than 5 pixels from the actual value and give the best results. This improves detection and tracking performance for aerial view vehicles in bad weather conditions.
Review of carbon emissions offsetting guidelines using instructional criteria
Authors: Helppi Olli , Salo Esko , Vatanen Saija , Pajula Tiina , Grönman Kaisa 
Published: April 2023

Abstract
Purpose
Carbon offsetting is one of the tools that companies can use to achieve their climate targets. The ability of a company to offset its emissions successfully depends on the availability and quality of guidance on the subject. This study explores how well existing offsetting guidelines equip corporations to achieve successful emissions offsetting.

Methods
Instructional criteria were developed and used to evaluate seven guidelines. The contents of each guideline were assessed based on how they instruct a company to achieve emissions offsetting that fulfills five criteria for appropriate offsetting: target affiliation, Paris compatibility, effectiveness, prioritizing removals, and transparency.

Results and discussion
The review revealed that necessary instructions enabling appropriate emissions offsetting were absent in the guidelines. Moreover, the degree of climate ambition and the role of offsetting varied between guidelines. Deficiencies in emissions offsetting guidance may increase the uncertainty of companies’ succeeding in offsetting their emissions.

Conclusions
Developing guidance on emissions offsetting could benefit society and corporations by increasing the certainty of achieving successful emissions offsetting. Standardizing corporate emissions offsetting could be considered as one solution for unifying the practice. The practical life-cycle implications of current ambiguity in guidelines are a direction for future research.
Deep learning-based overall survival prediction model in patients with rare cancer: a case study for primary central nervous system lymphoma
Authors: She Ziyu , Marzullo Aldo , Destito Michela , Spadea Maria Francesca , Leone Riccardo , Anzalone Nicoletta , Steffanoni Sara , Erbella Federico , Ferreri Andrés J. M. , Ferrigno Giancarlo , Calimeri Teresa , De Momi Elena 
Published: April 2023

Abstract
Purpose
Primary central nervous system lymphoma (PCNSL) is a rare, aggressive form of extranodal non-Hodgkin lymphoma. To predict the overall survival (OS) in advance is of utmost importance as it has the potential to aid clinical decision-making. Though radiomics-based machine learning (ML) has demonstrated the promising performance in PCNSL, it demands large amounts of manual feature extraction efforts from magnetic resonance images beforehand. deep learning (DL) overcomes this limitation.

Methods
In this paper, we tailored the 3D ResNet to predict the OS of patients with PCNSL. To overcome the limitation of data sparsity, we introduced data augmentation and transfer learning, and we evaluated the results using r stratified k-fold cross-validation. To explain the results of our model, gradient-weighted class activation mapping was applied.

Results
We obtained the best performance (the standard error) on post-contrast T1-weighted (T1Gd)—area under curve $$=0.81(0.03)$$

=
0.81
(
0.03
)

, accuracy $$=0.87(0.07)$$

=
0.87
(
0.07
)

, precision $$=0.88(0.07)$$

=
0.88
(
0.07
)

, recall $$=0.88(0.07)$$

=
0.88
(
0.07
)

and F1-score $$=0.87(0.07)$$

=
0.87
(
0.07
)

, while compared with ML-based models on clinical data and radiomics data, respectively, further confirming the stability of our model. Also, we observed that PCNSL is a whole-brain disease and in the cases where the OS is less than 1 year, it is more difficult to distinguish the tumor boundary from the normal part of the brain, which is consistent with the clinical outcome.

Conclusions
All these findings indicate that T1Gd can improve prognosis predictions of patients with PCNSL. To the best of our knowledge, this is the first time to use DL to explain model patterns in OS classification of patients with PCNSL. Future work would involve collecting more data of patients with PCNSL, or additional retrospective studies on different patient populations with rare diseases, to further promote the clinical role of our model.
A new genus, Complexispora, with two new species, C. multistratosa and C. mediterranea, and Epigeocarpum japonicum sp. nov.
Authors: Błaszkowski Janusz , Yamato Masahide , Niezgoda Piotr , Zubek Szymon , Milczarski Paweł , Malinowski Ryszard , Meller Edward , Malicka Monika , Goto Bruno Tomio , Uszok Sylwia , Casieri Leonardo , Magurno Franco 
Published: April 2023

Abstract
Morphological analyses of three glomoid spore-producing fungi suggested that two of them were undescribed species of Glomeraceae (phylum Glomeromycota), and the third differed slightly from Dominikia glomerocarpica and Epigeocarpum crypticum, recently described in Glomeraceae. The first two fungi originated from the Mediterranean Sea sand dunes of the Peloponnese, Greece, and the third was originally found in a tree plantation in Yokohama City, Japan. Phylogenetic analyses of sequences of the 45S nuc rDNA region and the RPB1 gene showed that (i) the three fungi belonged to Glomeraceae; (ii) the first two represented a new genus, here described as Complexispora gen. nov. with C. multistratosa sp. nov. and C. mediterranea sp. nov. and (iii) the third enlarged the monospecific genus Epigeocarpum, as E. japonicum sp. nov.
Explanatory Interactive Machine Learning
Authors: Pfeuffer Nicolas , Baum Lorenz , Stammer Wolfgang , Abdel-Karim Benjamin M. , Schramowski Patrick , Bucher Andreas M. , Hügel Christian , Rohde Gernot , Kersting Kristian , Hinz Oliver 
Published: April 2023

AbstractThe most promising standard machine learning methods can deliver highly accurate classification results, often outperforming standard white-box methods. However, it is hardly possible for humans to fully understand the rationale behind the black-box results, and thus, these powerful methods hamper the creation of new knowledge on the part of humans and the broader acceptance of this technology. Explainable Artificial Intelligence attempts to overcome this problem by making the results more interpretable, while Interactive Machine Learning integrates humans into the process of insight discovery. The paper builds on recent successes in combining these two cutting-edge technologies and proposes how Explanatory Interactive Machine Learning (XIL) is embedded in a generalizable Action Design Research (ADR) process – called XIL-ADR. This approach can be used to analyze data, inspect models, and iteratively improve them. The paper shows the application of this process using the diagnosis of viral pneumonia, e.g., Covid-19, as an illustrative example. By these means, the paper also illustrates how XIL-ADR can help identify shortcomings of standard machine learning projects, gain new insights on the part of the human user, and thereby can help to unlock the full potential of AI-based systems for organizations and research.
Comparative assessment of artificial intelligence (AI)-based algorithms for detection of harmful bloom-forming algae: an eco-environmental approach toward sustainability
Authors: Gaur Ashish , Pant Gaurav , Jalal Anand Singh 
Published: April 2023

AbstractOrganic effluent enrichment in water may selectively promote algal growth, resulting in water pollution and posing a threat to the aquatic ecosystem. Recent harmful algal blooms (HABs) incidents have highlighted information gaps that still exist, as well as the heightened need for early detection technology developments. Although previous research has demonstrated the importance of deep learning in the identification of algal genera, it is still a challenge to identify or to develop the best-suited convolution neural network (CNN) model for effective monitoring of bloom-forming algae. In the present study, efficiency of deep learning models (MobileNet V-2, Visual Geometry Group-16 (VGG-16), AlexNet, and ResNeXt-50) have been evaluated for the classification of 15 bloom-forming algae. To obtain a high level of accuracy, different convolution layers with adaptive moment estimation (Adam), root-mean-square propagation (RMSprop) as optimizers with softmax and rectified linear unit (ReLU) as activation factors have been used. The classification accuracies of 40, 96, 98, and 99% have been achieved for MobileNet V-2, VGG-16, AlexNet, and ResNeXt-50 model, respectively. We believe that the ResNeXt-50 has the potential to identify algae in a variety of situations with high accuracy and in real time, regardless of the underlying hardware. Such studies pave the path for future AI-based cleaner technologies associated with phycological studies for a sustainable future.
Obstacle avoidance of a wheeled robotic swarm using virtual spring-damper mesh
Authors: Wiech Jakub 
Published: April 2023

AbstractSwarm robotics, in which groups of robots coordinate to achieve a common goal, has the potential to revolutionize a wide range of industries. However, one of the key challenges in developing effective swarm systems is enabling the robots to navigate around obstacles while maintaining cohesion. In this article, the problem of trajectory tracking for a self-organized swarm of wheeled robots in the shape of a regular polygon operating in an environment with obstacles is addressed. The goal is for the swarm to follow a reference point by the geometric center of the swarm, while avoiding collision with obstacles and maintaining a given distance between neighboring robots and the reference point. To solve this problem, a method based on virtual forces that act on each individual robot in the swarm is proposed. These virtual forces are derived from virtual spring-damper connections acting from the obstacles, adjacent robots, and the reference point. The virtual forces allow the robots to dynamically adjust their trajectories to avoid obstacles and maintain the shape of the swarm. The proposed approach is evaluated through simulation and experiment with a swarm of two-wheeled robots. During the experiment the maximum deviation of inter-robot distances was equal to 0.08 m. With the set limit distance to obstacles equal to 0.2 m, from which virtual forces start to push the robot away from the edge of the obstacle, the shortest distance to obstacles was 0.11 m. The results demonstrate the effectiveness of the method in enabling the swarm to accurately follow the reference point while avoiding obstacles and maintaining the desired distances between robots. Overall, the approach represents a step forward in the development of virtual force-based algorithms for obstacle avoidance in swarm robotics.
Digitalisation promotes adoption of soft information in SME credit evaluation: the case of Indian banks
Authors: Hardik Nimbark 
Published: April 2023

AbstractSmall and Medium Enterprises
(SMEs) account for half of the employment in developing economies and are a significant part of their economic growth. In spite of this, SMEs are under-financed by banks, which have been disrupted by financial technology (fintech) firms. This qualitative multi-case study examines how Indian banks are utilising digitalisation, soft information, and Big data to improve SME financing. The participants shared their insights on the way banks adopt digital tools, sources of soft information (e.g., customer and supplier relationships, business plans), and factors that influence the implementation of Big data in the SME credit evaluation process. The major themes include: banks are improving SME financing operations through digitalisation, and IT tools can verify SME soft information. Soft information attributes that emerge from addressing SME information opacity include supplier relationships, customer relationships, business plans, and managerial successions. For SME credit managers, developing partnerships to access publicly available soft information created by industry associations and "online B2B trade platforms" is a high-priority recommendation. To enhance the efficiency of SME financing, banks should obtain the consent of SMEs before they access their private hard information through trade platforms.
Augustine's Theology of the Resurrection
Authors: Reisenauer Augustine M. 
Published: April 2023

In this volume, Augustine M. Reisenauer, O.P. provides a comprehensive study of Augustine's theology of the resurrection, the human return from death to life. Contextualizing Augustine within the early Church and the intellectual and religious cultures of the late Roman Empire,he interrogates the development of Augustine's thoughts on the historical resurrection of Jesus Christ, the spiritual resurrection of the soul in time, and the fleshly resurrection of the body at the end of time. Augustine offers profound insights into issues of personal and communal identity, human continuity and transformation, historical and eschatological events, and the God of the resurrection. He also elaborates a biblical paradigm that acknowledges how the resurrected Christ offers an intrinsic participation in his paschal mystery to the souls and bodies of the rest of humanity. Proposing fresh ideas regarding a central topic in Christian theology, Reisenauer's, study also reveals Augustine's defenses of the resurrection against its pagan, philosophical and heretical opponents.
Implications of Emission Sources and Biosphere Exchange on Temporal Variations of CO2 and δ13C Using Continuous Atmospheric Measurements at Shadnagar (India)
Authors: Pathakoti Mahesh , Kanchana A. L. , Mahalakshmi D. V. , Guha Tania , Raja P. , Nalini K. , Rajan K. S. , Sesha Sai M. V. R. , Dadhwal V. K. 
Published: April 2023

AbstractDiurnal variation of atmospheric CO2 and its driving factors are studied using high‐precision in situ mixing ratio measurements and stable carbon isotopic CO2 (δ13C‐CO2) for the first time over Shadnagar, a suburban site in India, from November 2018 to October 2019. The annual mean mixing ratios of atmospheric CO2 and δ13C‐CO2 are observed to be 414.76 ± 4.26 ppm and −11.19 ‰ ± 1.63 ‰. Maximum diurnal variability of atmospheric CO2 was observed in summer monsoon (17.30 ± 9.29 ppm), and the minimum was noticed in winter (7.19 ± 0.11 ppm), indicating strong seasonality in diurnal variability of amplitude. To characterize the atmospheric CO2 sources, the Miller‐Tans model was implemented separately using the CO2 and its δ13C‐CO2 isotopic data during day and night. However, a strong source signature of δ13C‐CO2 was observed during the night‐time of summer monsoon with a slope of −37.42‰ ± 0.73‰, obtained using a reduced major axis regression. The identified source indicates possible emissions from fossil fuel combustion. Further, a Lagrangian back trajectory analysis was performed to study the influence of transport on the observed CO2 mixing ratios. The model trajectory confirms the influence of the Indian summer monsoon on the variations of atmospheric CO2 mixing ratios. Our study shows that fossil fuel emissions are one of the major contributors of CO2 in the study location (about ∼2 to 4 ppm) with biogenic fluxes adding a clear seasonality ranging from −8 to 4 ppm.
New Particle Formation Occurrence in the Urban Atmosphere of Beijing During 2013–2020
Authors: Shang Dongjie , Hu Min , Tang Lizi , Fang Xin , Chen Shiyi , Zeng Limin , Guo Song , Zhang Yuanhang , Wu Zhijun 
Published: April 2023

AbstractNew particle formation (NPF) has strong influences on air quality and global climate. However, it was still ambiguous under which atmospheric condition NPF can occur, due to lack of long‐term observation data analysis. The long‐term measurement of NPF was conducted from 2013 to 2020 at PeKing University Urban Atmosphere Environment MonitoRing Station in Beijing, during which strong pollution control measures were conducted. However, the NPF frequency (NPF‐freq) remained stable within 40.4%–48.9% in an average of 42.5%. The occurrence of NPF events was characterized by lower levels of NOx, condensational sink, relative humidity, and higher levels of wind speed and north wind proportion. SO2 may act as one limiting factor only in summer. An H2SO4‐base nucleation indicator, I275K was calculated. It was found that I275K > 243 can separate the NPF and non‐NPF days quite well during the 8 years, with bias at 17%. Accordingly, it was also revealed that the simultaneous control of SO2 and PM2.5 (serving as condensation sink) in Beijing happened to cause the stable level of I275K, as well as the NPF frequency during 2013–2020. The results implicated that along with the reduction of primary emission, the secondary NPF process may become more important in aerosol evolution by its stable frequency, and the precise control of gaseous precursors is required for particle pollution mitigation in the future.