bluuebunny/crossref_metadata_2025_split huggingface dataset:
Transfer learning rolling bearing fault diagnosis model based on deep feature decomposition and class-level alignment
Authors: Dong Jingchuan , Jiang Hongyu , Su Depeng , Gao Yubo , Chen Tao , Sheng Kai 
Published: January 2024

Abstract
Research on transfer learning in rolling bearing fault diagnosis can help overcome challenges such as different data distributions and limited fault samples. However, most existing methods still struggle to address the zero-shot cross-domain problem within the same equipment and the few-shot cross-machine problem. In response to these challenges, this paper introduces a transfer learning rolling bearing fault diagnosis model based on deep feature decomposition and class-level alignment. The model consists of two stages. In the first stage, the original vibration signals undergo continuous wavelet transform to obtain time–frequency diagram. Subsequently, a convolutional neural network extracts features from the diagram. The obtained deep features are decomposed into four types: uncertain features, domain-shared features, domain-specific features and category features. Multiple loss functions are then employed to remove extraneous features beyond the category features. In the second stage, category features are further extracted, and convolutional block attention module is introduced to further reduce the potential interference of unexcluded irrelevant information within the category features with classification results. Simultaneously applying a class-level alignment strategy effectively alleviates inter-domain class distribution discrepancies. Experimental validation was conducted on three distinct datasets, revealing a significant improvement in the classification performance of the proposed method over alternative methods. Furthermore, the model demonstrated robustness and noise resistance.
Density-controlled electrochemical synthesis of ZnO nanowire arrays using nanotextured cathode
Authors: Eom Hyeonjin , Hur Junyoung , Sung Sang-Keun , Jeong Jun-Ho , Park Inkyu 
Published: January 2024

Abstract
Zinc oxide (ZnO) nanowires fabricated via wet chemical synthesis on flexible polymer substrates are inherently unstable against mechanical bending stress because of their high density and weak adhesion to the substrate. We introduce a novel method for controlling the density of such ZnO nanowire arrays using a three-dimensional corrugated metal substrate. These metal substrates, featuring extruded and recessed patterns fabricated via nanoimprint lithography, were employed as cathodes during the electrochemical deposition of ZnO nanowire arrays. The ZnO nanowire arrays synthesized on the patterned metal thin film exhibited smaller diameters and lower densities compared to those on non-patterned metal films. This reduction in density can be attributed to aligned nucleation and limited growth on the patterned metal surface. Crucially, ZnO nanowires synthesized on patterned metal substrates displayed remarkable mechanical robustness against external forces, a direct consequence of their reduced density. In contrast, nanowires synthesized on non-patterned metal substrates were broken under mechanical bending. Detailed morphological analyses performed after mechanical bending tests confirm that ZnO nanowires synthesized on nanoimprinted metal electrodes exhibited enhanced mechanical characteristics compared to those on non-patterned metal electrodes. These findings clearly demonstrate the promise of utilizing density-controlled ZnO nanowires in piezoelectric devices.
An efficient modeling workflow for high-performance nanowire single-photon avalanche detector
Authors: Li Zhe , Tan Hark Hoe , Jagadish Chennupati , Fu Lan 
Published: January 2024

Abstract
Single-photon detector (SPD), an essential building block of the quantum communication system, plays a fundamental role in developing next-generation quantum technologies. In this work, we propose an efficient modeling workflow of nanowire SPDs utilizing avalanche breakdown at reverse-biased conditions. The proposed workflow is explored to maximize computational efficiency and balance time-consuming drift-diffusion simulation with fast script-based post-processing. Without excessive computational effort, we could predict a suite of key device performance metrics, including breakdown voltage, dark/light avalanche built-up time, photon detection efficiency, dark count rate, and the deterministic part of timing jitter due to device structures. Implementing the proposed workflow onto a single InP nanowire and comparing it to the extensively studied planar devices and superconducting nanowire SPDs, we showed the great potential of nanowire avalanche SPD to outperform their planar counterparts and obtain as superior performance as superconducting nanowires, i.e. achieve a high photon detection efficiency of 70% with a dark count rate less than 20 Hz at non-cryogenic temperature. The proposed workflow is not limited to single-nanowire or nanowire-based device modeling and can be readily extended to more complicated two-/three dimensional structures.
Two-dimensional reductions of the Whitham modulation system for the Kadomtsev–Petviashvili equation
Authors: Biondini Gino , Bivolcic Alexander J , Hoefer Mark A , Moro Antonio 
Published: January 2024

Abstract
Two-dimensional reductions of the Kadomtsev–Petviashvili(KP)–Whitham system, namely the overdetermined Whitham modulation system for five dependent variables that describe the periodic solutions of the KP equation, are studied and characterized. Three different reductions are considered corresponding to modulations that are independent of x, independent of y, and of t (i.e. stationary), respectively. Each of these reductions still describes dynamic, two-dimensional spatial configurations since the modulated cnoidal wave, generically, has a nonzero speed and a nonzero slope in the xy plane. In all three of these reductions, the integrability of the resulting systems of equations is proven, and various other properties are elucidated. Compatibility with conservation of waves yields a reduction in the number of dependent variables to two, three and four, respectively. As a byproduct of the stationary case, the Whitham modulation system for the classical Boussinesq equation is explicitly obtained.
A systematic review on automatic identification of insomnia
Authors: Ingle Manisha , Sharma Manish , Kumar Kamlesh , Kumar Prince , Bhurane Ankit , Elphick Heather , Joshi Deepak , Acharya U Rajendra 
Published: January 2024

Abstract

Background. Insomnia is a prevalent sleep disorder characterized by difficulties in initiating sleep or experiencing non-restorative sleep. It is a multifaceted condition that impacts both the quantity and quality of an individual’s sleep. Recent advancements in machine learning (ML), and deep learning (DL) have enabled automated sleep analysis using physiological signals. This has led to the development of technologies for more accurate detection of various sleep disorders, including insomnia. This paper explores the algorithms and techniques for automatic insomnia detection. Methods. We followed the recommendations given in the Preferred Reporting Items for systematic reviews and meta-analyses (PRISMA) during our process of content discovery. Our review encompasses research papers published between 2015 and 2023, with a specific emphasis on automating the identification of insomnia. From a selection of well-regarded journals, we included more than 30 publications dedicated to insomnia detection. In our analysis, we assessed the performance of various methods for detecting insomnia, considering different datasets and physiological signals. A common thread across all the papers we reviewed was the utilization of artificial intelligence (AI) models, trained and tested using annotated physiological signals. Upon closer examination, we identified the utilization of 15 distinct algorithms for this detection task. Results. The major goal of this research is to conduct a thorough study to categorize, compare, and assess the key traits of automated systems for identifying insomnia. Our analysis offers complete and in-depth information. The essential components under investigation in the automated technique include the data input source, objective, ML and DL network, training framework, and references to databases. We classified pertinent research studies based on ML and DL model perspectives, considering factors like learning structure and input data types. Conclusion. Based on our review of the studies featured in this paper, we have identified a notable research gap in the current methods for identifying insomnia and opportunities for future advancements in the automation of insomnia detection. While the current techniques have shown promising results, there is still room for improvement in terms of accuracy and reliability. Future developments in technology and machine learning algorithms could help address these limitations and enable more effective and efficient identification of insomnia.
Assessment of bioimpedance spectroscopy devices: a comparative study and error analysis of gold-plated copper electrodes
Authors: Mussnig Sebastian , Krenn Simon , Hecking Manfred , Wabel Peter 
Published: January 2024

Abstract

Objective. Bioimpedance spectroscopy (BIS) is a non-invasive diagnostic tool to derive fluid volume compartments from frequency dependent voltage drops in alternating currents by extrapolating to the extracellular resistance (R
0) and intracellular resistance (R
i). Here we tested whether a novel BIS device with reusable and adhesive single-use electrodes produces results which are (in various body positions) equivalent to an established system employing only single-use adhesive electrodes. Approach. Two BIS devices (‘Cella’ and the ‘Body Composition Monitor’ [BCM]) were compared using four dedicated resistance testboxes and by measuring 40 healthy volunteers. In
vivo comparisons included supine wrist-to-ankle (WA) reference measurements and wrist-to-wrist (WW) measurements with pre-gelled silver/silver-chloride (Ag/AgCl) electrodes and WW measurements with reusable gold-plated copper electrodes. Main results. Coefficient of variation were <1% for all testbox measurements with both BIS devices. Accuracy was within ±1% of true resistance variability, a threshold which was only exceeded by the Cella device for all resistances in a testbox designed with a low R
0/R
i ratio. In
vivo, WA-BIS differed significantly between BIS devices (p < 0.001). Reusable WW electrodes exhibited larger resistances than WW-BIS with Ag/AgCl electrodes (R
0: 738.36 and 628.69 Ω; R
i: 1508.18 and 1390 Ω) and the relative error varied from 7.6% to 31.1% (R
0) and −15.6% to 37.3% (R
i). Significance. Both BIS devices produced equivalent resistances measurements but different estimates of body composition both in
silico and in WA setups in
vivo, suggesting that the devices should not be used interchangeably. Employing WW reusable electrodes as opposed to WA and WW measurement setups with pre-gelled Ag/AgCl electrodes seems to be associated with measurement variations that are too large for safe clinical use. We recommend further investigations of measurement errors originating from electrode material and current path.
Characterization of a functionally graded material produced using a new designed blender
Authors: Rahmani Kaveh , Majzoobi G H , Das R 
Published: January 2024

Abstract
One of the techniques widely used for fabrication of FGMs (Functionally Graded Materials) is powder metallurgy. However, producing a FGM with smooth gradient in its constituents mixture has remained a challenge yet to be met. The purpose of this research is design and manufacturing a blender for producing a functionally graded material with smooth gradient of its constituent materials. The blender was used to mix magnesium and titanium dioxide to produce Magnesium/titanium dioxide FGMs. The powder mixture was then compacted by Instron, drop hammer, and split Hopkinson bar at 600 °C. The microstructure and relative density of the functionally graded samples fabricated at different strain rates were studied in this work. SEM images clearly demonstrated a continuous and functional distribution of Mg and TiO2 particles in the produced FGM samples. It was also shown that increasing the compaction loading rate resulted in a higher relative density in the FGM samples. The maximum relative density (92%) was observed for the samples produced by Hopkinson bar. This level of density was 7% and 17% higher than the densities of the samples fabricated using drop hammer and Instron, respectively. The improvement of relative density in dynamic compaction methods is attributed to the increased compaction pressure and process being adiabatic.
Study of optical and structural properties of CZTS thin films using copper capping layer and sulfurization to correct stoichiometry
Authors: Bhale Devendra , Bute Arundhati , Tokas R B , Phatak Rohan , Vishwanadh B , Sharma R K , Biswas Arup , Bhattacharyya Dibyendu , Maiti Namita 
Published: January 2024

Abstract
Non-toxic, environment friendly and low cost Cu2ZnSnS4 (CZTS) thin films were deposited on sodalime glass substrates by radio frequency magnetron sputter deposition technique. Stoichiometry analysis revealed that the as-deposited films were Cu deficient. Hence, Cu capping layer was deposited, to study its’ effect on optical and structural properties of the films. These films were subsequently sulfurized in a quartz setup. Ultimately, stoichiometric CZTS films with improved Cu content up to 27.4% were obtained. The kesterite CZTS phase was identified in the samples using x-ray diffraction measurements. The morphological studies revealed that, grain size varies as a function of the thickness of Cu capping layer. The formation of undesired Cu2S and ZnS phases were also observed in the film with Cu content above and below the optimum range. Optical bandgap (Eg) of the films could also be optimized by controlling the Cu content in the film. An optimal Cu content between 24% and 26% was found to be appropriate for achieving a desired bandgap within a range of 1.55 to 1.4 eV, suitable for application in thin film solar cells. The CZTS films with corrected stoichiometry have a charge carrier density in the rage of ∼1021 cm−3, which are much better than values reported so far in the literature.
Calorimetry measurement for energy balance and energy distribution in WEST for L-mode plasmas
Authors: Gaspar J. , Gerardin J. , Corre Y. , Daumas J. , Fedorczak N. , Lamaison V. , Bourdelle C. , Ciraolo G. , Ekedahl A. , Gallo A. , Rivals N. , Tsitrone E. ,   the WEST Team
Published: January 2024

Abstract
This paper presents the energy balance of 602 pulses from four different experimental campaigns for the WEST tokamak. Different magnetic configurations have been studied, with lower single null (LSN) and upper single null (USN) configuration with deuterium or helium plasmas. The energy balance is closed with an imbalance of about 5% of the total injected energy for most of the campaigns and for different magnetic configurations. The distribution over the whole machine is shown, with the outer first wall receiving most of the energy due to its large surface area with about 30% of the total heat load, and the divertor with 25% due to the heat loads deposited by the convected power in the scrape-off layer (SOL). Finally, the tomography inversion of the bolometry measurement allows us to disentangle the contribution of the radiated and convected power in the energy absorbed by each type of plasma-facing component. We show that in the USN configuration about 63% of the available energy in the SOL is deposited in the upper divertor (UDIV) through convected heat loads, while in LSN this value is spread over the lower divertor with 45% and the baffle and UDIV with about 10% for both.
The effectiveness of enhanced Moringa oleifera leaves extract powder (MOLEP) in beef patties as a fortification on a well-done cooking method
Authors: Swastike W , Kartikasari L R , Nuhriawangsa A M P , Barido F H , Hertanto B S 
Published: January 2024

Abstract
Fast food like patties has become more popular with the lifestyle changes. However after the pandemic, consumers have shown an increased interest in functional foods that not only provide essential nutrients but also possess potential health-promoting properties. The tree that is rich in nutrition and also has health benefits is Moringa oleifera called the “miracle tree”. The cooking method used when serving will also help to keep you safe and will have an impact on beef patties quality. This study investigates the effectiveness of enhanced Moringa oleifera leaves powder in beef patties on the well-done cooking method. The ingredients used in this study were beef ground meat, Moringa oleifera leaves extract powder (MOLEP), tapioca meal, seasonings, salt, sugar, egg whites, and cold water. The commercial beef meat that used as a raw material of beef patties with the ratio of MOLEP as fortification are 0 (P0), 1 (P1), 2 (P2), 3 (P2), and 4% (P4). The data were analyzed using analysis of variance (ANOVA). The results of the analysis showed that there was a significant difference (P<0.05) in the pH value after cooking, diameter patties after cooking, Springiness, Water holding capacity, and also cooking loss. The general conclusion of this research are The general conclusion of this research is that MOLEP can be applied and enhanced into beef patties as a fortification but not more than 0,3%. Enhancement of MOLEP in beef patties can inhibit the shrinkage of the product when it is cooked in a well-done cooking method.
Improving cross-subject classification performance of motor imagery signals: a data augmentation-focused deep learning framework
Authors: Ozelbas Enes , Tülay Emine Elif , Ozekes Serhat 
Published: January 2024

Abstract
Motor imagery brain-computer interfaces (MI-BCIs) have gained a lot of attention in recent years thanks to their potential to enhance rehabilitation and control of prosthetic devices for individuals with motor disabilities. However, accurate classification of motor imagery signals remains a challenging task due to the high inter-subject variability and non-stationarity in the electroencephalogram (EEG) data. In the context of MI-BCIs, with limited data availability, the acquisition of EEG data can be difficult. In this study, several data augmentation techniques have been compared with the proposed data augmentation technique adaptive cross-subject segment replacement (ACSSR). This technique, in conjunction with the proposed deep learning framework, allows for a combination of similar subject pairs to take advantage of one another and boost the classification performance of MI-BCIs. The proposed framework features a multi-domain feature extractor based on common spatial patterns with a sliding window and a parallel two-branch convolutional neural network. The performance of the proposed methodology has been evaluated on the multi-class BCI Competition IV Dataset 2a through repeated 10-fold cross-validation. Experimental results indicated that the implementation of the ACSSR method (80.47%) in the proposed framework has led to a considerable improvement in the classification performance compared to the classification without data augmentation (77.63%), and other fundamental data augmentation techniques used in the literature. The study contributes to the advancements for the development of effective MI-BCIs by showcasing the ability of the ACSSR method to address the challenges in motor imagery signal classification tasks.
Briefing: How a mutiny became a(nother) coup: The politics of counterinsurgency and international military partnerships in Niger
Authors: McCullough Aoife , Sandor Adam 
Published: January 2024

Abstract
This briefing explores the factors that led to the consolidated position of the Nigerien Security and Defence forces in support of the coup d’état launched the night of 26th July 2023. The initial blockade of the former President’s palace by the head of the Presidential Guard and 350 of its members looked like a mutiny. However, instead of moving swiftly to end the blockade, commanders from across the security forces appeared on national television that night to announce a coup. We show how dominant theories that have been used to explain the coup’s (unlikely) success, including those relying on patrimonialism and coups as intra-military coordination games, do not adequately explain the support for the coup across rank and file, and the direction the coup took once announced. We argue that this coup cannot be fully explained without considering how the coup leaders advanced powerful ideological messages to garner support. By making the coup about ‘correcting’ the government’s counter-insurgency strategy and regaining Niger’s sovereignty through the removal of French counter-terrorism troops from the country, members of the military believed that everyone else would support the coup, thus making it less likely that they would resist the coup leaders.
Development of the lyrics-based deep learning algorithm for identifying alcohol-related words (LYDIA)
Authors: Bonela Abraham Albert , He Zhen , Luxford Dan-Anderson , Riordan Benjamin , Kuntsche Emmanuel 
Published: January 2024

Abstract

Background
Music is an integral part of our lives and is often played in public places like restaurants. People exposed to music that contained alcohol-related lyrics in a bar scenario consumed significantly more alcohol than those exposed to music with less alcohol-related lyrics. Existing methods to quantify alcohol exposure in song lyrics have used manual annotation that is burdensome and time intensive. In this paper, we aim to build a deep learning algorithm (LYDIA) that can automatically detect and identify alcohol exposure and its context in song lyrics.

Methods
We identified 673 potentially alcohol-related words including brand names, urban slang, and beverage names. We collected all the lyrics from the Billboard’s top-100 songs from 1959 to 2020 (N = 6110). We developed an annotation tool to annotate both the alcohol-relation of the word (alcohol, non-alcohol, or unsure) and the context (positive, negative, or neutral) of the word in the song lyrics.

Results
LYDIA achieved an accuracy of 86.6% in identifying the alcohol-relation of the word, and 72.9% in identifying its context. LYDIA can distinguish with an accuracy of 97.24% between the words that have positive and negative relation to alcohol; and with an accuracy of 98.37% between the positive and negative context.

Conclusion
LYDIA can automatically identify alcohol exposure and its context in song lyrics, which will allow for the swift analysis of future lyrics and can be used to help raise awareness about the amount of alcohol in music.
Highlights Developed a deep learning algorithm (LYDIA) to identify alcohol words in songs. LYDIA achieved an accuracy of 86.6% in identifying alcohol-relation of the words. LYDIA’s accuracy in identifying positive, negative, or neutral context was 72.9%. LYDIA can automatically provide evidence of alcohol in millions of songs. This can raise awareness of harms of listening to songs with alcohol words.