bluuebunny/crossref_metadata_2025_split huggingface dataset:
Superposition and nonseparable states in reconfigurable time-varying metasurfaces
Authors: Cui Tie Jun , Chen Long , You Jian Wei , Ma Qian , Su Jian Lin , Qin Shi Long , Peng Qiao Cong , Xiao Qiang 
Published: July 2024

Abstract

Classical and quantum bits serve as cornerstones in the field of information science. With the rapid development of this field, the classical and quantum bits are complementing and inspiring to each other. Here, we explore intermediate states between the classical and quantum bits in a reconfigurable time-varying metasurface, and demonstrate their superposition and nonseparable properties through theoretical analysis and experiments. To implement two quantum-inspired states in the classical system, we develop a reconfigurable time-varying metasurface that can synthesize the superposition states across the temporal dimension and the nonseparable states across the spatial and polarization dimensions. Due to the unique features of superposition and nonseparability, the proposed metasurface holds great potentials to enhance the information processing capabilities beyond those offered by the classical information metasurface. This work not only offers a reconfigurable and scalable physical platform to advance the classical and quantum information, but also will enable novel wave phenomena and provide new perspectives in the fields of information science, quantum physics and material science.
EEGEncoder: Advancing BCI with Transformer-Based Motor Imagery Classification
Authors: Liao Wangdan , Liu Hongyun , Wang Weidong 
Published: July 2024

Abstract

Brain-computer interfaces (BCIs) harness electroencephalographic signals for direct neural control of devices, offering significant benefits for individuals with motor impairments. Traditional machine learning methods for EEG-based motor imagery (MI) classification encounter challenges such as manual feature extraction and susceptibility to noise. This paper introduces EEGEncoder, a deep learning framework that employs modified transformers and Temporal Convolutional Networks (TCNs) to surmount these limitations. We propose a novel fusion architecture, named Dual-Stream Temporal-Spatial Block (DSTS), to capture temporal and spatial features, improving the accuracy of Motor Imagery classification task. Additionally, we use multiple parallel structures to enhance the model's performance. When tested on the BCI Competition IV-2a dataset, our proposed model outperforms the current state-of-the-art techniques.
How to handle high subgenome sequence similarity in allopolyploid Fragaria x ananassa: Linkage Disequilibrium Based Variant Filtering
Authors: Koorevaar Tim , Willemsen Johan H. , Hildebrand Dominic , Visser Ricard G.F. , Arens Paul , Maliepaard Chris 
Published: July 2024

Abstract

Background
The allo-octoploid F. x ananassa consistently follows a disomic inheritance. Therefore diploid variant calling pipelines can be followed but due to the high similarity among its subgenomes, there is an increased error rate for these variants. Especially when aligning short sequencing reads (150bp) to a reference genome, reads could be aligned on the wrong subgenome, resulting in erroneous variants. It is important to know which subgenome is important for a desired phenotypic value of a particular trait and filtering out these erroneous variants decreases the chance that a wrong subgenome is traced for certain traits. To mitigate the problem, we first need to classify variants in different categories: correct variants (type 1), and two erroneous variant types: homoeologous variants (type 2), and multi-locus variants (type 3).
Results
Erroneous variant types (type 2 and 3) often have skewed average allele balances (of heterozygous calls), but not always. So, the average allele balance of heterozygous variants is not sufficient to tag all erroneous variants in F. x ananassa. Not identified erroneous variants were further checked by an LD-based method in a diversity panel. This method predicted variant types with 99% similarity to a method utilizing a genetic map from a biparental mapping population that was used for validation of the method. The effect of the filtering methods on phasing accuracy was assessed by using SHAPEIT5 for phasing, and the lowest switch error rate (0.037) was obtained by a combination of LD-based and average allele balance filtering although the addition of the latter only improved the switch error rate slightly. This indicates that the LD-based method tags most erroneous variants with a skewed average allele balance and also other erroneous variants. The dataset resulting from the best filtering method (LD-based + AAB) had a 44% lower switch error rate than the original dataset and retained 72% of the overall variants.
Conclusions
In conclusion, erroneous variants that arise from high sequence similarity in allopolyploids could be identified without the need for genotyping many mapping populations. This LD-based filtering method improved phasing accuracy and ensures that important alleles are better traceable through the germplasm.
Mix proportion design and carbon emission assessment of high strength geopolymer concrete based on ternary solid waste
Authors: Liu Mo , Dai Wenting , Li Mingrui , Yang Xue , Han Yongming , Huang Mingxing 
Published: July 2024

Abstract

To accelerate the promotion and application of geopolymers in concrete (GPC), this study proposed a high strength geopolymer concrete (HSGPC) with ternary solid waste. The mix proportion of HSGPC was optimized and the impact of HSGPC on the environment was evaluated. Results indicate that the optimal mix proportion for HSGPC was 15% FA content, 10.30% SF content, a Na2SiO3/NaOH ratio of 2.5, and an NaOH molar concentration of 10 M. The molar concentration of sodium hydroxide has the greatest influence on the compressive strength of HSGPC, and the ratio of sodium silicate to sodium hydroxide solution has the greatest influence on the splitting tensile strength of HSGPC. Compared to ordinary Portland cement concrete, the carbon emissions of HSGPC have been reduced by approximately 30%. The production of HSGPC reduces the carbon emissions by 59.87% and 68.24% compared with ultra-high-performance concrete and reactive powder concrete of equivalent strength respectively.
Innovative non-pump gravity driving microfluid culture chip enhance mouse blastocyst formation
Authors: Hsieh Yao-Yuan , Huang Tsung-Tao , Kao Tzu-Chieh , Chang Chi-Chen , Hsieh Jen-Hsiang 
Published: July 2024

Abstract

Objectives: Various microfluidic technologies are currently used in ART. Herein we designed an innovative non-pump microfluidic culture platform to enhance the embryo culture in-vitro. We try to elucidate its feasibility and differences compared to traditional micro-drip culture systems.
Materials and methods: We innovated a microfluidic chips with series procedures, including fabrication of an SU-8 master mould; casting of polydimethylsiloxane (PDMS) on mould; and integrating microfluidic device. All mouse embryos were divided: (1) traditional static drops; (2) microfluid static controls; (3) dynamic microfluid chip. We used the gravity plateform with tilt angle 15 degree to drive the culture medium through the whole microchannels. The cytotoxicity surveys of chip materials were performed with MTT assay. The embryo developments in each groups was compared.
Results: The MTT surveys appeared the non-toxicity of PDMS chip materials. The percentages of 2 cell embryos reaching the stage of 4-8 cells, morula, and early blastocyst were comparable between both groups. In contrast, higher percentages of expanding blastocys and hatching blastocyst were observed in microfluid chip group. The percentages of 4-8 cells/morula/early/expanding/hatching blastocyst in each groups were 92.4/85.4/71.2/52.5/45.7%; 93.5/84.5/69.2/52.2/45.7%; and 93.8/86.5/74.5/67.2/60.4%, respectively.
Conclusions: Combined microfluid and non-pump tilting culture system results in the dynamic stimulation of the embryos and automatic waste swept, which could not provided by traditional culture system. The tilting gravity instead of the motor driving system improves the convenience and feasibility for improving the embryo qualities of embryo culture in-vitro.
Multi-Frame Binary-Phase Holograms Batched Optimization
Authors: Sha Jinze , Wojcik Antoni , Wetherfield Benjamin , Yu Jianghan , Wilkinson Timothy 
Published: July 2024

Abstract

Phase retrieval methods used in computer generated holograms (CGH) such as Gerchberg-Saxton (GS) and gradient descent give results which are prone to noise and other defects. This work builds up on the idea of time-averaging multiple hologram frames, first introduced in methods like One-Step Phase-Retrieval (OSPR) and Adaptive One-Step Phase-Retrieval (AD-OSPR). The proposed technique called Multi-Frame Holograms Batched Optimization (MFHBO) uses the L-BFGS optimization algorithm to simultaneously generate a batch of binary phase holograms which result in an average reconstructed image of improved fidelity and fast algorithmic convergence, both in the Fraunhoffer and the Fresnel regimes. The results are compared to OSPR and AD-OSPR in simulation and experimentally, proving the superiority of the proposed approach. This technique can be easily extended to other spatial modulation methods.
MSNet: A novel network with comprehensive multi-scale feature integration for gastric cancer and colon polyp segmentation
Authors: He Dongzhi , Li Chenxi , Ma Zeyuan , Li Yunqi 
Published: July 2024

Abstract

The precise and reliable segmentation of endoscopic images plays a pivotal role in the early diagnosis of gastrointestinal cancer lesions like gastric cancer and colon polyp. Due to the variations in appearance and blurred boundaries of cancer lesions, achieving precise automatic segmentation remains challenging. Therefore, this paper proposes a novel CNN-Transformer hybrid network named MSNet, which comprehensively utilizes multi-scale features. It aims to improve the segmentation accuracy of gastrointestinal cancer lesions by enhancing the ability to capture multi-scale features and distinguish boundaries. Firstly, a multi-scale perception module (MSPM) is designed specifically to capture and enhance multi-scale features, employing multiple pathways with stripe convolutions of various kernel sizes. Secondly, to enhance the model's ability to identify target boundaries, an improved dual feature pyramid (IDFP) strategy is proposed to obtain multi-scale prediction maps from various pathways and stages, thereby aggregating multi-scale features. Thirdly, to refine the network's recognition of target boundaries, a Boundary Enhancement Module (BEM) is devised, which integrates the multi-scale prediction maps obtained from IDFP with the final output of the decoder. This integration intends to mitigate spatial information loss caused by consecutive downsampling and upsampling operations, thereby achieving more precise segmentation outcomes. Extensive experiments are conducted on the privately collected gastric endoscopy dataset and five publicly available colonoscopy polyp datasets, aiming to assess the effectiveness and generalization of the proposed method. Results demonstrate that the proposed network achieves superior performance compared to other state-of-the-art methods in terms of accuracy. The source code will be available at https://github.com/0xChunFeng/MSNet.
Pattern Recognition Model for Identification of Healthy and Coronavirus Infected Samples Based on Optical Spectroscopy
Authors: Rumaling Muhammad Izzuddin , Chee Fuei Pien , Bade Abdullah , Juhim Floressy 
Published: July 2024

Abstract

This study proposes optical spectroscopy that utilizes the interaction of light with matter to identify healthy and virus-infected samples. The spectral differences between healthy and infected samples demonstrate the effectiveness of optical spectroscopy in differentiating infected samples from the healthy ones. However, optical spectral data contain numerous peaks which require additional expertise and time for interpretation. Thus, pattern recognition model is employed in conjunction with optical spectroscopy to enable practitioners to interpret results quickly. This study developed and evaluated the performance of several pattern recognition models in detecting SARS-CoV-2 in viral transport media (VTM). 75 healthy and 75 SARS-CoV-2 infected swab samples in separate vials of VTM were acquired and measured for ultraviolet absorbance, infrared absorbance and Raman spectral data. Four supervised classification algorithms, namely k-nearest neighbour (KNN), linear discriminant analysis (LDA), support vector machine (SVM) and artificial neural network (ANN), were developed with and without implementation of principal component analysis (PCA). A total of 8 pattern recognition models were developed using a training set and evaluated for performance. The results showed that the performance model predicts the class of testing set with generally higher performance when using Raman spectral data. Out of all the models predicting using Raman spectra, both LDA and SVM have the highest performance (accuracy, sensitivity and specificity of 100%). The application of PCA before the classification algorithm did not improve the performance of the models. The model developed in this study has demonstrated high performance in detecting SARS-CoV-2 infected samples, comparable to conventional detection methods.
Flux jumps, cluster distribution model and vortex phase diagram of oxygenated YBa2Cu3−xAlxO6+δ single crystal for H || ab
Authors: Babu Ashna , P. Sruthy S. , Jaiswal-Nagar D. 
Published: July 2024

Abstract

Flux jumps were observed in the magnetisation hysteresis loops of a single crystal of an oxygenated high temperature superconductor YBa2Cu3−xAlxO6+δ at fields similar to which symmetry reorientation transitions have been observed in small angle neutron scattering measurements of YBa2Cu3O6+δ previously. Bean’s critical state model was found to be insufficient to explain the observation of the flux jumps at fields larger than the field of full penetration. From x-ray photoelectron spectroscopy measurements, a single peak was observed in the spectra of Y, Ba, Cu and O, unlike the previous observation of double peaks in as-grown and oxygenated crystals. An aluminium substituted, oxygen vacant clusters of the kind (Al-O-Cu-..) n has been schematically made to explain the observation of the single peak in the XPS spectra. Similar to the observation of a field direction dependence in the as-grown crystal for µ0H || c, the second magnetisation peak anomaly was observed to have a field direction dependence for µ0H || ab, but only at low temperatures. At temperatures larger than 25 K, the second magnetisation peak anomaly was found to be field direction independent. The onset and peak fields were found to comply to an order-disorder transition at such fields and temperatures. The flux jump line was also found to fit with an order-disorder transition suggesting an intimate link between symmetry reorientation transition and the state of spatial order in the vortex lattice of oxygenated YBa2Cu3−xAlxO6+δ single crystal µ0H || ab. Incorporating all features in the magnetisation hysteresis loop, a vortex phase diagram has been made for oxygenated YBa2Cu3−xAlxO6+δ for µ0H || ab depicting various phases.
Super-enhancers reorganization controls re-sensitization of oxaliplatin-resistant FBXW7-mutated colorectal cancer
Authors: Giorgio Eros Di , Tolotto Vanessa , Gualandi Nicolò , Colitti Monica , Picco Raffaella , Cortolezzis Ylenia , D'Este Francesca , Gani Mariachiara , Hancock Wayne , Terrosu Giovanni , Degrassi Cristina , Brancolini Claudio , Xodo Luigi 
Published: July 2024

Abstract

Epigenetic plasticity and large-scale chromatin remodeling characterize tumor evolution and the emergence of subclones resistant to conventional therapies. Catalytically inactive class IIa HDACs (HDAC4, HDAC5, HDAC7, HDAC9) control the targeted recruitment of chromatin remodeling complexes, making them attractive therapeutic targets in oncology. In this study, we found that HDAC4 is proteasomal degraded in cancer cells impaired in DNA repair by homologous recombination. Genetic screening identified FBXW7 as the E3 ligase responsible for this degradation. FBXW7 loss-of-function mutations are frequently found in patients with colorectal cancer (CRC) and associated with the development of resistance to Oxaliplatin. Forced degradation of HDAC4 using a PROTAC-based compound restored oxaliplatin sensitivity in FBXW7-mutated CRC cells, patient-derived organoids (PDOs) and in mice. Mechanistically, removal of HDAC4 in FBXW7-mutated CRC cells and PDOs treated with oxaliplatin resulted in a profound restructuring of the super-enhancer landscape by restoring a super-enhancer repertoire typical of Oxaliplatin-sensitive cells. Furthermore, patient profiling based on the epigenetic state of super-enhancers controlled by HDAC4 successfully identifies a priori CRC patients resistant to platinum. This study supports HDAC4 as a key mediator of oxaliplatin resistance in FBXW7-mutated CRC and underlines the remodeling of a well-defined repertoire of super-enhancers as part of the process of re-sensitization.
Estimation of High-resolution Digital Elevation Model (DEM) From Sar Data Over the Confluence Region
Authors: Abubakar Ahmadu Ali , D. Dodo J. , O. Abubakar N. 
Published: July 2024

Abstract

Estimating a wide area high resolution digital elevation model (DEM) over the confluence region of Nigeria from sentinel-1synthetic aperture radar (SAR) data is crucial. This study leveraged geospatial technology to gain critical insights into the region's hydrology, terrain information and characteristics. These components play a crucial role in estimating the surface roughness of terrains for flood control and mitigation efforts, enabling the identification of areas vulnerable to erosion, rapid runoff, potential floodplains and possible drainage basins. The implications of this research are significant because it provides precise data for flood modeling, early warning systems, urban planning, watershed management and different types of possibly agriculturally influenced activities that are viable in the Confluence region. The research captured elevation values ranging from 79.59 meters to 529.27 meters. These detailed digital elevation model data allowed for precise terrain analysis, facilitating the identification of flood-prone areas, elevation extremes, and drainage patterns. A contour map was also generated from the DEM data, featuring contour intervals of 100 meters, which provides a clear visualization of the region's topography. Furthermore, the study delved into the analysis of key hydrological components, including slope, aspect and flow direction. As floods become increasingly severe and exacerbated by climate variables, these findings provide a foundation for building resilient communities and sustainable flood mitigation measures. The results underscore the major role of geospatial technology in addressing hydrological challenges and emphasize the importance of data-driven decision-making for a more secure and sustainable future in the face of evolving climate patterns.
Spatio-temporal analysis of meteorological drought return periods in a Mediterranean arid region, the center of Morocco
Authors: Hadri Abdessamad , Ndiaye Assane Salame , Khadir Lahcen , Jaffar Oumar , Zamzami Hamza Ait , Mahdi El Khalki El , Amazirh Abdelhakim , Bouchaou Lhoussaine , Chehbouni Abdelghani 
Published: July 2024

ABSTRACT

Morocco has recently witnessed a surge in drought occurrences, leading to considerable socioeconomic damage. The standardized precipitation index (SPI) at three- and 12-month timescales was employed to assess spatiotemporal drought characteristics across the Marrakech-Safi arid region. Mann–Kendall and Sen's slope methods were adopted to assess SPI trends. Furthermore, the Gumbel copula was employed to construct the joint distribution function of three drought variables: duration, severity, and intensity. The spatial distribution of drought return period for different scenarios was carried out. Results revealed notable interannual fluctuations of wet and drought cycles, with the driest years being 1983 and 2005 and a mixture of positive and negative trends of SPI-12 and SPI-3 over the 1972–2018 period. Nevertheless, statistical significance was observed in only 50% of the SPI-12 trends and 27% of the SPI-3 trends. The drought severity in the region was highest in the plain area, with droughts lasting 6–20 months for 75% of all drought events for SPI-12. The coastal strip exhibited the lowest drought recurrences. Furthermore, dry periods exhibited greater temporal variability over shorter timescales. Finally, trivariate return periods of drought events were the longer, averaging 93 months for SPI-12.
Extreme precipitation events in the Brahmaputra River basin during the Indian summer monsoon and its association with atmospheric moisture transport
Authors: Gupta Hariom , Raghuvanshi Akash Singh , Agarwal Ankit 
Published: July 2024

ABSTRACT
Due to climate change, rising temperatures cause the atmosphere to hold more moisture, likely increasing the frequency and intensity of extreme precipitation events. These events, lasting several days, are the leading causes of floods. Recently, floods from multi-day precipitation extremes (MPEs) have risen significantly. The Brahmaputra River basin (BRB) in India is particularly vulnerable to flooding during the Indian summer monsoon due to its transboundary nature. It is crucial to characterize and rank MPEs to understand their risk, impact, and underlying drivers. This study ranked MPEs of different durations based on intensity and spatial extent during the Indian summer monsoon over the BRB using a high-resolution daily precipitation dataset from 1951 to 2022. In addition, it evaluates the association between atmospheric moisture transport and MPEs by quantifying integrated water vapor transport (IVT) during top-ranked MPEs. The analysis indicates intense IVT in the mid-tropospheric layer (850–500 hPa) over the MPE regions during top-ranked events and identifies significant low-pressure anomalies and shifts from ridge to trough patterns. Quantifying the connection between MPEs and IVT can aid in early prediction and risk reduction.