bluuebunny/crossref_metadata_2025_split huggingface dataset:
Unveiling quantum correlations of SPDC biphoton spatial modes using spatial light modulator
Authors: Omshankar  , Venkataraman Vivek , Ghosh Joyee 
Published: October 2024

Abstract
We experimentally demonstrate the quantum entanglement in various spatial modes of the correlated photons generated via spontaneous parametric down-conversion in a type-II phase-matched periodically-poled KTP crystal. The two-fold coincidence measurement reveals the spatial entanglement between the photon pairs by projecting one of the correlated photons onto a Gaussian mode while scanning the spatial mode of its partner photon through selective projection (Gaussian, Laguerre–Gaussian (LG), Bessel-Gaussian (BG), and Airy–Gaussian (AG)) using a spatial light modulator (SLM). We also show the spatially resolved measurement of the non-conservation of orbital angular momentum via modal decomposition of the correlated photon in LG and BG bases.
Investigating the influence of periwinkle shell powder on the thermal and mechanical performance of high-density polyethylene composites
Authors: Valsadwala Abbas Saifee , Srinivasan Sriram , Balachandran Surya Rajan , Begum S Shamshath , Samui Asit Baran , Shukla Sandeep Kumar 
Published: October 2024

Abstract
In this study, the shell powder of Littorina littorea commonly called periwinkle was used as an eco-friendly filler in High-Density Polyethylene (HDPE) to form periwinkle/HDPE composites (PHPC). Understanding the effect of different particle sizes of periwinkle shell powder (PSP) and optimizing their influence on PHPC is the main scope of this work. Periwinkle shell (PS) particles with sizes ranging from <53 μm to 150 μm were chosen as reinforcements. The PSP sizes chosen in this study, <53 μm, 53 μm, 75 μm, 90 μm, 105 μm and 150 μm were named PHPCL53, PHPC53, PHPC75, PHPC90, PHPC105, and PHPC150, respectively. The composites were fabricated by incorporating 1 wt% PSP into the HDPE matrix using the compression molding technique and then subjected to morphological, thermal, and mechanical characterizations. Morphology studies using scanning electron microscopy (SEM) confirms 150 μm PSP had the best dispersion whereas 75 μm PSP resulted in agglomeration. PSP had little influence on the thermal stability of HDPE except for PHPC150 which showed an increase in the degradation temperature when compared to the virgin sample. Mechanical properties such as hardness, Young’s modulus, impact strength, and flexural modulus were enhanced by the addition of PSP. However, a decrease was noted in the elongation at break (%) and flexural strength of PHPC indicating the stiffening effect of the filler on the HDPE. In order to understand the particle size influence better, the extension evaluation method (EEM) was used for all samples and PHPC150 was found to be the best performing among all particle sizes.
High-voltage thermocell modules for direct device operation: eliminating the need for DC-DC converters
Authors: Jiang Lixian , Ebihara Teruo , Horike Shohei , Wei Qingshuo 
Published: October 2024

Abstract
This study explores the potential of thermocells as an efficient energy-harvesting solution that can power practical devices without the need for a DC-DC converter. We constructed thermocell devices comprising 35 legs using a modified soldering technique and electrode treatment to improve reliability. The devices achieved a peak voltage of 3.5 V at a hot-side temperature of 60 °C under natural cooling conditions. These thermocells were integrated with a voltage detector integrated circuit (IC) and beacon, initiating beacon operation within 100 s and transmitting signals over 600 times within a 15 min period. Our findings demonstrate the feasibility of thermocells as an alternative energy source, offering a cost-effective and streamlined approach for energy-harvesting applications without the complexity and expense of DC-DC converters.
Cascaded redundant convolutional encoder-decoder network improved apnea detection performance using tracheal sounds in post anesthesia care unit patients
Authors: Zhang Erpeng , Jia Xiuzhu , Wu Yanan , Liu Jing , Yu Lu 
Published: October 2024

Abstract

Objective. Methods of detecting apnea based on acoustic features can be prone to misdiagnosed and missed diagnoses due to the influence of noise. The aim of this paper is to improve the performance of apnea detection algorithms in the Post Anesthesia Care Unit (PACU) using a denoising method that processes tracheal sounds without the need for separate background noise. Approach. Tracheal sound data from laboratory subjects was collected using a microphone. Record a segment of clinical background noise and clean tracheal sound data to synthesize the noisy tracheal sound data according to a specified signal-to-noise ratio. Extract the frequency-domain features of the tracheal sounds using the Short Time Fourier Transform (STFT) and input the Cascaded Redundant Convolutional Encoder-Decoder network (CR-CED) network for training. Patients’ tracheal sound data collected in the PACU were then fed into the CR-CED network as test data and inversely transformed by STFT to obtain denoised tracheal sounds. The apnea detection algorithm was used to detect the tracheal sound after denoising. Results. Apnea events were correctly detected 207 times and normal respiratory events 11,305 times using tracheal sounds denoised by the CR-CED network. The sensitivity and specificity of apnea detection were 88% and 98.6%, respectively. Significance. The apnea detection results of tracheal sounds after CR-CED network denoising in the PACU are accurate and reliable. Tracheal sound can be denoised using this approach without separate background noise. It effectively improves the applicability of the tracheal sound denoising method in the medical environment while ensuring its correctness.
Identification of enset (ensete ventricoum (Welw.) cheesman) landraces and farmers preference in wolaita zone, Southern Ethiopia
Authors: Asfaw Abera , Wato Tamirat , Andualem Alemu 
Published: October 2024

Abstract
The enset farming system in Ethiopia, a significant agricultural system, is primarily cultivated by the Wolaita people, who prefer different landraces due to their unique traits. Thus, this study aimed to identify a landrace with specific traits that farmers preferred in the Wolaita Zone in 2021 from 14 Kebeles, each with 15 households. The study used both qualitative and quantitative data. SPSS software was used quantitative analysis: cross-tabulation, descriptive statistics, multidimensional preference analysis, Pearson correlation, t and chi square tests. As a result, 93 landraces and 4 cultivars were identified with varying frequency and distribution within and between farms, indicating farmers’ preference for different traits. Enset landraces preferred for high yield of Kocho were: Gena, Anko-gena, Halla, Mazia, Ala-gena, Godaria, Tuzuma; Bulla were: Halla, Mazia, Tuzuma, Gena, Gefetanuwa, Anko-gena, Argama, Ala-gena, Shalakumia; Amicho use; Arkia, Agina, chichia, Sutia, Kabaria, Nakaka, Katania; For fiber strength and durability were: Halla, Tuzuma, Mazia Anko-gena, Argama and Ala-gena. Medicinal use: Lochingia, Gefetanuwa, Argama, and Arkia. For overall traits, the most preferred landrace in the study area was Halla, Mazia, Tuzuma, Anko-gena, Argama, Shalakumia, Gena, Kabaria, Wanadia, Ala-gena, Gefetanuwa, Nakaka, Lembuwa, Sutia, Arkia, Katania, Godaria, Chichia, and Lochingia in order. Therefore, enset landraces–mainly used for amicho were extensively used and having medicinal values were rarely grown due to their susceptibility to both biotic and abiotic factors was under threat of erosion. So, to sustain the production of different landraces, it is recommended to collect, conserve, and raise awareness among the young generation about specific traits.
Enhancing selenium removal using pre-treated natural clay: experimental investigation and predictive modeling
Authors: Ramesh Gude , Ruj Biswajit , Bishayee Bhaskar , Chatterjee Rishya Prava , Kumar Ramesh , Khan Moonis Ali , Jeon Byong-Hun , Nayak Jayato , Chakrabortty Sankha 
Published: October 2024

Abstract
A heat treatment methodology was adopted as a pretreatment strategy, altering the porous structure of the clay to minimize leaching for selenium adsorption in an aqueous system. Rigorous experiments were carried out in batch mode to determine optimal parameters across various variables, including contact time, adsorbent dosages, selenium concentrations, pH, temperature, and stirring speed during selenium removal using natural clay. Investigating several kinetic and isotherm models revealed the best fitting for the pseudo-second-order and the Langmuir isotherm. Endothermic and spontaneous characteristics of the adsorption process were shown during thermodynamic analysis. In this study, a predictive model for the efficiency of selenium separation was established using Response Surface Methodology (RSM). Additionally, an Artificial Neural Network (ANN), a data-driven model, was employed for comparative analysis. The predictive model exhibited a high degree of agreement with experimental data, demonstrated by a low relative error of <0.10, a high regression coefficient of >0.97), and a substantial Willmott-d index of >0.95. Moreover, the efficacy of pre-activated clay in selenium removal was assessed, revealing the superior performance of ANN models over RSM models in forecasting the efficiency of the adsorption process. This research significantly advances an effective and sustainable material for selenium removal, providing valuable insights into predictive modeling techniques applicable to similar contexts to boost scale-up confidence during industrial implementation in affected regions.
Modelling and experimentation of failure modes to obtain safe operating range for improved dielectric elastomer actuation performance
Authors: Prabhakar O P , Sahu R K 
Published: October 2024

Abstract
Dielectric elastomers (DEs) possess high energy density, lightweight, and low response time making them suitable material candidatures for electromechanical transducers (ET). Performance enhancement of ET necessitates escalated electrical load making prone to failure and subsequently hindering reliability and life cycle. Existing models towards failure mitigation focus on mechanical and electrical loads individually with constant relative permittivity, whereas DEs undergoes combined loading and stretch-electrostriction in real-time application. This study aims to identify safe design and operating parameters by addressing various modes of failures under combined loads and stretch-electrostriction. Under combined load, pre-strain emerges as a crucial parameter to reduce EMI, while extensive pre-strain leads to diminish thickness which in turn promotes electrical breakdown of elastomer. The optimized design for DEs exhibit substantial failure suppression at a pre-strain value of 1.7 (

λ

pre

) under maximum electrical load of

37.66

MV
/
m

(

E

max

). The efficacy of optimized parameters for the failure suppression is verified through an in-house fabricated planar actuator. Operation within the identified range of parameters is observed to enhance the reliability and lifespan of DE-actuators, marking a noteworthy advancement in the field of soft robotics.
Model test of foundation pit excavation adjacent to the existing double-lane tunnel
Authors: He Jiuru , Cheng Xuansheng , Zhang Guiwen 
Published: October 2024

Abstract
To study the influence of foundation pit excavation on the adjacent existing double-track tunnel, a model box test with a similarity ratio of 1/50 was carried out. In this paper, the mechanical parameters of loess around the tunnel are determined through laboratory tests, and the miscellaneous fill, loess, sand, tunnel structure, support structure, and retaining wall are prepared by similar principles. The variation of the surface settlement, the deformation of the retaining wall, and the surrounding rock and soil pressure of the tunnel were analyzed by using equipment such as the miniature earth pressure box, dial gauge, and strain gauge. The results of the study showed that the existence of the supporting structure makes the bending moment of the retaining wall show the phenomenon of negative value first, then positive value, and finally negative value. In the process of foundation pit excavation, when the horizontal distance of the model surface is 4 times the excavation depth, the foundation pit excavation has little effect on the settlement of the model surface. The earth pressure value of the surrounding rock decreases with the increase of the excavation depth of the foundation pit. The earth pressure value of the surrounding rock is the right > the left > the lower part > the upper part and the excavation of the foundation pit has the greatest influence on the upper and left sides of the surrounding rock close to the side of the foundation pit, and the least impact on the right side of the surrounding rock.
Design and implementation of anti-mapping security access technology based on illegal scanning
Authors: Liu Zehui , Ma Dongjuan , Yang Hua , Jing Weizhe , Jin Xin 
Published: October 2024

Abstract
In the current field of information security, illegal network scanning activities are prevalent, and such behaviors are usually aimed at detecting security vulnerabilities in network systems and preparing for future attack activities. This study proposes a secure access system based on anti-mapping technology, which aims to effectively block illegal scanning behaviors while ensuring that the normal access of legitimate users is not affected. The system integrates advanced behavioral analysis algorithms that utilize machine learning techniques for deep learning and pattern recognition of network traffic, and is able to accurately distinguish between normal user activities and malicious scanning attempts. At the core of the system is a set of dynamic adaptive identification mechanisms that update the detection algorithms in real time to adapt to emerging scanning techniques and attack strategies by continuously learning from changes in network traffic. In addition, the system employs role-based access control (RBAC) policies to enhance the protection of sensitive resources. The Secure Access Gateway is deployed at the boundary of the network to monitor and filter all ingress traffic, effectively intercepting unauthorized scanning activities by comprehensively evaluating the source, behavior and frequency of traffic. Experimental results show that the proposed two-layer network structure performs well in detecting common threats such as port scanning, DDoS attacks, and SQL injections, with an accuracy rate of over 95%. Especially for complex and covert APT (advanced persistent threat) attacks, the system can significantly reduce the false alarm rate and effectively improve the detection speed. However, when dealing with some highly customized malware, the system’s recognition ability still needs to be improved, which indicates that future research needs to focus more on enhancing the ability to learn and adapt to unknown threats.
Dynamics of ecosystem services along ecological network seascapes
Authors: Guimarães Jr Paulo R , Melián Carlos J 
Published: October 2024

Abstract
Human societies depend on services provided by ecosystems, from local needs such as clean water and pest control to global services like the ozone layer and the ocean biological pump. Ecosystem services are linked to the states of the ecosystem, which are, in turn, governed by a web of ecological interactions. These interactions, along with the services they support, are under threat from environmental changes driven by human activities. Therefore, safeguarding these vital services requires an understanding of how the structure and dynamics of ecological interactions are affected by environmental change. A critical step towards this goal is the development of a theoretical framework that can elucidate how ecosystem services are sustained or impaired by interactions within ecosystems in fluctuating environments. Recent years have seen progress in characterizing the organization and dynamics of ecological networks. However, linking temporally varying network structure in fluctuating environments, the seascapes of ecological networks, and their impact on services remains a challenge. We propose an approach based on merging ecological network analysis with Boolean functions and modeling of fluctuating environments to address how services are affected by environmental change. We review aspects of Boolean Network models and illustrate the approach using biologically inspired Boolean rules that involve predator-prey cycles, trophic cascades, and mutualisms formed by plants and their frugivores. This approach aims to contribute to the study of how the organization of ecological interactions affects the persistence of ecosystem services. Specifically, we discuss how this approach can provide new insights into how environmental change affects the relationship between ecological networks and ecosystem services. The combination of information on the natural history of species interactions and ecosystem services, Boolean networks, and models for fluctuating environments may contribute to conservation strategies for preserving biodiversity and ecosystem services in the face of ongoing environmental change.
A review of artificial intelligence-based brain age estimation and its applications for related diseases
Authors: Azzam Mohamed , Xu Ziyang , Liu Ruobing , Li Lie , Meng Soh Kah , Challagundla Kishore B , Wan Shibiao , Wang Jieqiong 
Published: October 2024

Abstract
The study of brain age has emerged over the past decade, aiming to estimate a person’s age based on brain imaging scans. Ideally, predicted brain age should match chronological age in healthy individuals. However, brain structure and function change in the presence of brain-related diseases. Consequently, brain age also changes in affected individuals, making the brain age gap (BAG)—the difference between brain age and chronological age—a potential biomarker for brain health, early screening, and identifying age-related cognitive decline and disorders. With the recent successes of artificial intelligence in healthcare, it is essential to track the latest advancements and highlight promising directions. This review paper presents recent machine learning techniques used in brain age estimation (BAE) studies. Typically, BAE models involve developing a machine learning regression model to capture age-related variations in brain structure from imaging scans of healthy individuals and automatically predict brain age for new subjects. The process also involves estimating BAG as a measure of brain health. While we discuss recent clinical applications of BAE methods, we also review studies of biological age that can be integrated into BAE research. Finally, we point out the current limitations of BAE’s studies.
Therapeutic peptides identification via kernel risk sensitive loss-based k-nearest neighbor model and multi-Laplacian regularization
Authors: Zhang Wenyu , Ding Yijie , Wei Leyi , Guo Xiaoyi , Ni Fengming 
Published: October 2024

Abstract
Therapeutic peptides are therapeutic agents synthesized from natural amino acids, which can be used as carriers for precisely transporting drugs and can activate the immune system for preventing and treating various diseases. However, screening therapeutic peptides using biochemical assays is expensive, time-consuming, and limited by experimental conditions and biological samples, and there may be ethical considerations in the clinical stage. In contrast, screening therapeutic peptides using machine learning and computational methods is efficient, automated, and can accurately predict potential therapeutic peptides. In this study, a k-nearest neighbor model based on multi-Laplacian and kernel risk sensitive loss was proposed, which introduces a kernel risk loss function derived from the K-local hyperplane distance nearest neighbor model as well as combining the Laplacian regularization method to predict therapeutic peptides. The findings indicated that the suggested approach achieved satisfactory results and could effectively predict therapeutic peptide sequences.
Predictability of antigen binding based on short motifs in the antibody CDRH3
Authors: Scheffer Lonneke , Reber Eric Emanuel , Mehta Brij Bhushan , Pavlović Milena , Chernigovskaya Maria , Richardson Eve , Akbar Rahmad , Lund-Johansen Fridtjof , Greiff Victor , Haff Ingrid Hobæk , Sandve Geir Kjetil 
Published: October 2024

Abstract
Adaptive immune receptors, such as antibodies and T-cell receptors, recognize foreign threats with exquisite specificity. A major challenge in adaptive immunology is discovering the rules governing immune receptor–antigen binding in order to predict the antigen binding status of previously unseen immune receptors. Many studies assume that the antigen binding status of an immune receptor may be determined by the presence of a short motif in the complementarity determining region 3 (CDR3), disregarding other amino acids. To test this assumption, we present a method to discover short motifs which show high precision in predicting antigen binding and generalize well to unseen simulated and experimental data. Our analysis of a mutagenesis-based antibody dataset reveals 11 336 position-specific, mostly gapped motifs of 3–5 amino acids that retain high precision on independently generated experimental data. Using a subset of only 178 motifs, a simple classifier was made that on the independently generated dataset outperformed a deep learning model proposed specifically for such datasets. In conclusion, our findings support the notion that for some antibodies, antigen binding may be largely determined by a short CDR3 motif. As more experimental data emerge, our methodology could serve as a foundation for in-depth investigations into antigen binding signals.