Search

Photodepletion and action spectra of the laser-induced Ba...FCD3 fragmentation have been measured over the 16 075–16 380 cm-1 range. The observed band and peak structures allowed us to estimate the vibrational and rotational structures of the excited complex at the transition state configuration. The relative reaction probability PR(E) for the intracluster Ba...FCD3+hν→BaF+CD3 reaction has been determined over the cited energy range. PR(E) shows a peak structure with an energy spacing of 8.9 cm-1 which was attributed to an internal rotation of the CD3 group in the intermediate state. A comparison with previous Ba...FCH3 photofragmentation spectra reveals the dynamical role of the internal CX3 (X=H,D) motion which is manifested by the presence of rotational resonances in the laser-induced intracluster reaction. [ABSTRACT FROM AUTHOR]

Abstract: Deflection of a cold supersonic beam of Ba⋯FCH3 molecules has been observed when these molecules interact with both a static and a resonant oscillating electric field. The experimental observed deflection is about 0.2° when the employed resonant frequency between the two Stark levels of the FCH3 partner is 7.27MHz. This deflection angle is five orders of magnitude higher than the value one would expect from the FCH3 dipole moment and the employed RF field gradient. This new phenomenon suggests the possibility of a significant translational motion perpendicular to the beam axis which is induced by the resonant RF electric field on the cold and high density supersonic beam. [Copyright &y& Elsevier]

Using our spectroscopic technique based on molecular beam electric resonance without A and B fields [Chem. Phys. Lett. 341 (2001) 495] depletion spectra of both NO and (NO)2 were measured. The experimental results show an energy shift of the order of 10-8 cm-1 between the maxima of their (central) band spectra. This resolution enables to estimate changes in the NO dipole moment of the order of 0.6%, as this molecule forms the dimer. It is suggested that this technique could be used to investigate molecular rearrangements in gas-phase dynamical processes as well as to resolve variations in molecular bond distances in solvation or cluster processes. [Copyright &y& Elsevier]

The laser-induced intracluster (Ba⋯FCH3) + hν → BaF* (Ba*) + CH3 (CH3F) bimolecular reaction has been investigated in the 17 780–18 750 cm−1 energy range, corresponding to the electronic B-state of the Ba⋯FCH3 molecule. Both the photodepletion action spectrum and the action spectra of the reactive BaF and the non-reactive Ba photofragmentation channels were measured. The reaction probabilities of the two channels show an oscillatory behaviour with opposite phase. The oscillations in the energy dependence of both reactive and non-reactive probabilities for the cluster photofragmentation have been attributed to quantal interferences between the two photodissociation channels. [Copyright &y& Elsevier]

In this work, results on the Ba⋯FCH3+hν→BaF (Ba*)+CH3 (CH3F) reaction obtained under high laser field conditions are reported. We have found that the two-photodissociation channel yields, i.e. the reactive BaF and non-reactive Ba* products, exhibited opposite behaviour depending upon the laser fluence. Whereas the BaF yield rises as the laser fluence is increased, the Ba* yield decreases over the same laser fluence range. In other words, the product (Ba*)/(BaF) branching ratio changes significantly from low to high laser fluence in such a way that the (photoinitiated) Ba⋯FCH3→BaF+CH3 reaction may be controlled by changing the intensity of the excitation laser field. [Copyright &y& Elsevier]

We studied the time evolution of the wave packet formed by the excitation of the Ba⋯FCH3 complex with a 120 fs laser pulse at 745 nm to its electronic A˜′ state which is repulsive at the ground state geometry and decays to Ba and CH3F fragments. By applying probe pulses with different wavelengths of 400 and 267 nm, respectively, we observed strongly different time dependences of the pump–probe signals for the BaF+ and Ba+ ions determined by the Franck–Condon windows available in both cases. The experimental results are qualitatively confirmed by theoretical studies based on ab initio calculations of the corresponding potential energy surfaces. [Copyright &y& Elsevier]

In recent years, Convolutional Neural Network (CNN) models have demonstrated notable advancements in various domains such as image classification and Natural Language Processing (NLP). Despite their success in image classification tasks, their potential impact on medical image retrieval, particularly in text-based medical image retrieval (TBMIR) tasks, has not yet been fully realized. This could be attributed to the complexity of the ranking process, as there is ambiguity in treating TBMIR as an image retrieval task rather than a traditional information retrieval or NLP task. To address this gap, our paper proposes a novel approach to re-ranking medical images using a Deep Matching Model (DMM) and Medical-Dependent Features (MDF). These features incorporate categorical attributes such as medical terminologies and imaging modalities. Specifically, our DMM aims to generate effective representations for query and image metadata using a personalized CNN, facilitating matching between these representations. By using MDF, a semantic similarity matrix based on Unified Medical Language System (UMLS) meta-thesaurus, and a set of personalized filters taking into account some ranking features, our deep matching model can effectively consider the TBMIR task as an image retrieval task, as previously mentioned. To evaluate our approach, we performed experiments on the medical ImageCLEF datasets from 2009 to 2012. The experimental results show that the proposed model significantly enhances image retrieval performance compared to the baseline and state-of-the-art approaches.

There is a growing debate among scholars regarding the impact of artificial intelligence (AI) on the employment opportunities and professional development of people with disability. Although there has been an increasing body of empirical research on the topic, it has generally yielded conflicting findings. This study contributes to the ongoing debate by examining the linear and nonlinear effects of AI on the unemployment of people with disability in 40 countries between 2007 and 2021. Using the system Generalized Methods of Moments and panel smooth transition regression, the main conclusions are as follows. First, AI reduces the unemployment of people with disability in the full sample. Second, upon disaggregating the sample based on income level (high income/non-high income) and gender (men/women), the linear model only detects an inverse correlation between AI and unemployment among people with disability in high-income countries and among men, whereas it does not influence unemployment in non-high-income countries and women. Third, the panel smooth transition regression model suggests that the effects of AI on the unemployment of people with disability and among women are only observed once artificial intelligence interest search exceeds a specific threshold level. The effects of AI in non-high-income economies and among women are not significant in the lower regime, which confirms the nonlinear association between AI and the unemployment rate of people with disability. These findings have important policy implications for facilitating the integration of people with disability into the labor market., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

In this study, a newly developed setup based on laser-induced shockwave crystallization coupled with electric conductivity monitoring was employed to study the growth of crystals in supersaturated solutions and to investigate possible clustering in undersaturated solutions of potassium nitrate (KNO 3 ). A comparison was drawn between crystals induced by laser irradiation, by shockwaves, and spontaneously in terms of crystals' mean size, shape, and size distribution. The size distribution of produced crystals by shockwaves was also characterized in terms of laser irradiation time. The results show that produced crystals by shockwaves propagation have the sharpest size distribution and the smallest mean dimensions compared to crystals grown spontaneously or by direct laser induction. Real-time monitoring of nucleation was also performed in supersaturated solutions, while decrease in conductivity was observed in undersaturated solutions as a function of laser irradiation time., Competing Interests: The authors declare no competing financial interest., (© 2022 The Authors. Published by American Chemical Society.)

It has been noted that disease detection approaches based on deep learning are becoming increasingly important in artificial intelligence-based research in the field of agriculture. Studies conducted in this area are not at the level that is desirable due to the diversity of plant species and the regional characteristics of many of these species. Although numerous researchers have studied diseases on plant leaves, it is undeniable that timely diagnosis of diseases on olive leaves remains a difficult task. It is estimated that people have been cultivating olive trees for 6000 years, making it one of the most useful and profitable fruit trees in history. Symptoms that appear on infected leaves can vary from one plant to another or even between individual leaves on the same plant. Because olive groves are susceptible to a variety of pathogens, including bacterial blight, olive knot, Aculus olearius , and olive peacock spot, it has been difficult to develop an effective olive disease detection algorithm. For this reason, we developed a unique deep ensemble learning strategy that combines the convolutional neural network model with vision transformer model. The goal of this method is to detect and classify diseases that can affect olive leaves. In addition, binary and multiclassification systems based on deep convolutional models were used to categorize olive leaf disease. The results are encouraging and show how effectively CNN and vision transformer models can be used together. Our model outperformed the other models with an accuracy of about 96% for multiclass classification and 97% for binary classification, as shown by the experimental results reported in this study., Competing Interests: The authors declare that there are no conflicts of interest., (Copyright © 2022 Hamoud Alshammari et al.)

Computer science plays an important role in modern dynamic health systems. Given the collaborative nature of the diagnostic process, computer technology provides important services to healthcare professionals and organizations, as well as to patients and their families, researchers, and decision-makers. Thus, any innovations that improve the diagnostic process while maintaining quality and safety are crucial to the development of the healthcare field. Many diseases can be tentatively diagnosed during their initial stages. In this study, all developed techniques were applied to tuberculosis (TB). Thus, we propose an optimized machine learning-based model that extracts optimal texture features from TB-related images and selects the hyper-parameters of the classifiers. Increasing the accuracy rate and minimizing the number of characteristics extracted are our goals. In other words, this is a multitask optimization issue. A genetic algorithm (GA) is used to choose the best features, which are then fed into a support vector machine (SVM) classifier. Using the ImageCLEF 2020 data set, we conducted experiments using the proposed approach and achieved significantly higher accuracy and better outcomes in comparison with the state-of-the-art works. The obtained experimental results highlight the efficiency of modified SVM classifier compared with other standard ones., Competing Interests: The authors declare that there are no conflicts of interest., (Copyright © 2022 Olfa Hrizi et al.)

Here, we investigate effects of the size of pores in porous alumina powders on the broadening of the oxygen gas absorption line. The line broadening is caused by collisions of oxygen molecules with the pore walls and is extracted using gas in scattering media absorption spectroscopy (GASMAS), while the average pore size is determined using the gas adsorption technique. The average pore size of the samples studied lies within the range 10-40 nm. In this range, the contribution of the wall collision broadening is found to be approximately inversely related to the average pore diameter. Furthermore, the confined oxygen gas absorbance measured by GASMAS is found to be linearly correlated with the effective porosity evaluated by the saturation method.

In the medical image retrieval literature, there are two main approaches: content-based retrieval using the visual information contained in the image itself and context-based retrieval using the metadata and the labels associated with the images. We present a work that fits in the context-based category, where queries are composed of medical keywords and the documents are metadata that succinctly describe the medical images. A main difference between the context-based image retrieval approach and the textual document retrieval is that in image retrieval the narrative description is very brief and typically cannot describe the entire image content, thereby negatively affecting the retrieval quality. One of the solutions offered in the literature is to add new relevant terms to both the query and the documents using expansion techniques. Nevertheless, the use of native terms to retrieve images has several disadvantages such as term-ambiguities. In fact, several studies have proved that mapping text to concepts can improve the semantic representation of the textual information. However, the use of concepts in the retrieval process has its own problems such as erroneous semantic relations between concepts in the semantic resource. In this paper, we propose a new expansion method for medical text (query/document) based on retro-semantic mapping between textual terms and UMLS concepts that are relevant in medical image retrieval. More precisely, we propose mapping the medical text of queries and documents into concepts and then applying a concept-selection method to keep only the most significant concepts. In this way, the most representative term (preferred name) identified in the UMLS for each selected concept is added to the initial text. Experiments carried out with ImageCLEF 2009 and 2010 datasets showed that the proposed approach significantly improves the retrieval accuracy and outperforms the approaches offered in the literature., (Copyright © 2019 Elsevier Inc. All rights reserved.)

The 18 O/ 16 O and 2 H/ 1 H ratios of 18 water brands representing the most popular bottled water brands in the Saudi market were measured using a system based on the latest advancements in tunable off-axis integrated cavity output diode laser spectroscopy (OA-ICOS) in the near-infrared spectral region. Utilizing δ 18 O and the δ 2 H values of locally produced water samples, a meteoric water line (δ 2 H = 7.84 δ 18 O + 2.11) was extracted and found to be consistent with the slope of the global meteoric water line (GMWL) and the geographic location of Saudi Arabia.

Context: Pulmonary fibrosis is a devastating disease without effective treatment. Rosemary is appreciated since ancient times for its medicinal properties, while biomolecules originated from the plant have an antioxidant and antifibrotic effect., Objective: The effects of Rosmarinus officinalis L. (Lamiaceae) leaves extract (RO) on bleomycin-induced lung fibrosis were investigated., Materials and Methods: Male Wistar rats were given a single dose of bleomycin (BLM, 4 mg/kg, intratracheal), while RO (75 mg/kg, intraperitoneal) was administered 3 days later and continued for 4 weeks (BLM/RO1-curative group). Alternatively, RO was administered 2 weeks before BLM and continued 15 days thereafter (BLM/RO2-prophylactic group). Antioxidant activities of RO and lung tissues were studied by standard methods. Histological staining revealed lung architecture and collagen deposition. RO was characterized for its polyphenol content and by high-performance liquid chromatography., Results: RO polyphenol content was 60.52 mg/g of dry weight, carnosic and rosmarinic acids being major components (6.886 and 2.351 mg/g). Antioxidant effect of RO (DPPH and FRAP assay) expressed as IC 50 values were 2.23 μg/mL and 0.074 μg/mL, respectively. In BLM/RO1 and BLM/RO2 lung architecture was less compromised compared to BLM, which was reflected in lower fibrosis score (2.33 ± 0.33 and 1.8 ± 0.32 vs 3.7 ± 0.3). Malondialdehyde levels were attenuated (141% and 108% vs 258% of normal value). Catalase and glutathione-S-transferase activities were normalized (103% and 117% vs 59%, 85% and 69% vs 23%, respectively)., Discussion and Conclusion: RO has a protective effect against BLM-induced oxidative stress and lung fibrosis due to its phenolic compounds.