bluuebunny/crossref_metadata_2025_split huggingface dataset:
Unconventional pairing in Ising superconductors: application to monolayer NbSe2
Authors: Roy Subhojit , Kreisel Andreas , Andersen Brian M , Mukherjee Shantanu 
Published: September 2024

Abstract
The presence of a non-centrosymmetric crystal structure and in-plane mirror symmetry allows an Ising spin–orbit coupling to form in some two-dimensional materials. Examples include transition metal dichalcogenide superconductors like monolayer NbSe2, MoS2, TaS2, and PbTe2, where a nontrivial nature of the superconducting state is currently being explored. In this study, we develop a microscopic formalism for Ising superconductors that captures the superconducting instability arising from a momentum-dependent spin- and charge-fluctuation-mediated pairing interaction. We apply our pairing model to the electronic structure of monolayer NbSe2, where first-principles calculations reveal the presence of strong paramagnetic fluctuations. Our calculations provide a quantitative measure of the mixing between the even- and odd-parity superconducting states and its variation with Coulomb interaction. Further, numerical analysis in the presence of an external Zeeman field reveals the role of Ising spin–orbit coupling and mixing of odd-parity superconducting state in influencing the low-temperature enhancement of the critical magnetic field.
Construction of high-efficiency Fe–MnO@Fe electrocatalyst for methanol and ethanol oxidation in alkaline medium
Authors: Lv Jianbing , Liao Ye , E Yifeng , Lin Xi , Zuo Jingyu , He Gaihua 
Published: September 2024

Abstract
Acquiring effective, durable and economical non-precious metal electrocatalysts is crucial for the widespread utilization of fuel cells. In this work, an iron/manganese oxide nanocomposite (Fe–MnO@Fe) is synthesized as a novel electrocatalyst by direct-current arc plasma technology integrating transition metal and transition metal oxide. The Fe–MnO@Fe catalyst demonstrates excellent catalytic performance. By modulating the percentage of transition metal and transition metal oxide in the composite, the largest current density values of methanol and ethanol oxidation of the composite reach 20.18 and 7.83 mA cm−2, respectively. In addition, the composite catalyst exhibits excellent stability and conductivity, and the catalyst greatly reduces the experimental cost relative to the noble metal catalysts, indicating that the composite is a potential fuel cell catalyst candidate and provides an innovative concept for creation non-precious metal catalysts.
Evaluation of mode-I delamination toughness of composites for automotive applications
Authors: Britto S John , TM Chenthil Jegan , Franklin V Alfred 
Published: September 2024

Abstract
With the increasing need for energy efficiency, fiber-reinforced composites are being widely used in the automotive industry. This study investigates the inter-laminar fracture behavior of fiber-reinforced composite double cantilever beam specimens considering a non-zero slope at the root. Experiments were conducted on unidirectional and cross-ply glass fiber reinforced epoxy composite specimens and the critical delamination toughness

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has been evaluated by the proposed weighted residual equation. The proposed mathematical model requires crack length and the load versus displacement data to evaluate delamination fracture energy. This method accounts the measurement scatter and the rotation at the crack tip. For a particular specimen, the proposed model gives a unique value of critical delamination toughness value. The critical interface failure load estimates utilizing the evaluated delamination toughness agrees well with literature and in-house test results of double cantilever beam specimens.
SAL5087 bright welding wire two acid polishing process and its effect on surface roughness
Authors: Cai Ling Shan , Shui Ping Hu , Liang Jia Hao , Duan Xiao Ge , Zhong Qian Qian 
Published: September 2024

Abstract
In order to obtain bright aluminum alloy wires and eliminate the environmental and human hazards of traditional chemical polishing, the H3PO4-H2SO4 system chemical polishing process was developed for SAL5087, and the surface roughness was measured by laser confocal microscopy and the surface morphology was observed by sweeping electron microscopy as the evaluation index of the polishing quality. The process optimization was carried out by orthogonal experiments and the role of each component was analyzed. The experiments showed that phosphoric acid dominates the polishing process, which can effectively enhance the polishing effect under the synergistic effect with various additives. The surface roughness of the polished wire was reduced to 0.257 μm in the optimum process, and the surface was free of defects such as burrs and scratches.
Experimental study on residual compressive bearing capacity of cracked reinforced concrete columns by SCDA
Authors: Sun Jinshan , Liu Shaoyan , Hu Lingling , Jia Yongsheng , Yao Yingkang , Xia Yuqing 
Published: September 2024

Abstract
The static demolition of structures via soundless chemical demolition agent (SCDA) is attracting increasing attention because of its environmental friendliness and safety, especially in the dense urban areas. However, the research on the application of SCDA in demolishing the reinforced concrete is still scarce, seriously limiting the development of static demolition. In this work, with the combined efforts of experiments and theoretical analysis, we investigate the critical role of borehole layouts in the static demolition of reinforced concrete (RC) by determining the occurrence and expansion of cracks during static demolition, and the resulting residual mechanical properties of RC. The results show that the growth of cracks is significantly dependent on the reaction of SCDA. Additionally, the initiation time of cracking exhibits minimal sensitivity to variations in the layouts pattern of boreholes, while the progression of cracking is predominantly governed by the layouts of boreholes. We demonstrate that cracks formed in static demolition process can significantly reduce the strength of RC with about the remaining bearing capacity is only 10% to 33% of the original and change its failure mode. Further, it is point out that the key factor controlling the failure of RC under axial compression (i.e., the integrity of structures), and the RC with larger crack size and less structural integrity is more prone to fail. The results and findings in this work further clarify the role of borehole layouts and are of great significance in improving the efficiency of static demolition.
Advancing healthcare through piezoresistive pressure sensors: a comprehensive review of biomedical applications and performance metrics
Authors: Alghrairi Mokhalad , Farhan Basim Abdul Kareem , Ridha Hussein Mohammed , Mutashar Saad , Algriree Waleed , Sabbar Bayan Mahdi 
Published: September 2024

Abstract
Piezoresistive pressure sensors have transformed biomedical applications, enabling precise diagnostics and monitoring. This concise review explores the fundamental principles, key components, and fabrication techniques of piezoresistive pressure sensors, focusing on critical performance metrics such as sensitivity, accuracy, and response time. Biomedical design challenges, including biocompatibility and long-term stability, are examined, offering insights into solutions for optimal sensor integration. In diverse biomedical applications, piezoresistive pressure sensors play pivotal roles, from blood pressure monitoring to implantable medical devices. The paper emphasizes their versatility in enhancing patient care through continuous and accurate monitoring. Looking forward, the review discusses emerging trends and potential research directions, positioning piezoresistive pressure sensors as central contributors to the future of biomedical technology, promising improved patient outcomes and advanced healthcare delivery through precise and continuous monitoring.
The influence of moisture on precipitation patterns across the Western Tibetan Plateau and its response to sea surface temperature warming
Authors: Gao Yanhong , Jiang Haojun 
Published: September 2024

Abstract
The distribution of water resources in sub-basins across the Western Tibetan Plateau (WTP) is of critical importance due to not only ecological vulnerability resulting from the extremely arid climatology but also the political sensitivities surrounding the international rivers. In this study, we utilize an advanced water vapor tracer (WVT) embedded in the widely used regional climate model—Weather and Research Forecast (WRF), to quantify moisture contributions from four main sources towards precipitation over the WTP region. We also analyze influences on other sub-basins in the TP for comparison purposes. We examine how changes in sea surface temperature (SST) during 2010s compared to 1980s have influenced precipitation patterns and moisture contributions over recent decades. Our findings indicate that terrestrial moisture sources contribute more than oceanic sources towards the endorheic TP region. Recycling processes originating from highlands area are revealed to play a greater role in contributing moisture over WTP compared to those from lowlands areas. Furthermore, our results demonstrate stronger agreements between wetting distribution patterns and distributions of liquid/solid hydrometeors rather than water vapor distribution itself, highlighting condensation/freezing as critical factors. Notably, we observe different responses within Amu Dayra basin compared to the main WTP when subjected to SST changes. This study focuses on delineating distinct roles of terrestrial and oceanic moisture sources in driving precipitation changes over WTP, while specifically emphasizing condensation process’ contribution to inner TP’s precipitation and highlighting moisture transport form oceans’ influence on precipitation patterns over Amu Dayra basin.
Lung cancer classification model using convolutional neural network with feature ranking process
Authors: Aharonu Mattakoyya , Lokesh Kumar R 
Published: September 2024

Abstract
Lung cancer is the leading cause of cancer-related deaths worldwide, highlighting the importance of early detection to improve patient outcomes. The goal of this study is to create a computer-aided diagnosis (CAD) system that detects and classifies lung cancer based on medical images using a Convolutional Neural Network (CNN) and feature extraction techniques. By automating the process and reducing reliance on manual interpretation, the goal is to improve the accuracy and efficiency of lung cancer diagnosis. The study employs the LIDC-IDRI dataset, a comprehensive collection of lung cancer-related medical images, to achieve this goal. To improve the visual representation of the images, pre-processing techniques are used. The RGB images are converted to grayscale using a formula that considers the human perception of colour intensity. The images are then subjected to median filtering to reduce noise and smooth out irregularities. In addition, Contrast Limited Adaptive Histogram Equalization (CLAHE) is used to improve contrast and detail while reducing noise. To segment regions of interest based on grey-level intensities, thresholding techniques, specifically Otsu’s thresholding, are used. The Sobel operator is used to refine the segmentation process by enhancing edges and contours in binary images. Morphological operations such as dilation and filling are used to refine the segmented regions even further. Feature extraction is used to extract statistical data and texture characteristics from segmented regions. Mean and variance calculations reveal information about brightness and variability within regions, whereas co-occurrence matrices and Gray-Level Co-occurrence Matrix (GLCM) properties quantify texture features. The correlation between different regions is also evaluated to assess their relationships. The t-test statistic is used to rank all extracted features based on their relevance. Using the pre-processed and ranked features as inputs, a CNN model with five hidden layers is trained. To classify the segmented regions into cancerous and non-cancerous classes, the model learns patterns and relationships in the data. A confusion matrix is used to assess the accuracy, specificity, and sensitivity of the model’s predictions, with an emphasis on correctly identifying lung cancer-affected regions. The results show promising results, with the proposed CAD system identifying lung cancer-affected regions with an accuracy of 99.4375%. The system also outperforms other existing methods with a specificity of 99.12% and a sensitivity of 99.26%. These findings highlight the developed system’s potential as a valuable tool for early lung cancer detection, assisting doctors in making accurate diagnoses and improving patient outcomes.
Dual loop voltage droop regulated controller for DC microgrid using hybrid PSO and GGO algorithms
Authors: Prasad Neeraj Kumar , Singh Navdeep , Yadav Mukh Raj 
Published: September 2024

Abstract
DC microgrids are effectively employed in distribution network integration with renewable energy sources. The droop control technique is commonly utilized for DC microgrid regulation during load sharing. It minimizes the potential instability caused by disturbances such as input voltage changes, uncertainty parameters, and constant power load (CPL). Nevertheless, conventional droop control is inadequate in achieving precise current and satisfactory voltage regulation distribution for load sharing. The proposed approach comprises two separate loops for the DC bus voltage regulation and load current distribution, delivering a CVL and CPL to overcome the single optimization voltage controller technique. The primary loop uses the PSO algorithm to precisely manage the current load sharing among two parallel converters. Due to this the instability problems arising from disturbances, and it mitigated by the proposed topology. The multiobjective optimization technique provides notable resilience, rapid dynamic response, and robust stability over considerable variations in load. The secondary loop utilises the GGO algorithm to optimize the parameter to minimize the DC bus voltage regulation, voltage management, reasonable distribution of load power, and suitable reliability. The performance of the voltage regulation enhance & settling time for output voltage has been mitigated more than 31 ms times through the proposed controller compared to conventional controller under the variation of CVL, CPL, and input voltage disturbance demonstrated in simulation results.
Exploiting speech tremors: machine learning for early diagnosis of amyotrophic lateral sclerosis
Authors: Chauhan Ritu , Sharma Urvashi 
Published: September 2024

Abstract
Neurodegenerative diseases pose significant challenges in healthcare, with Amyotrophic Lateral Sclerosis (ALS) being one such rare yet debilitating condition affecting motor neurons. Machine learning (ML) and artificial intelligence (AI) have emerged as powerful tools in healthcare, offering insights and solutions for various medical conditions. This study investigates the application of ML to enhance early ALS diagnosis through the analysis of tremors in sustained speech. By focusing on tremor detection as a diagnostic marker, the research employs ML algorithms to develop predictive models capable of distinguishing ALS patients from healthy controls. The dataset comprises 54 patients from the Republican Research and Clinical Centre of Neurology and Neurosurgery in Belarus, Minsk. The study adopts a two-faceted approach: (1) Exploratory voice analysis to identify tremors associated with ALS in speech samples. (2) Development of ML algorithms to construct predictive models for early ALS diagnosis based on the identified tremors. The ML models exhibit promising results in distinguishing ALS patients from healthy controls based on speech analysis. Tremor detection in sustained speech proves to be an effective marker for early ALS diagnosis. While initial findings are encouraging, larger-scale studies are required to validate the clinical applicability of this approach. The successful application of ML and AI in early ALS diagnosis by leveraging innovative approaches, such as tremor detection in sustained speech, we can enhance early diagnosis and improve patient outcomes in neurodegenerative diseases like ALS on a broader scale.
Analysis of cement-stabilized soil on road embankment employing finite element analysis - a case study
Authors: Ashiq Hasan Muhommed , Sabab Shadman Rahman , Joy Jamil Ahmed , Zahid Chowdhury Zubayer Bin , Kabir Mozaher Ul 
Published: September 2024

Abstract
The application of stabilized soil is fast expanding around the world, and it is delivering improvements to the construction industry. Stabilized soil is a popular choice for reinforcing road embankments around the world. A layer of cement-stabilized soil can reduce the settlement of the soil layer below the embankments. This case study compares the long-term settlement of road embankments with and without cement-stabilized soil layers of the Chittagong Port Access Road located at Teknaf Bangladesh. The settlement analysis of the road embankment was performed using the Finite Element Analysis with Plaxis2D. In this study, two forms of sequence modeling were used. First, the settlement of a road embankment without reinforcement was examined. The second modeling step was to determine the settlement of an embankment reinforced with a cement-stabilized soil layer. Following the simulation, a comparison was performed. Additionally, the effect of cement content (4%, 8%, and 12%), compaction energy, and single, double, and triple layers of cement-stabilized soil on long-term settlement along with the factor of safety of the embankment have been addressed. The results demonstrated that using this specific cement-stabilized soil below the road embankment has decreased the long-term settlement of the soil while increasing the safety of the embankment compared to that without the cement-stabilized soil reinforcement.
Enhancing the Lifetime of STT-RAM by IFTRP
Authors: Priya Bhukya Krishna , Naick Bhukya Krishna , Ramasubramanian N 
Published: September 2024

Abstract
Spin Transfer Torque Random Access memory (STT-RAM) is a better alternative to overcome the shortcomings of the existing memory technologies. But, the implementation of STT-RAM memory technologies on the existing memory are limited due to its write restriction. The disadvantages of STT-RAM have been overcome by many of the existing techniques. The existing techniques either reduce the writes or variation in the writes on the cache block in inter-set(IeS) or intra-set(IrS) by writing the data from hot block to cold block, which may lead to a Repeated Address Attack. A IFTRP (Inter-secure Fault Tolerant Replacement Policy) is proposed to reduce the attacks and have lifetime enhancement on the STT-RAM. The variation in writes is reduced by IFTRP in both IeS and IrS by RSS (Randomized Swap Shift policy), RICL(Randomized Invalidation Cache Line) method with a variation in the threshold values, so that the location of data blocks is not predictable by the intruder. The proposed method also identifies the partial cell failures by flagging them as invalid, so that the future write is not performed on the invalid line. The proposed method reduces the attacks and improves the lifetime of the memory by 15% in cache and has negligible area overhead.
Remaining useful life prediction method for cross- condition tools based on parallel fusion
Authors: Ma Hongbo , Chen Bingquan , Kong Xianguang , Liu Zhenguo , Chen Ke , Huang Song , Yin Lei 
Published: September 2024

Abstract
The current tool remaining useful life (RUL) prediction models do not fully consider the importance of signals from different sensors and the differences in working conditions, which leads to a decline in the model’s robust performance under new conditions. This paper introduces a novel cross-condition RUL prediction approach that integrates parallel fusion, transfer learning, and deep learning. Considering the characteristics of different data sources, correlated signals are initially selected using correlation analysis and dynamic time warping (DTW). Subsequently, multi-scale Depthwise Separable Convolution and multi-attention mechanisms are combined to capture the importance of features at various scales. Finally, a dynamic domain adaptation technique is employed to adjust the importance weights of conditional distribution discrepancy and marginal distribution discrepancy, enabling the model to adapt to cross-condition scenarios.
Research on the vibration load spectrum extraction method for electric drive assembly
Authors: Zou Xihong , Wang Qinfeng , Yuan Dongmei , Zhang Yiwei , Qiu Weiben , Tan Yunlong , Xiong Feng , Wang Wanying 
Published: September 2024

Abstract
It is vital to precisely extract the actual vibration load spectrum of the electric drive assembly since it is a crucial piece of fundamental load data for vibration testing and vibration fatigue performance development. A simulation iteration-based vibration load spectrum extraction method for electric drive assemblies is proposed by actually measuring the six-component load spectrum. The entire vehicle system dynamical model was developed and validated using an electric car’s electric drive assembly as the study object. A simulated iterative system is established, and the frequency response function of the system is obtained. The excitation signals of the simulated iterative system are obtained by the simulated iterative algorithm and verified, with all relative errors within 5.0%. The results indicate that the simulation iteration-based method for extracting the vibration load spectrum of the electric drive assembly can accurately simulate the loads on the electric drive assembly during actual vehicle operation.
Synergistic xylan decomposition by a reducing-end xylose-releasing exo-oligoxylanase with other xylanolytic enzymes derived from Paenibacillus xylaniclasticus strain TW1
Authors: Taniguchi Koki , Karita Shuichi , Umekawa Midori 
Published: September 2024

Abstract
Paenibacillus xylaniclasticus strain TW1 is a promising tool for decomposing xylan-containing lignocellulosic biomass, since this strain possesses various genes encoding cellulolytic/hemicellulolytic enzymes. In this study, PxRex8A from the TW1 strain was found to be a reducing-end xylose-releasing exo-oligoxylanase of glycoside hydrolase family 8, which cleaves xylose from xylooligosaccharides of corn core xylan. In a synergistic assay, the efficient decomposition of oat spelt xylan (OSX) and beech wood xylan was exemplified in the combination of endo-β-1,4-xylanase (PxXyn11A) and PxRex8A from the TW1 strain in a molar ratio of 4:1. Furthermore, it was found that the addition of β-d-xylosidase/α-l-arabinofuranosidase (PxXyl43A) from this strain with PxXyn11A and PxRex8A achieved twice the amount of reducing sugars (1.1 mg/mL) against OSX after 24 h compared to PxXyn11A alone (0.5 mg/mL). These results demonstrate that synergy effect of PxRex8A and PxXyl43A with PxXyn11A promotes xylan degradation into xylose.