bluuebunny/crossref_metadata_2025_split huggingface dataset:
Scaling issues in the study of livestock depredation by carnivores
Authors: Slovikosky Sandy A. , Petracca Lisanne S. , Hunter Luke T. B. , Frair Jacqueline L. 
Published: July 2024

Abstract
Context
Many studies have documented the magnitude and socioecological drivers of livestock depredation, yet few have assessed how observations of depredation might vary with spatiotemporal scale. Understanding scaling relationships may allow for more accurate aggregation of observations collected across diverse extents and resolutions to better inform management actions.

Objectives
Herein we examine how reported metrics of livestock depredation varied by spatial and temporal scale (extent and resolution) after controlling for other drivers of differences among studies.

Methods
From 213 published studies we extracted conflict metrics (i.e., number of animals killed, number of attack incidents, and percent annual loss of stock) and regressed each against spatiotemporal extent (i.e., size of study area, duration of study) and resolution (i.e., minimum mapping unit, frequency of observations) while controlling for potential biases.

Results
The number of attacks or animals killed was positively related to spatial and temporal extent whereas percent annual loss of stock declined with extent. Further, the effects of scaling variables were modified through interactions with other factors (e.g., human density) known to influence human-carnivore conflict. The data available for spatiotemporal resolution were too sparse to draw conclusions. While scaling relationships were generally linear, they became increasingly noisy at broader extents.

Conclusions
Consistent with other ecological investigations, the scale of observation influenced the observed outcomes in human-carnivore conflict metrics. Authors should report the spatiotemporal dimensions of their observations to improve robust inference in comparative studies. Enhanced understanding of scaling relationships in human-carnivore conflict metrics should improve strategic allocation of resources to better mitigate future conflict.
Environmental Care: How Marine Scientists Relate to Environmental Changes
Authors: Schönbauer Sarah Maria 
Published: July 2024

AbstractMarine scientists have reported drastic environmental changes in marine and polar regions as a result of climate change. The changes range from species compositions in coastal regions and the deep-sea floor, the degradation of water and ice quality to the ever-growing plastic pollution affecting marine habitats. Marine scientists study these changes in their fieldwork, and communicate their findings in scientific publications. Some also rally in protests for the necessity of political programs to tackle changes. Based on ethnographic visits and interviews with marine scientists, this study examines how marine scientists experience and act on environmental changes as individuals and as collectives. In order to analyze their experiences and actions, I use the notion of care and portray care in different times and spaces, from work to protest. I demonstrate how care needs to be situated in different times and spaces, how care is embedded in a complex relationship of institutional requirements and structural demands that researchers experience, but also how care receives institutionalization and has an impact on research interests and agendas. In doing so, I show the social and epistemic consequences of care, opening up a view of individual and collective care in the marine sciences.
New directions in pass-and-swap queues
Authors: Dorsman Jan-Pieter , Gardner Kristen 
Published: July 2024

AbstractRecently, driven by redundancy systems and matching systems, there has been renewed interest in models with product form stationary distributions. By a “product form,” we mean that the stationary distribution can be expressed as a product of terms, each of which corresponds to a job in the system. Given the recent discovery of many such systems, it is natural to ask: how broad is this class of systems? In this paper, we consider extensions and generalizations of the recently-proposed pass-and-swap queue, which has a product-form stationary distribution. We make three main contributions. First, we identify sufficient conditions under which pass-and-swap queues can be connected in a closed network, while still preserving the product form. Second, we identify dimensions along which the pass-and-swap system can be extended while preserving the product-form stationary distribution. At the same time, we also identify cases in which generalizing the pass-and-swap queue causes the product-form nature of the stationary distribution to break. Finally, we identify questions that remain open and present a road map for future study.
FSSDroid: Feature subset selection for Android malware detection
Authors: Polatidis Nikolaos , Kapetanakis Stelios , Trovati Marcello , Korkontzelos Ioannis , Manolopoulos Yannis 
Published: July 2024

AbstractAndroid malware has become an increasingly important threat to individuals, organizations, and society, posing significant risks to data security, privacy, and infrastructure. As malware evolves in sophistication and complexity, the detection and mitigation of these malicious software instances have become more challenging and time consuming since the required number of features to identify potential malware can be very high. To address this issue, we have developed an effective feature selection methodology for malware detection in Android. The critical concern in the field of malware detection is the complexity of algorithms and the use of features that are used to detect malware. The present paper delivers a methodology for pre-processing datasets to select the most optimal features that will allow detecting malware, while maintaining very high accuracy. The proposed methodology has been tested on two real world datasets and the results indicate that the number of features is significantly reduced from 489 to between 19 and 28 for the first dataset and from 9503 to between 9 and 27 for the second dataset, whilst the accuracy is maintained as if all features were used.
Polar metabolomics using trichloroacetic acid extraction and porous graphitic carbon stationary phase
Authors: Day Francesca , O’Sullivan Justin , Ramzan Farha , Pook Chris 
Published: July 2024

Abstract
Introduction
Accurately identifying and quantifying polar metabolites using untargeted metabolomics has proven challenging in comparison to mid to non-polar metabolites. Hydrophilic interaction chromatography and gas chromatography–mass spectrometry are predominantly used to target polar metabolites.

Objectives
This study aims to demonstrate a simple one-step extraction combined with liquid chromatography–mass spectrometry (LC–MS) that reliably retains polar metabolites.

Methods
The method involves a MilliQ + 10% trichloroacetic acid extraction from 6 healthy individuals serum, combined with porous graphitic carbon liquid chromatography–mass spectrometry (LC–MS). The coefficient of variation (CV) assessed retention reliability of polar metabolites with logP as low as − 9. QreSS (Quantification, Retention, and System Suitability) internal standards determined the method's consistency and recovery efficiency.

Results
The method demonstrated reliable retention (CV < 0.30) of polar metabolites within a logP range of − 9.1 to 5.6. QreSS internal standards confirmed consistent performance (CV < 0.16) and effective recovery (70–130%) of polar to mid-polar metabolites. Quality control dilution series demonstrated that ~ 80% of annotated metabolites could be accurately quantified (Pearson’s correlation coefficient > 0.80) within their concentration range. Repeatability was demonstrated through clustering of repeated extractions from a single sample.

Conclusion
This LC–MS method is better suited to covering the polar segment of the metabolome than current methods, offering a reliable and efficient approach for accurate quantification of polar metabolites in untargeted metabolomics.
Numerical model for solid-like and fluid-like behavior of granular flows
Authors: Wang Yadong , Wu Wei 
Published: July 2024

AbstractWe propose a constitutive model for both the solid-like and fluid-like behavior of granular materials by decomposing the stress tensor into quasi-static and collisional components. A hypoplastic model is adopted for the solid-like behavior in the quasi-static regime, while the viscous and dilatant behavior in the fluid-like regime is represented by a modified $$\mu (I)$$

μ
(
I
)

rheology model. This model effectively captures the transition between solid-like and fluid-like flows. Performance and validation of the proposed model are demonstrated through numerical simulations of element tests.
Radiomics of pituitary adenoma using computer vision: a review
Authors: Zilka Tomas , Benesova Wanda 
Published: July 2024

Abstract
Pituitary adenomas (PA) represent the most common type of sellar neoplasm. Extracting relevant information from radiological images is essential for decision support in addressing various objectives related to PA. Given the critical need for an accurate assessment of the natural progression of PA, computer vision (CV) and artificial intelligence (AI) play a pivotal role in automatically extracting features from radiological images. The field of “Radiomics” involves the extraction of high-dimensional features, often referred to as “Radiomic features,” from digital radiological images. This survey offers an analysis of the current state of research in PA radiomics. Our work comprises a systematic review of 34 publications focused on PA radiomics and other automated information mining pertaining to PA through the analysis of radiological data using computer vision methods. We begin with a theoretical exploration essential for understanding the theoretical background of radionmics, encompassing traditional approaches from computer vision and machine learning, as well as the latest methodologies in deep radiomics utilizing deep learning (DL). Thirty-four research works under examination are comprehensively compared and evaluated. The overall results achieved in the analyzed papers are high, e.g., the best accuracy is up to 96% and the best achieved AUC is up to 0.99, which establishes optimism for the successful use of radiomic features. Methods based on deep learning seem to be the most promising for the future. In relation to this perspective DL methods, several challenges are remarkable: It is important to create high-quality and sufficiently extensive datasets necessary for training deep neural networks. Interpretability of deep radiomics is also a big open challenge. It is necessary to develop and verify methods that will explain to us how deep radiomic features reflect various physics-explainable aspects.

Graphical abstract
Radiogenomics as an Integrated Approach to Glioblastoma Precision Medicine
Authors: Sanchez Isabella , Rahman Ruman 
Published: July 2024

Abstract
Purpose of Review
Isocitrate dehydrogenase wild-type glioblastoma is the most aggressive primary brain tumour in adults. Its infiltrative nature and heterogeneity confer a dismal prognosis, despite multimodal treatment. Precision medicine is increasingly advocated to improve survival rates in glioblastoma management; however, conventional neuroimaging techniques are insufficient in providing the detail required for accurate diagnosis of this complex condition.

Recent Findings
Advanced magnetic resonance imaging allows more comprehensive understanding of the tumour microenvironment. Combining diffusion and perfusion magnetic resonance imaging to create a multiparametric scan enhances diagnostic power and can overcome the unreliability of tumour characterisation by standard imaging. Recent progress in deep learning algorithms establishes their remarkable ability in image-recognition tasks. Integrating these with multiparametric scans could transform the diagnosis and monitoring of patients by ensuring that the entire tumour is captured. As a corollary, radiomics has emerged as a powerful approach to offer insights into diagnosis, prognosis, treatment, and tumour response through extraction of information from radiological scans, and transformation of these tumour characteristics into quantitative data. Radiogenomics, which links imaging features with genomic profiles, has exhibited its ability in characterising glioblastoma, and determining therapeutic response, with the potential to revolutionise management of glioblastoma.

Summary
The integration of deep learning algorithms into radiogenomic models has established an automated, highly reproducible means to predict glioblastoma molecular signatures, further aiding prognosis and targeted therapy. However, challenges including lack of large cohorts, absence of standardised guidelines and the ‘black-box’ nature of deep learning algorithms, must first be overcome before this workflow can be applied in clinical practice.
A blockchain architecture with smart contracts for an additive symbiotic network - a case study
Authors: Ferreira Inês A. , Palazzo Guido , Pinto António , Pinto Pedro , Sousa Pedro , Godina Radu , Carvalho Helena 
Published: July 2024

AbstractAdopting innovative technologies such as blockchain and additive manufacturing can help organisations promote the development of additive symbiotic networks, thus pursuing higher sustainable goals and implementing circular economy strategies. These symbiotic networks correspond to industrial symbiosis networks in which wastes and by-products from other industries are incorporated into additive manufacturing processes. The adoption of blockchain technology in such a context is still in a nascent stage. Using the case study method, this research demonstrates the adoption of blockchain technology in an additive symbiotic network of a real-life context. The requirements to use a blockchain network are identified, and an architecture based on smart contracts is proposed as an enabler of the additive symbiotic network under study. The proposed solution uses the Hyperledger Fabric Attribute-Based Access Control as the distributed ledger technology. Even though this solution is still in the proof-of-concept stage, the results show that adopting it would allow the elimination of intermediary entities, keep available tracking records of the resources exchanged, and improve trust among the symbiotic stakeholders (that do not have any trust or cooperation mechanisms established before the symbiotic relationship). This study highlights that the complexity associated with introducing a novel technology and the technology’s immaturity compared to other data storage technologies are some of the main challenges related to using blockchain technology in additive symbiotic networks.
On the Shape of Hypersurfaces with Boundary Which Have Zero Fractional Mean Curvature
Authors: Onoue Fumihiko 
Published: July 2024

AbstractWe consider compact hypersurfaces with boundary in $${\mathbb {R}}^N$$

R

N

that are the critical points of the fractional area introduced by Paroni et al. (Commun Pure Appl Anal 17:709–727, 2018). In particular, we study the shape of such hypersurfaces in several simple settings. First we consider the critical points whose boundary is a smooth, orientable, closed manifold $$\Gamma $$
Γ
of dimension $$N-2$$

N
-
2

and lies in a hyperplane $$H \subset {\mathbb {R}}^N$$

H
⊂

R

N

. Then we show that the critical points coincide with a smooth manifold $${\mathcal {N}}\subset H$$

N
⊂
H

of dimension $$N-1$$

N
-
1

with $$\partial {\mathcal {N}}= \Gamma $$

∂
N
=
Γ

. Second we consider the critical points whose boundary consists of two smooth, orientable, closed manifolds $$\Gamma _1$$

Γ
1

and $$\Gamma _2$$

Γ
2

of dimension $$N-2$$

N
-
2

and suppose that $$\Gamma _1$$

Γ
1

lies in a hyperplane H perpendicular to the $$x_N$$

x
N

-axis and that $$\Gamma _2 = \Gamma _1 + d \, e_N$$

Γ
2

=

Γ
1

+
d

e
N

($$d >0$$

d
>
0

and $$e_N = (0,\cdots ,0,1) \in {\mathbb {R}}^N$$

e
N

=

(
0
,
⋯
,
0
,
1
)

∈

R

N

). Then, assuming that $$\Gamma _1$$

Γ
1

has a non-negative mean curvature, we show that the critical points do not coincide with the union of two smooth manifolds $${\mathcal {N}}_1 \subset H$$

N
1

⊂
H

and $${\mathcal {N}}_2 \subset H + d \, e_N$$

N
2

⊂
H
+
d

e
N

of dimension $$N-1$$

N
-
1

with $$\partial {\mathcal {N}}_i = \Gamma _i$$

∂

N
i

=

Γ
i

for $$i \in \{1,2\}$$

i
∈
{
1
,
2
}

. Moreover, the interior of the critical points does not intersect the boundary of the convex hull in $${\mathbb {R}}^N$$

R

N

of $$\Gamma _1$$

Γ
1

and $$\Gamma _2$$

Γ
2

, while this can occur in the codimension-one situation considered by Dipierro et al. (Proc Am Math Soc 150:2223–2237, 2022). We also obtain a quantitative bound which may tell us how different the critical points are from $${\mathcal {N}}_1 \cup {\mathcal {N}}_2$$

N
1

∪

N
2

. Finally, in the same setting as in the second case, we show that, if d is sufficiently large, then the critical points are disconnected and, if d is sufficiently small, then $$\Gamma _1$$

Γ
1

and $$\Gamma _2$$

Γ
2

are in the same connected component of the critical points when $$N \ge 3$$

N
≥
3

. Moreover, by computing the fractional mean curvature of a cone whose boundary is $$\Gamma _1 \cup \Gamma _2$$

Γ
1

∪

Γ
2

, we also obtain that the interior of the critical points does not touch the cone if the critical points are contained in either the inside or the outside of the cone.
Towards a cyberbullying detection approach: fine-tuned contrastive self-supervised learning for data augmentation
Authors: Al-Harigy Lulwah M. , Al-Nuaim Hana A. , Moradpoor Naghmeh , Tan Zhiyuan 
Published: July 2024

AbstractCyberbullying on social media platforms is pervasive and challenging to detect due to linguistic subtleties and the need for extensive data annotation. We introduce a Deep Contrastive Self-Supervised Learning (DCSSL) model that integrates a Natural Language Inference (NLI) dataset, a fine-tuned sentence encoder, and data augmentation to enhance the understanding of cyberbullying's nuanced semantics and offensiveness. The DCSSL model effectively captures contextual dependencies and the varied semantic implications inherent in cyberbullying instances, addressing the limitations of manual data annotation processes when compared against established models such as BERT and Bi-LSTM. Our proposed model registers a significant improvement, achieving a macro average F1 score of 0.9231 on cyberbullying datasets, highlighting its applicability in environments where manual annotation is impractical or unavailable.
Review of management of primary oesophagogastric cancers in East Asian versus Western countries
Authors: Chaganti Raja , Wong Mark 
Published: July 2024

AbstractOesophagogastric cancers are amongst the commonest cancers world-wide and this commentary explores the current state of curative management for primary oesophagogastric cancers and highlights the differences in treatment approaches between Eastern and Western countries. This commentary also examines the reasons behind these differences including variations in epidemiology, screening programs, surgical techniques and neoadjuvant/ adjuvant therapy and makes suggestions on future treatment approaches.
Enhancing Water Pollution Management Through a Comprehensive Fractional Modeling Framework and Optimal Control Techniques
Authors: Ebrahimzadeh Asiyeh , Jajarmi Amin , Baleanu Dumitru 
Published: July 2024

Abstract
Water pollution poses a significant threat to public health, particularly in developing nations. This study aims to enhance our understanding of water pollution transmission dynamics by introducing a novel mathematical model within a general fractional framework. The model offers a comprehensive analysis, including assessing solution feasibility and the stability of equilibrium points. To effectively implement this model, we develop an efficient numerical scheme based on the trapezoidal method. Furthermore, we conduct a thorough error discussion and convergence analysis. Notably, we devise and evaluate a new strategy to effectively tackle the associated fractional optimal control problem concerning water pollutant transmission. Applying this approach to a real case demonstrates its substantial impact, notably in reducing non-soluble contaminants within the water transmission system.
FPGA hardware kit for remote training platforms
Authors: Alhammami Muhammad 
Published: July 2024

AbstractThis paper outlines the development of a Hardware Development Kit (HDK) for a remote training platform on FPGA Devices designed to provide university students pursuing degrees in electronic and informatics engineering (at the bachelor’s, master’s, and PhD levels) with the tools they need to learn and develop systems related to artificial intelligence, digital signal processing, image processing, digital systems, integrated and embedded systems using an FPGA chip. Through the internet or a local network, students can engage with the platform to conduct engineering experiments. The HDK is equipped with a Raspberry Pi, a screen, a camera, and LEDs to facilitate the transfer of experiment results and to aid in assessing the validity of lab experiment execution. The Raspberry Pi plays a crucial role in the HDK, providing control and monitoring, remote access, data processing, user interface, and integration with other possible systems.
Endovascular embolization of a biliary–digestive loop bleeding
Authors: Vincenzi Matteo , Rigoni Benedetta , Manuppelli Carla , Mazzoli Matteo , Balestriero Giovanni 
Published: July 2024

AbstractDelayed massive hemorrhage after biliary surgery is a severe complication and carries a high mortality rate. Upper gastrointestinal (GI) tract bleeding is usually managed endoscopically. However, the presence of altered anatomy following hepato-biliary surgery does preclude endoscopic exploration of the anastomoted loop. In our case of hemorrhage from biliodigestive anastomosis, CT angiography provides accurate information about the presence of active bleeding and its source. We present a case of bleeding from a biliary–digestive loop. The patient exhibited several bleeding risk factors, notably cirrhosis and dual antiplatelet therapy. Furthermore, the site of bleeding, the biliarydigestive loop, had experienced a fistula with a hepatic abscess 2 years earlier, following percutaneous thermoablation. Endovascular embolization with microparticles 300–500 µm and one microcoil 2mx4cm proved to be a safe and effective treatment in this high-risk patient with persistent bleeding. Developments in interventional radiology equipment and techniques, such as low-profile catheter systems and advanced embolic agents, validate embolization as a suitable therapeutic option.