bluuebunny/crossref_metadata_2025_split huggingface dataset:
MMA-Net: A Multimodal Multitask Network Utilizing Dual Attention Mechanisms for Enhanced Modality Fusion and Task Exchange in Cognitive Load Assessment
Authors: Nguyen-Phuoc Long , Gaboriau Renald , Delacroix Dimitri , Navarro Laurent 
Published: June 2024

Abstract

In this paper, we introduce the Multimodal Multitask Dual Attention Network (MMA-Net), a novel neural architecture tailored for simultaneous multimodal and multitask learning. MMA-Net effectively processes audio and visual inputs through specialized encoders—AudioNet and VideoNet—to capture the distinct and complementary information present across different sensory modalities. A key element of our model is the implementation of Bidirectional Cross-Modality Attention, which refines the multimodal features by leveraging interdependencies between the modalities, enhancing the model's ability to handle complex datasets. This is complemented by the MultiTask Exchange Block, a sophisticated mechanism that dynamically integrates and refines task-specific features, promoting effective information exchange and synergy across multiple tasks. Experimental results demonstrate that MMA-Net achieves significant improvements over existing methods and sets a new benchmark in Cognitive Load Assessment.
Mixed-Variable Graphical Modeling for Medical Record-Based Predictive Models: Learning Pressure Injury Onset in Spinal Cord Injury Individuals
Authors: Li Yanke , Scheel-Sailer Anke , Riener Robert , Paez-Granados Diego 
Published: June 2024

Abstract

Developing machine learning (ML) methods for healthcare predictive modeling requires absolute explainability and transparency to build trust and accountability. Graphical models (GM) are key tools for this but face challenges like small sample sizes, mixed variables, and latent confounders. This paper presents a novel learning framework addressing these challenges by integrating latent variables using fast causal inference (FCI), accommodating mixed variables with predictive permutation conditional independence tests (PPCIT), and employing a systematic graphical embedding approach leveraging expert knowledge. This method ensures a transparent model structure and an explainable approach to feature selection and modeling, achieving competitive prediction performance. For real-world validation, data on hospital-acquired pressure injuries (HAPI) among individuals with spinal cord injuries (SCIs) were used, where the approach achieved a balanced accuracy of 0.941 and an AUC of 0.983, outperforming most benchmarks. The PPCIT method also demonstrated superior accuracy and scalability over other benchmarks in causal discovery validation on synthetic datasets that closely resemble our real dataset. This holistic framework effectively addresses the challenges of mixed variables and explainable predictive modeling for disease onset, which is crucial for enabling transparency and interpretability in ML-based healthcare.
Quantification of trihalomethanes in Nile tilapia fillets sanitized with chlorine
Authors: Júnior Nelson Abreu Delvaux , Queiroz Maria Eliana Lopes Ribeiro , Oliveira André Fernando , Neves Antônio Augusto , Heleno Fernanda Fernandes , Pimenta Amanda Maria Leal , Monção Flávio Pinto , Pimenta Samy , Ribeiro Regina Cássia Ferreira , Pereira Louiza Lourrane Mendes , Costa Luís Felipe Rodrigues , Rodrigues Luciano Pereira 
Published: June 2024

Abstract

Fish is a food with high nutritional value, but its quickly perishable nature requires a hygiene process to maintain product quality and increase shelf life. The objective of this study was to evaluate different chlorine concentrations in water for sanitizing Nile tilapia fillets on the formation of trihalomethanes (THM) in fresh and frozen stored fish fillets. A completely randomized experimental design was used, in a 2×3 split-plot arrangement, with 15 replications; the plots consisted of fresh and frozen fish fillets, and the subplots consisted of the three chlorine concentrations in the water for sanitizing the fillets (100, 175, and 250 mg L-1). Fresh fish analysis showed the following variations in THM levels: 1.4 to 6.8 μg kg-1 (chloroform), 0.5 to 3.2 μg kg-1 (bromodichloromethane), and 0.4 to 1.2 μg kg-1 (dibromochloromethane). Bromoform ranged from 3.7 μg kg-1 to undetected levels in the different tested storage times. Frozen fish analyses showed concentrations ranging from 2.1 to 5.2 μg kg-1 (chloroform) and 0.6 to 2.0 μg kg-1 (bromodichloromethane), while dibromochloromethane and bromoform levels were below the technique's quantification limits in all storage times. THM formation depends on the chlorine concentration in the washing water and the storage time and temperature; fresh fish presented higher THM levels than frozen fish.
BERE: Ensuring Immutability and Transparency with Blockchain in Real Estate Appraisal Industry
Authors: Kalkan Muruvvet , Karakus Murat 
Published: June 2024

Abstract

Real estate appraisal plays a pivotal role in various financial transactions and regulatory processes, necessitating the assurance of unalterable and transparent Real Estate Appraisal rePorts (REA_Ps). Authorized appraisal experts employ diverse methodologies to ascertain the market value of real estate properties, contributing to critical decisions in property transactions, loans, taxation, insurance, financial reporting, and lease calculations. The standardized format and jurisdiction-compliant methodologies used in official REA_Ps emphasize the need for their preservation in an unaltered state. Despite the importance of official REA_Ps, the potential for unauthorized alterations poses a significant challenge when stored in centralized databases. With its inherent tamper-proof nature, Blockchain (BC) technology emerges as an ideal solution to safeguard the integrity of the REA_Ps. This study proposes a Blockchain-Enhanced Real Estate appraisal management framework, BERE, to the immutability of ensure REA_Ps and enhance appraisal process transparency. The proposed BERE framework fosters trust among stakeholders in the real estate appraisal sector. By securely storing REA_Ps authored by on-field appraisal experts on the BC, the method prevents unauthorized modifications and ensures the confidentiality of private data. Simulations employ a variety of measures, such as BC size, transaction verification time, CPU usage, and message overhead on transaction generation, to assess system performance. The results indicate that the BERE framework successfully maintains the accuracy and reliability of REA_Ps.
To the best of our knowledge, this is the first comprehensive study showcasing a practical application of BC technology to demonstrate its ability to transform the appraisal process and inspire trust in the accuracy of REA_Ps.
Defining a Super App and Analyzing It from an Ecosystemic Perspective
Authors: Lucas Durval , Lopes Evandro Luiz 
Published: June 2024

Abstract

The creation of super apps is a recent development, establishing itself as the new pinnacle of increased technology and digital market participation in retail operations. It is primarily inspired by Chinese apps, open platforms where partners can create mini-applications and make them available to users. Although the rise of super apps is a widely disseminated phenomenon, there has been a lack of harmonization regarding the concept. Therefore, this paper aims to present a definition of the super app concept, analyzing how it consolidates the perspective oriented towards digital ecosystems. To this end, a systematic literature review was conducted, which included, at the end of the screening process, 26 scientific articles published in journals from the years 2018 to 2023. Following the analysis, a harmonized concept of super apps was achieved. Furthermore, it was observed that the concept is divided into two specific viewpoints, characterized by their geographical origin and the way these apps materialize digital ecosystems. These viewpoints support the perspective of DeSanctis and Poole (1994) and Schmitz et al. (2016) regarding the variability in technology appropriation processes, disciplined by the social context in which these artifacts are embedded.
Energy Equipartition a Tool to Study Tsunami/Bathymetry Interaction
Authors: Brazhnikov Dmitry , Kowalik Zygmunt 
Published: June 2024

Abstract

We propose a new approach to investigate tsunami propagation and interaction with bathymetry by considering the partitioning of the kinetic and potential energy in the total energy. Investigations of the total energy in time show that the equipartition of potential and kinetic energies is frequently disrupted due to wave interactions with varying bathymetry. First, the approach is tested in the channel with abrupt changes in the bathymetry. Along with usual, incident, reflected, and transmitted waves at the depth jump, the lack of equipartition of energy has been observed. The strength of differences between energies depends on the depth difference between the shallow and deep parts of the channel. Therefore, the differences between energies are correlated with reflection and transmission coefficients. Application to the ocean tsunami is made for the Japan/Tohoku tsunami of 2011. The observed kinetic and potential energies show equipartition along the considerable propagation distance until the wave impinges on prominent bathymetric features like Koko Guyot Seamount and Hess Rise. The energy difference again strongly depends on the relative depth between the ocean bottom plain and the seamount top. This research applied to Koko Guyot uncovered somewhat unusual signal amplification over the seamount. The modification of tsunami amplification and the wavefronts radiating energy to Hess Rise were related to the amplification of the negative sea level. This strong negative level is directed towards Crescent City as a secondary wave.
Evaluation of Henle's Fiber Layer with directional OCT in mild to moderate glaucoma patients
Authors: İlgüy Serdar 
Published: June 2024

Abstract

Purpose: To investigate the Henles’s fiber layer (HFL) to detect the damage that may develop in photoreceptors and axons in patients with mild to moderate glaucoma.
Study Design: Single center retrospective study
Methods: For this study, 25 eyes of 25 subjects diagnosed with glaucoma and 50 eyes of 50 age and sex matched healthy people were enrolled. Macular retinal thickness, peripapillary retinal nerve fiber layer (RNFL) thickness and macular ganglion cell-inner plexiform layer (GC-IPL) thickness measured on OCT. The OCT images with nasal, central and temporal tilt were processed with ImageJ software and HFL area was measured. Measurements were compared between the two groups.
Results: The two groups were similar in age and gender. When the two groups were compared, HFL measurements were thinner in glaucoma patients (p<0.001). The inner inferior, outer temporal and outer superior quadrants of the retinal thickness were significantly thinner in the glaucoma group (p=0.012, p=0.010, p=0.004, respectively). All quadrants of GC-IPL thicknesses were significantly thinner in the glaucoma group. Additionally, statistically significant changes were detected differences in the thickness of the superior, inferior quadrants and average RNFL (p=0.029, p<0.001, p=0.001, respectively).
Conclusion: From the early stages of glaucoma, automated HFL measurements can be used to monitor progression.
Anisotropy parameters from two-body dissociations
Authors: Olsson Emelie , Wallner Måns , Squibb Richard J. , Ideböhn Veronica , Parriani Marco , Parkes Michael A. , Price Stephen D. , Eland John H.D. , Feifel Raimund 
Published: June 2024

Abstract

When a molecule loses two electrons, Coulomb repulsion makes the resulting doubly charged system likely to fragment into two singly charged ions. These monocations can be detected in a correlated fashion using multiplex time-of-flight spectroscopy. The island shapes in the ion-ion coincidence maps derived from such two-body dissociations contain detailed information on the physical processes underlying the fragmentation. Here, a simple method is presented where a fit function is used to determine the anisotropy parameter β of the molecular distribution from the peak shape of the time-of-flight difference of the two ions. The validity of the method is demonstrated by performing fits to simulated peak shapes, recovering the β value of the input angular distribution, and by comparison of experimental peak shapes to β values known from the literature.
A Genomic Study to Explore the Coping Mechanisms of Bacteria to Hydrocarbon Stress
Authors: Dasgupta Abhisek 
Published: June 2024

Abstract

This study investigated the survival mechanisms of bacteria in a hydrocarbon-enriched environment, focusing on factors such as osmotic stress, biofilm formation, and complex carbohydrate utilization. The whole genome of a bacterial isolate (AR19) capable of degrading benzo[a]pyrene (B[a]P) was sequenced and analyzed. The draft genome size was 3.63 Mb, and the closest reference genome was identified as Bacillus altitudinis. The genome contained 3,777 coding genes. Noteworthy findings from the genome analysis included: (i) the involvement of biofilm and biosurfactant in B[a]P adsorption by AR19, (ii) the utilization of extradiol ring cleavage for B[a]P degradation, and (iii) the importance of osmotic stress protection in maintaining cell structure under hydrocarbon stress. The results of this study have potential implications for the development of bioremediation strategies using ex-situ and in-situ techniques.
Identification of BIRC5 and HMMR as Prognostic Biomarkers for Immune Infiltration in Prostate Cancer
Authors: Tang Huarui , Zhou Fanyang , Hu Wentao , Zhang Chen , Tao Jianping , Xing Fawang , Zhang Zhenxing , Gao Yukui 
Published: June 2024

Abstract

Background The delineation of molecular mechanisms and identification of prognostic markers between different subtypes and stages of prostate cancer is vital for the enhancement of therapeutic approaches against this disease. We aimed to identify novel immune-related biomarkers that could be instrumental in assessing and prognosticating prostate cancer across its different stages.Methods Transcriptomic and clinical data were obtained from the TCGA and GEO databases to identify differentially expressed genes (DEGs) associated with the progression of prostate cancer. The correlation between immune cell infiltration in the tumor microenvironment and the expression levels of BIRC5 and HMMR was investigated using xCELL and quanTISeq algorithms.Results Through our analysis, we identified ten key hub genes, and survival analysis revealed that elevated expressions of BIRC5 and HMMR are correlated with adverse outcomes and may be associated with tumor progression. Importantly, the expressions of BIRC5 and HMMR were significantly associated with the tumor-infiltrating lymphocytes (TILs) across different subgroups of prostate cancer. The results from immunohistochemistry (IHC) evaluations were consistent with our bioinformatics findings.Conclusion Our study identifies BIRC5 and HMMR as potential biomarkers for predicting the prognosis of prostate cancer.
Mechanically Enabled Formal Reductive Cross-Coupling Reaction of Two Inert Bonds
Authors: Lian Zhong , Liu Tianfen , Zhang Xuemei 
Published: June 2024

Abstract

Reductive cross-coupling reactions involving two electrophilic reagents have become increasingly important in modern synthetic chemistry. Previous studies have investigated electrophilic reagents featuring zero or one inert bond; however, reactions involving electrophilic reagents with two inert bonds remain unexplored. This study presents the inaugural nickel-catalyzed reductive cross-coupling reaction induced by mechanical force, involving aryl ethers and aryl fluorides, both of which contain inert bonds. This reaction results in the successful assembly of a series of versatile biaryl compounds and demonstrates excellent tolerance for various functional groups. This novel coupling reaction offers innovative approaches for polymer degradation and the development of luminescent materials.
Depression and Suicidal Ideation Among College Students with and without Learning Disabilities in Tamil Nadu Regions: Chennai and Coimbatore
Authors: R Anjit Raja , Nagarajan B. 
Published: June 2024

Abstract

This research investigates depression and suicidal ideation among college students with and without learning difficulties in Chennai and Coimbatore, two cities in Tamil Nadu, India. A total of 28,745 students aged 17 to 23 participated, comprising 11,203 male and 17,542 female students. Utilizing three validated instruments, the study employed a descriptive research approach with a purposive sample technique. Data analysis involved calculating mean, standard deviation, and Pearson product-moment correlation. Results indicated that students with learning disabilities were more prone to experiencing depression and suicidal thoughts compared to their peers without cognitive difficulties. Recommendations include the need for mental health professionals to address these issues among tertiary education students. The study also highlights the alarming rate of student suicides in India, particularly in Tamil Nadu, underscoring the inadequacy of support systems and mental health services. As such, this research aims to examine suicide behaviors and influencing variables among teenagers in Chennai and Coimbatore, employing web mining techniques and statistical analysis to identify key factors and develop machine learning strategies for the protection of India's younger generations.
Great Investment Returns in the COVID-19 Pandemic or Just Another Bubble?
Authors: Simpson Shawn 
Published: June 2024

Abstract

This study examines the impact of the COVID-19 pandemic on the S&P 500 Index through accounting return on equity. Previous literature examines the impact of the pandemic on the United States economy, government restrictions, and global market reaction. Accounting return on equity is employed due to the unmediated impact it has on shareholder wealth. The data obtained from Compustat comprise all available companies listed in the S&P 500 Index. This analysis stems from the first reported case of COVID-19 in the United States, January 21, 2020 and concludes on December 31, 2021. Significant (p-value < 0.0001) evidence conveys that the index had a strong negative initial response. However, the index recovered expeditiously. A multivariable regression model is employed to determine if the recovery led to inflated stock prices. For the purpose of this study, inflated stock prices are interpreted as return on equity is more pronounced than the intrinsic value of the companies that they represent. The results provide significant (p-value < 0.0001) evidence that return on equity is inflated on December 31, 2021. Controlled variables in this analysis include book to market, enterprise value multiple, price to operating earnings, price to earnings, price to sales, price to cash flow, total debt to total assets, total debt to equity, asset turnover, and price to book.
Bayesian adaptive designs in a breast cancer trial with a delayed binary outcome
Authors: Hong Wei , McLachlan Sue-Anne , Moore Melissa , Mahar Robert K 
Published: June 2024

Abstract

Background
Clinical trials using Bayesian adaptive designs can be more efficient than those using traditional fixed designs, but there is a vast range of possible design approaches described in literature. We performed virtual re-executions of a breast cancer clinical trial with a delayed binary outcome to demonstrate what would have happened if the trial had used various Bayesian adaptive designs instead, and to explore which specific designs yielded the most benefits.
Methods
We retrospectively “re-executed” a randomised controlled trial that compared two chemotherapy regimens for women with metastatic breast cancer (ANZ 8614) using Bayesian adaptive designs. We used computer simulations to estimate the power and sample sizes of a large number of different candidate designs and shortlisted designs with the either highest power or the lowest average sample size. Using the real-world data, we explored what would have happened had ANZ 8614 been conducted using these shortlisted designs.
Results
Adaptive designs that prioritised small sample size reduced the average sample size by up to 50% when there was no clinical effect and by up to 21% at the target clinical effect. Adaptive designs that prioritised high power yielded up to an absolute increase of 6.5% in power without a corresponding increase in type I error. The performance of the adaptive designs when applied to the real-world ANZ 8614 data was consistent with the simulations.
Conclusion
Bayesian adaptive designs improved power or lowered the average sample size substantially when applied to this data set. When designing new oncology trials, researchers should consider whether a Bayesian adaptive design may be beneficial.
Explainable Artificial Intelligence Methods for Analysis of Factors Influencing Covid-19 Cases in Türki̇ye
Authors: İçöz Cenk 
Published: June 2024

Abstract

COVID-19 disease emerged in Wuhan, China, in 2019 and evolved into a pandemic that negatively affected all countries worldwide. Researchers have employed methods such as machine learning and spatial machine learning methods, including spatial and multiple linear regression, geographically weighted regression and geographical random forests, to determine the importance of factors such as sociocultural, demographic, environmental, racial, and economic development related to COVID-19 cases or deaths caused by COVID-19. In this study, the explainability of the general factors analyzed by the random forest model of COVID-19 cases based on provinces in Türkiye was examined. In addition, traditional machine learning methods, compared with spatial machine learning models, as the Explainable Artificial Intelligence (XAI) methods directed toward employing spatial associations. The most important factors in the model might differ locally among provinces according to cluster. The spatial machine learning models performed better than the random forest model.
Impact of Direct İncorporation of Organic Waste on Soil Properties and Strawberry (Fragaria X Ananassa Duch.) Growth
Authors: GAMAL Mohammed , ElFEEL Rania A , CHEN Ri-zhao , KIZILKAYA Rıdvan , HUSSEN Adel 
Published: June 2024

Abstract

Aims This study delves into the impact of incorporating diverse organic waste materials on soil biological and chemical attributes within the rhizosphere of Albion strawberry plants (Fragaria x ananassa Duch.).Methods Eight organic waste types, including Cow Manure (CM), Poultry Manure (PM), Sheep Manure (SM), Vermicompost (VC), Rice Husk (RH), Wheat Straw (WS), Tea Waste (TW), and Hazelnut Husk (HH), were directly applied before strawberry transplantation. Effects on plant growth, soil chemical, and biological characteristics were assessed. In a controlled greenhouse, animal and plant waste impacts on rhizosphere and strawberry growth were examined. After applying organic waste (5% of pot weight), strawberries were transplanted. Soil moisture was monitored and maintained near field capacity. Employing a 61-day completely randomized design, soil samples were collected, analyzing microbial biomass C, basal soil respiration, dehydrogenase, and catalase.Results Findings revealed TW pronounced influence on basal soil respiration compared to controls, while WS significantly impacted microbial biomass carbon (MBC). SM notably affected DHA, while PM most influenced CA. All treatments augmented OM. SM, CM, and VC increased total N; conversely, TW, RH, WS, HH, and PM decreased it.Conclusions These findings highlight the ability of diverse organic waste to improve soil health and plant development within the rhizosphere of Albion strawberry plants. In addition to adds to our understanding of sustainable farming practices and provides useful information for farmers and policymakers who want to maximize organic waste usage in agricultural systems. More research and field experiments are needed to investigate the long-term impacts of these organic waste additions in real-world agricultural contexts.