bluuebunny/crossref_metadata_2025_split huggingface dataset:
Duplex surface engineering of cold spray Ti coatings and physical vapor-deposited TiN and AlTiN thin films
Authors: Alidokht Sima A , Liang Tongyue , Bessette Stéphanie , Brodusch Nicolas , Gauvin Raynald , Chromik Richard R 
Published: July 2024

Abstract
The feasibility of a duplex coating based on cold spray technology and magnetron sputtering was evaluated for repair applications requiring a ‘thin-on-thick’ layered structure. Commercially pure angular-shaped Ti grade 4 particles are fed to a cold spray gun and accelerated toward a Ti alloy substrate to deposit thick coatings (∼4.5 mm). TiN and AlTiN thin films are deposited on polished cold spray coatings using a four-source closed-field unbalanced magnetron sputtering (CFUBMS) system. Microstructure was characterized using focused ion beam (FIB) lift-out, scanning electron microscopy (SEM), and electron channeling contrast imaging (ECCI). The nanoindentation technique was used to evaluate the mechanical properties of coatings. The H/E ratios and H3/E2 ratios for TiN films were found to be 0.098 and 0.26 GPa, respectively, while those for AlTiN films were measured at 0.066 and 0.052 GPa, respectively, suggesting higher capacity of TiN films to withstand both elastic and plastic deformation. Using scratch testing, the adhesion of TiN and AlTiN thin films to cold spray Ti was investigated, with TiN-Ti duplex coatings exhibiting better performance compared to AlTiN-Ti coatings. Tribological testing was performed on duplex coatings using a reciprocating tribometer equipped with an alumina ball counterface. The wear rate for AlTiN-Ti coatings after 2000 sliding cycles was found to be (1.0 × 10−3 ± 0.1 × 10−3 mm3/Nm), three orders of magnitudes higher than that for TiN-Ti (8 × 10−6 ± 2 × 10−6 mm3/Nm. SEM was used to reveal worn surface morphologies and cross-sectional analysis of the wear track. Subsurface microstructural changes due to wear were examined using focused ion beam cross-sectioning, revealing bending cracks and tribofilm formation.
Evaluating spectral performance for quantitative contrast-enhanced breast CT with a GaAs based photon counting detector: a simulation approach
Authors: Ghammraoui Bahaa , Ghani Muhammad Usman , Glick Stephen J 
Published: July 2024

Abstract
Quantitative contrast-enhanced breast computed tomography (CT) has the potential to improve the diagnosis and management of breast cancer. Traditional CT methods using energy-integrated detectors and dual-exposure images with different incident spectra for material discrimination can increase patient radiation dose and be susceptible to motion artifacts and spectral resolution loss. Photon Counting Detectors (PCDs) offer a promising alternative approach, enabling acquisition of multiple energy levels in a single exposure and potentially better energy resolution. Gallium arsenide (GaAs) is particularly promising for breast PCD-CT due to its high quantum efficiency and reduction of fluorescence x-rays escaping the pixel within the breast imaging energy range. In this study, the spectral performance of a GaAs PCD for quantitative iodine contrast-enhanced breast CT was evaluated. A GaAs detector with a pixel size of 100 μm, a thickness of 500 μm was simulated. Simulations were performed using cylindrical phantoms of varying diameters (10 cm, 12 cm, and 16 cm) with different concentrations and locations of iodine inserts, using incident spectra of 50, 55, and 60 kVp with 2 mm of added aluminum filtration and and a mean glandular dose of 10 mGy. We accounted for the effects of beam hardening and energy detector response using TIGRE CT open-source software and the publicly available Photon Counting Toolkit (PcTK). Material-specific images of the breast phantom were produced using both projection and image-based material decomposition methods, and iodine component images were used to estimate iodine intake. Accuracy and precision of the proposed methods for estimating iodine concentration in breast CT images were assessed for different material decomposition methods, incident spectra, and breast phantom thicknesses. The results showed that both the beam hardening effect and imperfection in the detector response had a significant impact on performance in terms of Root Mean Squared Error (RMSE), precision, and accuracy of estimating iodine intake in the breast. Furthermore, the study demonstrated the effectiveness of both material decomposition methods in making accurate and precise iodine concentration predictions using a GaAs-based photon counting breast CT system, with better performance when applying the projection-based material decomposition approach. The study highlights the potential of GaAs-based photon counting breast CT systems as viable alternatives to traditional imaging methods in terms of material decomposition and iodine concentration estimation, and proposes phantoms and figures of merit to assess their performance.
Comprehensive review and future perspectives: 3D printing technology for all types of solid oxide cells
Authors: Kim Chanho , Jang Inyoung 
Published: July 2024

Abstract
As the urgency to address global warming increases, the demand for clean energy generation systems that can mitigate greenhouse gases is intensifying. Solid oxide cells (SOCs) have emerged as a key technology for clean energy conversion, offering the benefits of power generation without submission of any pollutants including greenhouse gases. As the consumption of energy rises, the electrochemical performance of SOCs must be enhanced to meet the future energy demand. With the advent of 3D printing technology, the fabrication of SOCs has undergone a transformative shift, enabling precise structural control beyond the capabilities of traditional ceramic processes. This technology facilitates the creation of complex geometries, optimising functionality through structural innovation and maximising the electrochemical performance by enhancing reaction sites. Our review covers the brief outlook and the profound impact of 3D printing technology on SOC fabrication, highlighting its role in surpassing the structural constraints of conventional SOCs and paving the way for advanced applications like metal supported SOCs and integrated stack modules. Through the review, it is evident that continued, in-depth research into 3D printing for SOCs is crucial for maximising their role as a sustainable energy resource in the future.
An active learning enhanced data programming (ActDP) framework for ECG time series
Authors: Gupta Priyanka , Gupta Manik , Kumar Vijay 
Published: July 2024

Abstract
Supervised machine learning learns a mapping from input data to output labels, based on the patterns and relationships present in a huge labelled training data.Getting labelled data generally requires a substantial allocation of resources in terms of cost and time. In such scenarios, weak supervised learning techniques like data programming (DP) and active learning (AL) can be advantageous for time-series classification tasks. These paradigms can be used to assign data labels in an automated manner, and time-series classification can subsequently be carried out on the labeled data. This work proposes a novel framework titled AL enhanced data programming (ActDP). It uses a combination of DP and AL for electrocardiogram (ECG) beat classification using single-lead data. ECG beat classification is pivotal in cardiology and healthcare applications for diagnosing a broad spectrum of heart conditions and arrhythmias. To establish the usefulness of this proposed ActDP framework, the experiments have been conducted using the MIT-BIH dataset with 94,224 ECG beats. DP assigns a probabilistic label to each ECG beat using nine novel polar labelling functions and a generative model in this work. Further, AL improves the result of DP by replacing the labels for sampled ECG beats of a generative model with ground truth. Subsequently, a discriminative model is trained on these labels for each iteration. The experimental results show that by incorporating AL into DP in the ActDP framework, the accuracy of ECG classification strictly increases from 85.7% to 97.34% in 58 iterations. Comparatively, the proposed framework (ActDP) has demonstrated a higher classification accuracy of 97.34%. In contrast, DP with data augmentation (DA) achieves an accuracy of 92.2%, while DP without DA results in an accuracy of 85.7%, few-shot learning techniques yield 87.5%–89.2%, and multi-instance learning methods achieve accuracies in the range of 88.9%–94.1%
Guided quantum compression for high dimensional data classification
Authors: Belis Vasilis , Odagiu Patrick , Grossi Michele , Reiter Florentin , Dissertori Günther , Vallecorsa Sofia 
Published: July 2024

Abstract
Quantum machine learning provides a fundamentally different approach to analyzing data. However, many interesting datasets are too complex for currently available quantum computers. Present quantum machine learning applications usually diminish this complexity by reducing the dimensionality of the data, e.g. via auto-encoders, before passing it through the quantum models. Here, we design a classical-quantum paradigm that unifies the dimensionality reduction task with a quantum classification model into a single architecture: the guided quantum compression model. We exemplify how this architecture outperforms conventional quantum machine learning approaches on a challenging binary classification problem: identifying the Higgs boson in proton-proton collisions at the LHC. Furthermore, the guided quantum compression model shows better performance compared to the deep learning benchmark when using solely the kinematic variables in our dataset.
An extended de Bruijn graph for feature engineering over biological sequential data
Authors: Cakiroglu Mert Onur , Kurban Hasan , Sharma Parichit , Kulekci M Oguzhan , Buxton Elham Khorasani , Raeeszadeh-Sarmazdeh Maryam , Dalkilic Mehmet M 
Published: July 2024

Abstract
In this study, we introduce a novel de Bruijn graph (dBG) based framework for feature engineering in biological sequential data such as proteins. This framework simplifies feature extraction by dynamically generating high-quality, interpretable features for traditional AI (TAI) algorithms. Our framework accounts for amino acid substitutions by efficiently adjusting the edge weights in the dBG using a secondary trie structure. We extract motifs from the dBG by traversing the heavy edges, and then incorporate alignment algorithms like BLAST and Smith–Waterman to generate features for TAI algorithms. Empirical validation on TIMP (tissue inhibitors of matrix metalloproteinase) data demonstrates significant accuracy improvements over a robust baseline, state-of-the-art PLM models, and those from the popular GLAM2 tool. Furthermore, our framework successfully identified Glycine and Arginine-rich motifs with high coverage, highlighting it is potential in general pattern discovery.
Implementation of two-step gradual reset scheme for enhancing state uniformity of 2D hBN-based memristors for image processing
Authors: Woo Dong Yeon , Noh Gichang , Park Eunpyo , Kim Min Jee , Lee Dae Kyu , Sung Yong Woo , Kim Jaewook , Jeong YeonJoo , Park Jongkil , Park Seongsik , Jang Hyun Jae , Choi Nakwon , Jo Yooyeon , Kwak Joon Young 
Published: July 2024

Abstract
In-memory computing facilitates efficient parallel computing based on the programmable memristor crossbar array. Proficient hardware image processing can be implemented by utilizing the analog vector-matrix operation with multiple memory states of the nonvolatile memristor in the crossbar array. Among various materials, 2D materials are great candidates for a switching layer of nonvolatile memristors, demonstrating low-power operation and electrical tunability through their remarkable physical and electrical properties. However, the intrinsic device-to-device (D2D) variation of memristors within the crossbar array can degrade the accuracy and performance of in-memory computing. Here, we demonstrate hardware image processing using the fabricated 2D hexagonal boron nitride-based memristor to investigate the effects of D2D variation on the hardware convolution process. The image quality is evaluated by peak-signal-to-noise ratio, structural similarity index measure, and Pratt’s figure of merit and analyzed according to D2D variations. Then, we propose a novel two-step gradual reset programming scheme to enhance the conductance uniformity of multiple states of devices. This approach can enhance the D2D variation and demonstrate the improved quality of the image processing result. We believe that this result suggests the precise tuning method to realize high-performance in-memory computing.
Improving cycling performance of the NaNiO2 cathode in sodium-ion batteries by titanium substitution
Authors: An Siyu , Karger Leonhard , Dreyer Sören L , Hu Yang , Barbosa Eduardo , Zhang Ruizhuo , Lin Jing , Fichtner Maximilian , Kondrakov Aleksandr , Janek Jürgen , Brezesinski Torsten 
Published: July 2024

Abstract
O3-type layered oxide cathodes, such as NaNi0.5Mn0.5O2, have garnered significant attention due to their high theoretical specific capacity while using abundant and low-cost sodium as intercalation species. Unlike the lithium analog (LiNiO2), NaNiO2 (NNO) exhibits poor electrochemical performance resulting from structural instability and inferior Coulomb efficiency. To enhance its cyclability for practical application, NNO was modified by titanium substitution to yield the O3-type NaNi0.9Ti0.1O2 (NNTO), which was successfully synthesized for the first time via a solid-state reaction. The mechanism behind its superior performance in comparison to that of similar materials is examined in detail using a variety of characterization techniques. NNTO delivers a specific discharge capacity of ∼190 mAh g−1 and exhibits good reversibility, even in the presence of multiple phase transitions during cycling in a potential window of 2.0‒4.2 V vs. Na+/Na. This behavior can be attributed to the substituent, which helps maintain a larger interslab distance in the Na-deficient phases and to mitigate Jahn–Teller activity by reducing the average oxidation state of nickel. However, volume collapse at high potentials and irreversible lattice oxygen loss are still detrimental to the NNTO. Nevertheless, the performance can be further enhanced through coating and doping strategies. This not only positions NNTO as a promising next-generation cathode material, but also serves as inspiration for future research directions in the field of high-energy-density Na-ion batteries.
Implementing the information system for older adult patients post-discharge self-management: a qualitative study
Authors: Wang Dorothy Yingxuan , Wong Eliza Lai-Yi , Cheung Annie Wai-Ling , Tam Zoe Pui-Yee , Tang Kam-Shing , Yeoh Eng-Kiong 
Published: July 2024

Abstract

Background
Discharging older adult patients from the hospital poses risks due to their vulnerable conditions, complex instructions and limited health literacy. Insufficient information about medication side effects adds to patient concerns. To address this, a post-discharge information summary system was developed. While it has shown positive impacts, concerns exist regarding implementation fidelity.

Objective
This study employed a theory-driven approach to understand health providers’ perspectives on effective implementation.

Method
Individual semi-structured interviews were conducted via telephone with nurses, doctors and pharmacists from local public hospitals. All interviews were audio-recorded and transcribed verbatim. Theoretical Domains Framework (TDF) was applied for direct content analysis. Belief statements were generated by thematic synthesis under each of the TDF domains.

Results
A total of 98 participants were interviewed. Out of the 49 belief statements covering eight TDF domains, 19 were determined to be highly relevant to the implementation of the post-discharge information summary system. These TDF domains include knowledge, skills, social/professional role and identity, beliefs about consequences, intentions, memory, attention and decision processes, environmental context and resources and social influences.

Conclusion
Our study contributes to the understanding of determinants in implementing discharge interventions for older adult patients’ self-care. Our findings can inform tailored strategies for frontline staff, including aligning programme rationale with stakeholders, promoting staff engagement through co-creation, reinforcing positive programme outcomes and creating default settings. Future research should employ rigorous quantitative designs to examine the actual impact and relationships among these determinants.
Towards an explanation for ‘unexplained’ dizziness in older people
Authors: Castro Patricia , Ibitoye Richard , Ellmers Toby , Kaski Diego , Arshad Qadeer , Bronstein Adolfo M 
Published: July 2024

Abstract

Background
Subjective unsteadiness or dizziness, usually without increase in body sway, is common in older people. The absence of mechanistic understanding of such symptoms renders clinical management difficult. Here, we explore the mechanisms behind such idiopathic dizziness (ID), focusing on postural control abnormalities.

Methods
Thirty patients with ID and 30 age-matched controls stood on a moving platform. Platform oscillations were randomly delivered at different velocities (from 0 to 0.2 m/s). Markers of postural control, including objective sway (trunk sway path, recorded via a sensor attached to vertebrae C7), stepping responses, subjective instability and anxiety ratings were obtained. MRI scans were available for correlations with levels of cerebral small vessel disease in 28 patients and 24 controls.

Results
We observed a significant relationship between objective and subjective instability in all groups. The slope of this fit was significantly steeper for patients than controls, indicating greater perceived instability for the same body sway. Stepwise linear regression showed that the slopes of this objective–subjective instability relationship were best explained by concerns about falling (Falls Efficacy Scale-International), clinical physical functioning (Short Physical Performance Battery) and, to some degree, by neuroimaging markers of cerebral small vessel disease. In addition, patients had a reduced stepping threshold, suggesting an overly cautious postural response.

Conclusion
The distorted perception of instability and subtle impairments in balance control, including abnormal and overly cautious stepping responses, underlies the emergence of ID. It appears to relate to changes in postural performance, psychological functioning and disruption of postural brain networks associated with cerebral small vessel disease.
Assessing the quality of cause-of-death reporting before and during the COVID-19 pandemic
Authors: Tai Shu-Yu , Kawachi Ichiro , Lu Tsung-Hsueh 
Published: July 2024

Abstract
This study assessed the quality of cause-of-death reporting in the United States before and during the COVID-19 pandemic. We used the selection rate and the adjusted odds ratio (aOR) to analyze each cause identified by the National Center for Health Statistics as unsuitable for the underlying cause of death (UCOD). The selection rate was defined as the proportion of deaths with mention of a particular unsuitable UCOD on the death certificate where that cause was ultimately selected as the UCOD. Out of 36 unsuitable UCODs, 33 exhibited a significant decline in selection rates from 2019 to 2021. However, when deaths with mention of COVID-19 on the death certificate were excluded, only 19 causes revealed a significant decline. In analyses that controlled for the age of decedents, aORs in 2021 were significantly lower compared with 2019 for 26 causes, and this number decreased to 17 causes in analyses that excluded COVID-19-related deaths. In conclusion, the overall quality of COD reporting improved during the COVID-19 pandemic, attributable mainly to the fact that over one-tenth of the deaths were related to COVID-19. Yet, for deaths that did not involve COVID-19, improvements in the quality of COD reporting were less prominent for certain causes.
What is the Scope and Contribution of Lived Experience in Social Work? A Scoping Review
Authors: Parsell Cameron , Kuskoff Ella , Constantine Skye 
Published: July 2024

Abstract
Lived experience, or people’s expertise and perspective derived from their involvement with events and interventions, represents an important resource for social work. Despite the appeal, lived experience is an ambiguous concept and the way social work knowledge is informed by lived experience is difficult to grasp. This article reports on a scoping review that maps the social work academic literature to examine how lived experience is used to inform social work. Over a thirty-three year period (between 1990 and 2022), we identified 1,877 studies. Of these, 110 met the inclusion criteria and were analysed for this study. Most studies (52 per cent) were published between 2019 and 2022, and a majority (43 per cent) were published from research conducted in the UK. The studies contribute knowledge to social work practice, education, research, and about the practical management of lived experience. The results show that lived experience contributions benefit both social work and the people contributing. An important implication is the opportunity for social work to lead the changes required to enable lived experience contributions to continuously inform the profession and contribute to social work realising its aspirational version of itself.
Pyrolysis technologies for biochar production in waste management: a review
Authors: V Kavan Kumar , Panwar N L 
Published: July 2024

Abstract
Pyrolysis is a thermal conversion process in the absence of air to derive energy components from the residues. Renewable-energy technologies will play a major role in addressing future challenges related to environmental safety and energy security. One of the many easily available renewable energy sources is biomass—an organic material that is thought to be carbon-neutral. Pyrolysis technology is a thermochemical process that can be used to produce useful products from biomass, such as biochar, bio-oil and combustible pyrolysis gases. The structure and relative product yield are impacted by the pyrolysis method employed. This article evaluates different approaches for biomass pyrolysis. Fast, slow and advanced pyrolysis methods using various pyrolyser reactors have been studied in the literature and are provided to increase the variety and use of these methods in upcoming studies and research. Slow pyrolysis can lead to increased ecological well-being, as it increases the amount of biochar produced using auger and rotary-kiln reactors. Rapid pyrolysis, mainly in fluidized-bed reactors with bubbling and rotating circulation, can be used to obtain bio-oil. Advanced pyrolysis methods offer a good probability of yielding great prosperity for specific applications. The selection of a pyrolysis process is based on the required output in terms of solid, liquid and gaseous fuels, and the parameter plays a crucial role in the pyrolysis performance.