bluuebunny/crossref_metadata_2025_split huggingface dataset:
The symbiotic relationship and collaboration between commercial banks and fintechs in Turkey
Authors: Nabiyev Abdul Baghi , Ovenc Gokhan 
Published: December 2023

AbstractFintech startups have forced traditional banks to change their business models using new IT-enabled technologies. These startups have become a potential threat to the financial industry in recent years. Therefore, traditional banks form strategic alliances with fintech startups to gain a competitive advantage and improve their technological infrastructure. This study examines which form of collaboration banks prefer with fintech firms and in which segment these firms operate. Using a hand-collected dataset, the probit model is applied to analyze 430 bank-fintech alliances in Turkey in 2021. The results indicate that in Turkey, large banks tend to collaborate with fintech startups in a product-related manner rather than investing in them. Large banks prefer to form partnerships with non-payment fintech startups rather than payment service fintechs. Lastly, young fintech startups mainly operate in payment services, and those established in Istanbul are likely to form strategic partnerships with commercial banks in this area.
Impact of chilling injury on global trade in tropical products
Authors: Heyes Julian A. 
Published: December 2023

Abstract
International trade in fresh products is dominated by products capable of long-term refrigerated storage, allowing marketing flexibility. For tropical products, postharvest chilling injury (PCI) is a barrier to long-term storage that limits trade by exacerbating the already-challenging requirements to minimise physical or biotic damage to products. In this review, the practical consequences of PCI in terms of international trade are described. Options for choosing to grow more resistant varieties or to predict the chilling injury risk of batches of fresh products can reduce overall losses by allowing better-informed sequencing of product marketing. Recent advances in technologies that reduce PCI risk or predict its severity, some of which can be used before harvest, are reviewed. At a more fundamental level, there is a growing number of excellent reviews on the potential for gene editing to alleviate postharvest chilling injury in tropical fruits and vegetables. Most are focused on research into ways to alleviate the consequences of chilling injury: i.e. oxidative stress arising from membrane dysfunction. A few recent reports show that reducing membrane dysfunction itself is possible, for example by increasing desaturation of membrane lipids, but this approach comes with a new and predictable problem: increased preharvest susceptibility to heat stress. Refined suggestions for strategies that could produce durable improvements in product quality hold the potential for significant increases in global trade with resulting economic benefits, particularly for developing countries.
Size effects on atomic collapse in the dice lattice
Authors: Oriekhov D O , Voronov S O 
Published: December 2023

Abstract
We study the role of size effects on atomic collapse of charged impurity in the flat band system. The tight-binding simulations are made for the dice lattice with circular quantum dot shapes. It is shown that the mixing of in-gap edge states with bound states in impurity potential leads to increasing the critical charge value. This effect, together with enhancement of gap due to spatial quantization, makes it more difficult to observe the dive-into-continuum phenomenon in small quantum dots. At the same time, we show that if in-gap states are filled, the resonant tunneling to bound state in the impurity potential might occur at much smaller charge, which demonstrates non-monotonous dependence with the size of sample lattice. In addition, we study the possibility of creating supercritical localized potential well on different sublattices, and show that it is possible only on rim sites, but not on hub site. The predicted effects are expected to naturally occur in artificial flat band lattices.
An optimized Laplacian of Gaussian filter using improved sparrow search algorithm for bearing fault extraction
Authors: Feng Kezhu , Yang Rongrong , Wei Zhongbin 
Published: December 2023

Abstract
Precise detection of fault characteristics in rolling bearings is imperative for machine health management. However, due to the presence of interfering components including noise and periodic components caused by vibration sources, the extraction of weak fault-related information cannot be achieved precisely. In this study, we propose an optimized Laplacian of Gaussian (LoG) filtering technique to handle this issue. The proposed algorithm utilizes the envelope entropy and Gini of square envelope as an objection function to optimize two important parameters, namely standard deviation and filter order of the LoG filter, through an improved sparrow search algorithm (SSA) named adaptive spiral flying SSA. Afterward, the LoG filtering method with the optimal parameters is employed to filter the raw vibration data. Finally, the filtered signal undergoes envelope analysis for fault feature detection. A simulated test and two case studies demonstrate the effectiveness and superiority of the LoG technique.
LiDAR-SLAM loop closure detection based on multi-scale point cloud feature transformer
Authors: Wang Shaohua , Zheng Dekai , Li Yicheng 
Published: December 2023

Abstract
Loop closure detection is an important part of simultaneous localization and mapping (SLAM), used to detect and correct map and trajectory drift issues caused by accumulated errors. For the LiDAR-SLAM system, the sparsity and disorder of point clouds make position recognition based on 3D point clouds more challenging. In recent years, many deep learning-based closed-loop detection algorithms have been proposed. However, due to the sparsity of point clouds, current deep learning algorithms often convert point clouds into histograms or depth maps and then process them using deep learning algorithms, undoubtedly causing information loss. In this paper, we propose a closed-loop detection method based on multi-scale point cloud features transformer, which introduces multi-scale point cloud feature extraction and transformer global context modeling. We use voxel sparse convolution to obtain features of original point clouds at different resolutions and establish contextual relationships between features at different resolutions using the transformer network to achieve multi-scale feature fusion, and then obtain global descriptors. The obtained global descriptors can be used not only for closed-loop detection but also for front-end registration to address the challenges of point cloud processing in the SLAM system, especially in enhancing global modeling capabilities and reducing information loss. Our method directly processes point cloud data and integrates multi-scale point cloud feature information, which can better adapt to the characteristics of LiDAR-SLAM systems, improving the accuracy and robustness of localization and map construction, thus having broad application prospects in the field of measurement. We evaluated our method on multiple sequences of the KITTI and KITTI-360 datasets, each containing more than 5000 frames of point clouds, and extensively evaluated on a self-collected dataset of over 3.6 km. The experimental results show that our method achieves an accuracy of over 80% on multiple datasets and demonstrates superior performance in different environments.
A self-healing artificial muscle was realized by fitting the electrode membrane with the self-healing actuating membrane with a folded structure
Authors: Wang Junyao , Chen Yansong , Wang Jing , Liu Huan , Lang Tianhong , Hou Qi , Wang Rui , Cui Bowen , Quan Jingran , Pan Hongxu , Yang Hanbo , Xu Jianxin , Liu Yahao , Wang Baoliang 
Published: December 2023

Abstract
As a part of biomimetic gelatinous polymer actuator (BGPA), hydrogel artificial muscle has the advantages of extreme flexibility, low driving voltage and controllable driving direction. However, such artificial muscles do not have self-healing properties and it is difficult to continue using them if they break, which considerably reduces their lifespan. In this paper, we propose a hydrogel artificial muscle with self-healing capability by gluing a membrane of electrodes with a pleated structure to a self-healing actuator layer. The crosslinking reaction between polyacrylic acid molecular chains and carboxylated chitosan (CLC) molecular chains was utilized to fabricat e self-healing actuator layers, while multi-walled carbon nanotubes and chitosan were employed for electrode films. The impact of CLC doping content on the self-healing properties, mechanical properties, electrical response output force properties, and electrochemical properties of self-healing artificial muscles was investigated. Experimental results demonstrated that the output force density of the self-healing artificial muscle could reach 14.7 mN g−1 with an addition of 0.2 g CLC; even after fracture-self-healing, the maximum output force density of the artificial muscle still remained above 90%, and the maximum stretching stress of the actuator film maintained a range from 91% to 99%, showcasing exceptional self-healing performance.
The Impact of Serpentinization on the Initial Conditions of Satellite Forming Collisions of Large Kuiper Belt Objects
Authors: Farkas-Takács Anikó , Kiss Csaba 
Published: December 2023

Abstract
Kuiper Belt objects are thought to be formed at least a few million years after the formation of calcium–aluminum-rich inclusions (CAIs), at a time when the 26Al isotope—the major source of radiogenic heat in the early solar system—had significantly depleted. The internal structure of these objects is highly dependent on any additional source that can produce extra heat in addition to that produced by the remaining, long-lasting radioactive isotopes. In this paper, we explore how serpentinization, the hydration of silicate minerals, can contribute to the heat budget and to what extent it can modify the internal structure of large Kuiper Belt objects. We find that the extent of restructuring depends very strongly on the start time of the formation process, the size of the object, and the starting ice-to-rock ratio. Serpentinization is able to restructure most of the interior of all objects in the whole size range (400–1200 km) and ice-to-rock ratio range investigated if the process starts early, ∼3 Myr after CAI formation, potentially leading to a predominantly serpentine core much earlier than previously thought (≤5 Myr versus several tens of million years). While the ratio of serpentinized material gradually decreases with the increasing formation time, the increasing ice-to-rock ratio, and the increasing start time of planetesimal formation in the outer solar system, in the case of the largest objects a significant part of the interior will be serpentinized even if the formation starts relatively late, ∼5 Myr after CAI formation. Therefore it is feasible that the interior of planetesimals may have contained a significant amount of serpentine, and in some cases, it could have been a dominant constituent, at the time of satellite-forming impacts.
Influence of low-energy supersymmetric vector-like quirk particles on W mass increment and muon g–2 anomaly*
Authors: Zhou 周 Ping 平 
Published: December 2023

Abstract
In the low energy realization of the quirk assisted Standard Model, the couplings between the exotic particles "quirks" and gauge bosons may contribute to the W mass and muon

anomaly reported by FermiLab. We calculate the contributions from supersymmetric quirk particles as an example. By imposing the theoretical constraints, we determined that the CDF II W-boson mass increment strictly constrains the mixing and coupling parameters and the quirk mass

, while the muon

anomaly cannot be solely attributed to the involvement of exotic particles, considering their significantly large masses.
Strong decay of
in light cone sum rules*
Authors: Xie 谢 Yiling 易凌 , Sun 孙 Hao 昊 
Published: December 2023

Abstract
In this study, we assign the tetraquark state for the

resonance and investigate the mass and decay constant of

in the framework of SVZ sum rules through a different calculation technique. Then, we calculate the strong coupling

by considering soft-meson approximation techniques within the framework of light cone sum rules, and we use the strong coupling

to obtain the width of the decay

. Our prediction for the mass agrees with the experimental measurement, and that for the decay width of

is within the upper limit.
Machine learning-based real-time kinetic profile reconstruction in DIII-D
Authors: Shousha Ricardo , Seo Jaemin , Erickson Keith , Xing Zichuan , Kim SangKyeun , Abbate Joseph , Kolemen Egemen 
Published: December 2023

Abstract
Kinetic equilibrium reconstruction plays a vital role in the physical analysis of plasma stability and control in fusion tokamaks. However, the traditional approach is subjective and prone to human biases. To address this, the consistent automatic kinetic equilibrium reconstruction (CAKE) method was introduced, providing objective results. Nonetheless, its offline nature limits its application in real-time plasma control systems (PCSs). To address this limitation, we present RTCAKENN, a machine learning model that approximates 7 CAKE-level output profiles, namely pressure, inverse q, toroidal current density, electron temperature and density, carbon ion impurity temperature and rotation profiles, using real-time available inputs. The deep neural network consists of an encoder layer, where the scalars and interdependent inputs such as plasma boundary coordinates and motional Stark effect data are encoded using multi-layer perceptrons (MLPs), while profile inputs are encoded by 1D convolutional layers. The encoded data is passed through a MLP for latent feature extraction, before being decoded in the decoding layers, which consist of upsampling and convolutional layers. RTCAKENN has been implemented in the DIII-D PCS and our model achieves accuracy comparable to CAKE and surpasses existing real-time alternatives. Through clever dropout training, RTCAKENN exhibits robustness and can operate even in the absence of Thomson scattering data or charge exchange recombination data. It executes in under 8 ms in the real-time environment, enabling future application in real-time control and analysis.
Ultrafast erbium-doped fiber laser using electrodeposition coated MoS2 thin film as saturable absorber
Authors: Hong Jason Lee Lian , Tiu Zian Cheak , Batumalay Malathy , Harith Zuraidah , Diblawe Abdulkadir Mukhtar , Anand T Joseph Sahaya , Harun Sulaiman Wadi 
Published: December 2023

Abstract
In this work, we have experimentally demonstrated the ultrafast laser in Erbium-doped fiber laser using MoS2 as saturable absorber. The MoS2 SA in this work is prepared by bottom-up method. A three-electrode configuration is used to perform the electrodeposition to coat the MoS2 onto conductive film. The MoS2 saturable absorber is incorporated into the fiber laser system to induce mode-locking operation centered at 1560.8 nm. The proposed saturable absorber has achieved pulse width of 1.47 ps, with highest average pulse energy of 0.44 nJ and repetition rate of 1.88 MHz. The venture of electrodeposition coated MoS2 thin film as saturable absorber is potentially to pave the foundation toward the sustainable industry, innovation and infrastructure.
Improving bonding strength and reliability of brazed titanium/copper dissimilar joint using vanadium interlayer
Authors: Mengen Liu , Li Bai 
Published: December 2023

Abstract
Conventional practice of brazing titanium (Ti) to copper (Cu) using Ag-28Cu eutectic filler alloy is of limited success because of the generation of brittle Ti-Cu intermetallic compounds (IMCs). The bonding strength is unsatisfactory owing to embrittlement of IMCs, and it is difficult to reproduce the bonding strength as the joint microstructure is highly sensitive to brazing parameters. In the current study, it was demonstrated that the formation of such undesirable IMCs can be suppressed and strong Ti/Cu joint can be obtained by brazing at 850 °C using a refractory vanadium (V) interlayer 15 μm in thickness deposited on Ti substrate, in combination with an Ag-Cu-Ti active filler alloy. The V interlayer successfully blocked the interaction between Ti and filler alloy. Bonding mechanisms of the resultant joint can be deduced to be a synergy of active brazing in the V/Ag-Cu-Ti/Cu half and solid-state diffusion bonding at the Ti/V interface. The thus developed joint was comprised of continuous Ti-V solid solution, unconsumed V interlayer and remaining filler alloy. Attributed to elimination of brittle IMCs at joint interface, excellent bonding strength of ∼220 MPa which is comparable with strength of Cu base metal was achieved. Moreover, since the joint evolution was dominated by sluggish solid-state diffusion at the Ti/V interface and very limited erosion of V interlayer by the Ag-Cu-Ti filler, desirable IMC free joint can be obtained over a wide range of brazing time (2 ∼ 5 min), enabling outstanding reliability of the high bonding strength.
Inferring comparative advantage via entropy maximization
Authors: Bruno Matteo , Mazzilli Dario , Patelli Aurelio , Squartini Tiziano , Saracco Fabio 
Published: December 2023

Abstract
We revise the procedure proposed by Balassa to infer comparative advantage, which is a standard tool in Economics to analyze specialization (of countries, regions, etc). Balassa’s approach compares a country’s export of a given product with what would be expected from a benchmark based on the total volumes of countries and product flows. Based on results in the literature, we show that implementing Balassa’s idea leads to conditions for estimating parameters conflicting with the information content of the model itself. Moreover, Balassa’s approach does not implement any statistical validation. Hence, we propose an alternative procedure to overcome such a limitation, based upon the framework of entropy maximization and implementing a proper test of hypothesis: the ‘key products’ of a country are, now, the ones whose production is significantly larger than expected, under a null-model constraining the same amount of information defining Balassa’s approach. What we found is that country diversification is always observed, regardless of the strictness of the validation procedure. Besides, the ranking of countries’ fitnesses is only partially affected by the details of the validation scheme employed for the analysis while large differences are found to affect the rankings of product complexities. The routine for implementing the entropy-based filtering procedures employed here is freely available through the official Python Package Index PyPI.
MLGN: multi-scale local-global feature learning network for long-term series forecasting
Authors: Jiang Maowei , Wang Kai , Sun Yue , Chen Wenbo , Xia Bingjie , Li Ruiqi 
Published: December 2023

Abstract
Although Transformer-based methods have achieved remarkable performance in the field of long-term series forecasting, they can be computationally expensive and lack the ability to specifically model local features as CNNs. CNN-based methods, such as temporal convolutional network (TCN), utilize convolutional filters to capture local temporal features. However, the intermediate layers of TCN suffer from a limited effective receptive field, which can result in the loss of temporal relations during global feature extraction.To solve the above problems, we propose to combine local features and global correlations to capture the overall view of time series (e.g. fluctuations, trends). To fully exploit the underlying information in the time series, a multi-scale branch structure is adopted to model different potential patterns separately. Each pattern is extracted using a combination of interactive learning convolution and causal frequency enhancement to capture both local features and global correlations. Furthermore, our proposed method,multi-scale local-global feature learning network (MLGN), achieves a time and memory complexity of O(L) and consistently achieve state-of-the-art results on six benchmark datasets. In comparision with previous best method Fedformer, MLGN yields 12.98% and 11.38% relative improvements for multivariate and univariate time series, respectively. Our code and data are available on Github at https://github.com/Zero-coder/MLGN.
A stable undoped low-voltage memristor cell based on Titania (TiOx)
Authors: Ullah Fateh , Tarkhan Mahdi , Fredj Zina , Su Yi , Wang Tianjun , Sawan Mohamad 
Published: December 2023

Abstract
An asymmetric memristive device fabricated with a titania (TiOx)-based switching layer deposited through atomic layer deposition with a thickness of ∼37 nm was investigated. X-ray photoelectron spectroscopy and high-resolution transmission electron microscopy coupled with energy-dispersive x-ray spectroscopy were employed for device structural characterization. A unipolar resistive switching behavior (both at positive and negative voltages) was observed through the memristor’s current–voltage characteristics. A remarkably smaller forming voltage (from the top Pt electrode to the grounded Au electrode) of 0.46 V was achieved, while it approached (positive bias from the Au electrode and holding Pt electrode as grounded) 0.25 V, which is a much smaller forming voltage than has ever been reported for titanium-based oxides without doping. The retention and endurance characterization over 2000 switching cycles were satisfactory without degradation.
Comprehensive whole-genome analyses of the UK Biobank reveal significant sex differences in both genotype missingness and allele frequency on the X chromosome
Authors: Chen Desmond Zeya , Roshandel Delnaz , Wang Zhong , Sun Lei , Paterson Andrew D 
Published: December 2023

Abstract
The UK Biobank is the most used dataset for genome-wide association studies (GWAS). GWAS of sex, essentially sex differences in minor allele frequencies (sdMAF), has identified autosomal SNPs with significant sdMAF, including in the UK Biobank, but the X chromosome was excluded. Our recent report identified multiple regions on the X chromosome with significant sdMAF, using short-read sequencing of other datasets. We performed a whole genome sdMAF analysis, with ~410 k white British individuals from the UK Biobank, using array genotyped, imputed or exome sequencing data. We observed marked sdMAF on the X chromosome, particularly at the boundaries between the pseudo-autosomal regions (PAR) and the non-PAR (NPR), as well as throughout the NPR, consistent with our earlier report. A small fraction of autosomal SNPs also showed significant sdMAF. Using the centrally imputed data, which relied mostly on low-coverage whole genome sequence, resulted in 2.1% of NPR SNPs with significant sdMAF. The whole exome sequencing also displays sdMAF on the X chromosome, including some NPR SNPs with heterozygous genotype calls in males. Genotyping, sequencing and imputation of X chromosomal SNPs requires further attention to ensure the integrity for downstream association analysis.