bluuebunny/crossref_metadata_2025_split huggingface dataset:
Ultrasound assisted synthesis and spectral correlations of some bio-active E-imines
Authors: Mayavel P. , Divya J. , Gayathri P. , Balasundari S. , Usha V. , Muthuvel I. , Thirunarayanan G. 
Published: May 2024

Abstract

Some aryl E-imines were synthesized by nano fly-ash H3PO4 catalyzed condensation of various aryl anilines and benzaldehydes under ultrasound irradiated greener solvent medium. In this condensation the obtained yield was more than 75%. These E-imines were characterized by their physico-chemical data, micro analysis and spectroscopic data. The characteristic spectral frequencies were employed for spectral QSAR study. The spectral QSAR study was performed with single and multi-regression analysis using Hammett σ, σ, σI, σR F R and Swain-Lupton’s constants. From the outcome of the regression, the effect of substituents on the spectral frequencies were predicted. The molecular docking study of these imines were performed by the assessment of protein -ligand interaction study with a characteristic protein. The antimicrobial activities of these imines were studied using Bauer-Kirby disc diffusion technique with various bacterial and fungal microbes. The in-vitro antimalarial activity of these imines was measure using P. Falciparum Thai protein-microbes.
Enhancing Carbapenem Resistance Detection: LAMP Coupled with Melting Curve Analysis- for Rapid Molecular Diagnostics in Clinical Specimens
Authors: Naik Vaishnavi Vishram , Kumar Sumit , Thrimurthy Tejal , Channareddy Vani , Shaw Tushar 
Published: May 2024

Abstract

Carbapenem-resistant Enterobacteriaceae have become a major clinical and public health challenge due to the need for rapid administration of effective antimicrobials and implementation of supplemental infection control practices. Identifying genes associated with carbapenem resistance is crucial for managing these cases. The timely initiation of effective antimicrobial therapy and infection control interventions to prevent spreading are critical. Therefore, rapid diagnostic tests for detecting CP-CRE are necessary. This study aimed to evaluate the use of LAMP and melting curve analysis in RT-PCR to identify carbapenem resistance genes (NDM and OXA) in clinical specimens. This study emphasizes the benefits of the LAMP strategy in comparison to traditional methods, demonstrating its effectiveness and practicality. LAMP has proven to be more sensitive than RT-PCR and conventional multiplex PCR, with a detection rate of 96.7% and 91.6% respectively. While LAMP has a slightly lower specificity rate of 88.7%, it remains a promising method for rapid and accurate identification. Furthermore, the study found that LAMP can detect bacterial DNA even in low quantities, with a limit of detection of 102 CFU/mL for both K. pneumoniae and E. coli. In settings where MDR bacteria are prevalent, this capability is crucial for early diagnosis and treatment. In conclusion, RT-PCR systems that combine melting curve analysis with LAMP offer promise for quick and precise identification of carbapenem resistance genes in clinical specimens. This approach can enhance the diagnosis and management of multidrug-resistant bacterial infections.
Clinical Causal Analysis via Iterative Active Structure Learning
Authors: Tao Zhenchao , Chi Meiyan , Chen Lyuzhou , Ban Taiyu , Tu Qiang , Gao Fei , Wang Wei 
Published: May 2024

Abstract

Machine Learning (ML) has achieved considerable success in clinical applications such as image-based diagnostics, predictive modeling for patient outcomes, and personalized treatment planning. However, the black-box nature of deep neural networks (DNNs) often results in poor interpretability and reliability of predictions. Traditional neural network architectures, focusing primarily on correlations, fall short in elucidating underlying causal medical mechanisms. Addressing this, causal discovery, aimed at elucidating the structure of causal graphical models from observational or experimental data, is gaining prominence in clinical fields demanding high reliability. Nevertheless, the complexity of search algorithms, the scarcity of real-world data, and the challenges in identifying unique results significantly hinder the reliability of these approaches. To overcome these challenges, we propose an iterative active structure learning approach to ensure reliable clinical causal analysis. Our method begins with the recovery of a causal structure, guided by a set of prior causal presence, followed by an iterative process of active refinement to enhance the output reliability. This involves using violations of known clinical mechanisms as structural constraints to guide successive rounds of learning, thereby correcting and refining the model iteratively. The process continues until there is a convergence between expertise and the data-derived solutions. Our experiments on real-world clinical data demonstrate that Our approach can improve the quality of causal findings and discover new causal associations beyond the basis of expert knowledge. Furthermore, our approach has yielded novel and significant insights from various datasets, which we explore in our discussion.
The exceptional find of a Bronze Age votive bog offering of millet accompanied by a human sacrifice. Or was it the other way around?
Authors: Dreslerová Dagmar , Vondrák Daniel , Hošek Jan , Brychová Veronika , Hunt Harriet , Pokorný Petr 
Published: May 2024

Abstract

The first discovery of prehistoric human remains at the same wetland as millet plants (Panicum miliaceum), which is reported here for the Zahájí site in the Czech Republic, sheds curiously new light on the central European Middle Bronze Age. The millet and a human fingernail (both dated ca. 1400–1300 BC) were found in what was possibly a newly formed shallow lake in a small brook valley located in a fertile and continuously populated region. Their joint presence in the same layer does not appear accidental. Although a wealth of contextual information has been gleaned from multi-proxy investigations (such as sediment lithostratigraphy, geo and water chemistry, pollen, plant macrofossils, aquatic invertebrae and organic residua analyses, and aDNA of millet and the fingernail), archaeological interpretation of the find is still highly challenging. We present some possible scenarios for what might have happened, with ritual behaviour (in the sense of sacrifice or offering) being high on the list of probabilities. The find may also be related to reverence for the millet plant, a crop that had only recently become established in central Europe around the mid-2nd millennium BC following its rapid westward migration from southeastern Asia.
Integration of Kalman Filter with Ant Colony Optimization for Solving the Job Shop Scheduling Problem
Authors: Kriouich Mohamed , Sarir Hicham 
Published: May 2024

Abstract

In smart manufacturing, the job-shop scheduling problem (JSP) is a major obstacle that must be solved by the best possible sequencing of task operations. Dynamic job-shop environments require flexible scheduling systems that can adjust to changing conditions due to unpredictabilities like machine breakdowns. Traditional methods, which only provide the best answers when they are put into practice, are not adaptable enough to take into account shifting circumstances. Because of this limitation, temporal complexity has increased, highlighting the importance of sophisticated, flexible scheduling techniques in smart manufacturing. Several metaheuristic techniques, such as the well-known Ant Colony Optimization (ACO), are inspired by natural phenomena and are remarkably successful and efficient at solving extremely difficult (NP-hard) combinatorial optimization problems. This paper presents the implementation of an Ant Colony Optimization with Kalman Filter (ACO_KF) model algorithm applied to solve the JSP. ACO_KF is a combination of the recursive estimating algorithm for dynamic systems with the metaheuristic optimization algorithm inspired by ant foraging behavior to solve optimization problems. Our proposed approach aims to implement an ACO algorithm for solving a JSP and optimizing the makespan time by adjusting pheromone levels on paths. Also, the algorithm incorporates a Kalman filter to adaptively adjust pheromone levels according to recorded makespan times, to improve the convergence and efficiency of the ACO algorithm. Comparing the quality of the solutions to the most well-known outcomes from the most successful methods was necessary to evaluate the algorithm's performance on reference JSP. The solutions were obtained with remarkable efficiency and excellent quality.
An Efficient Method for Converting Irregular Logical Formula into DNF/CNF with Graph Structure
Authors: Park Sangmork 
Published: May 2024

Abstract

In this paper, we introduce an effective technique that converts an irregular logical formula into a set of Disjunctive Normal Form (DNF) or a Conjunctive Normal Form (CNF) [1][3] using Graph structure. In a situation where we must evaluate complex conditions containing diverse factors, we develop a convoluted logical formula and evaluate the formula from the beginning to the end. As the complexity of the logical formula grows, the time complexity for evaluation computation increases exponentially. The most efficient evaluation will be achieved if we convert the complex propositional logic formula into a set of DNF or CNF expressions and then evaluate each DNF or CNF expressions one by one. The suggested method in this paper efficiently converts a convoluted logical formula into a set of DNF/CNF expressions with the time complexity of O (log N * 2log N). We employed Graph structure to develop the proposed DNF conversion method in this paper. A Tree structure with a root node and one or more sibling nodes is constructed to represent a local irregular logical formula. A branch in the Tree is a disjunctive literal and each branch in the Tree has a corresponding conjunctive relationship with another. To describe the global logical formula, a Graph structure includes every Tree connected in all logical sequence from the front literal to the end literal. All paths from the root node to a leaf node in the Graph represent a disjunctive iteral of conjunctive relationship with another path and the set of the disjunctive literals will be a full disjunctive normal form if we prune redundant nodes. A logical formula evaluation process is repeated operations following the rules of logical equivalences, such as double negative elimination, De Morgan’s law, and distributive law. The time complexity of the operations can exponentially increase as the length of logical formula grows. The conversion method we present in this paper can reduce the complexity of conversion operations and it can be used for not only DNF conversion, but also Conjunctive Normal Form (CNF) conversion with minor modification of relational representations.
NS-Fuse: Noise-Suppressed Fusion of Infrared and Visible Images via Improved Generative Adversarial Network
Authors: Cheng Yong , Li Xiang 
Published: May 2024

Abstract

Aiµing at the traditional discriµinator types based on the generated counterµeasure network, the coµplex network structure and the noise probleµs in infrared equipµent of the existing iµage fusion µethods, a noise suppression iµage fusion µethod based on the iµproved generated counterµeasure network (NS-fuse) is proposed. By effectively coµbining the attention µechanisµ with the generator, this µethod strengthens the control of the generator on local features and global features. Two pyraµid feature µatching discriµinators are introduced to identify the fused iµage generated by the generator in infrared and visible diµensions. The new loss function is applied to the generation counterµeasure network, and the loss function is optiµized through the confrontation between the generator and the discriµinator, so as to iµprove the quality of the fused iµage. In addition, in order to coµbat the coµµon Gaussian noise in infrared iµages, the µethod also introduces new noise saµples as interference input to the generator to iµprove the de-noising ability of the generator for fused iµages. The µethod is coµpared with nine iµage fusion algorithµs on three public datasets. The results show that NS-fuse is better than the µost advanced µethod in qualitative analysis and quantitative analysis, and the optiµal structure of NS-fuse network is also obtained through experiµental exploration. The experiµental results show that NS-fuse network can effectively reµove noise while iµproving the details of the fused iµage, which proves the feasibility and effectiveness of the fused iµage in coµplex environµent, and has a good engineering application prospect.Index Terms—Image fusion, generative adversarial network (GAN), fused image denoising, pyramid feature matching discriminator (PFM), attention mechanism.
Enhancing Broadband Absorption and Photocurrent Generation in Carbon Dots via P3HT Integration
Authors: Gopi Pavithra , Ponnusamy Kathirvel 
Published: May 2024

Abstract

The growing interest in carbon dots (CDs) arises from their diverse applications and unique properties. This study addresses challenges in CDs for photodetector (PD) applications, specifically surface defects and trap states hindering efficient charge transport. CDs/P3HT composites were prepared to overcome these issues by incorporating CDs in a poly(3-hexylthiophene) (P3HT) matrix. Broad absorption in spectroscopic characterization revealed its utility in fabricating a broadband PD. The CDs/P3HT PD displays a remarkable broadband photoresponse, spanning both UV and visible regions. The CDs and P3HT are effectively combined via non-covalent π-π interactions constituted by their conjugated systems. The π-π interaction increases electron delocalization and facilitates efficient charge transfer due to band alignment at the junction interface. Hence, fabricated CDs/P3HT PD demonstrated enhanced photocurrent compared to pure CDs, exhibiting high responsivity of 6.12×10-3 AW-1 and detectivity of 0.69×109 Jones. This study highlights the potential of CD/P3HT composites for broadband photodetector applications with enhanced photoelectric conversion.
Status-neutral community-based multilevel intervention to address intersectional stigma and discrimination, and increase HIV testing, PrEP, and ART uptake among YGBMSM in Ghanaian Slums: A clustered randomized control trial protocol.
Authors: Abu-Ba’are Gamji Rabiu , Torpey Kwasi , Guure Chris , Nelson LaRon E , Jeon Sangchoon , McMahon James , Leblanc Natalie M , Shamrock Osman Wumpini , Zigah Edem Yaw , Apreku Amos , Dakpui Henry Delali , Agbemedu George Rudolph Kofi , Boakye Francis , Adu Prince , Attisoe Andrew , Adjaka Gideon 
Published: May 2024

Abstract

Background
While GBMSM constitute less than 2% of Ghana's population, their HIV prevalence surpasses the national average by more than eightfold, emphasizing the critical need for targeted interventions to improve detection, care linkage, and reduce community transmission. This study seeks to increase HIV testing, Pre-Exposure Prophylaxis (PrEP), and Antiretroviral Therapy (ART) uptake (HPART) among YGBMSM through the adaptation of an evidence-based intervention (LAFIYA).
Methodology
We will employ the ADAPTT-IT framework to adapt LAFIYA and evaluate its feasibility and effectiveness in addressing intersectional stigma and increasing HPART uptake among YGBMSM residing in Ghanaian slums. In aim 1, we will hold focus groups (n=5) and interviews (n=20) among YGBMSM and two FGDs among GBMSM-led organizations. At the HCF level, we will hold 6 FGDs and interviews (n=20) among nurses. In AIM 2, we will randomly assign 6 healthcare facilities (HCFs) to receive the LAFIYA (n=3) or wait-list control (n=3). Friend groups (cluster) of YGBMSM (N=240) will be assigned to receive LAFIYA (n=120) or a wait-list control (n=120). We will collect 3-, 6-, and 9-months post-intervention data among YGBMSM(n=240) and HCWs(n=300) to measure HPART adherence (primary outcomes), ISD reduction, HIV and status-neutral knowledge (secondary outcomes), and intervention acceptability, appropriateness, and feasibility (implementation outcomes).
Conclusion
The intervention group will observe increased HPART adherence, reduced ISD, and enhanced HPART knowledge and efficacy relative to the wait-list control group. The findings will inform ISD reduction and HIV status-neutral implementation strategies – and place-based interventions that address access to HIV prevention and care among YGBMSM, slum and in different settings.
Trail Registration
This study was registered on clinicalTrail.gov, with identifier number NCT06312514 on 03/14/2023. https://classic.clinicaltrials.gov/ct2/show/NCT06312514.
Nature as a Therapeutic Place and tool for Enhancing Service Users’ Engagement in Mental Health Services: A Comprehensive synthesis of Evidence
Authors: MSN Alexandra Troughton; , Chin Mellisa , PhD MS Isaac Amankwaa; 
Published: May 2024

Abstract

This systematic review explored how nature-based care settings influence engagement in mental health therapy. We relied on Joanna Briggs Institute's guidelines for systematic reviews to synthesise data from nine articles selected from an initial pool of 649 records retrieved from PubMed, CINAHL, Science Direct, SocINDEX, and JBI EPB. Synthesis revealed six analytical themes: nature as a therapeutic tool, therapeutic relationships, nature’s impact on power balance, nature as a safe space, risk of nature and patient-centered care. The findings shed light on the benefits while highlighting some of the challenges that influence participation in mental health interventions. The findings suggest that integrating nature into mental health care could be an alternative or complementary approach to enhancing patient engagement. However, the specific types of mental health care of this impact vary. Robust clinical trials that examine the effectiveness of the reported benefits is recommended. Such studies must target specific patient groups, such as the young and the elderly. Longitudinal studies that examine the long-term effects and moderating factors are needed to strengthen the evidence base and enhance patient-centered care.
The diagnostic performance of Cochlear Endolymphatic Hydrops and Perilymphatic Enhancement in Stratifying Ménière's Disease probabilities: A Meta-Analysis of semi-quantitative MRI-based grading systems
Authors: Azarpey Neda , Seyed-Bagher-Nazeri Shahrzad-Sadat , Yazdani Omid , Esbati Romina , Boustani Paria , Hajiabbasi Mobasher , Torabi Pouya , Farazandeh Dorreh , Farzaneh Hana , Azizi Ashkan , Amini Behnam , Ghasemi Zohre 
Published: May 2024

Abstract

Background
The diagnosis of Meniere’s Disease (MD) presents significant challenges due to its complex symptomatology and the absence of definitive biomarkers. Advancements in MRI technology have spotlighted endolymphatic hydrops (EH) as a key pathological marker, necessitating a reevaluation of its diagnostic utility amidst the need for standardized and validated MRI-based grading scales.
Methods
Our meta-analysis scrutinized the diagnostic efficacy of semi-quantitative MRI-based cochlear EH and perilymphatic enhancement (PLE) grading systems in delineating clinically relevant discriminations: “Spotting” the shift from normal or asymptomatic ears to pMD, “Confirming” the progression to dMD, and “Establishing” the presence of dMD. A thorough literature search up to October 2023 resulted in 35 pertinent studies, forming the basis of our analysis through a bivariate mixed-effects regression model.
Results
Using criteria from the American Academy of Otolaryngology-Head and Neck Surgery (AAO-HNS) and Barany Society, across varying thresholds and disease probabilities; the Establishment model at an EH grade 1 threshold revealed a sensitivity of 85.4% and a specificity of 82.7%. Adjusting the threshold to EH grade 2 results in a sensitivity increase to 92.1% (CI: 85.9–95.7) and a specificity decrease to 70.6% (CI: 64.5–76.1), with a DOR of 28.056 (CI: 14.917–52.770). The Confirmation model yields a DOR of 5.216, indicating a lower diagnostic accuracy. The Spotting model demonstrates a sensitivity of 48.3% (CI: 34.8–62.1) and a specificity of 88.0% (CI: 77.8–93.9), with a DOR of 6.882. The normal ears subgroup demonstrated a notably high specificity of 89.7%, while employing Nakashima's criteria resulted in a reduced sensitivity of 74.9%, significantly diverging from other systems (p-value < 0.001). The PLE grading system showcased exceptional sensitivity of 98.4% (CI: 93.7–99.6, p-value < 0.001).
Conclusion
Our findings advocate for a deliberate, informed choice of grading system, encapsulating the essence of a customized diagnostic approach in MD management.
Exploring the Typhoon Intensity Forecasting through Integrating AI Weather Forecasting with Regional Numerical Weather Model
Authors: Xu Hongxiong , Zhao Yang , Zhao Dajun , Duan Yihong , Xu Xiangde 
Published: May 2024

Abstract

Recent advancements in artificial intelligence (AI) have notably enhanced global weather forecasting, yet accurately predicting typhoon intensity remains challenging. This is largely due to constraints inherent in regression algorithm properties and inability of coarse resolution to capture the finer-scale weather processes. To address these insufficient in typhoon intensity forecasting, we propose an attractive approach by initiating regional Weather Research and Forecasting (WRF) model with Pangu-weather, a state-of-the-art AI weather forecasting system (AI-Driven WRF), whose forecasting power can be further augmented by the implementation of dynamic vortex initialization. The results revealed a significant gap in Pangu-weather's ability to predict typhoon intensity, while the AI-Driven WRF model, on the other hand, exhibited a remarkable improvement over Pangu-weather with dramatic reductions in the Root Mean Square Errors (RMSEs) for Typhoons Doksuri and Hato, demonstrating 87% and 63% improvements in forecast accuracy, respectively. The AI-Driven WRF model also showed potential advancements over traditional global numerical model-driven WRF models, performing more accurately typhoon intensity and wind details. Our analysis underscores the potential of AI weather forecasting models as a viable alternative for driving regional models, suggesting a promising avenue for future research in meteorology.