bluuebunny/crossref_metadata_2025_split huggingface dataset:
Chrysanthemum Classification Method Integrating Deep Visual Features from both the Front and Back Sides
Authors: Chen Yifan , Yang Xichen , Yan Hui , Liu Jia , Jiang Jian , Mao Zhongyuan , Wang Tianshu 
Published: February 2024

Abstract

Chrysanthemum morifolium Ramat(hereinafter referred to as Chrysanthemum) is one of the most beloved and economically valuable Chinese herbal crops, which contains abundant medicinal ingredients and wide application prospects. Therefore, identifying the classification and origin of Chrysanthemum is important for producers, consumers, and market regulators. The existing Chrysanthemum classification methods mostly rely on visual subjective identification, are time-consuming, and always need high equipment costs. A novel method is proposed to accurately identify the Chrysanthemum classification in a swift, non-invasive, and non-contact way. The proposed method is based on the fusion of deep visual features of both the front and back sides. Firstly, the different Chrysanthemums images are collected and labeled with origins and classifications. Secondly, the background area with less available information is removed by image preprocessing. Thirdly, a two-stream feature extraction network is designed with two inputs which are the preprocessed front and back Chrysanthemum images. Meanwhile, the incorporation of single-path residual connections and cross-path residual connections is employed to extend the receptive field of the network and fully fusion the features from both the front and back sides. Experimental results demonstrate the proposed method outperforms existing methods, which gain higher accuracy and stability. The proposed method presents an effective and dependable solution for identifying the classification and origin of Chrysanthemum. Code and data are available at https://github.com/dart-into/CCMIFB.
Artificial Intelligence in Healthcare: A Closer Look at ChatGPT's Usages and Challenges
Authors: khonakdar kimia , moradimajd parisa , babajani alireza , sedighmaroufi shahnam , sajadi Atiyeh sadat 
Published: February 2024

Abstract
Background
Artificial intelligence, a rapidly advancing field, is characterized as a branch of computer science capable of human-like thinking and actions. Among the extensively utilized forms of AI is Natural Language Processing (NLP), enabling machines to think and communicate in a manner resembling humans. Numerous chatbots, such as Jasper chat, DialoGPT, Replika, and ChatGPT, have been introduced as part of these advancements.
Methods
The study employs a scoping review methodology, searching five databases with specific keywords related to ChatGPT and healthcare. Inclusion and exclusion criteria are defined, resulting in 17 relevant articles for analysis. The research process involves a thorough review, removal of duplicates, and quality assessment. Data analysis is conducted using content analysis.
Results
The 17 articles are categorized into three themes: Education & Evaluation, Research & Publication, and Care & Cure. Subthemes within each category detail the usages and challenges of ChatGPT in various contexts, including education, research, and patient care.
Conclusion
The paper evaluates the impact of ChatGPT in different domains, emphasizing its positive influence on data availability, learning experiences, and research processes. However, ethical concerns such as plagiarism, biases, and potential risks in patient care are identified. The authors advocate for the responsible use of ChatGPT, suggesting solutions like academic integrity rules and staff training.
Composition and variations diet of the European eel Anguilla anguilla (Linnaeus, 1758) in Tonga Lake, North-East Algeria.
Authors: DERBAL Farid , ARIBA Souheila 
Published: February 2024

Abstract
Despite the economic and ecological status of the European eel Anguilla anguilla throughout the North African coasts, this species remains little known. This study aims to provide information on the trophic habits of the fraction of the population that occupies Tonga Lake, a wetland located in Algeria extreme East. During one year (January - December 2009), a total of 477 eels (26
Heavy metals transfer in leaves, stem and roots of olive tree near roads in the region of Sfax (Tunisia) and their impact on physiological characteristics
Authors: Mbadra Chaker , Gargouri Kamel , Mbarek Hadda Ben , Trabelsi Lina , Soua Nabil , Ncube Bhekumtheto , Chaabouni Semia Ellouz 
Published: February 2024

Abstract
In this study we try to evaluate the accumulation of heavy metals, including Pb, Zn, Cu and Cr in the soil, the entry into plants, and the effect on the physiological and biochemical processes of olive tree. We try to study the contamination of the leaves, stem and roots of olive trees collected from 24 sampling sites throughout three roads in the region of Sfax, Tunisia. To better study this contamination, we used two contamination indices, the accumulation and translocation factor. For Gremda road, Pb and Zn contamination does not exceed 50m. For Tunis road, contamination of leaves stems and roots by Pb and Cr arrived up to 50m in the eastern and western side of the road. In contrast, Zn contamination reached 50m in the eastern and 25m in the western side of road. Likewise, metal contamination affects the roots and leaves of Manzel Chaker olive trees at a distance of 3m but this contamination does not affect the physiological and biochemical mechanisms of the latter. The contamination of sites near Tunis and Gremda roads affects the chlorophyll assimilation of olive trees planted in these sites but, the chlorophyll contents of Manzel chaker sites are less affected. On the other hand, for soluble sugars, the olive leaves close to Manzel Chaker and Tunis roads recorded higher sugar contents than those of Gremda sites. The leaves of olive trees in the sites close to Gremda and Tunis roads showed a weakened antioxidant activity compared to that recorded in the control sites.
RQPoA: A random quantum PoA Consensus Mechanism in Blockchain Based on Quantum Methods
Authors: WANG ZHUO , Li Jian , Liu Ang , Ota Kaoru , Dong Mianxiong , Chen Xiubo 
Published: February 2024

Abstract
As a distributed network, the operational efficacy of a blockchain system relies heavily on the consensus mechanism it adopts. Among the mainstream consensus mechanisms in blockchain, proof-of-authority (PoA) is appealing for its post-quantum security and block generation efficiency and, therefore, has gained academic attention. Nevertheless, the traditional PoA faces three major problems: (1) Low decentralization. The allocation of bookkeeping rights has a low degree of decentralization in the blockchain network. (2) Low availability. The total exposure of the leader node makes it susceptible to centralized attacks, leading to a single point of failure (SPOF) dilemma and reducing the availability of the entire blockchain system. (3) Non-robustness (low fault tolerance). Generating a new block must be done jointly by all validator nodes, with this voting process potentially delayed due to invalid voter participation. To address these issues, this paper improves PoA and proposes a new consensus mechanism scheme, the random quantum proof of authority (RQPoA). First, RQPoA develops the leader node election algorithm with a verifiable delay function (VDF) to realize fair and impartial leader node selection, enforcing the blockchain’s decentralization level. Second, RQPoA adopts the multi-party quantum secret sharing protocol to share the leader node’s identity among validator nodes confidentially. This strategy eliminates SPOF caused by the leader node, increasing the blockchain system availability. Third, RQPoA incorporates a candidate block voting protocol based on a quantum threshold signature to complete the block proposal, which is fault-tolerant and thus enhances the blockchain system's robustness. A security analysis of RQPoA demonstrates its security, efficiency, and better fault tolerance than related quantum consensus mechanisms. In conclusion, the RQPoA makes a useful exploration for researching secure consensus mechanisms in the post-quantum era and enriches the related research.
Study on Workability and Durability Characteristics of Self- Compacting Geopolymer Concrete Composites
Authors: Jeyaseela J , Engineering S Sowmya Civil , Engineering M Shahul Hameed Civil 
Published: February 2024

Abstract
Increase in production of cement causes the huge amount of carbon-dioxide emission which results in global warming. In order to overcome this problem many researchers put their effects to achieve the optimum strength and durability of concrete by replacing the cement with fly ash and when it combines with the alkali activator solution it becomes geopolymer concrete. These concretes have the ability to fail due to lack of compaction. Self-compacting concrete is the innovative way of concreting; it can be compressed into every corner of formwork by means of its own weight. In order to trounce poor compaction and global warming; the new way of concreting is introduced as self-compacting geopolymer concrete. This concrete has two limitations such as delay in setting time and heat curing to gain strength. The present research intended to set right these limitations by replacing the fly ash by OPC up to 20% on a mass basis. Also an attempt to make to use of manufactured sand instead of using river sand due to scarcity of river sand and natural resource depletion. The durability of self-compacting geopolymer concrete composites is based on the tests conducted such as acid attack, sulphate attack and Sorptivity.
Improved Diabetic Retinopathy Severity Classification Using Squeeze-and-excitation and Sparse Light Weight Multi-level Attention U-net With Transfer Learning From Xception
Authors: Bhandari Sachin , Pathak Sunil , Jain Sonal Amit , Agarwal Basant 
Published: February 2024

Abstract

Diabetic Retinopathy (DR) is a leading cause of vision loss among diabetic patients. Early detection and accurate severity classification of DR play a crucial role in preventing its progression and managing patient care effectively. In this paper, we propose an improved DR severity classification approach that leverages advanced architectural enhancements and transfer learning techniques. Specifically, we introduce a novel model named Xception Squeeze-and-Excitation Sparse Lightweight Multi-Level Attention U-Net (XceSE_SparseLwMLA-UNet) for DR severity classification using fundus images from the Messidor 1 and Messidor 2 datasets. Our proposed XceSE_SparseLwMLA-UNet architecture combines the benefits of multiple innovations, including the Squeeze-and-Excitation (SE) mechanism for feature recalibration, the Sparse Lightweight Multi-Level Attention (SparseLwMLA) mechanism for capturing contextual information, and transfer learning from the Xception architecture to enhance feature extraction. The SE mechanism enables the model to emphasize important features by adaptively recalibrating channel-wise feature responses. The SparseLwMLA mechanism facilitates efficient integration of multi-level contextual information, enhancing the model's ability to recognize complex patterns associated with different DR severity levels. Furthermore, the utilization of transfer learning from the Xception architecture allows our model to leverage pre-trained weights learned from a large-scale image dataset, enabling the network to better generalize to the task of DR severity classification, even when trained on large medical imaging data. Our experimental results demonstrate the superiority of the SE_SparseLwMLA-UNet model, achieving significantly improved higher accuracy and multi-class higher F1 scores for DR severity classification. The color-coded segmentation outputs generated by the model provide interpretable visual representations, aiding medical professionals in understanding the severity levels identified by the algorithm. Hence the proposed SE_SparseLwMLA-UNet model holds promise for enhancing early diagnosis and management of DR, contributing to better patient care and visual health outcomes.
Isolation, biochemical characterization and comparative study of diagnostic methods for Helicobacter pylori infection from dyspepsia patients
Authors: Mujtaba Ahmed , Ibrahim Muhammad Suhail , Parveen Sana , Sarwar Noreen , Alsagaby Suliman A. , Memon Anjuman Gul , Raza Muhammad Ahsan , Abdelgawad Mohamed A. , Ghoneim Mohammed M. , Alhossan Abdulaziz , Abdulmonem Waleed Al , Hussain Muzzamal , Yehuala Tadesse Fenta 
Published: February 2024

Abstract
The success of any therapy especially against H. pylori depends upon the prompt and authentic diagnosis. This research was designed to study the patients with dyspepsia for possible H. pylori infection and to compare different commonly used diagnostic techniques. Ninety-Six gastrointestinal biopsies and blood samples were collected after obtaining an informed consent having gastro duodenal symptoms. The obtained samples were tested and compared by using different invasive and non-invasive diagnostic tests. The results showed that 40 (41.67%) subjects were positive for H. pylori in RUT and histopathology. Contrariwise, 46 subjects were positive for ELISA test against anti-IgA and IgG. Culture tests showed 18 biopsies against the bacteria. On the other side, tests like endoscopic examination, urease, catalase and oxidase further confirmed the findings. A high resistance was observed to antibiotics like kanamycin (77.78%), amoxicillin (88.89%) and polymyxin B (100%) respectively. A gene (Cag A) was also detected by using molecular technique and 18 subjects emerged as positive. Sensitivity/specificity (%) of used diagnostic method was 95/77 for histology, 100/83.5 for rapid urease, 85.7/90 for gram staining, 100/66.6 for IgG serology, 100/79.5 for IgA serology, 100/75.0 for PCR, 100/79.04 for a combination of RUT and IgG serology and 100/92.4 for a combination of RUT, gram staining and IgG serology. It is concluded that PCR appeared the most reliable test among different diagnostic methodologies. However, the higher sensitivity and specificity values were also observed for other tests. Hence, comparative detection methods should be used for accurate detection rather than a single methodology.
Unique metabolite profile of Pacific white shrimp Litopenaeus vannamei intestine after diverse Vibrio parahaemolyticus vp5 challenges
Authors: Sutanti Sutanti , Sukenda Sukenda , Widanarni Widanarni , Alimuddin Alimuddin , Aliah Ratu Siti 
Published: February 2024

Abstract
The Pacific white shrimp Litopenaues vannamei is the most widely cultivated shrimp in the world, particularly in Indonesia. Vibrio parahaemolyticus is a bacterial pathogen that severely affects shrimp, causing extensive economic losses in L. vannamei culture. This study is the first report of the effects on the L. vannamei intestine after Vibrio parahaemolyticus Vp5 local isolate challenge with different concentrations using a metabolomics approach. Healthy shrimp cultured weighing 2±0.5 g were challenged using the intramuscular method with four different concentrations (103, 104, 105, and 106 cfu/ml) of V. parahaemolyticus Vp5 then subjected to gas chromatography-mass spectrometry (GC-MS) non-targeted metabolomics analysis. Out of the five groups, three groups (104, 105, and 106 cfu/ml) demonstrated significant mortalities (P<0.05). A total of 176 metabolites were annotated from shrimp samples and targeted to principal component analysis (PCA) and heatmap analysis. Metabolomic results showed that Vibrio parahaemolyticus Vp5 changes the metabolite profiling. Ten volatile metabolites showing high variable importance in projection (VIP) scores were diethyl phthalate, 2-propanol, 1, 1’-oxybis; cyclopenta [g]-2-benzopyran, 1-propanol, 2-(2-hydroxypropoxy); 1, 2-benzenedicarboxilic acid, heneicosane, pentacosane, docosane, dibutyl phthalate, dan cyclopentadecanone. Diethyl phthalate was the conserved metabolite both in the Control and different concentrations challenges. The healthy shrimp have a significantly higher concentration of pentacosane compared to the infected shrimp (P<0.05). Pentacosane was lost abundance in acute infection of V. parahemolyticus Vp5. The differential metabolites (DMs) was detected in intestine of L. vannamei, exhibiting that this bacteria may disrupt the immune system and cause metabolic disorder in the shrimp. Our findings showed that pentacosane may serve as essential fragrant for healthy shrimp and candidate markers to indicate the acute condition caused by the infection.
Coagulation hallmark indicators of menorrhagia in a comparative study between menorrhagic and healthy women attending Bungoma County Referral Hospital in Kenya
Authors: Marabi Phidelis Maruti , Musyoki Stanslaus Kiilu , Monari Fred , Kosiyo Paul , Ouma Collins 
Published: February 2024

Abstract
Background
Despite the significant burden of menorrhagia among women in Western Kenya, it remains unknown whether coagulation disorders are one of the important underlying causes of this condition in the region. This study evaluated the differences in coagulation profiles, associations between menorrhagia and coagulation profiles and compared the morphological features of platelets between menorrhagic patients and healthy women attending Bungoma County Referral Hospital in Kenya.
Methods
A comparative cross-sectional study of women [n = 428 (214 per group), aged 18–45 years] was performed. A Humaclot junior analyzer was used to evaluate prothrombin time (PT), activated partial thromboplastin time (aPTT), thrombin time (PT), fibrinogen, and the international normalized ratio (INR). The platelet count was determined using Celtac F ME822K, and Leishman-stained blood films were analyzed using an Olympus light compound microscope. The Chi-square test was used to analyze categorical descriptive data. The Mann‒Whitney U test was used to compare the data between the menorrhagic and control groups. Binary logistic regression was applied to determine the association between coagulation profile and menorrhagia incidence. The platelet morphological characteristics were reported as frequencies and percentages. Statistical significance was set at p≤0.05 indicated statistical significance.
Results
The results showed that a history of bleeding disorders (p < 0.0001), PT (p < 0.0001) and INR (p < 0.0001) were greater in menorrhagic women than in non-menorrhagic women. Menorrhagia was significantly associated with a high PT [OR = 2.129, 95% CI = 1.658–2.734; p < 0.0001] and INR [OR = 7.479, 95% CI = 3.094–18.080; p < 0.0001]. No morphological abnormalities in the platelets were observed in either healthy or non-menorrhagic women.
Conclusions
Family history of bleeding disorders, increased PT and INR are the hallmark indicators of menorrhagia in western Kenya. Therefore, routine assessment of the coagulation profile and history of bleeding disorders is critical for the diagnosis and management of menorrhagia.
X-CTCANet：3D spinal CT reconstruction directly from 2D X-ray images
Authors: Gao Tian , Zhang Lei , Wang Zechang , Shi Zhengjin , Ying Yuhan , Li Andi , Wu Chen , Song Guoli 
Published: February 2024

Abstract

2D X-ray images are extensively employed for intraoperative navigation and localization owing to their high imaging efficiency, low radiation risk, and affordability. However, this method can only yield overlapped anatomical information from a restricted number of projected views. Conversely, intraoperative CT scanning techniques, offering 3D images, elevate the risk of radiation exposure for both patients and healthcare professionals. For this purpose, we propose a V-shaped convolutional attention mechanism network (X-CTCANet) designed for X-ray reconstruction of CT images. The network enhances reconstruction performance by promoting task consistency in encoding-decoding, minimizing semantic differences between feature mappings. Additionally, it introduces an adaptive convolutional channel attention (CCA) mechanism to compel the network to prioritize essential feature regions. Experimental results demonstrate the successful CT image reconstruction from spine X-rays using X-CTCANet, achieving an SSIM value of 0.805 and a PSNR value of 34.64 dB. This underscores the considerable potential of accurate 3D CT reconstruction from 2D X-ray images in offering image support for surgical robots.
Dependence of micro-structure regularity on the degree of molecular asymmetry in polynorbornene derivatives
Authors: Pan Rui , He Shuyin , Qiu Sili 
Published: February 2024

Abstract
Although the correlation between the regularity of micro-structures and the length of side chains has been widely reported, experiments still require precursors of polynorbornene derivatives due to the complex inter-and intra-chain interplay. This research utilizes hydrogen bonding between amide groups to design molecular models of polynorbornene derivatives (PNbs). The PNbs are abbreviated as Pm-8, where “m” indicates the amount of methylene groups as spacers in the side chain, and “8” refers to the oxyoctyl tail. The aim is to obtain a gradual variation of inter-and intra-chain interplay for further elucidation. Based on an analysis of spatial descriptors, mean square displacement (MSD), and radial distribution function (RDF) by using molecular mechanics (MM) and molecular dynamics (MD) methods, it is found that the asymmetry of side chains plays a more significant role in determining micro-structural regularity than the length. Highly asymmetric side chains are favorable for forming inter-chain hydrogen bonding, promoting interdigitation, and creating a larger scale of regularity in P8-8 and P10-8 molecules. In contrast, the relatively symmetric side chains tend to form intra-chain bonding, resulting in limited regularity within P2-8 and P4-8 molecules. Therefore, unlike regular structures that usually require symmetrical molecules, polynorbornene derivatives with asymmetric side chains have achieved highly ordered orthorhombic lattices in experiments. This study aims to offer an alternative approach to screening effective precursors of long-side chain polymers by means of semi-quantitative geometric parameters, providing a fresh perspective on the subject.