bluuebunny/crossref_metadata_2025_split huggingface dataset:
Hybrid modeling approaches for agricultural commodity prices using CEEMDAN and time delay neural networks
Authors: Pandit Pramit , Sagar Atish , Ghose Bikramjeet , Paul Moumita , Kisi Ozgur , Vishwakarma Dinesh Kumar , Mansour Lamjed , Yadav Krishna Kumar 
Published: November 2024

AbstractImproving the forecasting accuracy of agricultural commodity prices is critical for many stakeholders namely, farmers, traders, exporters, governments, and all other partners in the price channel, to evade risks and enable appropriate policy interventions. However, the traditional mono-scale smoothing techniques often fail to capture the non-stationary and non-linear features due to their multifarious structure. This study has proposed a CEEMDAN (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise)-TDNN (Time Delay Neural Network) model for forecasting non-linear, non-stationary agricultural price series. This study has evaluated its suitability in comparison with the other three major EMD (Empirical Mode Decomposition) variants (EMD, Ensemble EMD and Complementary Ensemble EMD) and the benchmark (Autoregressive Integrated Moving Average, Non-linear Support Vector Regression, Gradient Boosting Machine, Random Forest and TDNN) models using monthly wholesale prices of major oilseed crops in India. Outcomes from this investigation reflect that the CEEMDAN-TDNN hybrid models have outperformed all other forecasting models on the basis of evaluation metrics under consideration. For the proposed model, an average improvement of RMSE (Root Mean Square Error), Relative RMSE and MAPE (Mean Absolute Percentage Error) values has been observed to be 20.04%, 19.94% and 27.80%, respectively over the other EMD variant-based counterparts and 57.66%, 48.37% and 62.37%, respectively over the other benchmark stochastic and machine learning models. The CEEMD-TDNN and CEEMDAN-TDNN models have demonstrated superior performance in predicting the directional changes of monthly price series compared to other models. Additionally, the accuracy of forecasts generated by all models has been assessed using the Diebold-Mariano test, the Friedman test, and the Taylor diagram. The results confirm that the proposed hybrid model has outperformed the alternative models, providing a distinct advantage.
Novel optical optode for selective detection and removal of ultra-trace level of mercury ions in different environmental real samples
Authors: Dayra Hager A. , Abdelaal Magdy Y. , Khalifa Magdi E. , Abdallah A. B. 
Published: November 2024

AbstractOwing to the high cost and unavailability of different analytical techniques, there is an urgent need to develop new techniques not only for detecting but also removing mercury ions in real samples. Thus, an optical chemical sensor based on the anchoring of phenanthraquinone monophenylthiosemicarbazone in a plasticized cellulose triacetate membrane was fabricated and applied to the recognition and removal of mercury ions from aqueous solutions. The synthesized optode was characterized by FT-IR, SEM, AFM, and thermal analysis. Several parameters, including the pH, temperature, contact time, washing solvent, and washing time, were optimized. Under optimal conditions, a promising optode film platform was utilized for sensing mercury ions, and the concentrations were calculated based on colorimetric analysis (Histogram, RGB) of digital images, visualization, and spectrophotometry. Also, an optical optode was used for complete adsorption of mercury ions from aqueous solutions. In addition, the regeneration of the synthesized optode was evaluated using 0.1 mol L− 1 nitric acid, which effectively removed all adsorbed mercury ions. The obtained data indicated good linearity in the sensing and adsorption of Hg2+ over a concentration range of 0.005–5000 µgL− 1 with a low limit of detection (LOD = 0.066 µgL− 1) and limit of quantification (LOQ, 0.22 µgL− 1). Furthermore, it showed good distinctions in the presence of coexisting ions, high stability (five months), good applicability, and reproducibility (RSD = 1.31%), making it a promising sensor for Hg2+ detection. On the other hand, the kinetic studies revealed that the pseudo-second-order was the best model for describing the adsorption behavior of mercury ions on the optode surface. Also, the thermodynamic parameters indicate spontaneous (ΔG0 < 0) and endothermic (ΔH0 < 0) reactions. Also, the maximum adsorption capacity was found to be 73.2 mg g− 1. Thus, the optodes were successfully applied for the detection and/or removal of Hg2+ in different real samples, including cucumber, fish, soil, and water samples, with excellent recoveries of 98.1–99.5%.
Effects of miR-204-5p modulation on PAX6 regulation and corneal inflammation
Authors: Abbasi Mojdeh , Amini Maryam , Moustardas Petros , Gutsmiedl Quirin , Javidjam Dina , Suiwal Shweta , Seitz Berthold , Fries Fabian N. , Dashti Ava , Rautavaara Yedizza , Stachon Tanja , Szentmáry Nóra , Lagali Neil 
Published: November 2024

AbstractCongenital aniridia is a rare eye disease characterized by loss of PAX6 protein leading to aniridia-associated keratopathy that significantly reduces vision. The miR-204-5p is a possible regulator of PAX6 function and here we evaluate its effect in multiple in vitro and in vivo models. In vitro, miR-204-5p overexpression suppressed vascular factor ANGPT1 in human limbal stem cells (T-LSC) and Pax6-knockdown LSC (mut-LSC), and in primary human limbal epithelial cells (LEC) at the gene and protein levels and following LPS stimulation. However, miR-204-5p inhibited VEGFA expression only in mut-LSCs and LPS-stimulated LEC. Also, miR-204-5p increased PAX6 expression in mut-LSC and differentiated corneal epithelial cells, but not in LEC. Topical miR-204-5p after LPS-induced keratitis in mice failed to suppress Vegfa, Angpt1, Il-1β, and Tnf-α or rescue Pax6 levels in contrast to in vitro results, although it significantly reduced the inflammatory infiltrate in the cornea. In Pax6Sey/+ aniridia mice, miR-204-5p did not rescue PAX6 levels or suppress Vegfa, Angpt1, or inhibit the ERK1/2 pathway. While short-term miR-204-5p treatment effectively suppresses VEGFA and ANGPT1 and enhances PAX6 expression in multiple corneal epithelia, effects are variable across primary and immortalized cells. Effects of longer-term in vivo treatment, however, require further study.
Optomechanical motions of gold dimer’s spin, rotation and revolution manipulated by bessel beam
Authors: Liu Chao-Kang , Ku Yun-Cheng , Kuo Mao-Kuen , Liaw Jiunn-Woei 
Published: November 2024

AbstractThe optomechanical motion of a gold nanoparticle (GNP) dimer—a pair of optically bound GNPs—in fluid, manipulated by a Bessel beam, is theoretically studied using the multiple multipole (MMP) method. Since a Bessel beam possesses orbital angular momentum (OAM) and spin angular momentum (SAM) simultaneously, complicated rigid-body motions of the dimer can be induced. The mechanism involves the equilibrium between the optical force with the reactive drag force exerted by the fluid. Our results demonstrate that the dimer rotates around its center of mass (COM), while the COM performs an orbital revolution around the optical axis. Additionally, each individual GNP undergoes spinning. The directions of the GNPs’ spin and the orbital revolution of COM depend on the handedness and the order (topological charge) of Bessel beam, respectively. Nevertheless, the rotation direction of the dimer depends on the size of GNP. In the case of a smaller dimer, the direction of dimer’s rotation with respect to the COM is consistent with the handedness of the light. Conversely, a larger dimer performs a reverse rotation, accompanied by a precession during the orbital revolution. There are multiple turning points in the radius of the GNP for the alternating rotation of the dimer caused by positive or negative optical torque. Our finding may provide an insight to the optomechanical manipulation of optical vortexes on the motions of GNP clusters.
Affine transform representation for reducing calibration cost on absorption-based LWIR depth sensing
Authors: Kushida Takahiro , Nakamura Ryutaro , Matsuda Hiroaki , Chen Wenhao , Tanaka Kenichiro 
Published: November 2024

AbstractMultispectral long-wave infrared (LWIR) ranging is a technique that estimates the distance to the object based on wavelength-dependent absorption of LWIR light through the air. Prior works require time-consuming measurements for calibration and solve non-linear inverse problems, which sometimes falls into a local minimum. In this paper, we propose a linear representation that connects the measurements and the scene parameters using the affine matrix. In this representation, the distance and the temperature of the object can be obtained as a closed-form solution and the calibration cost can be reduced to at least three observations. In real-world experiments, we demonstrate that our method is effective to reduce the calibration cost while keeping the precision of the depth estimation.
Environmental challenge trials induce a biofluorescent response in the green sea urchin Strongylocentrotus droebachiensis
Authors: Juhasz-Dora Thomas , Lindberg Stein-Kato , James Philip , Evensen Tor , Ortega Samuel 
Published: November 2024

AbstractStress in sea urchins leads to high mortality and economic losses in both the environment and aquaculture. The green sea urchin Strongylocentrotus droebachiensis has been documented emitting complex biofluorescence, yet how this responds to external stressors is unknown. Adult sea urchins (n = 210) were divided between control (n = 30) and experimental groups (n = 180), using three transport variables: out of water, in water at elevated temperatures, (warm-water) and in water at seawater temperature (cold-water). Hyperspectral imaging of external fluorescence and fluorospectrometric analysis on coelomic fluid was measured at five intervals (hour 0,3,6,9,12). External green emissions (∼580 nm) responded to all treatments, peaking at h9. External red emissions (∼680–730 nm) in the cold-water remained low until an h9 peak. The warm water increased emissions at each interval, peaking at h9. The out of water gradually increased, with the highest at h12. The coelomic fluid fluorescence (∼680 nm) was low to nonexistent except in warm-water, whose elevated levels suggest that fluorescent emissions are a measurable byproduct of internal adaptation(s) to stress. Early detection of fluorescent emissions (broken spines, lesions) may prevent economic losses. The observed link between fluorescence and the applied stressors provides a baseline for developing non-invasive technology for improving echinoderm welfare.
Optimization of curdlan production and ultrasound assisted extraction processes from Priestia megaterium
Authors: Aquinas Natasha , Ramananda Bhat M. , Selvaraj Subbalaxmi 
Published: November 2024

AbstractIn this study, the upstream and downstream production processes of curdlan from Priestia megaterium were optimized to enhance its yield. Additionally, a novel extraction method was developed for curdlan recovery. Optimization studies were conducted using Central composite design (CCD). Curdlan yield improved from 0.15 g/L (unoptimized) to 0.46 g/L (3-fold increase) when fermentation was carried out in CCD-optimized media of (w/v) sucrose 20%, urea 0.1%, KH2PO4 0.02%, agitation speed 250 rpm. To further enhance curdlan yield during extraction, ultrasonication was incorporated as a novel step into the conventional method of acid/alkali-assisted curdlan recovery. A two-step optimization was chosen for extraction, namely, one-factor-at-a-time (OFAT) and CCD, wherein the optimized extraction parameters were determined to be 25 s sonication, 1 N NaOH, and 2 h solubilization time. The curdlan yield improved by 1.5-fold (0.70 g/L) post optimization, in comparison with unoptimized conventional extraction step. Finally, the biopolymer was validated through characterization by nuclear magnetic resonance (NMR) which showed characteristic curdlan spectra in the13C and1H NMR studies. To the best of our knowledge, this study represents the first documented report on curdlan extraction using this novel method of ultrasonication.
Neurotrophic and synaptic effects of GnRH and/or GH upon motor function after spinal cord injury in rats
Authors: Martínez-Moreno C. G. , Calderón-Vallejo D. , Díaz-Galindo C. , Hernández-Jasso I. , Olivares-Hernández J. D. , Ávila-Mendoza J. , Epardo D. , Balderas-Márquez J. E. , Urban-Sosa V. A. , Baltazar-Lara R. , Carranza M. , Luna M. , Arámburo C. , Quintanar J. L. 
Published: November 2024

AbstractThoracic spinal cord injury (SCI) profoundly impairs motor and sensory functions, significantly reducing life quality without currently available effective treatments for neuroprotection or full functional regeneration. This study investigated the neurotrophic and synaptic recovery potential of gonadotropin-releasing hormone (GnRH) and growth hormone (GH) treatments in ovariectomized rats subjected to thoracic SCI. Employing a multidisciplinary approach, we evaluated the effects of these hormones upon gene expression of classical neurotrophins (NGF, BDNF, and NT3) as well as indicative markers of synaptic function (Nlgn1, Nxn1, SNAP25, SYP, and syntaxin-1), together with morphological assessments of myelin sheath integrity (Klüver-Barrera staining and MBP immunoreactivity) and synaptogenic proteins (PSD95, SYP) by immunohystochemistry (IHC) , and also on the neuromotor functional recovery of hindlimbs in the lesioned animals. Results demonstrated that chronic administration of GnRH and GH induced notable upregulation in the expression of several neurotrophic and synaptogenic activity genes. Additionally, the treatment showed a significant impact on the restoration of functional synaptic markers and myelin integrity. Intriguingly, while individual GnRH application induced certain recovery benefits, the combined treatment with GH appeared to inhibit neuromotor recovery, suggesting a complex interplay in hormonal regulation post-SCI. GnRH and GH are bioactive and participate in modulating neurotrophic responses and synaptic restoration under neural damage conditions, offering insights into novel therapeutic approaches for SCI. However, the intricate effects of combined hormonal treatment accentuate the necessity for further investigation that conduce to optimal and novel therapeutic strategies for patients with spinal cord lesions.
A novel proxy for energy flux in multi-era wildfire reconstruction
Authors: Theurer Thomas , Mauquoy Dmitri , Hadden Rory , Muirhead David , Campbell-Lochrie Zakary , Córdoba Sergio Vargas , von Scheffer Clemens , Coathup Daniel Thomas 
Published: November 2024

AbstractEscalations in wildfire activity are of significant global concern, particularly within vulnerable wetland ecosystems integral to natural carbon sequestration and climate change mitigation. Our understanding and management of future wildfire activity may be better contextualised through the study of historic and ancient fire records, independent of human influence. Methods of study include ‘geothermometry’ - approximating ancient fire intensity from temperature-dependent changes in the chemistry of fossil charcoal. Though well established in their relation to experimental charcoalification, these methods still fail to quantify the true intensity of ancient fires, as a measure of energy release. As a result, their applicability, and contributions to the characterisation of modern fire activity, remain uncertain. Here, we present a novel measure of wildfire energy release, as a proxy for true intensity, through the co-application of cone calorimetry and Raman spectroscopy of charcoal. By applying a range of wildfire heat fluxes to variable peatland fuel mixes, this research demonstrates the complexity in correlating fire behaviour and charcoal microstructure. Further statistical analyses suggest a correlation between spectroscopic results, measures of CO and CO2 release, and fire severity. This offers a principal measure of ancient wildfire intensity, consistent with modern practices in wildfire modelling, monitoring, and management.
Catastrophic impact of extreme 2019 Indonesian peatland fires on urban air quality and health
Authors: Grosvenor Mark J. , Ardiyani Vissia , Wooster Martin J. , Gillott Stefan , Green David C. , Lestari Puji , Suri Wiranda 
Published: November 2024

AbstractTropical peatland fires generate substantial quantities of airborne fine particulate matter (PM2.5) and in Indonesia are intensified during El Niño-related drought leading to severe air quality impacts affecting local and distant populations. Limited in-situ data often necessitates reliance on air quality models, like that of the Copernicus Atmosphere Monitoring Service, whose accuracy in extreme conditions is not fully understood. Here we demonstrate how a network of low-cost sensors around Palangka Raya, Central Kalimantan during the 2019 fire season, quantified extreme air quality and city-scale variability. The data indicates relatively strong model performance. Health impacts are substantial with estimates of over 1200 excess deaths in the Palangka Raya region, over 3200 across Central Kalimantan and more than 87,000 nationwide in 2019 due to fire-induced PM2.5 exposure. These findings highlight the need for urgent action to mitigate extreme fire events, including reducing fire use and landscape remediation to prevent peat fire ignition.
Increase in insurance losses caused by North Atlantic hurricanes in a warmer climate
Authors: Comola Francesco , Märtl Bernhard , Paul Hilary , Bruns Christian , Sapelza Klaus 
Published: November 2024

AbstractNorth Atlantic hurricanes are a major driver of property losses in the United States and a critical peril for the reinsurance industry globally. We leverage insurance loss data and stochastic modeling to investigate the impacts of projected changes in hurricane climatology on the insurance industry, for +2 °C and +4 °C warming scenarios. We find that, relative to the historical baseline 1950-2022, expected changes in wind speed and rainfall may increase hurricane losses by 5% −15% (+2 °C) and 10% − 30% (+4 °C), with greater impacts at lower return periods than in the tail. The historical 100-year loss event may therefore be exceeded on average every 80 years (+2 °C) and 70 years (+4 °C). The expected changes in average annual loss are projected to be 10% (+2 °C) and 15% (+4 °C), with the largest relative increase attributable to precipitation-induced losses. Under the extreme SSP5-8.5 scenario, the expected loss inflation due to climate change is thus on the order of 0.5% per annum.
The interrupted modern energy transition to LPG and the charcoal renaissance in urban Senegal
Authors: Rose Julian , Ankel-Peters Jörg , Hodel Hanna , Sall Medoune , Bensch Gunther 
Published: November 2024

AbstractFossil fuel subsidy removal may hinder access to clean fuels like LPG. Our analysis of urban Senegal shows that LPG use fell sharply after subsidies ended in 2009, despite later price drops. Households switched to charcoal, and the new availability of energy-efficient charcoal stoves made a return to LPG less appealing. This highlights how energy transitions among the poor are price sensitive, with implications for subsidy and carbon-tax policies.
Perceptions of Artificial Intelligence and ChatGPT by Speech-Language Pathologists and Students
Authors: Austin Julianna , Benas Keith , Caicedo Sara , Imiolek Emily , Piekutowski Anna , Ghanim Iyad 
Published: November 2024

Purpose:
This project explores the perceived implications of artificial intelligence (AI) tools and generative language tools, like ChatGPT, on practice in speech-language pathology.

Method:

A total of 107 clinician (
n
= 60) and student (
n
= 47) participants completed an 87-item survey that included Likert-style questions and open-ended qualitative responses. The survey explored participants' current frequency of use, experience with AI tools, ethical concerns, and concern with replacing clinicians, as well as likelihood to use in particular professional and clinical areas. Results were analyzed in the context of qualitative responses to typed-response open-ended questions.

Results:
A series of analyses indicated participants are somewhat knowledgeable and experienced with GPT software and other AI tools. Despite a positive outlook and the belief that AI tools are helpful for practice, programs like ChatGPT and other AI tools are infrequently used by speech-language pathologists and students for clinical purposes, mostly restricted to administrative tasks.

Conclusion:
While impressions of GPT and other AI tools cite the beneficial ways that AI tools can enhance a clinician's workloads, participants indicate a hesitancy to use AI tools and call for institutional guidelines and training for its adoption.
Multi‐omics graph convolutional networks for digestive system tumour classification and early‐late stage diagnosis
Authors: Zhou Lin , Zhu Zhengzhi , Gao Hongbo , Wang Chunyu , Khan Muhammad Attique , Ullah Mati , Khan Siffat Ullah 
Published: November 2024

AbstractThe prevalence of digestive system tumours (DST) poses a significant challenge in the global crusade against cancer. These neoplasms constitute 20% of all documented cancer diagnoses and contribute to 22.5% of cancer‐related fatalities. The accurate diagnosis of DST is paramount for vigilant patient monitoring and the judicious selection of optimal treatments. Addressing this challenge, the authors introduce a novel methodology, denominated as the Multi‐omics Graph Transformer Convolutional Network (MGTCN). This innovative approach aims to discern various DST tumour types and proficiently discern between early‐late stage tumours, ensuring a high degree of accuracy. The MGTCN model incorporates the Graph Transformer Layer framework to meticulously transform the multi‐omics adjacency matrix, thereby illuminating potential associations among diverse samples. A rigorous experimental evaluation was undertaken on the DST dataset from The Cancer Genome Atlas to scrutinise the efficacy of the MGTCN model. The outcomes unequivocally underscore the efficiency and precision of MGTCN in diagnosing diverse DST tumour types and successfully discriminating between early‐late stage DST cases. The source code for this groundbreaking study is readily accessible for download at https://github.com/bigone1/MGTCN.
Pressure sensor placement in water distribution networks based on enhanced Rafflesia optimization algorithm
Authors: Huang Yu‐Chung , Yang Qingyong , Chu Shu‐Chuan , Pan Jeng‐Shyang 
Published: November 2024

AbstractThe pressure sensor placement in the water distribution networks (WDNs) is a typical NP‐Hard problem. The goal is to monitor the running state of the whole network area by placing a small number of pressure sensors. This letter proposes an enhanced Rafflesia optimization algorithm (EROA) to solve this problem. The simulation results show that compared with the original ROA algorithm and the traditional intelligent optimization algorithms, the proposed EROA algorithm exhibits superior optimization capabilities and solution efficiency, and can provide a more reasonable sensor placement scheme.
Statistical resolution analysis of microwave computational imaging
Authors: Zhang Jinyu , Hu Taiyang , Xiao Mengxuan , Zhang Haoxuan , Wu Li , Xiao Zelong 
Published: November 2024

AbstractThis letter presents a model based on the Cramér–Rao bound theory for calculating the theoretical lower limit of imaging resolution of microwave computational imaging aided by re‐configurable metasurface. The model takes into account two crucial parameters: the signal‐to‐noise ratio and the number of measurements in the imaging process. By simultaneously considering the signal‐to‐noise ratio and the number of measurements, as well as other parameters, the model allows for a comprehensive assessment of the imaging resolution of the imaging system. In addition, both position estimation and intensity estimation are considered, and a new composite statistical resolution concept is proposed, which is more suitable for microwave computational imaging.