bluuebunny/crossref_metadata_2025_split huggingface dataset:
Discriminative Frontal Face Synthesis by Using Attention and Metric Learning
Authors: Cevikalp Hakan , Turgut Kaya , Topal Cihan 
Published: May 2024

Abstract

This paper introduces a novel approach for obtaining distinctive frontal facial representations from
collections of multiple facial images. The primary objective is to ensure that the profound features extracted
through a deep Convolutional Neural Network (CNN) from these learned facial representations exhibit notable separability
within the feature space. The acquisition of frontal facial representations capable of effectively representing entire
sets of images holds significant value as it considerably reduces the number of image samples requiring processing.
This acceleration proves especially advantageous during the classification testing phase.
The proposed method combines three fundamental components: attention mechanisms, adversarial methodologies, and metric learning strategies.
We adopt a U-Net architecture enhanced by attention modules for the facial aggregation network that generates frontal faces
that approximate multiple face images within image sets. Furthermore, we employ both a discriminator network and a pre-trained
facial classification network to successfully achieve the goals of adversarial and metric learning.
The experimental studies on different face recognition datasets demonstrate that using only attention mechanisms
and metric learning strategy is sufficient to synthesize discriminative frontal face images yielding high classification accuracies.
Bayesian Prediction of Afghanistan Annual Population Growth Using ARIMA and BSTS Models
Authors: Haqbin Sayed Rahmi Khuda , Khan Athar Ali 
Published: May 2024

Abstract

This study analyzes Afghanistan’s annual population growth using historical time series data from 1950 to 2022. The objectives are modeling, comparison, and prediction. Two models are employed: the auto-regressive integrated moving average (ARIMA) and the Bayesian Structural Time Series (BSTS). These models are fitted to the data, and their accuracy is evaluated using the Leave-One-Out Information Criteria (LOOIC), a Bayesian model selection criterion. The ARIMA(2,1,2) model is identified as suitable based on its minimum LOOIC value. The analysis is conducted using R statistical software and Stan proba-bilistic programming language for Bayesian modeling. The model comparison indicates the ARIMA model outperforms the BSTS model for this population growth data, exhibiting lower LOOIC values, suggesting better model fit and pre-dictive ability. The key result is a next-decade prediction of annual population growth from 2023 to 2032, generated using the preferred ARIMA(2,1,2) model. This model is deemed most appropriate for forecasting Afghanistan’s population growth, as evidenced by its superior performance on the LOOIC evaluation metric. The study demonstrates the effectiveness of the ARIMA model in capturing population growth dynamics and providing accurate forecasts for the upcoming decade.
Printing of Fine, Continuous, and Soft Fibers in Complex 3D Trajectories via Embedded Solvent Exchange
Authors: Tawfick Sameh , Eom Wonsiik , Hossain Mohammad Tanver , Parasramka Vidush , Kim Jeongmin , Siu Ryan , Sanders Kate , Piorkowski Dakota , Lowe Andrew , Koh Hyun Gi , De Volder Michael , Fudge Douglas , Ewoldt Randy 
Published: May 2024

Abstract

Nature uses hairy and fibrous structures for sensing and structural functions as observed in beetle hairs, mammalian whiskers, ear stereocilia, spider silks, and hagfish slime thread skeins. However, replicating these intricate, thin, high aspect ratio geometries has been a longstanding manufacturing challenge. It is currently not possible to fabricate 3D fibrous structures with microscale diameters and centimeter lengths. Here, we demonstrate 3D printing of fine hair having freeform trajectories and substrate fixation, with fiber diameters as fine as 1.5 µm and continuous length. This is realized via 3D printing by solvent exchange (3DPX), where the nozzle is embedded in a bath of microgranular gel. The yield stress rheology of the bath material allows the free-form placement of the fibers with bioinspired trajectories. The solvent exchange induces rapid radial solidification of the extruded polymer solutions at a rate of 2.33 µm/s, resulting in an elastic plateau modulus increase by four orders of magnitude, which prevents capillarity-induced fiber breakage. Furthermore, 3DPX offers unparalleled versatility in material selection, including the ability to print with commercially available thermoplastic polymers and nanocomposite materials. This study introduces and demonstrates the potential of 3DPX in replicating natural fibrous structures and paving the way for applications in biomimetic functions.
Numerical model of N-level cascade systems for atomic RF sensing applications
Authors: Bussey Liam , Kale Yogeshwar , Winter Samuel , Burton Fraser , Lien Yu-Hung , Bongs Kai , Costantinou Costas 
Published: May 2024

Abstract

The numerical model has been developed for atomic ladder (cascade) systems, which proves to be suitable for Rydberg RF sensors.This model is designed with the capability to be extended to N-level non-thermal atomic systems with ease. The adaptability of this model to Rydberg RF sensors is validated by comparing its results with those of earlier reported experiments. The numerical model here provides a good approximation to the experimental results. Thus, such a dynamic model can be useful for quantum technologists in designing, optimising, and developing the Rydberg RF-based sensors in a user-friendly manner.
Optimal Decentralized PI Control Design for a TimeDelay Benchmark Process Control System : AModified Whale Optimization Algorithm Approach
Authors: Mahapatro Soumya ranjan , Khadanga Rajendra Kumar , Das Mangal 
Published: May 2024

Abstract

This paper presents a new optimal decentralized PIcontroller design based on a modified hybrid whale optimization approach that is addressed for a typical industrial benchmark process control device. This research work determines the gain parameters of the PID controllers using the modified whale optimization algorithm (mWOA), aiming to achieve efficient performance by taking into account the integral time absolute error (ITAE) cost function. Further, the stability and performance of the proposed controller are verified by considering multiplicative input and output uncertainty. A comparison has beenmade between the performance of the proposed decentralized control method and a traditional decentralized PID controllerarchitecture. The obtained results highlight that the proposedcontroller exhibits superior control performance in inevitablepractical scenarios, such as in the face of additive stochasticuncertainties and load perturbation.
Rapid trajectory planning under multiple constraints in unknown environment
Authors: Xu Chenghong , Qi Wenjie , Zhang Zhigang , Ge Shuaishuai , Cheng Junjie , Zhang Xiangkui , Wang Guojian , Peng Fei 
Published: May 2024

Abstract

In this work, we propose a rapid trajectory planning method based on robotic kinematic constraints, specifically applied to ground-based robot navigation in unknown environments. We address this by formulating a Quadratically Constrained Quadratic Program problem, integrating third-order polynomials and multiple motion states to generate trajectories while considering kinematic constraints. The solutions derived from this method are utilized in initial path point generation. Leveraging the geometric properties of convex decomposition enables path point corrections, while employing Bézier curves enhances trajectory smoothness. We introduce a novel replanning framework encompassing local waypoint generation, trajectory collision detection, and replanning strategies. The viability and effectiveness of our method are demonstrated through simulations and real-world experiments.
Daytime Heat Wave Seasonal Variability over India
Authors: KUMAR PRAMOD 
Published: May 2024

Abstract

Around thirty percent of the world's population is already exposed to potentially life-threatening heat waves (HW) for a period of twenty days or plus yearly. The heat wave is not just intensifying, its spatiotemporal variability in the current climate crisis scenario is also persuading towards less warming-prone as well as the prominent warming regions. The present study is the discussion of seasonal variability in daytime heat waves (HW) over entire Indian states for 1991–2020 with the help of the NOAA Climate Prediction Center (CPC) global temperature dataset. The monthly variation of HW over India is observed, which clearly explains the more frequent occurrence of heat waves after the year 2000, especially during the months of March and July. However, the monthly HW distribution offers clarity on long-term events. March revealed an increasing trend of daytime HW in northern India, providing valuable insights into regional climate patterns.The month of July also evidences the rising rate of daytime HW, especially over the western and central parts of India. The seasonality index indicates that HW is rising during the months of March and June over the entire India as well. Understanding the HW seasonality of the entire Indian state will assist policymakers in designing regional-scale policies accordingly.
The anther transcriptome of cold-tolerant rice cultivars is largely insensitive to temperature changes
Authors: Yamamori Koichi , Ishiguro Seiya , Ogasawara Kei , Lubba Kayyis , Fujino Kaien , Onishi Kazumitsu , Sato Yutaka , Kishima Yuji 
Published: May 2024

Abstract

Many studies of stress tolerance in plants have characterized genes that show differences among a small number of lines with clearly distinct tolerance or sensitivity to the given stress. From the few cloned genes, it is difficult to genetically interpret intermediate tolerance or susceptibility levels and explain the complexity of stress responses and tolerance. In this study, we explored the changes in the transcriptome of anthers from 13 rice lines with different cold tolerance grown under control conditions or exposed to 4 days of cold stress to look for correlations between cold tolerance at the booting stage and expression levels. When examining the overall expression patterns in anthers at low temperature, the cold-tolerant lines tended to have relatively few highly expressed genes, and the expression levels of ribosome-related genes tended to be lower in cold-tolerant lines than in cold-sensitive lines. Importantly, we observed these different expression patterns between the cold-tolerant and -sensitive lines regardless of whether cold stress had been applied. Minimal expression changes under cold stress tended to be characteristic of the cold-tolerant lines, especially in repetitive sequences. We also identified unknown genes whose expression was cold responsive and common to all the lines studied. We conclude that rice lines whose transcriptome remains constant or insensitive in response to cold stress are more tolerant to low-temperature exposure during the booting stage than rice lines with more widespread expression changes.
Air Quality and Related Health Effects in Urban Areas of Iran During COVID-19 Pandemic Using AirQ + Tool
Authors: Ghobakhloo Safiye , Khoshakhlagh Amir Hossein , Gruszecka-Kosowska Agnieszka , Moda Haruna M , Fakhraee Ali , Alwan Nisreen , Tehrani Ashraf Mazaheri 
Published: May 2024

Abstract

The study investigated the impact of sudden frozen economy during the COVID-19 pandemic on air pollution and related health effects. The concentrations of PM10, PM2.5, SO2, O3, NO2, and CO for Air Quality Index (AQI) calculations were obtained from the Iranian Air Pollution Monitoring System in 7 provinces of central Iran between 2018 and 2021. AirQ+ software was employed to estimate health risk of respiratory mortality due to O3 exposure. AQI values decreased significantly during the lockdown (39.42%) and increased after (19.09%). Comparing to 2018–2019, air pollution in 2021 decreased by: PM2.5 4.73%, PM10 0.5%, SO2 50.4%, NO2 21.16%, and CO 10.84%. However, O3 concentrations increased during lockdown by 8.2%. Mortality due to O3 exposure increased during the lockdown comparing with the baseline in 2018–2019, except for Tehran province. Death rates due to long-term exposure to ambient O3 in investigated Iranian urban areas also increased. The use of air quality and emission standards is crucial towards the reduction of adverse health impacts associated with ambient air pollution.
Influence of the cross-equatorial Chaco-Northwest Africa pressure gradient on the South American monsoon
Authors: Menendez Claudio G. , Russmann Juan Eugenio , Giles Julian A. , Carril Andrea F. , Ledo Pablo Coria , Perron Remy , Turban Matthieu , Zaninelli Pablo G. 
Published: May 2024

Abstract

In December and January, the pressure contrast between the subtropical plains of South America (CHA) and northwestern Africa (WEM) reaches its maximum. This happens because of simultaneous and opposite surface pressure variations in these two regions. The low tropospheric winds flow from the tropical Atlantic into Amazonia and then divert southwards into the Gran Chaco region, suggesting a possible strengthening of this circulation associated with the CHA-WEM gradient. This article analyses the effect of a cross-equatorial CHA-WEM gradient intensification on the monsoon system of South America. The reinforcement of the gradient is associated with increased easterlies in the eastern equatorial Atlantic, a stronger low-level jet east of the Andes, increased moisture transport towards the CHA region, and reduced precipitation over much of Brazil. The large quasi-stationary wave in the upper troposphere, consisting of the Bolivian High and the Nordeste Low, weakens in amplitude, altering the return flow to the North Atlantic. Extratropical dynamics, such as Rossby wave trains that reach southern South America and the North Atlantic Oscillation, affect the variability in the CHA and WEM regions and modulate the CHA-WEM gradient intensity.
Camouflage Object Detection Based on Multi-Task Learning
Authors: Lin Di , Han Chunyu 
Published: May 2024

Abstract

Camouflaged object detection(COD) is an emerging and challenging visual detection task that can recognize and segment objects that perfectly blend into their surroundings. This paper introduces a novel multi-task learning framework(DEENet). It primarily features a dual-branch structure: one branch is the edge enhancement module based on multi-head attention mechanism, and the other branch is the semantic feature extraction module. We effectively utilize the gradient information to integrate two kinds of features through the gradient-induced transition module. Extensive experiments on three challenging test datasets demonstrate that our DEENet outperforms existing state-of-the-art methods.
A Computational Framework of Marangoni Convective Flow of Trihybrid Nanofluid with Thermo-Bioconvection and Oxytactic Microorganisms Based on the Extended Version of Xue and Yamada-Ota Models
Authors: Mohsin Bandar Bin , Abbas Munawar , Asamoah Joshua Kiddy K. , Rehman Muhammad Junaid U , Umer Muhammad , Mahariq Ibrahim 
Published: May 2024

Abstract

This paper exhibits a brief study of Marangoni convective, Activation energy, non-uniform heat generation, thermal-bioconvection and Oxytactic microorganisms, and time-dependent and stagnation point flow of MHD trihybrid nanofluid around a rotating sphere based on the extended model, referred to as the YOM (Yamada-Ota model) and XM (Xue model). The porous medium impact of Darcy-Forchheimer is included in the momentum equation. Some acceptable transformations are implemented to translate the governing system of PDEs (partial differential equations) and the relevant boundary conditions for computation. This model aims to evaluate the efficiency of two well-known trihybrid nanofluid models, Yamada-Ota and Xue. The shooting approach (BVP4C) is applied to derive the numerical estimates for the relevant differential expression system. The detailed analysis and simulation of trihybrid nanofluid flow patterns are facilitated by the variation in changing factors, which include temperature, velocity, concentration, and gyrotactic and Oxytactic microorganisms the profiles. The results indicate that the YOM performs significantly superior than the XM flow model in terms of heat transmission ability.
Galactia lindenii lectin type-II. Proposal of its potential use in diagnostic tools
Authors: Cortázar Tania M. , Vega Nohora A. , Reyes-Montaño Edgar A. , Ballen-Vanegas Manuel A. , Acosta Jinneth , Ricuarte Orlando 
Published: May 2024

Abstract

Galactia lindenii lectin type-II (GLL-II) belongs to the group of the legume lectins. The present study investigated the GLL-II staining patterns in histological sections of neoplastic and non-neoplastic thyroid tissues. Besides, hemagglutination assays (HA) using the GLL-II on red blood cells (RBCs) of different glycomic profile were performed, complementing previous results. The differential staining in Papillary Thyroid Cancer (PTC), Invasive Encapsulated Follicular Variant Papillary Thyroid Carcinoma (IEFV-PTC), Hashimoto's thyroiditis (HT), and non-neoplastic thyroid with goiter changes, together with the HA results and along with reviewed glycoprofiles of unhealthy conditions in other organs, allowed us to propose the potential utility of GLL-II in lectin platforms used to discriminate human pathological samples from normal ones. The present study shed light on potential applications of GLL-II in determining alterations of glycosylation patterns in specific cells, tissues, or body fluids, as well as glycotopes biomarkers of healthy or pathological conditions.
Logging curve reconstruction based on multivariate feature fusion of Attribute Co- occurrence Relationships and BiLSTM
Authors: Song YunPeng , Zhang XiaLin , Li ZhangLin , Weng ZhengPing , Li Xiang 
Published: May 2024

Abstract

Logging data play an important role in resource exploration, but in actual production, the logging attributes are often distorted or missing, which affects the geological work based on logging data. In this paper, a multivariate feature fusion model is proposed to address the problem of insufficient extraction of the intrinsic correlation between logging attributes in the traditional logging curve reconstruction method. Analogous to the method of extracting texture features in images, the Gray-Level Co-occurrence Matrices (GLCM) and Gradient Co-occurrence Matrices (GCM)are used to extract some kind of co-occurrence relationship that inherently exists between attribute values and trends, the fused features are fed into the BiLSTM network as a priori features in conjunction with the original attribute features, and the missing data are reconstructed using the trained model. The purpose of using this hybrid architecture is to extract the correlations between logging attributes of human interest in a previous step of network training, and to translate this type of important information into a form that can be learnt by the neural network, helping the network to learn more directly about the effective features in the logging data. In order to verify the effectiveness of the model, this paper uses some common evaluation metrics to assess the feature extraction results and model effectiveness. The experimental results of the test boreholes show that the reconstruction results of this paper's model compared with some common machine learning models (XGBoost, RNN, LSTM, BILSTM) are improved up to 33.18% and 44.12% in accuracy (MAE), and 21.67% and 19.85% in relevance (PCC), which has a practical engineering has some application value.
Curvature effects on stimulated parametric down-conversion process: an analog model
Authors: Akbari-Kourbolagh Saeed , Mahdifar Ali , Mohammadi Hamidreza 
Published: May 2024

Abstract

In this paper, we utilize an analog model of the general relativity and investigate the influence of spatial curvature on quantum properties of stimulated parametric down-conversion process. For this purpose, we use two-mode sphere coherent state as the input beams of the aforementioned process. These states are realization of coherent states of two-dimensional harmonic oscillator, which lies on a two-dimension sphere. We calculate the entanglement of output states of stimulated parametric down conversion process, measured by linear entropy, and show that it depends on the spatial curvature. So, by preparing the suitable two-mode sphere coherent states, it is possible to control the entanglement between the output states in the laboratory. In addition, we consider mean number and Mandel parameter of the output states of the process and also, their cross-correlation function, as the convince measures of non-classical behaviors.
Gaps in In-situ and Ex-situ Conservation of Threatened Medicinal Plant Species of Sri Lanka; Towards Their Effective Conservation
Authors: Jayaweera C. L. , Rajakaruna R. W.M.T.N. , Jayasuriya K. M.G.G. , Yakan D. M.D. 
Published: May 2024

Abstract

Sri Lanka is a country rich in biodiversity, harboring 1476 plant species with medicinal values, 307 of which are threatened. These medicinal plants have long been a valuable resource in traditional medicine, reflecting a well-documented historical legacy. However, the fact that 20% of these medicinal plants are listed as threatened poses a major conservation challenge. This study addresses the need for effective conservation strategies for threatened medicinal plants in Sri Lanka. The study focuses on using Geographic Information System (GIS) technology to monitor the distribution of threatened medicinal plants in Sri Lanka, prioritizing the species and areas for conservation and needs for ex-situ conservation. Using comprehensive datasets on the distribution of threatened medicinal plants from scientific literature, institutes, and herbarium collections, we assessed the priority areas within existing protected areas for in-situ conservation and ex-situ conservation status in Government Medicinal Plant Gardens. The research highlights the significance of Sinharaja rainforest and Peak Wilderness Sanctuary (Sri Padaya) as important protected areas with high species richness, housing 68 and 83 threatened medicinal plants respectively. Our findings show that a substantial proportion of threatened medicinal plants are distributed outside of protected areas, highlighting the need of establishing new protected areas as well as expanding the existing protected areas to conserve these unprotected threatened medicinal plants. Ex-situ conservation efforts were found to be crucial, especially for overexploited and endangered species. Our study highlights the importance of integrating GIS technology into conservation management to ensure the effective conservation of Sri Lankan medicinal plants for future generations.