bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Novel Wavelet-Based Model For Android Malware Detection Utilizing System Calls Features
Authors: CHHAYBI Akram , LAZAAR Saiida 
Published: September 2024

Abstract

Security is a significant concern in the realm of Android platforms, given the ability to allow installations from unverified sources exposing devices to possible malware infiltration and malicious attacks. Malware classification remains a challenging task in the field of mobile malware detection, where dynamic analysis plays a central role in intrusion detection systems (IDS). This process aims real-time examination of application behavior enabling extraction of dynamic patterns exhibited by malware. System calls, provided by the device’s operating system and accessible from user applications, are deployed in machine learning methods for malware detection. The performance of machine learning-based detection algorithms requires improving the feature selection within system calls. This work suggests an original approach based on wavelets to enhance detection of a wide spectrum of Android malwares, such as adware, riskware, banking and SMS. The present methodology uses the Chi-Square test for feature selection, and Haar wavelet for converting selected attributes into wavelet coefficients. In our experiments, we use several machine-learning classifiers such as Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF), and Neural Network (NN). To assess the performance of these classifiers, we employ evaluation metrics in terms of Accuracy, Recall, F-Score, and Precision. Among the selected classifiers, the Random Forest model coupled with wavelet feature selection provided the highest performance, achieving an accuracy rate of 99.99%. The results clearly demonstrate the effectiveness of our proposed model.
Incorporating additive genetic effects and full LD information to discover genome-level gene-environment interactions with summary statistics of complex traits
Authors: Dong Zihan , Jiang Wei , Shen Jiangnan , Li Hongyu , DeWan Andrew , Zhao Hongyu 
Published: September 2024

Abstract

Uncovering environmental factors interacting with genetic factors to influence complex traits is important in genetic epidemiology and disease etiology. Existing methods examining gene-environment (G\(\:\times\:\)E) interactions either test G\(\:\times\:\)E interaction for each genetic variant individually, ignoring correlations with additive genetic effects, or use only partial information of the Linkage Disequilibrium (LD), leading to potential loss of statistical power to uncover interacting factors. In this paper, we introduce BiVariate Linkage-Disequilibrium Eigenvalue Regression for Gene-Environment interactions (BV-LDER-GE), a novel statistical method that detects the overall contributions of G\(\:\times\:\)E interactions in the genome using summary statistics of complex traits. BV-LDER-GE harnesses both correlations with additive genetic effects and full LD information to enhance the statistical power to detect genome-scale G\(\:\times\:\)E interactions. Extensive simulations demonstrate that the BV-LDER-GE is more powerful than existing methods while the type-I error rate is well-controlled. When examining 151 environmental covariate-phenotype (E-Y) pairs from the UK Biobank data, BV-LDER-GE identified 28 (80%) more statistically significant interacting environmental covariate phenotype pairs (E-Y pairs) than existing methods.
Hepatitis B and Hepatitis C Mortality in a Local Health Jurisdiction, Alameda County, CA 2005- 2022
Authors: Taye Beimnet F. , Chitnis Amit S. , Yette Emily , Beyers Matt , Wong Robert J. , Dunne Eileen F. 
Published: September 2024

Abstract

Background: Local data to characterize Hepatitis B Virus (HBV) and Hepatitis C Virus (HCV) mortality supports targeted use of public health resources.
Aims:To assess HBV and HCV mortality in Alameda County to guide elimination efforts.
Methods: Cross-sectional analysis of data from California’s Integrated Vital Records System, 2005–2022, consisting of Alameda County residents who died in California with International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) codes specific to HBV, HCV, or both listed as a cause of death. Main outcome measures were HBV and HCV death counts; crude mortality rates; and 2013–2022 Average Annual Percent Changes (AAPC) of HCV age-adjusted mortality rate by sex, race/ethnicity, and census tract Healthy Places Index (HPI) quartile.
Results: A total of 2165 HBV and HCV deaths were identified in Alameda County from 2005–2022 (313 HBV, 1809 HCV, 43 co-infected deaths). Most (73.2%) HBV decedents were Asian and/or born outside the United States (78.9%). Age-adjusted HCV mortality rates decreased for all groups from 2013–2022. African American/Black and Hispanic/Latinx residents had smaller percent decreases in HCV mortality than Asian residents (AAPC difference: 6.6% [0.4%,12.9%]; P=0.04 and 9.3% [3.5%,15.1%]; P=0.002). HPI quartile 1 (least advantaged) had a smaller percent decrease in HCV mortality than quartile 4 (AAPC difference: 8.3% [3.6%,12.9%]; P=0.01).
Conclusions: We identified disparities in the burden of HBV deaths and the declines in HCV deaths in Alameda County. Focused efforts to expand viral hepatitis screening, vaccination, and treatment in specific populations are needed to close these gaps and reach elimination targets.
Autonomous navigation with minimal sensors in dynamic warehouse environments: a multi-agent reinforcement learning approach with curriculum learning enhancement
Authors: Shen Zhilong , Chi Yongwei , Bai Yu , Liao Xiaojing , Zhao Peiyu , Lu Jianjiang , Niu Qian , Dai Qinya 
Published: September 2024

Abstract

This paper introduces an innovative approach to augmenting the capabilities of autonomous mobile robots (AMRs) in dynamic, multi-agent warehouse environments. We propose an integrated system that combines Multi-Agent Deep Deterministic Policy Gradient (MADDPG) a cutting-edge multi-agent reinforcement learning (MARL) algorithm with Curriculum Learning (CL). This hybrid method is specifically tailored to meet the complex demands of warehouse operations. Our approach is distinguished by the development of decentralized policies within a centralized training framework, progressively incorporating complexity through a carefully structured curriculum. This strategy enables autonomous agents to master sophisticated navigation and coordination tactics, adapting adeptly to the variable requirements of real-time warehouse scenarios. Through comprehensive experiments, we demonstrate significant enhancements in autonomous decision-making and operational efficiency, thereby validating the effectiveness of merging MARL with CL in intricate, multi-agent contexts. Our findings align with recent advancements in MARL applications for robotics 1 and underscore the potential of curriculum-based learning strategies in complex environments.
Enhancing Quantum Machine Learning: The Power of Non-Linear OpticalReproducing Kernels
Authors: Dehdashti Shahram , Tiwari Prayag , Safty Kareem H. El , Bruza Peter , Nötzel Janis 
Published: September 2024

Abstract

Amidst the array of quantum machine learning algorithms, the quantum kernel method has emerged as a focal point, primarily owing to its compatibility with noisy intermediate-scale quantum devices and its promise to achieve quantum advantage. This method operates by nonlinearly transforming data into feature space constructed with quantum states, enabling classification and regression tasks. In this study, we present a novel feature space constructed using Kerr coherent states, which generalize su(2), su(1,1) coherent states, and squeezed states. Notably, the feature space exhibits constant curvature, comprising both spherical and hyperbolic geometries, depending on the sign of the Kerr parameter. Remarkably, the physical parameters associated with the coherent states, enable control over the curvature of the feature space. Our study employs Kerr kernels derived from encoding data into the phase and amplitude of Kerr coherent states. We analyze various datasets ranging from Moon to breast cancer diagnostics. Our findings demonstrate the robustness of Kerr coherent states, attributed to their flexibility in accommodating different hyperparameters, thereby offering superior performance across noisy datasets and hardware setups.
Tick repellent activities of supercritical fluid extract from Perilla frutescens against Haemaphysalis longicornis
Authors: Jang Young Su 
Published: September 2024

Abstract

Supercritical fluid extraction (SFE) is a rapid, selective, and convenient method for sample preparation prior to the analysis of compounds in a volatile product of plant matrices. In this study, insecticidal activity and repellency were examined in a laboratory study to determine the active constituent of Perilla frutescens and a supercritical fluid extract from Perilla frutescens against hard tick Haemaphysalis longicornis. The repellency of P. frutescens oil extract was evaluated in comparison with two commercial repellents (Icaridin and DEET) against H. longicornis. Filter-paper diffusion assay revealed that P. frutescens oil (3.2 µg/cm2) had a 100% repellency and icaridine (3.2 µg/cm2) had a 96.1% repellency at 120 min. Repellencies of 4 compounds and microcapsule P. frutescens of against H. longicornis were compared with the repellency of Icaridine and DEET at 3.2 µg/cm2. Responses varied according to the test compound and exposure time. Repellencies of Perillaldehyde and microcapsule P. frutescens were 100% at 30 min post-treatment. P. frutescens oil merit further study as potential fumigants or lead compounds for the control of H. longicornis.
Spiral Polycyclic Aromatic Hydrocarbons Embedded in the B/N Center for Constructing Highly Efficient Multiple Resonance TADF Emitters
Authors: Ge Ziyi , Wu Lin , Liu Chunyu , Liu Denghui , Li Deli , Li We , Zhang Jiasen , Mu Xilin , Wang Zhiheng , Liu Di , Zhou Yubo , Wu Siyao , Su Shi-Jian 
Published: September 2024

Abstract

Introducing spiral polycyclic aromatic hydrocarbons (PAHs) in multiple resonances thermally activated delayed fluorescence (MR-TADF) emitters to achieve small FWHM values and high efficiency remains a formidable challenge. This research departs from the conventional molecular design strategy that utilizes spiral PAHs solely as side chains, terminal groups, or linkage units. Instead, we integrate the rigid 9,9'-spirobi[fluorene] (SF) unit fully or partially into the boron/ nitrogen MR (B/N-MR) emitting core and successfully developed a range of brand-new proof-of-concept isomerized MR-TADF emitters, SF-BN1, SF-BN2, SF-BN3, and SF-BN4. Remarkably, these novel emitters exhibit exceptionally narrow full-width at half-maximum (FWHM) values of 15～21 nm in dilute toluene solutions and high photoluminescence quantum yields (PLQYs) of up to 90% in doped films. The corresponding organic light-emitting diode (OLED) based on SF-BN1 achieved high external quantum efficiency (EQE) of up to 29.0%, with CIE coordinates of (0.13, 0.08), closely aligning with the BT.2020 blue emission standard. Sky-blue OLEDs based on SF-BN3 can achieve a high EQE of 29.8%, with an exceptionally highly narrow FWHM value of 18 nm; the hyperfluorescent OLEDs based on SF-BN3 improved the EQE of 35.5%. Moreover, we elucidated subtlevariations in the connectivity of chemical functional groups within emitters, as well as the polar environment and doping concentrations of OLEDs, which can significantly impact the optical and electroluminescent properties of these isomers. This research unveils new opportunities for exploration and yields potential novel luminescent materials suitable for ultra-high-definition (UHD) display technologies.
Neural dynamics of perceived agreement and disagreement with peer and expert opinions: An MEG study
Authors: Irani Fatemeh , Lyyra Pessi , Muotka Joona , Maunula Sini , Parviainen Tiina , Hytönen Kaisa , Monto Simo 
Published: September 2024

Abstract

Individuals change their opinions under the influence of others' opinions; however, the extent and nature of this influence critically depend on their attitudes toward those exerting the influence. In this study, we compare two sources of influence that drive conformity behavior: an expert group, and a peer group. Furthermore, we investigate the underlying neural dynamics using magnetoencephalography to determine whether the processing of these two influences shares their neural mechanisms. Twenty-two participants performed a task in a fashion choice context and received feedback from a peer and an expert group. When participants re-evaluated the clothing after a delay, we found that participants' opinions changed in line with disagreement feedback when feedback was lower than the participant's first rating – without distinct conformity to the social sources. On the neural level, however, there was a difference between conflict with peer and expert groups, with a stronger response for peers in 170–590 ms time window in gradiometer channels. Furthermore, agreement evoked stronger neural responses than conflict, in 590–960 ms time window in magnetometer channels. Taken together, our findings suggest that conflicting feedback from peers and experts regarding clothing preferences elicits distinct temporal dynamics. However, conformity behavior is influenced solely by the feedback valence.
Enhancing PID Control Robustness in CSTRs: A Hybrid Approach to Tuning Under External Disturbances with GA, PSO, and Machine Learning
Authors: AJlouni Naim , Osyavas Adem , Ajlouni Firas , Almassri Abdelrahman , Takaoglu Faruk , Takaoglu Mustafa 
Published: September 2024

Abstract

The optimization of PID controllers for Continuous Stirred Tank Reactors (CSTRs) is critical for ensuring stable and efficient chemical processing under varying operational conditions and external disturbances. This study presents a novel approach that integrates advanced tuning techniques, including Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and their hybrid combinations with a Machine Learning (ML) surrogate model, to improve PID controller performance in disturbed environments. A unique evolutionary framework is employed, where populations of both controllers and plants are co-evolved to handle the most challenging plant models. An adversarial testing approach is utilized to evaluate the best-tuned controller against the four most difficult CSTR plants, with disturbances such as feed concentration and temperature fluctuations. The results demonstrate that both GA and PSO, when enhanced with the ML surrogate model, effectively tune PID controllers to manage disturbances, with the GA tuned controller achieving faster convergence and the PSO tuned controller showing greater robustness. Additionally, the hybrid ML surrogate model significantly improved control performance and disturbance rejection. The findings highlight the ability of the ML_GA and ML_PSO controllers to maintain stability and accuracy across a range of challenging conditions, providing a robust solution for optimizing control systems in nonlinear dynamic environments. This research contributes to the field of process control, showcasing the potential of combining evolutionary algorithms with machine learning surrogate models for adaptive, resilient control strategies in complex chemical systems.
Pollution load and dynamics of phytoplankton communities in relation to environment variables in Lake Babogay, Ethiopia
Authors: Hiruy Andualem Mekonnen , Yibel Zeleke Balew , Alehegne Kassahun Atalay , Endalew Yirgalem Esuneh 
Published: September 2024

ABSTRACT

The objective of this study was to investigate the water quality of Lake Babogaya in Ethiopia. Water samples were collected from six different sites, and analyzed for total coliform (TC), fecal coliforms (FC), and Escherichia coli (E.coli) content. Additionally, potential of hydrogen (pH), dissolved oxygen (DO), total dissolved solids (TDS), electrical conductivity (EC), and Secchi depth were measured. Nutrients and heavy metals were determined using standard methods. The abundance of phytoplankton was estimated by calculating the biovolume of the species. The results indicated that pH, TDS, DO, and turbidity ranged from 8.78 + 0.31 to 9.52 + 0.04, 129 + 4 to 419 + 3 ppm, 5.50 to 7.25 mg/l, and 0.96 to 17.29 NTU, respectively. TC bacteria exceeded the World Health Organization (WHO) standards at all sites, while FC levels exceeded in most samples. Cadmium and chromium were low in both seasons, whereas lead in the dry season exceeded the WHO limits. The levels of iron were high across all sites in the dry season and low in most sites in the wet season. Five major taxonomic phytoplankton groups were identified, comprising a total of 31 species with highest richness of green algae, blue-green algae, and diatoms species.
Dysbiosis and Regulation of Gut Microbiota in Type 2 Diabetes Mellitus
Authors: Minakshi  , Kumari Hemlata , Prakash Shaurya , Kumar Antresh 
Published: September 2024

Abstract:
Type 2 diabetes mellitus is a serious metabolic disease having a high growth rate and
becoming a global threat. An unhealthy lifestyle, food intake, and genetic susceptibility are the
major factors responsible for this metabolic disorder. This disease results in hyperlipidemia,
hyperglycemia, glucose intolerance, restricted insulin synthesis, and insulin resistance. Despite a
variety of treatments currently available, cases of diabetes and resulting complications are on the
rise. One promising approach to diabetes focuses on gut microflora and their associated metabolites.
Gut microbiota has attracted widespread attention due to its crucial role in disease pathophysiology.
This study explores the dysbiosis in the human gut microflora in Type 2 Diabetes
Mellitus and how the gut microbiota influences metabolites related to T2DM. It also sheds light
on early identification and targeted intervention for this. Understanding these mechanisms
could potentially lead to more effective strategies for managing and preventing T2DM. The
findings of our literature study are that gut microbiota can serve as biomarkers for early disease
detection. Finally, we also highlight gut microecological therapeutic strategies focused on
shaping the gut flora to emphasize the improvement of T2DM progression.
Examination of Triazolinedione-Derived Dihydropyrrole Hybrid Compounds: ER Stress-Related Apoptosis in Breast Cancer Cells, Molecular Docking, and ADMET Analysis
Authors: Mesci Seda , Ayar Arif , Yazgan Burak , Gül Melek , Yıldırım Tuba 
Published: September 2024

Abstract:
In many types of cancer, uncontrolled growth and proliferation of cells
occur due to abnormalities in their genes, mutations of pro-apoptotic proteins, or upregulation of
anti-apoptotic proteins. Triazolinedione and pyrrole derivatives are compounds with anti-microbial,
anti-fungal, anti-inflammatory, and anti-cancer activities. Pyrrole and its derivatives are critical
heterocycle compounds that are significant in anticancer studies and highly preferred in research.

Objective:
This study aimed to investigate the effects of dihydropyrrole derivatives substituted with
triazolinedione on the MCF-7 (breast cancer) cell line’s apoptosis, ER stress, and heat shock genes.

Methods:
The mRNA levels of apoptosis, ER stress, and heat shock proteins were assessed by
qRT-PCR method in the MCF-7 cell line. The investigation of ADMET features, crucial pharmacokinetic
indices for the potential candidacy of compounds as drugs, has been meticulously designed.
In silico-induced molecular docking studies were conducted to further explore the interaction
and elucidate the orientation of hybrid compounds within the active sites of BCL-2, PARP,
HSP70, HSP90, and GRP78.

Results:
It was determined that the compounds caused cell death by modulating apoptotic (compound
IV), ER stress, and heat shock proteins (compounds XI and XVI) through up- and downregulation.
Our findings have pointed to the effects of triazolinedione-substituted dihydropyrrole
derivatives, exhibiting antitumor activity on apoptosis, ER stress, and heat shock genes in the MCF-
7 cell line.

Conclusion:
The compounds investigated in this study have been found to be promising for anticancer
research.
Development and Validation of an Eco-Friendly Liquid Chromatography- Tandem Mass Spectrometry Method for Rapid Quantification of Γ-Aminobutyric Acid in Cordyceps sinensis and its Related Species
Authors: Wang Wenhao , Lei Qinggui , Wang Chuanxi , Huang Qi , Qian Zhengming 
Published: September 2024

Introduction:
Cordyceps sinensis, a renowned traditional Chinese medicinal substance,
contains pharmacologically beneficial compounds, including γ-aminobutyric acid (GABA), necessitating
efficient analytical methods for quality assessment. This study aims to develop a rapid,
cost-effective, and eco-friendly liquid chromatography-tandem mass spectrometry (LC–MS/MS)
method for GABA quantification in Cordyceps sinensis and its related species. The objective is to
enhance the quality control standards within the Cordyceps sinensis industry and for those of its related
species.

Methods:
The study innovated a swift analytical procedure. Optimal conditions were determined
through systematic evaluations of extraction solvents, durations, and chromatographic settings, prioritizing
speed and solvent minimization.

Results:
The proposed LC–MS/MS method achieved precise quantification of GABA in a reduced
time frame and significantly lowered solvent consumption, enhancing method efficiency by 22-fold
compared to reported LC methods. The method exhibited robustness and scored the highest blue
applicability grade index score, underscoring its suitability for academic and industrial applicaition.

Conclusion:
The validated LC–MS/MS approach offers a sustainable avenue for the rapid and accurate
measurement of GABA in Cordyceps sinensis and its related species, improving the quality
control process and ensuring product authenticity. This method stands out as a model for green analytical
chemistry, propelling forward the eco-friendly analytical evaluation of health-related compounds.
Normal Range of Ocular Biometry in Healthy Saudi Children
Authors: Raffa Lina H. , Hamdi Talaat J. , Balbaid Nasser T. , Shikdar Noorah A. , Basalem Esraa A. , Almarzouki Nawaf 
Published: September 2024

Aim
This study aims too describe typical ocular component growth patterns in healthy Saudi children.

Background
Prior literature has explored ocular growth patterns in various populations, but there is a gap in understanding these patterns in Saudi Arabia.

Objectives
To establish normative values for ocular biometric parameters and understand correlations between age and ocular development.

Methods
This cross-sectional study involved 141 children aged 3–17 years. The participants were examined at the eye clinic at King Abdulaziz University Hospital, Jeddah, Saudi Arabia, between 2018 and 2023. Every participant underwent a comprehensive ophthalmological assessment, including ocular biometric data [axial length (AXL), corneal curvature, and anterior chamber depth (ACD)].

Results
The mean age of participants was 9.03 ± 3.5 years. The AXL and ACD were 22.49 ± 1.1 mm and 3.34 ± 0.3 mm, respectively. As expected, there was a significant myopic shift with increasing age (p < 0.030). Both the ACD and AXL grew deeper and longer as the children grew older (r = 0.595, p < 0.001 and r = 0.559, p < 0.001, respectively), but corneal curvatures and corneal astigmatism remained constant. AXL values were significantly longer in boys (p = 0.019), but corneal curvatures (K1 and K2) were significantly steeper in girls (p = 0.007 and <0.001, respectively).

Conclusion
This study presents a comprehensive pediatric normative database of ocular biometric parameters for healthy Saudi children. These data will serve as a foundation for tracking the development of refractive errors and may be used as normative values for the evaluation of eye growth in children from the Middle East.