bluuebunny/crossref_metadata_2025_split huggingface dataset:
Influence of preparation method on structural, optical and magnetic properties of synthesized Zn0.5Mg0.5−xNixFe2O4 nano-ferrite
Authors: Khalafallah Abdien Y , Siddig Mohamed A , Algethami Obaidallah A , Elbadawi Abdelrahman A , Mohammed Abdalskhi S 
Published: May 2024

Abstract
Structural, optical, and magnetic properties of ferrite series with chemical formula Zn0.5NixMg0.5−xFe2O4 (x = 0.0, 0.1, 0.2, 0.3 and 0.4) were investigated. Co-precipitation and sol–gel methods were used to synthesis the samples and to investigate further the influence of these preparation methods on the properties of Zn0.5NixMg0.5−xFe2O4 nano-ferrites. X-ray diffraction and Fourier-transform infrared spectroscopy revealed that the samples crystallize in a cubic spinel structure with space group Fd3m:1. The crystallite size of Zn0.5NixMg0.5−xFe2O4 nanoparticles was decreased from 19.15 to 11.65 nm with increasing Ni content for the samples of co-precipitation method, while the opposite behavior was observed in case of sol–gel method which was increased from 13.90 to 21.90 nm. Moreover, the optical investigation by UV-visible spectroscopy revealed that the energy band gap of the samples of co-precipitation method ranged (6.04–6.08) eV compared to (4.44–4.50) eV of sol–gel method. For both methods, substitution of Ni+2 by Mg+2 in Zn0.5NixMg0.5−xFe2O4 affects the remanent magnetization (Mr), coercivity (Hc) and saturation magnetization (Ms) suggesting the super-paramagnetic behavior of the samples. The substitution of Ni resulted in small values of coercivity ranged 0.758 to 164.62 Oe. The Ms was ranged 20.39 to 127.57 emu g−1 and an enhancement in magnetization was observed which increases the value of Ms that was found to be maximum with x = 0.4 of co-precipitation method (127.57 emu g−1). The magnetic behavior of synthesized Ni doped nanoparticles makes them suitable candidate for sensing and high-frequency applications and for use in hyperthermia treatment. The characterization outcomes may recommend that co-precipitation method is preferred over sol–gel method for refined structure and preferable properties of Zn0.5NixMg0.5−xFe2O4 nanoparticles.
Graphene reinforced ceramic matrix composite (GRCMC) – state of the art
Authors: A Madhankumar , M Anthony Xavior 
Published: May 2024

Abstract
Ceramic Matrix Composites (CMC) are widely used for manufacturing automobile parts aircraft components, biomedical and electronic devices. In recent years, graphene has found extensive applications in various fields like effective reinforcement in the development of metal matrix, polymer matrix and ceramic matrix composites. Literature reveals that more focus was on polymer and metal matrix composites reinforced with graphene when compared to ceramic matrix composites. In this manuscript, the effect and suitability of graphene as a reinforcement for fabricating ceramic matrix composite are presented. Further, the property evaluation, including mechanical and physical, characterization and microstructural evaluation of the CMCs developed with graphene as reinforcement is elaborated. The investigation of the reinforcing mechanisms and failure behavior of ceramic matrix composites reinforced with graphene, together with the development of novel processing techniques to solve manufacturing difficulties, are key areas of focus. Although many investigations have concentrated on enhancing the mechanical and electrical characteristics of ceramics by integrating graphene, more investigation is required to investigate the interfacial interaction between graphene and the ceramic matrix, as well as the influence of graphene size on the properties of the composite. Furthermore, there is a need for future research to explore the possibility of using graphene-reinforced ceramic composites in several multifunctional applications, including microwave absorption, electromagnetic interference shielding, ballistic armors, self-monitoring damage sensors, and energy storage and conversion. Future research should focus on developing innovative processing procedures that guarantee the uniform distribution and precise alignment of graphene sheets within the ceramic matrix. The incorporation of graphene into ceramic matrix composites presents novel prospects for augmenting the characteristics and capabilities of ceramics, rendering it a very interesting area for further investigation.
Multi-criteria optimization of Al2O3-ZrO2(Y2O3)-based self-lubricating composite cutting tools for increasing shear angle, and wear resistance in machining processes
Authors: Pradeep Kumar Bagadi , Rao Pujari Srinivasa , K Adarsha Kumar , Venkatesh Durga Janaki 
Published: May 2024

Abstract
In the present investigation, self-lubricating cutting tools were fabricated by adding various elements such as NiCr, Ag, Mo, SrSO4, and CaF2 to Zirconia Toughened Alumina in order to overcome the difficulties that occurred in dry machining. Three significant process parameters; cutting speed (50, 125, 200, and 275 m/min), feed rate (0.1 and 0.2 mm/rev), and depth of cut (0.1 and 0.2 mm) were selected for the machining process. The performance measures such as chip thickness ratio, shear angle, and tool wear, were investigated. The experimental runs were conducted with Taguchi L8 mixed-level orthogonal array. Technique for Order of Preference by Similarity to Ideal Solution is a multi-criteria decision-making method used to determine the best alternative from a set of options. Turning experiments have been performed on the AISI 4340 steel workpiece with various combinations of machining parameters. The findings revealed the incorporation of SrSO4 and Mo at 10Wt% and CaF2 at 5Wt% (i.e., self-lubricating cutting tool-4) achieved better mechanical properties and wear resistance due to the formation of a self-lubrication layer on the rake face of the cutting tool. A confirmation experiment is executed in order to verify the outcomes in the end. Finally, there is a noticeable improvement in the outcomes compared to the base cutting tool, i.e., chip thickness ratio is 35.77%, shear angle is 27.31%, and tool wear is 58%. The mechanism for improving performance measures is discussed in detail.
Application of self-learning interval type-2 fuzzy neural network in PM2.5 concentration prediction
Authors: Shao Kunpeng , Zhao Taoyan , Cao Jiangtao 
Published: May 2024

Abstract
The change of PM2.5 concentration in air quality is nonlinear and difficult to predict. Therefore, a self-learning interval type-2 fuzzy neural network (SLIT2FNN) is proposed. SLIT2FNN has two parts: online structure learning and parameter learning. In structure learning, to improve the training accuracy and speed of the model, the Possibilistic Fuzzy C-Means (PFCM) algorithm is used to process the input data and obtain the number of initial rules before model training. The PFCM algorithm introduces the concept of possibility P to Fuzzy C-Means (FCM), allowing PFCM to overcome the shortcomings of FCM that cannot accurately cluster a large number of nonlinear problems. SLIT2FNN can establish an appropriate number of rules in the preparation stage, and then use the firing strength of the antecedents of the rules to judge whether to generate fuzzy rules for online self-learning, thereby optimizing its network structure. Then, the improved Levenberg–Marquardt (ILM) algorithm is used to modify the relevant parameters of SLIT2FNN. The ILM algorithm can address the challenge of numerous parameters in the Jacobian matrix and complex calculations and improve the calculation speed and adaptive ability of SLIT2FNN parameter learning. Finally, SLIT2FNN is applied to the prediction of air quality PM2.5 concentration, and the performance is compared with other models. The experiment proves that SLIT2FNN has a high prediction accuracy and fast convergence.
Spike-based computation using classical recurrent neural networks
Authors: De Geeter Florent , Ernst Damien , Drion Guillaume 
Published: May 2024

Abstract
Spiking neural networks (SNNs) are a type of artificial neural networks in which communication between neurons is only made of events, also called spikes. This property allows neural networks to make asynchronous and sparse computations and therefore drastically decrease energy consumption when run on specialized hardware. However, training such networks is known to be difficult, mainly due to the non-differentiability of the spike activation, which prevents the use of classical backpropagation. This is because state-of-the-art SNNs are usually derived from biologically-inspired neuron models, to which are applied machine learning methods for training. Nowadays, research about SNNs focuses on the design of training algorithms whose goal is to obtain networks that compete with their non-spiking version on specific tasks. In this paper, we attempt the symmetrical approach: we modify the dynamics of a well-known, easily trainable type of recurrent neural network (RNN) to make it event-based. This new RNN cell, called the spiking recurrent cell, therefore communicates using events, i.e. spikes, while being completely differen-tiable. Vanilla backpropagation can thus be used to train any network made of such RNN cell. We show that this new network can achieve performance comparable to other types of spiking networks in the MNIST benchmark and its variants, the Fashion-MNIST and the Neuromorphic-MNIST. Moreover, we show that this new cell makes the training of deep spiking networks achievable.
Crossing Abyssal Lines
Authors: Taboada Manuela , Turner Jane 
Published: May 2024

Abstract
This chapter explores how decolonial thought can help us understand and recover our relationships with the more-than-human in urban design contexts. This is done through stories of place from Meanjin/Brisbane, Australia, that offer a lens to look at the tangles created by the visible and invisible ‘abyssal lines’ that separate the human from the more-than-human in the city. We start by taking a walk along a local river and finish in our homes, highlighting the encounters with and perceptions of the humans towards the more-than-human in these areas. The authors weave the stories together to reflect upon some contemporary approaches to bring the more-than-human closer to human urban ontologies, and their consequences. They offer some decolonial principles that can be applied to help us untangle our perspectives and positionings to find opportunities to return to living and sharing with the more-than-human.
Exploring More-than-Human Smart Cities
Authors: Gaver Bill , Boucher Andy , Brown Dean , Matsuda Naho , Ovalle Liliana , Sheen Andy , Vanis Mike 
Published: May 2024

Abstract
This chapter describes a process and line of thought developed by our design research team as we explored how smart cities might include ‘more-than-humans’, particularly living non-humans and for our team, mainly animals. The project was inspired by and pursued alongside researchers from five UK universities and two community organisations via a programme of participatory and design-led engagements with a variety of (human) stakeholders in London and elsewhere in the United Kingdom. As newcomers to the field, our design team investigated the topic by generating a workbook of design proposals and fictions: collections of simple propositions that contain a few images, words, and titles to convey an idea. The chapter provides examples from the workbook and discusses the value of an open, emergent design process and highlights the transformative potential of embracing a more-than-human perspective in reshaping our understanding and implementation of smart city designs.
The Logic of Status Competition
Authors: Beaumont Paul David 
Published: May 2024

Abstract
This chapter situates the book’s theoretical framework within the IR status literature. In so doing, it develops an ideal-typical account of status competition as a rule-governed competition for position whereby the relative performance of competitors is easily identifiable by virtue of the shared understanding of the rules of the game. While broadly consistent with prior research, the advantage of this ideal type is that it gives due emphasis to the rule-governed nature of status competition and the specific processual-relational logic of action it produces. The key value added is that it defines status dynamics by their processual-relational logic rather than by either the motivation or the substance of the behavior. This typology thus lays the groundwork for solving one of the methodological riddles of the introduction: differentiating status competition from other types of state action and status-seeking without defining substance of the status hierarchy prior to analysis.
The Grammar of Status Competition
Authors: Beaumont Paul David 
Published: May 2024

Abstract
This chapter elaborates how we can recognize theories of international status as they are manifested in discourse. To this second end, the chapter develops a metalinguistic framework—“Grammar of Status Competition”—for identifying the logic of status competition as it manifests in language. The chapter thereby leverages the processual-relational logic outlined in the previous chapter to develop a lens for identifying status and its effects without assuming (and reifying) the rules of an international social hierarchy or trying to infer motivations. Specifically, it outlines how three types of representations invoke and thus constitute competitive status hierarchies: (1) superlatives and relative comparisons, (2) positional identity constructions, and (3) sports metaphors. When uttered, each grammatical unit on its own (no matter how momentary) invokes the logic of status competition and specifies a distinct theory of status, which can be used to legitimize and delegitimize particular courses of action.
AI identifies potent inducers of breast cancer stem cell differentiation based on adversarial learning from gene expression data
Authors: Li Zhongxiao , Napolitano Antonella , Fedele Monica , Gao Xin , Napolitano Francesco 
Published: May 2024

Abstract
Cancer stem cells (CSCs) are a subpopulation of cancer cells within tumors that exhibit stem-like properties and represent a potentially effective therapeutic target toward long-term remission by means of differentiation induction. By leveraging an artificial intelligence approach solely based on transcriptomics data, this study scored a large library of small molecules based on their predicted ability to induce differentiation in stem-like cells. In particular, a deep neural network model was trained using publicly available single-cell RNA-Seq data obtained from untreated human-induced pluripotent stem cells at various differentiation stages and subsequently utilized to screen drug-induced gene expression profiles from the Library of Integrated Network-based Cellular Signatures (LINCS) database. The challenge of adapting such different data domains was tackled by devising an adversarial learning approach that was able to effectively identify and remove domain-specific bias during the training phase. Experimental validation in MDA-MB-231 and MCF7 cells demonstrated the efficacy of five out of six tested molecules among those scored highest by the model. In particular, the efficacy of triptolide, OTS-167, quinacrine, granisetron and A-443654 offer a potential avenue for targeted therapies against breast CSCs.
Meso-cortical pathway damage in cognition, apathy and gait in cerebral small vessel disease
Authors: Li Hao , Jacob Mina A , Cai Mengfei , Kessels Roy P C , Norris David G , Duering Marco , de Leeuw Frank-Erik , Tuladhar Anil M 
Published: May 2024

Abstract
Cerebral small vessel disease (SVD) is known to contribute to cognitive impairment, apathy and gait dysfunction. Although associations between cognitive impairment and either apathy or gait dysfunction have been shown in SVD, the inter-relations among these three clinical features and their potential common neural basis remain unexplored. The dopaminergic meso-cortical and meso-limbic pathways have been known as the important brain circuits for both cognitive control, emotion regulation and motor function. Here, we investigated the potential inter-relations between cognitive impairment, apathy and gait dysfunction, with a specific focus on determining whether these clinical features are associated with damage to the meso-cortical and meso-limbic pathways in SVD.
In this cross-sectional study, we included 213 participants with SVD for whom MRI and comprehensive neurobehavioural assessments were performed. These assessments comprised six clinical measures: processing speed, executive function, memory, apathy (based on the Apathy Evaluation Scale) and gait function (based on the time and steps in the Timed Up and Go Test). We reconstructed five tracts connecting the ventral tegmental area (VTA) and dorsolateral prefrontal cortex (PFC), ventral lateral PFC, medial orbitofrontal cortex, anterior cingulate cortex (ACC) and nucleus accumbens within meso-cortical and meso-limbic pathways using diffusion weighted imaging. The damage along the five tracts was quantified using the free water (FW) and FW-corrected mean diffusivity indices. Furthermore, we explored the inter-correlations among the six clinical measures and identified their common components using principal component analysis (PCA).
Linear regression analyses showed that higher FW values of tracts within meso-cortical pathways were related to these clinical measures in cognition, apathy, and gait (all P-corrected values &lt; 0.05). The PCA showed strong inter-associations among these clinical measures and identified a common component wherein all six clinical measures loaded on. Higher FW values of tracts within meso-cortical pathways were related to the PCA-derived common component (all P-corrected values &lt; 0.05). Moreover, FW values of the VTA-ACC tract showed the strongest contribution to the PCA-derived common component over all other neuroimaging features.
In conclusion, our study showed that the three clinical features (cognitive impairment, apathy, and gait dysfunction) of SVD are strongly inter-related and that the damage in meso-cortical pathway could be the common neural basis underlying the three features in SVD. These findings advance our understanding of the mechanisms behind these clinical features of SVD and have the potential to inform novel management and intervention strategies for SVD.
Prevention of Vascular Access Device–Associated Hospital-Onset Bacteremia and Fungemia: A Review of Emerging Perspectives and Synthesis of Technical Aspects
Authors: Garcia Robert , Septimus Edward J , LeDonne Jack , Sturm Lisa K , Moureau Nancy , DeVries Michelle , DeBaun Barbara 
Published: May 2024

Abstract
Significant events that have impacted healthcare over the last several years have been associated with escalating rates of healthcare-associated infections. This has resulted in increased efforts to reinstitute well-established and evidence-based infection prevention (IP) practices, particularly for central line–associated bloodstream infections. However, implementation of prevention initiatives beyond central lines has not received the same level of acknowledgment and response as being a considerable risk to patients. This article, authored by IP, infectious disease, and vascular access professionals, provides emerging perspectives and technical aspects associated with the complete “life cycle” of a vascular access device. The intent is to provide insight and perspective on enhancing current IP practices in the acute care hospital setting. This will also help prepare hospitals for upcoming broader surveillance and intervention activities aimed at reducing hospital-onset bacteremia and fungemia associated with all types of vascular access devices.
Usefulness of the Clinical Frailty Scale in patients with end-stage kidney disease
Authors: Lamberink Kyra , Vermeeren Yolande M , Moes Arthur D , Mulderij Jeanette , Rootjes Paul A , Zomer Tizza P 
Published: May 2024

ABSTRACT

Background
The frailty index (FI) is commonly used to estimate frailty in end-stage kidney disease (ESKD) patients. The Clinical Frailty Scale (CFS) is a less time-consuming alternative. We aimed to determine the test performance of the CFS for pre-dialysis and dialysis patients and patients receiving conservative therapy from the Dialysis Centre Apeldoorn.

Methods
In this cross-sectional study, haemodialysis, peritoneal dialysis, pre-dialysis patients and patients receiving conservative therapy from the Dialysis Centre Apeldoorn were included and subjected to frailty assessment. Nephrologists not familiar with the CFS completed the frailty score after medical consultation. The sensitivity, specificity and area under the curve (AUC) of the CFS were determined. The FI was used as the gold standard.

Results
Included were 144 patients, of whom 60 (41.7%) were considered frail according to the FI. The mean age was 67.4 ± 13.5 years and 56 (38.9%) were female. The cut-off point of the CFS for ‘vulnerable’ (CFS ≥4) had a sensitivity of 63.3%, a specificity of 81.0% and an AUC of 0.72. The cut-off point of the CFS for ‘frail’ (CFS ≥5) had a sensitivity of 50.0%, a specificity of 91.7% and an AUC of 0.71.

Conclusions
The CFS is a quick and easy-to-use tool for the determination of frailty in ESKD patients with a high prevalence of frailty. Nevertheless, the sensitivity of the CFS in the present study was considered too low to implement into daily clinical practice. The sensitivity might be increased by training nephrologists in the use of the CFS.
Prediction of Student Performance Using Random Forest Combined With Naïve Bayes
Authors: Manzali Youness , Akhiat Yassine , Abdoulaye Barry Khalidou , Akachar Elyazid , El Far Mohamed 
Published: May 2024

Abstract
Random forest is a powerful ensemble learning technique celebrated for its heightened predictive performance and robustness in handling complex datasets; nevertheless, it is criticized for its computational expense, particularly with a large number of trees in the ensemble. Moreover, the model’s interpretability diminishes as the ensemble’s complexity increases, presenting challenges in understanding the decision-making process. Although various pruning techniques have been proposed by researchers to tackle these issues, achieving a consensus on the optimal strategy across diverse datasets remains elusive. In response to these challenges, this paper introduces an innovative machine learning algorithm that integrates random forest with Naïve Bayes to predict student performance. The proposed method employs the Naïve Bayes formula to evaluate random forest branches, classifying data by prioritizing branches based on importance and assigning each example to a single branch for classification. The algorithm is utilized on two sets of student data and is evaluated against seven alternative machine-learning algorithms. The results confirm its strong performance, characterized by a minimal number of branches.