bluuebunny/crossref_metadata_2025_split huggingface dataset:
Blind Quality Evaluator for Multi-Exposure Fusion Image via Joint Sparse Features and Complex-Wavelet Statistical Characteristics
Authors: Yang Benquan , Cui Yueli , Liu Lihong , Chen Guang , Xu Jiamin , Lin Junhao 
Published: April 2024

Abstract

Multi-Exposure Fusion (MEF) technique aims to fuse multiple images taken from the same scene at different exposure levels into an image with more details. Although more and more MEF algorithms have been developed, how to effectively evaluate the quality of MEF images has not been thoroughly investigated. To address this issue, a blind quality evaluator for MEF image via joint sparse features and complex-wavelet statistical characteristics is developed. Specifically, considering that color and structure distortions are inevitably introduced during the MEF operations, we first train a color dictionary in the Lab color space based on the color perception mechanism of human visual system, and extract sparse perceptual features to capture the color and structure distortions. Given an MEF image to be evaluated, its components in both luminance and color channels are derived first. Subsequently, these obtained components are sparsely encoded using the trained color dictionary, and the perceived sparse features are extracted from the produced sparse coefficients. In addition, considering the insensitivity of sparse features towards weak structural information in images, complex steerable pyramid decomposition is further performed over the generated chromaticity map. Consequently, perceptual features of magnitude, phase and cross-scale structural similarity index are extracted from complex wavelet coefficients within the chromaticity map as quality-aware features. Experimental results demonstrate that our proposed metric outperforms the existing classic image quality evaluation metrics while maintaining high accordance with human visual perception.
Day – ahead traffic flow forecast using LSTM and Cuckoo Search Optimization
Authors: V Rajalakshmi , P Sharon Femi , A Kala 
Published: April 2024

Abstract

To monitor and regulate the traffic flow there is always a need to implement a dynamic and proactive traffic control system. Traffic flow forecasting is an essential and critical requirement for developing a traffic control system in intelligent transportation systems (ITS). In this paper, traffic flow forecasting is done on the England highways dataset of MIDAS site. LSTM networks play a critical role in sequence learning. It is used to forecast the patterns of forecast in the near future. Bio-inspired algorithms are commonly employed in numerous research to optimize the model's parameters. In this work, optimizing the performance of LSTM parameters is initiated with the Cuckoo Search Evolutionary Algorithm. Optimizing weights of various layers improves the performance of LSTM model. The cuckoo search optimization is used in finding the optimum weights and bias values for the LSTM network. The prior traffic flow data is imparted with intelligence using the LSTM – Cuckoo Search (CS) to provide adequate decisions to drivers. The results propagate that the Cuckoo Search has improved the performance of LSTM with the R2 of 0.98.
On-chip dual-microcomb based fiber-optic acoustic mapper
Authors: Yao Baicheng , He Xinyue , Tan Teng , Chang Bing , Xiang Jinlong , Yang Ziyi , Wang Shangce , Wang Zeping , Liang Yupei , Zhou Heng , Huang Yongjun , Yan Yingzhan , Su Yikai , Rao Yun-Jiang , Wu Yu , Guo Xuhan 
Published: April 2024

Abstract

Sense of hearing is a fairly basic but extraordinary feature in nature. It involves a complicated interplay of multi-dimensional signal detection, transmission, and analysis to fully accomplish acoustic detection, orientation, localization and recognition. Conventional design of a listening system typically necessitates numerous hardware components and intricate software interactions. Here, by assembling stabilized dual-microcombs, passive optical elements, optoelectronic photodetectors, electronic processors all-on-chip along with a fiber optic microphone array, we present an integrated optic-acoustic sensing and localization system that demonstrates coherent parallelism and compact integration. Our study establishes a paradigm in which an integrated soliton microcomb can simultaneously drive and demodulate networking sensors, thus overcoming the technological gaps between silicon-based photonics and fiber optic sensing technology. Utilizing two microcomb chips as the core, we boast sensitive optical acoustic detection, precise acoustic localization, and swift acoustic tracing capabilities together. Leveraging the unique advantages of soliton microcombs especially low noise and multi-channel outputs, we achieve a minimum detectable acoustic pressure of 0.79 μPa/Hz^(1/2) and a localization error of less than 1 cm. We further demonstrate its efficacy by detecting, tracking and identifying different targets such as a small drone in a field setting. Such a fusion of dual-comb technology and sensor-networking scheme could be a significant step towards the design of future listening systems.
Experimental characterization of wear and failure modes depending on different loading conditions applied on cylindrical polyamide 66 gears
Authors: Hriberšek Matija , Kulovec Simon 
Published: April 2024

Abstract
Engineering plastics are increasingly used for engineering applications, such as the automotive sector, e-mobility sector, and household appliances. The use of engineering plastics is conditioned by the lack of knowledge of their material properties, especially for gears. A selection of the materials for a given gear pair has a significant impact on the mechanical and thermal response of the whole system. In this paper, steel-Polyamide 66 gear pairs were tested under various torques to define failure life and associated failure modes of the polymer gears. Additionally, to discover new knowledge on the field of tribological contact between observed gear pairs, meshing temperatures were monitored and correlated with calculated wear coefficients of Polyamide 66 material according to technical guideline VDI2736-2. The results show that gear flank wear of PA66 HT is directly proportional to the meshing temperature and torque. Thermal melting is a characteristic failure mode of the polymer gears which are exposed to higher load levels. In the middle torques, the dominant failure mode is flank fracture known as pitch point fracture. The greater impact of the gear wear mechanism occurs at lower torques where initial crack propagation starts at the pitch point and ends in the tooth root area.
Characterizations and dielectric properties of Ag2O/Chitosan nanocomposites a combined impedimetric and computational study
Authors: Bashal Ali , Abdel-Basset T.A. , Alkayal Nazeeha S. , Habeeb Talaat , Khalil Khaled 
Published: April 2024

Abstract
We have synthesized nanocomposite films of silver oxide nanoparticles loaded on chitosan (Ag2O/CS) with different nanoparticle weight percentages. The Ag2O/CS nanocomposites were analysed using several techniques, including FTIR, SEM, EDX, TGA, and complete density functional theory analysis. The measured dielectric properties (via impedance spectroscopy) of the nanocomposite films are featured by an increase in the permittivity with the increase of the Ag2O content, suggesting that the underlying polarization mechanism is of the Maxwell-Wagner-Sillars type at the Ag2O/CS interfaces. Moreover, the film conductivities are governed by the correlated barrier hopping conduction between the nanoparticle sites. Semiempirical quantum calculations of the frontier orbitals show that the Ag2O/CS structure is stable and reactive. These electrochemical properties of our Ag2O/CS combined with the well-known antibacterial activities of Ag nanoparticles render the Ag2O/CS as a nanocomposite with a wide range of catalytic, biochemical and electrical applications.
Depleted housing elicits cardiopulmonary dysfunction after a single flaming eucalyptus wildfire smoke exposure in a sex-specific manner in ApoE knockout mice
Authors: Fiamingo Michelle , Toler Sydnie , Lee Kaleb , Oshiro Wendy , Krantz Todd , Evansky Paul , Davies David , Gilmour M. Ian , Farraj Aimen , Hazari Mehdi S. 
Published: April 2024

Abstract

Although it is well established that wildfire smoke exposure can increase cardiovascular morbidity and mortality, the combined effects of non-chemical stressors and wildfire smoke remains understudied. Housing is a non-chemical stressor that is a major determinant of cardiovascular health, however, disparities in neighborhood and social status have exacerbated the cardiovascular health gaps within the United States. Further, pre-existing cardiovascular morbidities, such as atherosclerosis, can worsen the response to wildfire smoke exposures. This represents a potentially hazardous interaction between inadequate housing and stress, cardiovascular morbidities, and worsened responses to wildfire smoke exposures. The purpose of this study was to examine the effects of enriched (EH) versus depleted (DH) housing on pulmonary and cardiovascular responses to a single flaming eucalyptus wildfire smoke (WS) exposure in male and female apolipoprotein E (ApoE) knockout mice, which develop an atherosclerosis-like phenotype. The results of this study show that cardiopulmonary responses to WS exposure occur in a sex-specific manner. EH blunts adverse WS-induced ventilatory responses, specifically an increase in tidal volume (TV), expiratory time (Te), and relaxation time (RT) after a WS exposure, but only in females. EH also blunted a WS-induced increase in isovolumic relaxation time (IVRT) and the myocardial performance index (MPI) 1-wk after exposures, also only in females. Our results suggest that housing alters the cardiovascular response to a single WS exposure, and that DH might cause increased susceptibility to environmental exposures that manifest in altered ventilation patterns and diastolic dysfunction in a sex-specific manner.
Ecological Inference for Relative Risks, with Application to Infrequent Mental Health Events
Authors: Congdon Peter 
Published: April 2024

Abstract

Mental health outcomes may show wide contrasts in incidence or prevalence between ethnic groups, often for relatively infrequent events (e.g. suicide). To gauge such relativities, one ideally seeks age standardised comparisons, given that ethnic groups may differ in age structure, and that the events themselves often show wide disparities in risk between ages. It is also advantageous to provide a geographically disaggregated (e.g. neighbourhood) perspective on relative risk differences, with sampling densities (e.g. Poisson) appropriate to possibly infrequent events. Often only total disease counts (with no socio-demographic disaggregation) are available for neighbourhoods, though data on ethnic mix (e.g. Census data) are available. We consider in this paper an ecological inference method, applicable to infrequent events, which can use such information, and which furthermore takes account of the impacts of neighbourhood age structure on the health outcome. We consider two case studies to estimate age standardised relative risks by neighbourhood and ethnicity, one involving suicide, the other concerning psychosis. The analyses are for 6856 neighbourhoods in England.
Stability Investigations and Insights on the Pseudodepth Domain- Based (1 st -order Riemannian Solver) Seismic Wavefield Modeling
Authors: MUHAMMED HUSSEIN , Li Zhenchun , Gadelmula Abdelhafiz 
Published: April 2024

Abstract
The enlightenment of Earth's nether regions presents itself as a matter of profound intrigue. A novel and avant-garde method, known as the Least-squares reverse-time migration is considered as the cutting-edge seismic imaging technique due to its outstanding structural irradiating. Its procedure consumes a lot of time and occupies huge memory amounts thus wavefield extrapolation has been juxtaposed in a Riemannian coordinate system to transform the vertical axis to overcome the above-mentioned issues and oversampling/aliasing for modelling deeper parts of the seismic section. The problem of stabilizing this Riemannian wavefield arises from the introduction of the mapping velocity and the vertical axis operator that converts the finite difference solution partially from time into frequency domains. For every point in the Cartesian space, there is a corresponding vertical-time point with the coordinates hence we can interpolate the reconstructed source wavefield by drawing a Cartesian-to-Riemannian mapping function. Our stability and convergence analysis suggests that the spatial derivatives of 1st order Riemannian axis can be approximated by a Fourier pseudo-spectral methods as well as fast-Fourier transform using a special Gaussian-like impulse function to generate the vector-matrix of the source term within the finite-difference operator, in addition to the mapping velocity, which is a differential form of the initial input velocity model, manifestly controls the CFL conditions of the associated Riemannian-finite difference operator. Numerical and synthetic examples indicate that this approach is more stable and efficient in extrapolating a smooth Riemannian wavefield while maintaining Claerbout’s principle for locating subsurface reflectors also choosing an appropriate sampling rate for the new vertical axis is related inversely by the maximum frequency of the impulse wavelet and directly with minimum velocity value in the given model.
Synthesis and characterization of copper bismuth oxide/rGO nanocomposite by sonochemical method and investigation of its photocatalytic performance in dibenzothiophene desulfurization
Authors: Tahmasbi Mohammad Reza , Mousavi-Kamazani Mehdi , Keramati Narjes 
Published: April 2024

Abstract

In order to improve the efficiency of desulfurization of petroleum derivatives, copper bismuth oxide/reduced graphene oxide (Bi7.38Cu0.62O11.69/rGO) nanocomposite was synthesized by sonochemical method. The synthesized nanocomposite was identified by FESEM, FT-IR, XRD, DRS, and EDS analyses. Based on XRD analysis, pure copper bismuth oxide is formed using hydrazine, while using sodium hydroxide does not lead to the desired product. FESEM images showed that the size of Bi7.38Cu0.62O11.69 particles decreases in the presence of rGO. The desulfurization results showed that the presence of rGO causes a significant increase in efficiency due to increased light absorption and reduced recombination rate, so that Bi7.38Cu0.62O11.69/rGOnanocomposite has an efficiency of 87%, while pure Bi7.38Cu0.62O11.69 has an efficiency of 68%. Also, it was found that the purity of the copper bismuth oxide has a great effect on the photocatalytic efficiency, so that by calcining the product and removing the impurities, the efficiency increases from 55% to 68%.
Quality Evaluation of Urban Low-carbon Development based on Fermatean Fuzzy Regret Theory Model
Authors: Pan Yan 
Published: April 2024

Abstract
In response to the complexity and fuzziness of the evaluation environment for urban low-carbon development quality, and by integrating the regret avoidance psychology of experts in the evaluation process, this paper proposes a Fermatean fuzzy regret-theory multi-index decision-making model incorporating bidirectional projection. Firstly, considering the defects of misjudgment of similarity and unlimited range of projection values in the projection, a novel Fermatean fuzzy bidirectional projection method is proposed. Secondly, since the conflictiveness of indexes should only be related to the degree of association of the indexes, independent of the positive and negative signs, a CRITIC (CRiteria Importance Through Intercriteria Correlation) weight assignment method harmonizing with Fermatean fuzzy bidirectional projection is proposed to eliminate the influence of the positive and negative signs of similarity on the weighting results. Finally, the viability of the presented model is verified by urban low-carbon development quality cases, and a comparison analysis is also conducted to show the superiority of the presented method.
AstroPlant Sentinel: Next-Gen Space AgricultureMonitoring and ADS
Authors: Das Rajdeep , Sharma Vaishnavi , Kumari Priyanka 
Published: April 2024

Abstract
With the expanding role of humanity in outer space, sustainable food production becomes crucial. The success of space farming is determined as much by thecultivation of crops as by the rigorous monitoring and control of environmentalfactors. In this undertaking, we are pleased to offer you AstroPlant Sentinel, acombination of IoT technology with ADS custom-made for space farming. Ourresearch stresses the need for real-time environmental monitoring within spaceshuttles, where any deviations from environmental conditions may lead to plantillness and drop in crop yield. To address this challenge, we designed a miniature IoT environment equipped with three key sensors: Soil Moisture Sensor,Sensor for Temperature and Humidity, and Air Quality Sensor. Through intensive experiments and sensitivity analysis, we discover what are the thresholdsof these factors that are suitable for plant growth in space. However, recognizing the inherent variability and occasional aberrations in environmental data,we delve into the development and evaluation of three distinct machine learningmodels for anomaly detection: One-Class Support Vector Machines (OCSVM),Isolation Forest, and Density-Based Spatial Clustering of Applications with Noise(DBSCAN). Essentially, our results prove that DBSCAN outperforms other techniques in finding out the anomalies in fluctuating data. Lastly, our methodologyfor data visualization using a Software as a Service (SaaS) cloud platform isthoroughly detailed, which improves interpretability and enables informed decision making. This paper identifies the synergy between IoT-based monitoring and advanced anomaly detection, which is instrumental in enhancing both therobustness and efficacy of space agriculture attempts.
Cross-Species Genetic Marker Adaptation: Enhancing Genetic Defect Screening in Water Buffaloes Using Cattle Genetic Markers
Authors: Paraguison-Alili Rubigilda , Cacho-Rubia Roxan Grace , Flores Ester B. , Mingala Claro N. 
Published: April 2024

Abstract

Genetic defect screening plays a fundamental role in advancing the livestock industry by systematically identifying inherited aberrations in various animal species. This study applies DNA markers originally established for screening genetic defects in cattle, such as Bovine leukocyte adhesion deficiency (BLAD), Deficiency of uridine monophosphate synthase (DUMPS), Citrullinemia, and Freemartin, to water buffaloes. Evaluating genomic relatedness between cattle and water buffaloes provides insights into leveraging well-established genetic information from cattle. A combination of PCR and Restriction Fragment Length Polymorphisms (RFLP) was employed to identify inherited heterozygous and recessive allele conditions. DNA sequencing validated PCR products, confirming specific base changes. Notably, a lethal mutation in cattle's BLAD was identified in normal buffaloes, raising questions about the suitability of established target gene markers for water buffaloes. However, the study highlights the efficacy of cattle gene markers for DUMPS, Citrullinemia, and Freemartinism in screening genetic defects in water buffaloes. This research establishes a standardized molecular method for breeders, facilitating the identification of at-risk animals and carriers to eliminate recessive defect genes in the Philippine livestock industry.
Identification of a novel favorable STAD subtype based on multi-omics data Integration
Authors: Dai Hui , Ren Jing , Wang Chun , Zhang Haotian , Lv Xinhui , Yang Jiyuan , Wang Xudong 
Published: April 2024

Abstract

Gastric cancer remains a leading cause of cancer-related deaths with considerable heterogeneity among patients. Accurate classifications play a pivotal role in prognosis prediction and personalized therapeutic strategies. Considering the practicality of typing and its closer clinical relevance, in this study, we leveraged multi-omics data, specifically transcriptomics RNA-sequencing (mRNA) and DNA methylation data from the TCGA-STAD cohort, for clustering analysis. The integration of multi-omics data concerning prognosis facilitated cluster analysis through the implementation of ten clustering algorithms. A total of 359 gastric cancer (GC) samples were collected and categorized. Moreover, external validation datasets from diverse sequencing technologies corroborated the robustness of the clustering model. The relationships between the separate subgroups and clinical pathological characteristics, immune infiltration characteristics, immune checkpoint, genomic mutation and so on were meticulously examined. Meanwhile, potential responses to immunotherapy and chemotherapy were also assessed to enhance the clinical applicability of the molecular subtypes. Three subtypes (CS1, CS2, and CS3) were identified for gastric cancer, and exhibiting distinct prognostic status, activation of cancer-related pathways, TME compositions, immune checkpoints, sensitivity to chemotherapy and immunotherapy.
Preparation and characterization of copper ferrite functionalized graphitic carbon nitride nanocomposite as an efficient and reusable catalyst for synthesis of spirooxindol quinazolinone derivatives
Authors: Shaabani Raheleh , Naeimi Hossein 
Published: April 2024

Abstract

In this study, CuFe2O4/g-C3N4 nanocomposite was fabricated and characterized by using various techniques including FT-IR, XRD, EDX, VSM and FE-SEM. Furthermore, the prepared nanocomposite was applied as a highly effective, heterogeneous and recyclable catalyst for multicomponent synthesis of spirooxindole derivatives as target heterocyclic compounds. Additionally, the current research was shown unique advantages such as; simple synthesis of the catalyst, remarkable magnetic properties, convenient separation of the catalyst using a permanent magnet and the application of cheap and available precursors. At the end of the reaction, it was gained the high yields of products at short reaction times. This indicates the strong catalytic performance of the catalyst that was prepared.
Emerging contaminants in municipal sewage/sludge: occurrence, risk assessment, and treatment technologies
Authors: Wang Bing , Jiang Tao , Wang Nana , Zou Qianqian 
Published: April 2024

Abstract
Emerging contaminants (ECs) have received widespread attention globally due to their potential ecological and human health impacts. Sludge, a byproduct of the sewage treatment processes, accumulates amounts of ECs or toxic byproducts that have not been fully degraded. Without proper treatment, it may affect the resource utilization of sludge and damage the ecological environment. Therefore, it is significant to investigate the generation mechanism of ECs in municipal sewage/sludge and explore effective treatment technologies to remove them. This chapter first reviews the main occurrence of ECs in municipal sewage/sludge. Secondly, the potential environmental risks of ECs are evaluated, and the treatment technologies of different ECs are discussed in detail, such as anaerobic consumption, aerobic composting, and advanced oxidation. Finally, suggestions and prospects are put forward for the removal of ECs from municipal sewage/sludge.