bluuebunny/crossref_metadata_2025_split huggingface dataset:
Effects of Aluminum Oxide Nanoparticles in the Spinal Cord of Male Wistar Rats and the Potential Ameliorative Role of Melatonin
Authors: Abdelhameed Nermeen G. , Ahmed Yasmine H. , Yasin Noha A. E. , Mahmoud Mohamed Y. , El‐sakhawy Mohamed A. 
Published: December 2024

ABSTRACTAluminum oxide nanoparticles (Al2O3 NPs) are widely utilized in vaccine manufacturing and other medical preparations. Melatonin has numerous effects as an antioxidant and anti‐apoptotic. The purpose of this study was to examine the beneficial impact of melatonin on Al2O3 NPs toxicity in the spinal cord. Forty male rats were divided into four groups: Group I, the negative controls (received standard diet and distilled water); Group II, Al2O3 NPs (received 30 mg/kg bw Al2O3 NPs); Group III, melatonin and Al2O3 NPs (received 30 mg/kg bw Al2O3 NPs + 10 mg/kg bw melatonin); Group IV, melatonin (received 10 mg/kg bw melatonin). All treatments were administered daily for 28 days by gastric gavage. After that, all rats were sacrificed, then, the samples from different spinal cords were subjected to histopathological, biochemical, and immunohistochemical analyses. Al2O3 NPs markedly elevated malondialdehyde and 8‐hydroxydeoxyguanosine while inhibiting superoxide dismutase and catalase. Al2O3 NPs also induced histological alterations in both gray and white matter manifested by neuronal degeneration, vacuolation, axonal degeneration, ballooning, and fusion of myelin sheaths. Furthermore, immunohistochemical results displayed a strong positive expression of caspase‐3. Conversely, melatonin significantly mitigated the effects of Al2O3 NPs by increasing the activities of antioxidant enzymes and inhibiting malondialdehyde and 8‐hydroxydeoxyguanosine. Moreover, melatonin alleviated most histological alterations induced by Al2O3 NPs and reduced caspase‐3 immunoreactivity. Collectively, melatonin could protect the spinal cord and mitigate Al2O3 NPs‐induced neurotoxicity.
Chronic and Idiopathic Pancreatitis—A Personalized Treatment Approach
Authors: von Widdern Julian Cardinal , Rosendahl Jonas , Ammer‐Herrmenau Christoph 
Published: December 2024

ABSTRACTChronic pancreatitis is a fibroinflammatory disease of the pancreas with heterogeneous clinical features and a significant socioeconomic burden. Assessing its aetiology and early diagnosis of associated complications remain challenging. Personalized therapy necessitates precise knowledge of the genetic, biological, and clinical differences within a patient population. In this context, the identification of the underlying aetiology represents an essential cornerstone. This review elucidates current standards for identifying underlying aetiologies and the diagnostic work‐up for idiopathic cases. It provides an overview of general therapeutic approaches and highlights individual treatment options. Additionally, the follow‐up management of pancreatitis‐associated complications, namely exocrine pancreatic insufficiency, post‐pancreatitis diabetes mellitus, pain management, pancreatic fluid collections, and pancreatic cancer risk, is summarized.
Carboxymethyl cellulose‐based bioactive food packaging films incorporated with Ficus religiosa/Psidium guajava extracts: Effect of additive ratio variation
Authors: Sharma Swati , Priyadarshi Ruchir , Mishra Narayan Chandra 
Published: December 2024

AbstractThe incorporation of plant extract into biopolymers is an environment‐friendly method of fabricating bioactive films (BFs) with multifunctional properties for food packaging. The current work explores the development of carboxymethyl cellulose (CMC)‐based BFs by combining Ficus religiosa (Peepal) extract (PLE) and Psidium guajava (Guava) extract (GLE) in varying ratios. The impact of extract concentration on the films' physiochemical and functional properties was examined. The structural analysis of the films showed good compatibility between the polymer and extracts. The findings indicate that BFs with PLE and GLE in a 50:50 ratio exhibited better mechanical properties than other films. All the BFs enhanced oxidative stability and UV‐blocking properties. Among all combinations, the CMC/GLE100 film (having only GLE and CMC) exhibited the highest antioxidant and UV‐blocking properties although transparency is slightly reduced. On employing the CMC/GLE100 film as UV protective packaging, it was able to preserve the color and shape of grapes even after 48 h of direct UV exposure. CMC/GLE100 film, when applied to pack fried items, exhibited enhanced oxidative stability. Overall, CMC/GLE100 film can find potential applications for packaging fruits and fried items.Highlights
CMC/GLE100 film exhibited enhanced oxidative stability for packing fried items.
CMC/GLE100 film showed potential as UV‐blocking film for fruits' packing.
CMC/GLE100 film exhibited very good antioxidant potential.
CMC/GLE100 film has very good UV‐blocking properties but decreased transparency.
BFs with PLE/GLE in a 50:50 ratio exhibits very good mechanical properties.
Radiative effects on 2D unsteady MHD Al2O3‐water nanofluid flow between squeezing plates: A comparative study using AGM and HPM in Python
Authors: Majidi Zar Pooriya , Shateri Amirali , Jalili Payam , Al‐Yarimi Fuad A. M. , Jalili Bahram , Ganji Davood Domiri , Ben Khedher Nidhal 
Published: December 2024

AbstractThis research uses Python programming to explore the influence of magnetic field strength on a two‐dimensional squeezing nanofluid flow confined between two parallel plates. The primary objective is to examine an aluminum oxide nanofluid's velocity and heat transfer characteristics which are governed by dimensionless parameters such as the Prandtl number and friction coefficient. The non‐dimensionalized differential equations are solved employing two analytical techniques: the Akbari‐Ganji Method and the Homotopy Perturbation Method, both implemented through Python programming. Using Python programming to solve these equations represents a novel approach that offers accurate and efficient results. The study's findings reveal that as the Prandtl number increases, the temperature and thermal properties of the nanofluid flow also increase while the concentration decreases. Additionally, the Nusselt number experiences a decline. The implementation of Python programming in this research showcases the versatility of the language in solving complex mathematical problems, particularly in fluid dynamics. Python's ability to provide accurate solutions efficiently enhances its potential for further applications and advancements in this area.
Concept for Direct Recycling of Battery Electrodes and Recovery of Active Material
Authors: Denk Florian , Wiechers Patrick , Lödige Lukas , Schabel Sebastian , Gleiß Marco , Scharfer Philip , Schabel Wilhelm , Fleischer Jürgen 
Published: December 2024

AbstractTo achieve the goal of sustainable and competitive production of battery electrodes, the integration of recycling approaches into the product life cycle is becoming increasingly important. In addition to the fixed recycling quotas for end-of-life batteries, which are legally stipulated for the future, production scrap also plays a decisive role.Production scrap is a result of quality requirements, which are directly correlated to production parameters. As a result, up to 40% of the material used in battery cell production is scrap, which also needs to be recycled to close the value chain. Currently, there are two established processes for recycling the functional materials of lithium ion batteries (LIBs), namely pyrometallurgical and hydrometallurgical recycling. An alternative process, which is not yet established due to the complexity of the physical and chemical relationships, is direct battery recycling. This function-preserving recycling enables a direct return to the LIBs value chain while simultaneously processing the materials in an environmentally friendly and resource-saving manner. Therefore, this paper presents technological approaches in the form of a process chain for the direct recycling of active materials (lithium iron phosphate and graphite) in order to significantly improve the battery sustainability. Anode layers with defined structural properties are treated in the process chain consisting of decoating, dispersing and thickening.Initial tests have looked at two different decoating methods. The mechanical removal of anode active material from the collector foil using brushes and removal in a high-power ultrasonic bath are demonstrated. Furthermore, the process for the preparation of the recyclate is presented. This involves dispersing the decoated material to break up large agglomerates. Finally, the anode suspension is thickened so that the concentrate has the same solids concentration as a newly produced paste.
Process Chain for Functionally Integrated Structures Based on Continuous Fibre Reinforced Thermoplastic Sheets
Authors: Reichel Vicky , Berlin Werner , Dröder Klaus 
Published: December 2024

AbstractThermoforming enables the efficient processing of continuous fibre reinforced thermoplastic (CFRT) such as organo sheets into geometrically complex and structurally advantageous structures; however, resulting components are often hybrized through additional processes for an increased rigidity. For this, injection moulding is a widespread option, as it allows for the precise integration of plastic ribs into the thermoformed products, leading to improved structural integrity and mechanical stiffness [1, 2]. As thermoforming and injection moulding offer synergistic advantages, especially based on many related process steps, the processes can be successfully integrated [3]. When further manufacturing steps such as cutting are combined within this integrated process, the previously required handling steps are avoided entirely and the overall process cycle time can be reduced [4].Within the present research, the integration of multiple process steps into an integrative process chain is examined through a prototypical experimental setup. The investigated one-shot injection moulding mould (IMM) combines the thermoforming and trimming of the final contour as well as the injection moulding step within an index plate mould. This enables comparably short process cycles while maintaining part quality, such as the defined position of CFRT in edge contours. The process chain is validated and resulting demonstrator components are analyzed regarding the adhesion between CFRT and applied thermoplastics by applying tensile loads. Even in comparison to conventionally established processes, the investigated integrated process chain enables feasible results. Within the examined process parameters, the temperatures within the different system components significantly influence the overall part quality. Overall, the concept for an integrated process chain shows promise for further investigations, as it offers significant gains in overall process efficiencies through reduced handling steps and potential time savings.
Comparative mechanisms for O2 storage and metabolism in two Florida diving birds: the anhinga (Anhinga anhinga) and the double-crested cormorant (Nannopterum auritum)
Authors: White Jeff , Schell Elizabeth R. , Dawson Neal J. , McCracken Kevin G. 
Published: December 2024

AbstractAir-breathing vertebrates face many physiological challenges while breath-hold diving. In particular, they must endure intermittent periods of declining oxygen (O2) stores, as well as the need to rapidly replenish depleted O2 at the surface prior to their next dive. While many species show adaptive increases in the O2 storage capacity of the blood or muscles, others increase the oxidative capacity of the muscles through changes in mitochondrial arrangement, abundance, or remodeling of key metabolic pathways. Here, we assess the diving phenotypes of two sympatric diving birds: the anhinga (Anhinga anhinga) and the double-crested cormorant (Nannopterum auritum). In each, we measured blood- and muscle-O2 storage capacity, as well as phenotypic characteristics such as muscle fiber composition, capillarity, and mitochondrial arrangement and abundance in the primary flight (pectoralis) and swimming (gastrocnemius) muscles. Finally, we compared the maximal activities of 10 key enzymes in the pectoralis, gastrocnemius, and left ventricle of the heart to assess tissue level oxidative capacity and fuel use. Our results indicate that both species utilize enhanced muscle-O2 stores over blood-O2. This is most apparent in the large difference in available myoglobin in the gastrocnemius between the two species. Oxidative capacity varied significantly between the flight and swimming muscles and between the two species. However, both species showed lower oxidative capacity than expected compared to other diving birds. In particular, the anhinga exhibits a unique diving phenotype with a slightly higher reliance on glycolysis and lower aerobic ATP generation than double-crested cormorants.
Mitigating content poisoning attacks in named data networking: a survey of recent solutions, limitations, challenges and future research directions
Authors: Ullah Syed Sajid , Hussain Saddam , Ali Ihsan , Khattak Hizbullah , Mastorakis Spyridon 
Published: December 2024

Abstract
Named Data Networking (NDN) is one of the capable applicants for the future Internet architecture, where communications focus on content rather than providing content. NDN implements Information-Centric Networking (ICN) with its unique node structure and significant characteristics such as built-in mobility support, multicast support, and efficient content distribution to end-users. It has several key features, including inherent security, that protect the content rather than the communication channel. Despite the good features that NDN provides, it is nonetheless vulnerable to a variety of attacks, the most critical of them is the Content Poisoning Attack (CPA). In this survey, the existing solutions presented for the prevention of CPA in the NDN paradigm have been critically analyzed. Furthermore, we also compared the suggested schemes based on latency, communication overhead, and security. In addition, we have also shown the possibility of other possible NDN attacks on the suggested schemes. Finally, we adds some open research challanges.
Analyzing customer preferences for hydrogen cars: a characteristic objects method approach
Authors: Shekhovtsov Andrii , Rafiei Oskooei Amirkia , Wątróbski Jarosław , Sałabun Wojciech 
Published: December 2024

Abstract
As hydrogen vehicles gain popularity, car manufacturers are introducing numerous models, presenting customers with the challenge of choosing the most suitable option. To address this, Multi-Criteria Decision Analysis methods are often used to evaluate and select the best alternative. This study applies the Characteristic Objects Method (COMET) to address the practical problem of selecting the most appropriate hydrogen car for decision-makers. Using data provided by manufacturers, we evaluate ten hydrogen vehicles and create six decision models based on the preferences of three decision-makers, utilizing both the recently proposed Triad Support and Expected Solution Point-COMET algorithms. The models provide insights into how customer preferences can be extracted and represented in decision models. Moreover, we analyze local weights derived from the models to understand customer expectations for hydrogen cars better. The results of our study highlight the effectiveness of the COMET approach in capturing and comparing decision-maker preferences, offering a valuable methodology framework for future applications in similar multi-criteria decision-making problems.
Landscape of machine learning evolution: privacy-preserving federated learning frameworks and tools
Authors: Nguyen Giang , Sáinz-Pardo Díaz Judith , Calatrava Amanda , Berberi Lisana , Lytvyn Oleksandr , Kozlov Valentin , Tran Viet , Moltó Germán , López García Álvaro 
Published: December 2024

Abstract
Machine learning is one of the most widely used technologies in the field of Artificial Intelligence. As machine learning applications become increasingly ubiquitous, concerns about data privacy and security have also grown. The work in this paper presents a broad theoretical landscape concerning the evolution of machine learning and deep learning from centralized to distributed learning, first in relation to privacy-preserving machine learning and secondly in the area of privacy-enhancing technologies. It provides a comprehensive landscape of the synergy between distributed machine learning and privacy-enhancing technologies, with federated learning being one of the most prominent architectures. Various distributed learning approaches to privacy-aware techniques are structured in a review, followed by an in-depth description of relevant frameworks and libraries, more particularly in the context of federated learning. The paper also highlights the need for data protection and privacy addressed from different approaches, key findings in the field concerning AI applications, and advances in the development of related tools and techniques.
Ratai: recurrent autoencoder with imputation units and temporal attention for multivariate time series imputation
Authors: Lai Xiaochen , Yao Yachen , Mu Jichong , Lu Wei , Zhang Liyong 
Published: December 2024

Abstract
Multivariate time series is ubiquitous in real-world applications, yet it often suffers from missing values that impede downstream analytical tasks. In this paper, we introduce the Long Short-Term Memory Network based Recurrent Autoencoder with Imputation Units and Temporal Attention Imputation Model (RATAI), tailored for multivariate time series. RATAI is designed to address certain limitations of traditional RNN-based imputation methods, which often focus on predictive modeling to estimate missing values, sometimes neglecting the contextual impact of observed data at and beyond the target time step. Drawing inspiration from Kalman smoothing, which effectively integrates past and future information to refine state estimations, RATAI aims to extract feature representations from time series data and use them to reconstruct a complete time series, thus overcoming the shortcomings of existing approaches. It employs a dual-stage imputation process: the encoder utilizes temporal information and attribute correlations to predict and impute missing values, and extract feature representation of imputed time series. Subsequently, the decoder reconstructs the series from the feature representation, and the reconstructed values are used as the final imputation values. Additionally, RATAI incorporates a temporal attention mechanism, allowing the decoder to focus on highly relevant inputs during reconstruction. This model can be trained directly using data that contains missing values, avoiding the misleading effects on model training that can arise from setting initial values for missing values. Our experiments demonstrate that RATAI outperforms benchmark models in multivariate time series imputation.
A Nash equilibria decision tree for binary classification
Authors: Suciu Mihai-Alexandru , Lung Rodica Ioana 
Published: December 2024

Abstract
Decision trees rank among the most popular and efficient classification methods. They are used to represent rules for recursively partitioning the data space into regions from which reliable predictions regarding classes can be made. These regions are usually delimited by axis-parallel or oblique hyperplanes. Axis-parallel hyperplanes are intuitively appealing and have been widely studied. However, there is still room for exploring different approaches. In this paper, a splitting rule that constructs axis-parallel hyperplanes by computing the Nash equilibrium of a game played at the node level is used to induct a Nash Equilibrium Decision Tree for binary classification. Numerical experiments are used to illustrate the behavior of the proposed method.
Varieties of Work with Preferred Futures in Solution-Focused Brief Therapy
Authors: Zatloukal Leoš 
Published: December 2024

AbstractThe term “preferred future” refers to the outcomes a client hopes to achieve through psychotherapy. Collaborative goal setting is a crucial component of many psychotherapy approaches. This article describes the six ways to focus on a preferred future in solution-focused brief therapy: (1) common project, (2) Vision of a preferred future, (3) goal(s), (4) skill(s), (5) values, and (6) small signs of progress. All variants are briefly expounded upon with accompanied case examples. The second part of the article focuses on the transitions from problem descriptions to various forms of work related to a preferred future, and on the transitions among different methods of preferred future conversations. The primary purpose of this article is to broaden therapists’ strategies when working with preferred futures, enhancing their adaptability in managing these types of conversations with their clients.
Assessing the impact of federalism on constitutional compliance
Authors: Kantorowicz Jaroslaw , Voigt Stefan 
Published: December 2024

AbstractFederalism is a constitutionalized version of multi-tier governance. Proponents of veto-player theory argue that a high number of veto players leads to a high degree of policy stability. Compared to states with a unitary structure, federal countries, in which at least one sub-central level of government is constitutionally recognized and endowed with some degree of exclusive competences, dispose of a higher number of veto players, who can voice and challenge constitutional non-compliance by the central government. In this paper, we therefore ask whether federally constituted states also enjoy a higher degree of constitutional compliance, i.e. have governments that respect and enforce the promises made in their country’s constitution. At the same time, with a higher number of governments under federalism, there are more chances that some of them will not comply with constitutional constraints leading to greater non-compliance. To test these hypotheses, we employ data from up to 163 countries and apply standard panel data estimation techniques. Our results indicate that federalism is neither positively nor negatively correlated with constitutional compliance of the respective countries.
Regression analysis with independent variables in shares: a guide and an empirical example
Authors: Morawetz Ulrich B. , Klaiber H. Allen 
Published: December 2024

Abstract
The use of shares or percentages as independent variables in regressions is a popular modeling technique for aggregated data but can be particularly challenging when it comes to model specification and interpretation. Reviewing theory and providing an empirical application to building height in Vienna, Austria, we discuss aggregation bias and show how to appropriately identify and interpret share coefficients. We further distinguish interpretation for constant and non-constant share sums and extend our analysis to the case of two stage least squares. Finally, we suggest a test for the conditional correlation of an instrument with shares which is needed for exogeneity if only one instrument is available.