bluuebunny/crossref_metadata_2025_split huggingface dataset:
Optimization of porous volcanic ash-based geopolymer for crystal violet adsorption using the Box-Behnken design
Authors: Lenou Idriss , Tchakounte Armand , Shikuku Victor , Kemdjien Ludovic , Valere Kuisseu , Dika Joseph , Kede Charles 
Published: November 2024

Volcanic ash was used as a precursor for the synthesis of a geopolymer activated by sodium hydroxide and using hydrogen peroxide as a pore-forming agent. Factors controlling geopolymer synthesis such as sodium hydroxide concentration (6–12 mol/L), liquid/solid mass ratio (0.3–0.5), and H2O2 mass concentration (0%–2%) were optimized using the Box-Behnken design method. The chosen process variables were optimized to enhance both the geopolymer's porosity and its effectiveness in removing crystal violet. Sodium hydroxide concentration and H2O2 mass concentration had a significant effect on both responses. Under optimal conditions of 6 mol/L NaOH concentration, a 0.3 liquid/solid ratio, and 2% H2O2 mass concentration, the model-predicted and experimental values for both responses were highly comparable. Additionally, response surface methodology was used to assess the removal of crystal violet from an aqueous solution, employing the geopolymer produced under these optimal conditions as the adsorbent. Experiments were carried out according to the Box-Behnken statistical surface design with four input parameters, namely, contact time (A: 10–120 min), initial crystal violet concentration (B: 20–100 mg/L), adsorbent dose (C: 0.1–0.6 g), and pH (D: 3–9). Regression analysis indicated a strong fit of the experimental data to the second-order polynomial model, with a coefficient of determination ( R2) of 0.9864 and a Fisher's F value of 61.97. Optimization of the parameters A (35.415 mg/L), B (98.184 min), C (0.359 g), and pH (6.950) achieved a maximum crystal violet removal of 98.413% by the geopolymer.
Productions of bottom and bottom-strange mesons in pion and kaon induced reactions
Authors: Liu Jing , Guo Quan-Yun , Wu Qi , He Jun , Chen Dian-Yong 
Published: November 2024

AbstractIn the present work, we propose to explore the productions of the bottom and bottom-strange mesons in the high-energy pion and kaon-induced reactions on a proton target. The cross sections are evaluated with an effective Lagrangian constructed by the heavy-quark limit and chiral symmetry. Our estimations show that at $$P_\pi =80$$

P
π

=
80

GeV, the cross sections for B(5279), $$B^*(5325)$$

B
∗

(
5325
)

, $$B_0^*(5722)$$

B
0
∗

(
5722
)

, $$B_1^\prime (5716)$$

B
1
′

(
5716
)

, $$B_1(5721)$$

B
1

(
5721
)

and $$B_2^*(5747)$$

B
2
∗

(
5747
)

production processes are estimated to be 3.19–86.26, 1.86–51.29, 0.88–24.33, 0.86–23.69, 162.35–4477.66, and 57.16–1604.43 nb, respectively, where uncertainties arise from the model parameter. In addition, the cross sections for the corresponding bottom-strange mesons production processes are very similar. From the heavy quark effective theory point of view, the ratios between the cross sections for bottom mesons and bottom-strange mesons should be similar and the present estimations are consistent with the expectation of heavy quark limit. In particular, the cross-section ratios related to the states in the same doublets are of order one. The cross sections related to the states in the T doublets are about two orders larger than those related to the states in the S doublets.
Phenomenology of extended multiHiggs doublet models with $$S_4$$ family symmetry
Authors: Hernández A. E. Cárcamo , Espinoza Catalina , Gómez-Izquierdo Juan Carlos , Marchant González Juan , Mondragón Myriam 
Published: November 2024

AbstractWe propose extended 3HDM and 4HDM models where the SM gauge symmetry is enlarged by the spontaneously broken $$S_4$$

S
4

group, the preserved $$Z_2$$

Z
2

and broken $$Z_4$$

Z
4

cyclic symmetries. Each model has three active SU(2) scalar doublets, in addition, the first one has an extra inert scalar singlet, whereas the second model has an inert scalar doublet. Furthermore, each model has several extra gauge singlet scalars, which are triplets under $$S_4$$

S
4

. Both models yield the same structure of the mass matrices for the fermion sector, where a radiative seesaw generates the tiny light active neutrino masses at one-loop level, through the $$S_4$$

S
4

triplets. The presence of flavor changing neutral currents mediated by heavy scalars allowed us to study the $$(K^0-\overline{K}^0)$$

(

K
0

-

K
¯

0

)

and $$(B_{d,s}^0-\overline{B}_{d,s}^0)$$

(

B

d
,
s

0

-

B
¯

d
,
s

0

)

meson mixings, in the parameter space that currently satisfies the experimental constraints. On the other hand, due to the preserved $$Z_2$$

Z
2

symmetry, our proposed models have stable scalar and fermionic dark matter candidates. Furthermore, these models are consistent with the current pattern of SM fermion masses and mixings, with the measured dark matter relic abundance and successfully accommodate the constraints arising from meson oscillations and oblique parameters. The extra scalars in our models provide radiative corrections to the oblique parameters, where due to the presence of the scalar inert doublet, renders the 4HDM less restrictive than the 3HDM one.
Impact of adding bismuth oxide nanoparticles functionalized with graphene oxide nanosheet on the polyvinyl chloride shielding properties
Authors: Salem Elsayeda F. , Khalil Waleed F. , Zaky Rasha S. , Kamel Fatma A. 
Published: November 2024

AbstractThis study investigates the enhancement of polyvinyl chloride (PVC) by incorporating Bi2O3-GO nanocomposites. We produced nanocomposites with varying concentrations (1, 2.5, 5, and 10 wt%). The prepared samples were characterized using scanning electron microscopy (SEM) and Fourier transform infrared spectroscopy (FTIR). X-ray diffraction (XRD) and thermogravimetric analysis (TGA) were also used. As well as the shielding effectiveness against ultraviolet rays and gamma radiation from 137Cs and ⁶⁰Co sources at energies of 662, 1173, and 1275 keV. Results indicate that the addition of Bi2O3-GO significantly improves the thermal stability of PVC, as evidenced by higher decomposition temperatures from TGA. The absorption coefficient increases, while energy gap values decrease with increased nanofiller weight concentrations. The linear attenuation coefficient (LAC) also rises with increasing nanofiller content and decreases with higher gamma-ray energies. Results were consistent with Phy-X software simulations. The sample with 10% wt of Bi2O3-GO composite demonstrated a highly fast neutron removal cross section (FNRC) and reduced mean free path (MFP). This indicates strong potential for use as a safe, environmentally friendly alternative to lead-based shielding materials. These PVC/Bi2O3-GO nanocomposites could be effectively utilized in radiation barriers and protective screens in settings involving neutron and low-energy gamma radiation, such as in radiography and radioactive material management.
Microarray gene expression classification: dwarf mongoose optimization with deep learning
Authors: Balaraman Shyamala Gowri , Nair Anu H. , Kumar Sanal 
Published: November 2024

<span lang="EN-US">The deoxyribonucleic acid (DNA) microarray model holds significant promise for revealing expression data from thousands of genes. It serves as a valuable tool for investigating gene expressions in diverse biological research fields. This study explores advancements in gene selection for cancer detection through artificial intelligence, with a focus on the challenge of extracting pertinent information from vast databases. The application of deep learning architecture in detecting chronic diseases and aiding medical decision-making has proven effective across various domains. Therefore, this study designs an enhanced microarray gene expression classification by utilizing a dwarf mongoose optimization with deep learning (MGEXC-DMODL) approach. The MGEXC-DMODL approach intends to classify the microarray gene expression (MGE). For this, the MGEXC-DMODL technique initially applies the wiener filtering (WF) technique to eradicate the noise. In addition, the MGEXC-DMODL technique employs a deep residual shrinkage network (DRSN) to learn feature vectors. Meanwhile, the convolutional autoencoder (CAE) model was executed for identifying and classifying the MGE data. Furthermore, the dwarf mongoose optimization (DMO)-based hyperparameter tuning is performed to enhance the detection outcomes of the CAE model. The investigational evaluation of the MGEXC-DMODL model is validated using a benchmark database. The comprehensive comparison outcome highlighted the betterment of the MGEXC-DMODL model over recent approaches. </span>
Comparison of faster region-based convolutional network for algorithms for grape leaves classification
Authors: Sarosa Moechammad , Ma'rifah Puteri Nurul , Kusumawardani Mila , Al Riza Dimas Firmanda 
Published: November 2024

The shapes of leaves distinguish the Indonesian grape variants. The grape leaves might look the same at first glance, but there are differences in leaf shapes and characteristics when observed closely. This research uses a deep learning method combined with the faster region-based convolutional neural network (R-CNN) algorithm with the Inception network architecture, ResNet V2, ResNet-152, ResNet-101, and ResNet-50, and uses COCO weights trained to classify five grape varieties through leaf images. The study collected 500 images to be used as an independent dataset. The results show that network improvements can effectively improve operating efficiency. There are also limitations to training scores because the F1 score value tends to stabilize or decrease at a certain point. In the Inception ResNet V2 architecture, with the highest average F1 score of 92%, the average computing time for training and testing is longer than other network architectures. This suggests that the algorithm can classify types of grapes based on their leaves.
Comparative analysis of genetic algorithms for automated test case generation to support software quality
Authors: Hadiningrum Tiara Rahmania , Rochimah Siti 
Published: November 2024

<span lang="EN-US">Software testing is crucial for enhancing software quality, but designing test cases is a labor-intensive, resource-intensive, and time-consuming process. Additionally, test case designers often introduce subjectivity when creating test cases manually. To address these challenges, this paper compares three different approaches for automatically generating program branch coverage test cases: the parallel data generation algorithm (PDGA), a standard genetic algorithm (SGA), and a random test generation method. By leveraging genetic algorithms and parallel data generation techniques, these automated approaches aim to reduce the manual effort, resources, and potential biases involved in test case design, while improving the efficiency and effectiveness of achieving comprehensive branch coverage during software testing. The experimental results, conducted using five datasets with programs written in PHP, demonstrate that PDGA outperforms both SGA and random methods across various tested programs, achieving higher maximum and average coverage. Specifically, PDGA achieved an average coverage of 100% in the "calculator" program, highlighting its superior stability and efficiency. While SGA also shows good performance, it is not as optimal as PDGA, and the random method shows the lowest performance among the three. These findings underscore the potential of genetic algorithms, particularly PDGA, to enhance the coverage and quality of software testing, thereby significantly improving system reliability. </span>
Machine learning-assisted decision support in industrial manufacturing: a case study on injection molding machine selection
Authors: Tayalati Faouzi , Idiri Soulaimane , Boukrouh Ikhlass , Azmani Abdellah , Azman Monir 
Published: November 2024

<p>Selecting the right injection molding machine for new products remains a challenging task that significantly influences the profitability and flexibility of companies. The conventional approach involves performing theoretical calculations for clamping force, conducting mechanical validations of the mold, and carrying out real trials for new parts. This approach is time-consuming, costly, and requires a high level of expertise to ensure the optimal machine choice. This study explores the use of machine learning (ML) methods for efficient machine selection based on product, material, and mold criteria. Six supervised learning techniques were tested on a dataset comprising 70 plastic parts and five machines. Evaluation metrics like F1-score, recall, precision, and accuracy were used to compare models. The results indicate that ML can provide guidance for predicting machine selection, with a preference for the random forest (RF), decision tree (DT), and support vector machine (SVM) models. The most favorable outcome is demonstrated by the RF model, displaying an accuracy of 93%. In this manner, these findings may be helpful for injection molding businesses that are considering the significance of using classification algorithms in their manufacturing process. </p>
An efficient load-balancing in machine learning-based DC-DC conversion using renewable energy resources
Authors: Shankara Kavitha Hosakote , Hosakote Shankara Kavitha , Srikantaswamy Mallikarjunaswamy , Nagaraju Sharmila , Nagaraju Sharmila 
Published: November 2024

<span lang="EN-US">This paper introduces the machine learning-based DC-DC conversion algorithm (ML-DC2A), a pioneering machine learning (ML) approach designed to enhance load-balancing in DC-DC conversion systems powered by renewable energy sources. Traditional control strategies, such as pulse-width modulation (PWM), maximum power point tracking (MPPT), and basic voltage and current controls, are foundational yet often fall short in adapting to the rapid fluctuation’s characteristic of renewable energy supply. The ML-DC2A optimizes crucial performance indicators including conversion efficiency, reliability, adaptability to energy supply variability, and response time to changing loads. By leveraging predictive analytics and adaptive algorithms, it dynamically manages the conversion process, offering superior performance over traditional techniques. A notable drawback of conventional methods is their inability to anticipate and adjust to real-time changes in energy availability and demand, leading to inefficiencies and potential system instability. The proposed ML-DC2A addresses these challenges by incorporating a sophisticated ML framework that predicts future energy scenarios and adaptively adjusts system parameters to maintain optimal performance. Initial results highlight the transformative potential of integrating ML into renewable energy conversion systems, promising significantly enhanced efficiency and system resilience, thus marking a significant step forward in sustainable energy management.</span>
Prediction of metabolic syndrome in mexicans using machine learning
Authors: Pineda-Rico Zaira , Rojas Mendoza Diana Luz de los Angeles , Pineda-Rico Ulises 
Published: November 2024

<span lang="EN-US">Metabolic syndrome (MetS) is a compelling public health issue in Mexico, with high prevalence rates of overweight, obesity, arterial hypertension, diabetes, high triglycerides, low high-density lipoprotein cholesterol, and high total cholesterol. Despite this, predictive models tailored for under-researched professional groups with sedentary habits are scarce. This study introduces a novel predictive model for MetS using data from the National Center for Health Statistics and a unique dataset of higher education staff. By employing and comparing machine learning algorithms such as decision trees, random forest, artificial neural networks, and adaptive boosting, the research provides new insights into gender and race-specific aspects of MetS. The data was labeled using standards from the International Diabetes Federation and the National Cholesterol Education Program Adult Treatment Panel III to create classification models, which were tested on the higher education staff dataset. Model predictions were assessed using F1-score, accuracy and area under the curve - receiver operating characteristic (AUC-ROC), with random forest, decision tree, and adaptive boosting performing best. The key predictive features identified for MetS prediction include triglycerides, glucose, high-density lipoprotein cholesterol, waist-to-height ratio, and body mass index. </span>
Machine learning for mental health: predicting transitions from addiction to illness
Authors: Alkhazraji Ali , Alsafi Fatima , Dbouk Mohamed , Ibrahim Zein Al Abidin , Sbeity Ihab 
Published: November 2024

<span lang="EN-US">The increasing prevalence of infection-causing diseases due to environmental factors and lifestyle choices has strained the healthcare system, necessitating advanced techniques to save lives. Disease prediction plays a crucial role in identifying individuals at risk, enabling early treatment, and benefiting governments and health insurance providers. The collaboration between biomedicine and data science, particularly artificial intelligence and machine learning, has led to significant advancements in this field. However, researchers face challenges related to data availability and quality. Clinical and hospital data, crucial for accurate predictions, are often confidential and not freely accessible. Moreover, healthcare data is predominantly unstructured, requiring extensive cleaning, preprocessing, and labeling. This study aims to predict the likelihood of patients transitioning to mental illness by monitoring addiction conditions and constructing treatment protocols, with the goal of modifying these protocols accordingly. We focus on predicting such transformations to illuminate the underlying factors behind shifts in mental health. To achieve this objective, data from an Iraqi hospital has been collected and analyzed yielding promising results. </span>
Patient Perceptions of Emerging Gene Therapies for Arrhythmogenic Right Ventricular Cardiomyopathy
Authors: Schopp Emma M. , Okwara Leonore , Tichnell Crystal , Turriff Amy , Murray Brittney , Barth Andreas S. , Calkins Hugh , Jamal Leila , James Cynthia A. 
Published: November 2024

BACKGROUND:
No disease-specific therapy currently exists for arrhythmogenic right ventricular cardiomyopathy (ARVC), a progressive cardiogenetic condition conferring elevated risk for ventricular arrhythmias, heart failure, and sudden cardiac death. Emerging gene therapies have the potential to fill this gap. However, little is known about how adults with ARVC, or any other inherited cardiomyopathy or arrhythmia syndrome, appraise the risks and benefits of gene therapy research and which considerations may influence their decisions about clinical trial participation.

METHODS:
Twenty adults with clinically diagnosed and gene-positive ARVC participated in semi-structured interviews that explored perceptions of gene therapy and hypothetical decision-making for gene therapy clinical trial participation. Interview transcripts were qualitatively coded and analyzed.

RESULTS:
Participants expressed enthusiasm for gene therapy with varied levels of personal interest in trial participation. Although clinical severity appeared to be associated with an elevated interest in early trial participation, participants anticipated weighing both personal and trial-specific factors including life stage, trial stage, risks, benefits, participation burden, study leadership, and anticipated cost of future gene therapy. Adaptation to living with ARVC and involvement in the ARVC patient community were also relevant to decision-making about trial participation. Potential ethical concerns included unquestioning trust in clinical teams collaborating on industry-led trials and vulnerability of those recently diagnosed or with high perceived severity of ARVC symptoms.

CONCLUSIONS:
Several characteristics of the individual and trial warrant consideration during the informed consent process. Insights from this study may affect trial planning and communication with participants who have inherited cardiac conditions.
Unraveling Comorbidities Contribution to Cardiac Diastolic Dysfunction and Heart Failure
Authors: Villalba-Orero María , López-Olañeta Marina , Campos-Olmo Belén , Jimenez-Carretero Daniel , Sánchez Lucía , Sánchez-Cabo Fátima , Ausiello Antonella , Cañas-Álvaro Rodrigo , Camafeita Emilio , Vázquez Jesús , García-Pavía Pablo , Pascual-Figal Domingo , Lara-Pezzi Enrique 
Published: November 2024

BACKGROUND:
Heart failure with preserved ejection fraction (HFpEF) is a major public health problem characterized by multiple simultaneous comorbidities whose specific contribution is challenging to disentangle in humans, leading to a generalized therapeutic approach that may not account for the underlying pathology.

METHODS:
We followed distinct mouse models of major HFpEF comorbidities for 2.5 years to unveil their specific contribution to the syndrome.

RESULTS:
All comorbidities contributed to HFpEF through partially distinct routes. Aging alone resulted in HFpEF in old age, with delayed left ventricular relaxation and kidney fibrosis. Obesity induced a faster deterioration of relaxation associated with enlarged left ventricle and liver fibrosis. Hypertension caused delayed ventricular relaxation independent from structural changes that preceded left atrial dilatation linked to aortic stiffness and increased fibrosis in myocardium and kidney. Chronic intermittent hypoxia led to HFpEF and relaxation impairment associated with pulmonary hypertension. Hyperglycemia accelerated diastolic dysfunction and HFpEF onset associated with reduced arterial flow and left ventricular remodeling. Therefore, the pathological substrates contributing to HFpEF included cardiac and noncardiac alterations with differential features for each comorbidity. Critically, the characteristics linked to diastolic dysfunction and HFpEF across the various comorbidities agreed with phenogroups observed in human patients.

CONCLUSIONS:
The identification of time-dependent pathological features provides a comprehensive picture of HFpEF progression associated with each comorbidity.
Interactions between Javan slow lorises (Nycticebus javanicus) and domestic and wild carnivores in an anthropogenic landscape in Java, Indonesia
Authors: Nekaris K.A.I. , Balestri M. , Hedger K. , Campera M. , Adinda Esther , Ahmad Nabil , Imron M.A. , Manson S. , Saepurohman Ferryandi , Nijman V. 
Published: November 2024

Abstract
With increasing replacement of native forests with agriculture, it is important to understand the factors allowing non-human primates to persist, including interactions with potential predators. For small-bodied nocturnal primates, smaller carnivores and domestic dogs that often characterise agroforestry landscapes may be a particular threat, especially for primates like slow lorises, which rely on canopy connectivity and are particularly vulnerable on the ground. We present data collected over 12 years in Indonesia. During a long-term study on Javan slow lorises, we used ad libitum sampling to record 703 instances of slow lorises in proximity to wild small carnivores (Javan palm civets, small Indian civets, Sunda leopard cats, Javan ferret badgers) and interactions with small carnivores (62) and domestic dogs (58) respectively. Most of these interactions were neutral or affiliative, including sharing canopy bridges and sleeping sites. Two negative interactions occurred between lorises and wild carnivores; five potential predation events were linked to dogs. Lorises were less active and more vigilant when carnivores were present. A WhatsApp® group facilitated data collection, increasing detail of observations, especially on interactions with domestic dogs. We saw an overall decline in small carnivores, especially nearer to the village, which could be linked to competition with domestic dogs. A better understanding of the role of dogs in this changing landscape is vital.