bluuebunny/crossref_metadata_2025_split huggingface dataset:
Maritime Fleet Management Transformation Using Big Data and IoT
Authors: Setiawan Ariyono , Wahy Anak Agung Istri Sri , Hasugian Sereati , Wiratno Daviq , Umasangadji Fahmi 
Published: January 2025

Abstract

In today's digital era, maritime fleet management faces major challenges related to port congestion and unpredictable traffic patterns. The main objective of this study is to investigate how the use of big data and the Internet of Things (IoT) can predict maritime traffic patterns and reduce port congestion. [
1] The methodology used includes analysis of historical data on sea traffic, predictive modeling, and the use of IoT sensors for real-time data collection. Key findings show that the application of this technology can improve the accuracy of traffic predictions by up to 30% and reduce ship waiting times at ports by up to 20%. The conclusion of this study confirms that the integration of big data and IoT can not only optimize fleet management, but also make a significant contribution to port operational efficiency and the overall sustainability of the maritime industry[2][3][4]
A case of chronic lymphocytic leukaemia with initial symptoms of cognitive impairment
Authors: Ye Baoye , Huang Yilin , Lai Wanying 
Published: January 2025

Abstract

Background: Chronic lymphocytic leukaemia (CLL) is a type of leukaemia that is often ignored in the initial stages. Slight fever, fatigue and night sweats are common presentations, but cognitive decline is rare.
Case presentation: A 69-year-old patient with early-stage CLL presented with rapid cognitive decline. The patient was taken by his wife to seek medical attention in our hospital. In his medical reports from other hospitals, the Mini-mental State Examination(MMSE) score was 10, and the Montreal Cognitive Assessment(MoCA) score was zero since he could not cooperate. MRI revealed bilateral hippocampal atrophy. The apolipoprotein E (APOE) gene test revealed that his genotype was e3/e4. A blood test also revealed high leucocyte and lymphocyte counts. Peripheral blood lymphocyte immunotyping was subsequently performed, and the results revealed abnormal monoclonal mature B cells. The final diagnosis was CLL.
Discussion: To our knowledge, memory loss is rare in the primary phase of CLL. However, we linked this patient’s cognitive impairment with CLL.
An unsupervised learning model that integrates clinical and MRI radiomics features outperforms existing models in predicting the 5-year progression-free survival of prostate cancer patients after prostatectomy: a multicenter study
Authors: Hu Guoqing , Liu Xiaofeng , Chen Zhangzhe , Zhou Bingni , Gan Hualei , Chen Ruchuan , Zhou Liangping , Gu Yajia , Liu Xiaohang 
Published: January 2025

Abstract

Background
Prostate cancer (PCa) is the second most common male cancer. Despite undergoing radical prostatectomy (RP), 20–30% of patients experience recurrence within 5 years. Unsupervised learning method based on radiomics features has proved its efficiency for predicting recurrence in patients with breast and lung cancer. In this study, we sought to identify subgroups of PCa patients after RP using an unsupervised clustering method based on clinical and MRI radiomics features, and further evaluate the prognostic value in predicting 5-year progression-free survival (PFS).
Materials:
Preoperative MRI and clinical data from 400 PCa patients (185 with recurrence) were collected from three centers (one training and two external validation groups). Radiomics features were extracted from index lesions. PFS-associated clinical and radiomics features were selected by least absolute shrinkage and selection operator (LASSO)-Cox analysis. The K-means clustering method was used to identify subgroups and construct a Radiomic-Clinical model. PFS differences across subgroups were assessed using Kaplan-Meier survival analyses. The predictive performance of the Radiomic-Clinical model was compared with the European Association of Urology (EAU), University of California, San Francisco (UCSF) Cancer of the Prostate Risk Assessment (CAPRA), and PIPEN models using the concordance index (C-index).
Results
Five clinical and 13 radiomics features were selected, and three distinct prognostic subgroups were identified within the Radiomic-Clinical model. The Radiomic-Clinical model demonstrated superior predictive accuracy with C-indices of 0.82 (training group), 0.78 (validation group 1), and 0.79 (validation group 2), outperforming the EAU (0.68, 0.70, 0.65), CAPRA (0.71, 0.67, 0.70), and PIPEN models (0.71, 0.70, 0.68) (p < 0.05).
Conclusion
Unsupervised learning using radiomics and clinical data effectively identifies distinct prognostic subgroups in PCa patients after RP, offering superior predictive performance over existing models for 5-year PFS.
The role of geology in flood risk assessments: a systematic literature review and a comprehensive bibliometric analysis
Authors: Erbil Cemre , Ayat Berna , Zabcı Cengiz 
Published: January 2025

Abstract

Floods have emerged as a critical global issue due to climate change, leading to increased research interest across various fields. However, the complex relationship between floods and geological factors remains insufficiently explored in the literature. This bibliometric analysis addresses this gap by examining the intellectual structure of research on floods and geology through a systematic review of 71 articles published between 1989 and 2024. The study reveals that environmental science dominates the field (44%), followed by earth and planetary sciences (16%), engineering (12%), and computer sciences (7%). Analysis of research terms demonstrates the field's breadth, with hydrology-related keywords comprising 58.4% of total terms, while flood-related and geology-related terms represent 21.9% and 19.7%, respectively. This study was conducted using data from the Scopus database, and co-word, co-citation, and co-author network analysis were performed through VOSviewer software. Key topics, influential publications, citation patterns, and international collaborations were identified and visualized using VOSviewer. The United States leads with 22 publications and 771 citations, followed by China with 15 publications and 117 citations. The analysis identified seven distinct international collaboration clusters, highlighting the global nature of flood research while also revealing geographical disparities in coverage. Notably, previous research demonstrates that integrating geological layers into hydrological models yields results closely matching real flood measurements, even in basins lacking measurement stations. This finding emphasizes the significance of understanding lithological characteristics for enhanced flood risk assessment. The analysis highlights an increasing application of advanced technologies, such as remote sensing, GIS, and machine learning, particularly in post-2020 studies, marking a shift toward data-driven approaches.
Subjective probability is modulated by emotions
Authors: Abel Lara , Schulz Eric , Nelson Jonathan D. 
Published: January 2025

Abstract

Information about risks and probabilities is ubiquitous in our environment, forming the basis for decisions in an uncertain world.Emotions are known to modulate subjective probability when probabilistic information is desired (as in gambles) or undesired (as in risks). Yet little is known about the subjective probability of affectively neutral events. We investigated this in one correlational study (Study 1, N = 162) and one experimental study (Study 2, N = 119). As predicted, we found that participants higher in emotional dominance were more conservative in their probability estimates, avoiding the extremes. Remarkably, this pattern also transferred to realistic risk assessments. Furthermore, respondents' tendency to use the representativeness heuristic as a proxy for probability was increased in high dominance individuals. Our findings suggest that emotional dominance may be a unifying construct explaining previously reported effects of emotions on probabilistic cognition.
BIRC5 as a Potential Biomarker and Therapeutic Target for Lung Adenocarcinoma: A Comprehensive Bioinformatics Analysis
Authors: Chang Jianliang , Yang Shuaibo , Peng Xiaocui , Wang Xue , Xu Dandan , Zhang Zhihua 
Published: January 2025

Abstract

Lung adenocarcinoma (LUAD), the most prevalent type of non-small cell lung cancer (NSCLC), has the highest incidence and mortality rates among thoracic cancers worldwide. BIRC5, a protein linked to tumor cell proliferation, differentiation, migration, and invasion, has been insufficiently studied as a potential LUAD biomarker. In this study, we used bioinformatics techniques to analyze LUAD-related data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases to identify biomarkers associated with LUAD onset, progression, and prognosis and evaluate their clinical significance. Immune cell infiltration and single-cell RNA sequencing analyses assessed the expression of core genes in various immune environments. Drug sensitivity analysis evaluated the impact of these biomarkers on treatment response, providing a basis for early diagnosis and personalized LUAD treatment. Gene expression and clinical data from TCGA and GEO underwent weighted correlation network analysis (WGCNA), differential analysis, and univariate and multivariate Cox regression to identify prognostic core genes. High BIRC5 expression emerged as an independent risk factor for poor overall survival (OS) in LUAD, exhibiting strong diagnostic performance. Enrichment analysis indicated that BIRC5 was involved in the cell cycle and P53 signaling pathways. Single-cell RNA sequencing and immune infiltration analyses revealed that BIRC5 plays a critical role in the immune microenvironment. Drug sensitivity analysis showed that high BIRC5 expression correlated with increased sensitivity to several anticancer drugs. These findings establish BIRC5 as a promising biomarker for LUAD diagnosis and prognosis. Its role in immune regulation and drug sensitivity highlights its potential for guiding personalized treatment strategies.
Analysing stickiness related to changes in Customer Behaviour Due to the COVID-19 Pandemic
Authors: B Meenal 
Published: January 2025

Abstract

This research analyses the effects of the COVID-19 pandemic on consumer preferences for shopping locations and its duration through a quantitative method. A revised version of the Huff gravity model is employed to measure changes over time in customer preferences regarding store selection for shopping. The research utilises extensive mobility and location datasets along with census data to examine department stores in New York City. Employing clustering methods and statistical inference models, this research assesses the short-term and long-term effects of the COVID-19 pandemic on changes in customer behaviour, emphasising the varied responses across different socioeconomic groups. The results of this study indicate that the suggested model successfully represents the dynamics of shopping site choices and time-based visitation trends, enabling managers and marketers to grasp customer preferences and adjust strategies as needed. Retailers ought to apply quantitative techniques to revise their assumptions according to new consumption trends, instead of anticipating a full return to consumer behaviour from before the pandemic.
Assessment of naturally occurring radionuclides in the coastal sediment with statistical analysis of radiological risk parameters
Authors: Pandion Kumar , Arunachalam Kantha Deivi , Gaafar Abdel-Rhman Z. , Almunqedhi Bandar M. , Kuppusamy Sowmya , Singh Rajeev Pratap , Chandrasekaran Murugesan , Dowlath Mohammed Junaid Hussain , Chang Soon Woong , Balasubramani Ravindran 
Published: January 2025

ABSTRACT

This study reports on the spatial distributions of naturally occurring 238U, 232Th, 40K, 222Rn, and 220Rn from sediments across the coastal zone of the Bay of Bengal (BoB). The mean values of 238U, 232Th, and 40K activity are 23.42, 80.95, and 292.49 Bq kg−1, and the 220Rn surface mass exhalation rate value of 1,225.33 was calculated by 2.54 mBq kg−1h−1. The radionuclide concentration range in all the samples estimated in the coastal region was 220Rn &gt; 40K &gt; 232Th &gt; 238U and the radiological risk parameters (Raeq, DR, AEDE, AGDE, Iɤr, AUI, Hex, Hin, and ELCR) were higher in the Kanyakumari region and lower in the Puducherry region of the BoB. The radionuclide levels of 232Th, 238U, and 220Rn displayed a strong step of positive association with all parameters of radiological hazards through constants of r &gt; 0.75. This showed that radiation risks were correlated with, and regulated by, 238U and 232Th concentrations. Therefore, coastline sediments do not pose any serious hazard, and the statistics obtained from this analysis will serve as the reference data for the activity of natural radionuclides in sediments across the coastal zone of the BoB.
Simulation and prediction of hydraulic jump characteristics over expanding rough beds using FLOW-3D and soft computing techniques
Authors: Ziari Morteza , Karami Hojat , Ostadi Ali , Ghazvinian Hamidreza 
Published: January 2025

ABSTRACT

In this study, hydraulic jumps over expanding beds with artificial roughness were simulated using FLOW-3D across Froude numbers ranging from 4.34 to 9.37. The simulations were conducted on both smooth and rough beds, with roughness in the form of half-spheres of 3, 4, and 5 cm in diameter, and divergence angles of 7°, 14°, and 90°. The results showed that for maximum discharge in a sudden divergent channel, a rough bed with 5-cm diameter elements reduced flow depth by 19.77% compared to a smooth bed. Additionally, in all scenarios, the ratio of y2/y1 increased as the Froude number increased. In the second phase, soft computing models – such as Linear Regression, Support Vector Regression, Decision Tree, Random Forest, Bagging, Gradient Boosting, MLP, and Stacking – were employed to model the relationships between input parameters (Fr1, θ, D/b1, and Kb) and outputs (y2/y1 and Lj/y1). The R2 coefficient value in the training stage of the Stacking model for the parameter (y2/y1) was 0.978 and in the testing stage it was 0.988, and for the parameter (Lj/y1) in the training and testing stages this coefficient was estimated to be 0.971 and 0.987, respectively.
Ononin’s Antagonistic Activity towards TRPV1: Insights from Molecular Dynamics and Capsaicin-Evoked Calcium Response in DRG Neurons
Authors: Saqallah Fadi G. , Abbas Manal A. , AL-Najjar Belal O. , Al-Kabariti Aya Y. , Yusuf Muhammad , Abdulwahed Omar A. , Barakat Noor A. , Alsalem Mohammad 
Published: January 2025

Background:
The search for effective painkillers has led to intensive research,
with a particular focus on the transient receptor potential vanilloid-1 (TRPV1)
channel as a possible target.

Methods:
One promising candidate is ononin, which is investigated for its binding with
TRPV1 through a 200-ns molecular dynamic simulation and analysed via root-meansquare
deviation (RMSD), root-mean-square fluctuation (RMSF), hydrogen-bond interactions,
radius of gyration (RadGyr), and MM-PBSA energy calculations. The results
were further validated experimentally via calcium imaging studies.

Results:
Molecular dynamics revealed that the ononin-TRPV1 complex achieved stable
binding within a remarkably short time of approximately 38 ns while maintaining the degree
of compactness of the receptor throughout a 200 ns simulation period. In contrast,
the capsazepine-TRPV1 complex displayed more significant structural deviations during
the whole simulation. Moreover, MM-PBSA energy calculations showed a relatively
strong binding affinity between ononin and TRPV1, mainly attributed to favourable hydrophobic
interactions. Pre-incubation of dorsal root ganglia (DRG) neurons with
ononin (1 and 10 μM) or capsazepine (10 μM) for 4 min prior to stimulation with capsaicin
significantly reduced capsaicin-evoked calcium responses. No significant difference
between capsazepine and ononin was found at a concentration of 10 μM.

Conclusion:
Overall, this research demonstrates the potential of ononin as a potential antagonist
for developing analgesics targeting TRPV1. Hence, and to our best knowledge,
this study represents the first report of ononin’s antagonistic activity towards TRPV1.
Exploring the Mechanism of Shi-San-Wei-He-Zhong-Wan in the Treatment of Functional Dyspepsia Based on Network Pharmacology and Experimental Validation
Authors: Li Xiao Min , Zhu Jing Wei , Li Kui Wu , Han Xiao Yu , Ruan Jing Ru , Chu Hao Ran 
Published: January 2025

Purpose:
The incidence of Functional Dyspepsia (FD) is gradually increasing, yet
there are currently no effective treatment methods available. This study explored the effective
components, potential targets, and pathways of Shi-San-Wei-He-Zhong-Wan (SSWHZW) in the
treatment of FD, aiming to provide new insights into its treatment.

Methods:
First, the Chinese Medicine System Pharmacology Database and Analysis Platform
(TCMSP) and GeneCards databases were utilized to identify the major active components of
SSWHZW and potential therapeutic targets of FD. Subsequently, functional enrichment analyses
were performed to elucidate the mechanisms of SSWHZW on FD. Molecular docking simulations
were then conducted to assess the binding affinity of key targets and major active components.
Next, an FD animal model was established, and the therapeutic effects of SSWHZW were
validated using Hematoxylin and Eosin (HE) staining and Enzyme-linked Immunosorbent Assay
(ELISA). Finally, Western blot analysis was performed to validate the involvement of key signaling
pathways.

Results:
A total of 229 active ingredients and 283 putative targets were identified from
SSWHZW, of which 173 overlapped with the targets of FD and were considered potential therapeutic
targets. Key ingredients, such as quercetin, kaempferol, wogonin, and baicalein, were
identified as pivotal components of SSWHZW, potentially acting on the 173 overlapping targets
and influencing FD through related signaling pathways. Functional enrichment analysis revealed
that the PI3K-Akt signaling pathway, VEGF signaling pathway, and NF-kappa B signaling
pathway may be involved in the mechanism of SSWHZW in treating FD. Molecular docking
predicted that all five ingredients could firmly bind with the top-ranked target TP53 in the Protein-
protein Interaction (PPI) network. Further experiments demonstrated that SSWHZW protected
the intestinal tissues of FD rats from inflammatory damage by inhibiting the PI3K/AKT
signaling pathway.

Conclusion:
Based on network pharmacology, this study explored the multi-component, multitarget,
and multi-pathway characteristics of SSWHZW in treating FD. The findings suggest that
SSWHZW exerts its anti-FD effects by inhibiting the expression of the PI3K/AKT signaling
pathway, providing new insights and methods for further research on the mechanism of
SSWHZW in treating FD.
Artificial Intelligence Revolution in Pharmaceutical Sciences: Advancements, Clinical Impacts, and Applications
Authors: Halagali Praveen , Nayak Devika , Seenivasan Raagul , Manikkath Jyothsna , Rathnanand Mahalaxmi , Tippavajhala Vamshi Krishna 
Published: January 2025

Abstract:
Artificial intelligence (AI) is a rapidly transforming drug discovery and development
process, significantly impacting the pharmaceutical industry and enhancing human
health. This review article examines the tremendous role of AI in analyzing complex biological
data, optimizing research processes, and reducing costs of production. Implementation of
AI in the pharmaceutical sector can store a vast dataset of manufacturing processes, identify
potential disease targets, simulate physiological conditions, and predict drug interactions. The
review article also discusses the AI concepts and their applications, particularly in developing
solid dosage forms. Advanced algorithms optimize formulation processes, predict pharmacokinetics
profiles, and assess drug toxicity profiles, facilitating a more efficient pathway from
pilot study to market. Additionally, this review highlights the advancements in 3D printing
technologies of dosage forms that have the ability to provide personalized treatment to different
individuals. Furthermore, the article explores the opportunities and challenges of AI in
healthcare, focusing on applications such as disease diagnosis, digital therapy, and epidemic
forecasting. Prominent AI technologies like deep learning and neural networks are examined
for their roles in predicting outbreaks of diseases like influenza and COVID-19. As the pharmaceutical
landscape evolves, AI is poised to redefine traditional methods. This paves the way
for more efficient healthcare solutions. By harnessing the interplay of technology and science,
AI not only increases productivity; but it also promotes a new era of precision medicine tailored
to the needs of each patient.
Introducing the Concept of Hypertensive Heart Disease to Improve
Hypertensive Left Ventricular Hypertrophy
Authors: Koracevic Goran , Stojanovic Milovan , Zdravkovic Marija , Simic Dragan , Lovic Dragan , Djordjevic Dragan , Otasevic Suzana , Tomasevic Miloje , Sakac Dejan 
Published: January 2025

Background:
Among the organ damage mediated by hypertension, cardiac lesions hold
significant importance. Numerous authors focus on hypertensive heart disease (HHD) rather than
exclusively on left ventricular hypertrophy (LVH).

Objective:
This narrative review aims to assess the incorporation of the concept of 'hypertensive heart disease' (HHD) in hypertension (HTN) guidelines. Furthermore, if HHD is not addressed, the review will evaluate the potential benefits of including this concept in future studies

Methods:
The following databases were searched: Scopus, Medline, Springer, Science Direct, Wiley,
SAGE, Cambridge, Oxford Journals, and Google Scholar. Attention was given to the guidelines related to hypertension(HTN); the search items were “guidelines” and “hypertension.” Within these
guidelines, we specifically sought references to ‘hypertensive heart disease.’

Results:
The concept of “HHD” is clearly advantageous compared to “HTN LVH,” as it not only
addresses LVH but also considers other structures of the heart that may be severely affected, which
can significantly influence treatment. The concept of “hypertensive heart disease” is mentioned in
only 8 out of 36 guidelines on HTN. The therapeutic implications and recommendations are absent in
the guidelines.

Conclusion:
The concept of HHD is reasonable and evidence-based, and there is no reason to focus
only on LVH when considering HTN-induced damage to the heart. It is time to update our recommendations for heart treatment by using the phrase "Treatment of hypertensive heart disease" instead
of "Treatment of hypertensive LVH." This update can enhance our awareness of the need to improve
not only HTN LVH but the other parts of the heart as well.