bluuebunny/crossref_metadata_2025_split huggingface dataset:
In Tough Seas: Overcoming Field Challenges Through Innovation and Partnerships With DPAA
Authors: Mires Calvin , Kovacs Evan , Lickliter-Mundon Megan , Mazza Alba 
Published: February 2024

Abstract

In 2022 and 2023, the Defense POW/MIA Accounting Agency (DPAA) partnered with Marine Imaging Technologies (Marine Imaging, hereafter) to conduct remote sensing survey operations in Guam to search for and possibly confirm the locations of four separate B-29 Superfortress wreck sites. The reported locations for these sites were in challenging environments; high seas, fast currents, and strong winds pose hazardous conditions for survey teams and equipment. Additionally, the bathymetry of the areas consists of dynamic changes in depths with sudden valleys, rises, and seamounts, mirroring the rocky coastline which poses threats to successful, strategic surveying and quality results. Marine Imaging Technologies worked with Eastern Search and Survey to develop an innovative survey system that allowed flexibility and pliability for small boat, deeper water, and limited crew operations. This paper discusses the goals, challenges, and results of this project and looks at ways forward on similar missions partnering with DPAA to account for MIA service members.
Modelling and Optimization of Electrodeposited Amorphous Fe-P Alloys Using CCC Design and RSM
Authors: Zhang Shuai , Yu Jing , Liu Zhengda , Li Yuetong 
Published: February 2024

Abstract
In this study, central composite circumscribed (CCC) design and response surface methodology (RSM) were used to model and optimize the electrodeposition characteristics of amorphous Fe-P alloys. Based on the results of a previous single-factor experiment, the significance of the influencing factors was determined using analysis of variance (ANOVA). Three factors significantly impacting the P content, hardness and corrosion current density of a deposit were identified: the bath temperature, pH and H2PO2− concentration. The statistical relationships between the process parameters and individual responses were established based on the CCC experimental data and RSM. The optimal parameters for each response were derived, and the influences of interaction terms were investigated. A desirability function was applied to determine the sample with the optimal comprehensive performance, featuring both high hardness and low corrosion current density. Due to the complex electrodeposition mechanisms of amorphous Fe-P alloys, the predicted P contents in deposits largely deviated from the experimental contents. However, the predictions of the hardness, corrosion current density and comprehensive performance were very accurate.
EDMHA: Encrypting data using Merkle Hellman algorithm for secure sharing in Wireless Body Area Network (WBAN)
Authors: CHINNADURAI Gomathi , NAGARAJAN Santhiyakumari 
Published: February 2024

Abstract
With its many potential applications and huge impact on people’s health, wireless body area networks have gained a lot of attention and are now considered a promising technology in the health care sector. It’s a network tailored to the human body, with the express purpose of tracking and relaying vital signs like heart rate, blood pressure, and electrocardiogram readings. As the demand of individual health monitoring rose, Wireless Body Area Networks (WBAN) are becoming highly distinctive within health applications. Nowadays, WBAN is much easier to access then what it used to be. However, due to WBAN’s limitation , properly sophisticated security protocols do not exist. As WBAN devices deal with sensitive data and could be used as a threat to the owner of the data or their family, securing individual devices is highly important. Despite the importance in securing data, existing WBAN security methods are focused on providing light weight security methods. This led to most security methods for WBAN providing partial security protocols, which left many possibilities in compromising the system. Initially, a Compression Aware Reliable and Accurate Data Transmission Network (CRADTN) is studied as a solution to this security issue because it is secure, but also it is introduced to enhancement and prevent malicious access. Then an Encrypting Data using Merkle Hellman Algorithm (EDMHA) framework is proposed which employs MHA to encrypt the data contents. This helps to considerably reduce the risk of unauthorised access to the data. The proposed scheme is evaluated in terms of message size, energy consumption, and computation cost, and the results are compared with those of existing schemes. The result shows that the proposed method can achieve higher efficiency than conventional methods.
Transparent-ATC-DNN: Deep Neural Network Based ATC Drug Class Prediction Using 17 Molecular Properties and Transparency Analysis Using SHAP Explanation for Property Categorization.
Authors: Chaurasia Anushka , Kumar Deepak , a Yogita 
Published: February 2024

Abstract
The Anatomical Therapeutic Chemical (ATC) system assigns unique codes to drugs for tracking, aiding in prescription, public health policies, and research. Different classifiers with distinct properties have been used earlier to predict ATC drug classes. However, Limited research has been conducted on 17 molecular properties and classifier transparency, emphasising the need of categorising each feature to increase model explainability. In this paper,a Transparent-ATC-DNN (Transparent ATC drug class prediction using Deep Neural Network) deep neural network and SHAP based framework was proposed for ATC drug class prediction and feature categorisation based on their contribution towards prediction. The 17 molecular properties and ATC data were extracted from PubChem and SIDER databases and then mapped based on drug identifier. The resulting dataset contained information on 1117 drugs, including their molecular properties, and 14 ATC drug classes. The proposed framework was applied to this dataset utilizing MLSMOTE to manage multilabel class imbalance and compared with eight classifiers, namely the ETC, KNN, SVM, RF, DT, XGBoost, LSTM, and 1D-CNN. The results of multilabel DNN architecture demonstrated promising performance, achieving an average accuracy of 98.89 ± .002, a hamming loss of 1.50 ± .002, and an ROC-AUC of 98.47 ± .003. Features were evaluated and categorized into three distinct categories: highest, lowest, and none, showcasing the proposed framework's effectiveness and explanatory power in predicting ATC drug classes.
The Egyptian National HPC Grid (EN-HPCG): Open-Source Slurm Implementation from Cluster to Grid Approach
Authors: Elshambakey Mohammed , Maiyza Aya I. , Kashkoush Mona S. , Fathy Ghada M. , Hassan Hanan A. 
Published: February 2024

Abstract

Recently, Egypt has recognized the pivotal role of High-Performance Computing in advancing science and innovation. The Egyptian National High Performance Computing Grid (EN-HPCG) advocates for consolidating high-performance computing resources, creating a unified pool accessible to all universities and scientific research centers using the PBS Pro scheduler. Therefore, this paper delves into the architecture and capabilities of the EN-HPCG grid using two different workload management systems: (i) Slurm (Open-Source) and (ii) PBS Pro (Licensed). Additionally, it assesses the grid's performance in specific high-throughput computing (HTC) applications using the NAS Grid parallel benchmark (NGB). The evaluation includes grid-level performance metrics such as throughput, and the number of tasks completed as a function of time. Also, the presented methodology aims to assist potential partners in their decision-making process to join the EN-HPCG grid, with a focus on the site speed-up metric. Our results showed that it is not advisable to integrate a cluster with high-speed hardware with a cluster possessing outdated hardware when using the Slurm scheduler. In contrast, the PBS Pro scheduler takes into account online decision-making in a dynamic environment using a unified grid.
Advancing Daily Streamflow Forecasting in Snow-Fed Mountainous Region Using a Novel Hybrid SWAT-BiLSTM Approach
Authors: Ahmad Khalil , Iqbal Mudassar , Tariq Muhammad Atiq Ur Rehman , Shahid Muhammad Laiq Ur Rahman , Khan Afed Ullah , Nadeem Abdullah , Adnan Muhammad 
Published: February 2024

Abstract

Precise prediction of streamflow ensures reliable planning and management of water resources. Physical-based prediction models are prone to significant uncertainties due to the complexity of processes involved as well as due to the uncertainties in model parameters and parameterizations. This study evaluates the performance of daily streamflow prediction in Astore a snow-fed mountainous region, by coupling physical-based semi-distributed hydrological Soil and Water Assessment Tool (SWAT) with data-driven (DD) Bidirectional Long Short-Term Memory (BiLSTM) model. Firstly SWAT and BiLSTM models are calibrated individually then coupled in three modes; SWAT-D-BiLSTM: flows obtained from SWAT with default parameters values used as one of the input in BiLSTM, SWAT-T-BiLSTM: flows obtained from SWAT with three most sensitive parameters values used as one of the input in BiLSTM and SWAT-A-BiLSTM: flows obtained from SWAT with all sensitive parameters values used as one of the input in BiLSTM. Input selection for DD model was carried out by cross correlation analysis of temperature, precipitation, and total rainfall with streamflow. The calibration, validation, and prediction of coupled models are carried out for periods 2007–2011, 2012–2015 and 2017–2019, respectively. Prediction performance is evaluated based on Nash-Sutcliffe Efficiency (NSE), coefficient of determination (R2), and Percentage Bias (PBIAS). Temperature showed greater correlation of 0.7 at 1-day lag as compared to precipitation and total rainfall with streamflow at daily time scale. The results showed that integrated model SWAT-A-BiLSTM outperformed SWAT-T-BiLSTM followed by SWAT-D-BiLSTM, BiLSTM and SWAT respectively. This study recommends coupling of hydrological models facing uncertainties with DD models.
Comparative assessment of seismic soil- shallow/pile foundation-structure responses in the saturated and dry soil deposits using 3D parallel finite element analysis
Authors: Asgari Ali , Bagheri Mohsen , Hadizadeh Mohammad 
Published: February 2024

Abstract

The research aims to investigate and compare the seismic responses of various models, including free field, pile group, fixed base, and shallow/deep foundation-structure with different structure’s height/width (h/b) ratios in saturated and dry sands during realistic earthquakes with varying intensities to realize the detrimental or beneficial effects of soil-foundation-structure-interaction (SFSI). None of the comparative research has simultaneously considered the shallow/deep foundations response in saturated and dry soil. This paper fills this gap using 3D non-linear parallel finite element models validated with two sets of centrifuge tests, and the extended analysis of nonlinearity effects of seismic SFSI considering large deformation performed. Focusing on the time-frequency content distribution result, the input acceleration amplitudes at different times are intensified by passing through the stiffer system (e.g., dry site, remediated soil, and shorter structure) at high frequencies. However, they decrease in the softer system, especially in liquefiable soil, due to the excess pore pressure build-up. The time of PGA alters at the foundation level, and correspondingly, the commencement time of significant settlement occurs quicker or later. A structure with a higher flexibility base increases rocking and, as a result, reduces flexural drifts, internal forces, and base shear force to seismic weight ratio in the structure, and subsequently decreases the structure's local damage. In contrast to lower h/b, the structure’s base shear force in the saturated soil site is more than in the dry one due to the higher peak structural acceleration. The results need attention in seismic design to help engineers in practical applications.
A quantitative method to compare regional tumor contrast between prone and supine breast MRI
Authors: Byrd Brook K. , Krishnaswamy Venkataramanan , Fox Misty J. , Gui Jiang , DiFlorio Roberta , Paulsen Keith , Barth Richard J. , Rooney Timothy B. 
Published: February 2024

Abstract
Purpose
For surgical guidance applications, supine breast MRI tumor contrast should be non-inferior to prone MRI, currently considered the standard-of-care. However, comparing image contrast quantitatively between different MRI sequences and breast orientations presents a significant challenge. Herein, we present a method for quantitatively comparing regional tumor contrast in the prone and supine breast MRI orientations for the purpose of tumor localization, and we apply this framework to assess the performance of two investigational supine scans (i.e. independent and prone-to-supine, P2S) compared to diagnostic prone MRI.
Methods
Patient tumors from two studies (NCT03573804, NCT03573661) were outlined slice-by-slice by a breast radiologist using Gd-enhanced, T1-weighted MRI. Image data were derived from subjects undergoing standard-of-care prone imaging (n = 78), independent supine imaging (n = 17), and P2S supine imaging (n = 61). Normalized tumor contrast was computed between the segmented tumor and neighboring normal tissue regions and compared for statistical differences amongst cohorts and non-inferiority to prone MRI.
Results
The independent supine cohort possessed non-inferior tumor-to-fibroglandular contrast compared to prone (p = 0.002), while tumor-to-fibroglandular contrast from the P2S supine cohort was found inferior to the prone cohort. However, both investigational supine scans produced non-inferior tumor-to-adipose contrast when compared to prone MRI (p < 0.001 in both cases).
Conclusions
Regional contrast between tumor and surrounding fibroglandular tissue suffered at later timepoints observed in the P2S supine study, resulting in inferior tumor contrast. However, when contrast-enhanced supine breast MRI is acquired independently, ratiometric comparisons indicate that tumor contrast is non-inferior to prone MRI.
Discovery of potential protein tyrosine phosphatase non-receptor type 2 inhibitors from phytochemical database through structure- based high-throughput virtual screening and machine learning Discovery of protein tyrosine phosphatase non-receptor type 2 inhibitors
Authors: Tu Vo Linh , Chen Tran Van , Hoang Thai Minh , Tran Pham Nguyen Bao , Quynh Tran Thi Thuy , Triet Nguyen Thanh 
Published: February 2024

Abstract
Protein tyrosine phosphatase non-receptor type 2 (PTPN2) has recently become a viable target for cancer immunotherapy. Our study aimed to predict compounds with potential to inhibit PTPN2 for anticancer immunotherapy through a combination of structure-based and ligand-based virtual screening. Using a virtual screening method based on the protein structure of PTPN2 (PDB: 7UAD), we were able to identify 94 compounds from the PhytoHub database that have the ability to bind to PTPN2. These compounds include flavonoids, stilbenes, alkaloids, carotenoids, coumarin, ellagitannins, diterpenoids, curcuminoids, and phenolic acids. The predicted IC50 value for the PTPN2 inhibitory activity of the compounds is from 15.25 µM to 3.26 µM through our machine learning model. Among them, the leading structures of each group also had stable complexes with proteins during molecular dynamics simulations and tightly bound to proteins while exhibiting low binding free energy (MM/GBSA). The two leading groups of compounds are metabolites derived from hesperetin and trans-resveratrol (sulfate and glucuronide) that can be further investigated as potential PTPN2 inhibitors for anti-tumor immunotherapy.
Rethinking of BERT Sentence Embedding for Text Classification
Authors: Galal Omar , Abdel-Gawad Ahmed H. , Farouk Mona 
Published: February 2024

Abstract
Text classification is a fundamental task in NLP that is used in several real-life tasks and applications. Large pre-trained language models such as BERT achieve state-of-the-art performance in several NLP tasks including text classification tasks. Although BERT boosts text classification performance, the common way of using it for classification lacks many aspects of its advantages. This work rethinks the way of using BERT final layer and hidden layers embeddings by proposing different aggregation architectures for text classification tasks such as sentiment analysis and sarcasm detection. This research also proposes different approaches for using BERT as a feature extractor without fine-tuning whose performance surpasses its fine-tuning counterpart. It also proposes promising multi-task learning aggregation architectures to improve the performance of the related classification problems. The experiments of the different architectures show that freezing BERT can outperform fine-tuning it for sentiment analysis. The experiments also show that multi-task learning while freezing BERT boosts the performance of yet hard tasks such as sarcasm detection. The best-performing models achieved new state-of-the-art performance on the ArSarcasm-v2 dataset for Arabic sarcasm detection and sentiment analysis. For multi-task learning and freezing BERT, a new SOTA F1-score of 64.41 was achieved for the sarcasm detection with a 3.47% improvement and near SOTA FPN of 75.78 for the sentiment classification. For single-task learning, a new SOTA FPN of 75.26 was achieved for the sentiment with a 1.81% improvement.
Assessment of surface water quality using the Water Quality Index (WQI) and multivariate statistical analysis (MSA), around tannery industry effluent discharge areas
Authors: Monira Umama , Sattar Golam Sabbir , Mostafa Md Golam 
Published: February 2024

Abstract

The study attempted to assess the water quality around the tannery effluent discharge areas for suitability for irrigation purposes using different indices (Water Quality Index (WQI)) and statistical analysis in Savar Upazilas, Dhaka, Bangladesh. The samples were collected three times, from monsoon 2021 to pre-monsoon 2023. The analysis results showed that the concentrations of various parameters at most of the sampling points exceeded the surface water standard. Pollution levels were found in the following order: pre-monsoon, post-monsoon, and monsoon. At SW1 point, the CWQI, WWQI, and MWQI of the surface water quality showed a ‘poor’ category. The IWQI values showed that the surface water at all the sampling points was suitable for irrigation purposes, except for the SW1 sampling point. The NPI indicates that EC, BOD, COD, TSS, Cl−, Na+, and NO3--N parameters were potentially responsible for polluting most sampling sites. Multivariate statistical analyses like principal component analysis, cluster analysis, and Pearson correlation matrix showed significant anthropogenic intrusions of these variables in surface water in the area. A strong correlation between these parameters indicated their common origin, i.e., poorly treated tannery industry effluent entered the surface water, suggesting an improvement in the efficiency of the Central Effluent Treatment Plant (CETP).
Kidney Injury: Focus on Molecular Signaling Pathways
Authors: Liu Wei , Hu MengDi , Wang Le , Mirzaei Hamed 
Published: February 2024

Abstract:
Acute kidney injury (AKI) is a syndrome in which kidney function reduces
suddenly. This syndrome which includes both structural changes and loss of function
may lead to chronic kidney disease (CKD). Kidney regeneration capacity depends on
the cell type and severity of the injury. However, novel studies indicated that regeneration mostly relies on endogenous tubular cells that survive after AKI. Regenerative pharmacology requires a great knowledge of fundamental processes involved in the development and endogenous regeneration, leading to a necessity for investigating related signaling molecules in this process. Regulatory non-coding RNAs (ncRNAs) including microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and circular RNAs (circRNAs) are currently known as critical regulators of gene expression in various cellular
processes, and this regulatory function is also observed in nephrotic tissue, following damaging insults, which may promote or inhibit the progression of damage. Thus, studying signaling molecules and pathways involved in renal injury and repair results in a
comprehensive prospect of these processes. Moreover, these studies can lead to new opportunities for discovering and enhancing therapeutic approaches to renal diseases.
Herein, we review studies dealing with the role of different signaling pathways involved
in renal injury. Besides, we discuss how some signaling pathways are useful for the repair process following AKI.
Diabetic Retinopathy - Pathophysiology to Treatment: A Review
Authors: Singh Randhir , Walia Aditya , Kaur Jasleen , Kumar Praveen , Verma Inderjeet , Rani Nidhi 
Published: February 2024

:
Diabetic retinopathy (DR) is a microvascular disease affecting the eyes of diabetic patients,
and is the most prevalent complication of diabetes mellitus. Vision improvement is not
possible in the majority of DR patients. Several studies have indicated that microvascular changes,
inflammation, oxidative stress, and retinal neurodegeneration are involved in the pathogenesis
of DR. Therefore, there is an urgent need for the development of new and effective treatment
for DR. Understanding the molecular mechanisms involved in the pathogenesis of disease will
pave a way for better treatment and management of DR. This article has emphasized the molecular
pathogenesis and treatment of DR.
Molecular Absorption Scanning and Assay of Serotonin Reception
Inhibitor Antidepressant Drugs
Authors: Santana Silva Amanda , de Paula Victor Hugo Abreu , dos Santos Xavier Lucas Gabriel , da Silva Ludimila Nascimento , Silva Lucas Ana Carolyne , Lourenço Alves Aléxia Mariany , dos Santos Fernando Machado 
Published: February 2024

Background::
Spectrophotometric techniques are based on the absorption of electromagnetic
energy by the molecules of the substance analyzed and can be applied to drug
analysis.

Objective::
This study has been proposed to carry out molecular absorption scanning and assay
in the ultraviolet region of generic antidepressant drugs of the serotonin reuptake inhibitor
class produced by three Brazilian laboratories, addressing the importance of good manufacturing
practices, drug quality control, and the use of qualitative and quantitative analytical techniques.

Methods::
The physicochemical analyses were performed in a Marte Científica (580 UVP)
UV-Vis spectrophotometer, and the result was evaluated according to the acceptance interval
described in the Brazilian Pharmacopoeia.

Results::
When calculating the dosage of the fluoxetine hydrochloride sample, the value of
90.95% was obtained and, therefore, the drug was approved for the dosage test. The dosage
test for the drugs, citalopram hydrobromide and sertraline hydrochloride, was also approved,
obtaining values of 102.4% and 98.2%, respectively.

Conclusion::
The results presented provide future perspectives regarding the rigor of the need
for good drug handling practices, maintenance and qualification of quality control laboratory
equipment, and the guarantee of validation of analytical methodologies used in the pharmaceutical
industries, contributing to the improvement of the quality of production and pharmaceutical
products analysis.