bluuebunny/crossref_metadata_2025_split huggingface dataset:
Semi-industrial Bio-fabrication of ZnO/MnO2 Nanocomposite Using Endophytic Streptomyces coelicolor: Characterization, Statistical Design, Exponential Pulse Fed-Batch Fermentation, and Its Antimicrobial Application
Authors: EL-Moslamy Shahira H. , Rezk Ahmed H. , Elkady M. F. , Shokry Hassan Hassan 
Published: January 2024

AbstractIn our study, we examined how well six Streptomyces strains bio-fabricated ZnONPs, MnONPs, and/or ZnO/MnO2 nanocomposite. The most potent strain that generated efficient antimicrobial nanoparticles was then picked to increase the production of those particles in a semi-industrial pilot plant unit. Consequently, the intracellular extract of endophytic Streptomyces coelicolor strain E72 was used to achieve the bio-fabrication reaction of the spherical ZnO/MnO2 nanocomposite (6–18 nm). The bio-fabricated ZnO/MnO2 nanocomposite was validated and characterized using FTIR, XRD, SEM, TEM, TGA, and EDS analyses. Additionally, the production of this ZnO/MnO2 nanocomponent was scaled up to a pilot plant unit with a semi-industrial size. The Plackett–Burman experimental method was used to maximize the production of ZnO/MnO2 nanocomposites, which had increased 2.7-fold from their initial state. The bio-fabricated ZnO/MnO2 nanocomposite was subsequently scaled up 31.25 times using an exponential pulse-feeding fermentation technique in a 70-L bioreactor. This ZnO/MnO2 nanocomposite exhibited effective antimicrobial efficacy against all tested antibiotic-resistant human pathogens. The antimicrobial effects against Salmonella paratyphi (53.17 ± 2.8 mm) and Candida albicans (50.2 ± 1.01 mm) were the most potent at 90 and 130 µg/ml of ZnO/MnO2 nanocomposite, respectively. This is the first full explanation of the ZnO/MnO2 nanocomposite bio-fabrication at a semi-industrial scale employing endophytic strain E72 extract as a reducing/capping agent that reacted with MnCl2·4H2O and Zn (CH3COO)2·2H2O as precursors. This bio-fabricated ZnO/MnO2 nanocomposite has the potential to be utilized in the development of pharmaceuticals, cosmetics, wound dressings, and burn therapy due to its powerful antimicrobial capabilities.
Helix surfaces for Berger-like metrics on the anti-de Sitter space
Authors: Calvaruso Giovanni , Onnis Irene I. , Pellegrino Lorenzo , Uccheddu Daria 
Published: January 2024

AbstractWe consider the Anti-de Sitter space $$\mathbb {H}^3_1$$

H

1
3

equipped with Berger-like metrics, that deform the standard metric of $$\mathbb {H}^3_1$$

H

1
3

in the direction of the hyperbolic Hopf vector field. Helix surfaces are the ones forming a constant angle with such vector field. After proving that these surfaces have (any) constant Gaussian curvature, we achieve their explicit local description in terms of a one-parameter family of isometries of the space and some suitable curves. These curves turn out to be general helices, which meet at a constant angle the fibers of the hyperbolic Hopf fibration.
Evaluation of Aflatoxins Occurrence and Exposure in Cereal-Based Baby Foods: An Update Review
Authors: Açar Yasemin , Akbulut Gamze 
Published: January 2024

Abstract
Purpose of Review
The first stages of human life, which include the fetal period, infancy, and early childhood, are the most critical for human growth and development. This is the most vulnerable phase to health challenges due to the immature immune system and rapid development. Mycotoxins such as aflatoxins, ochratoxin A, patulin, fumonisins, zearalenone, and deoxynivalenol are secondary metabolites secreted by various fungal species, primarily Aspergillus, Fusarium, Penicillium, and Alternaria. Aflatoxins are one of the major mycotoxins produced in cereals and cereal-based foods by several species of Aspergillus, mainly Aspergillus flavus. In this context, this review provides a brief overview of the occurrence, exposure, legal regulations, and health effects of aflatoxins (B1, B2, G1, G2, and M1) in cereal-based baby foods and breast milk.

Recent Findings
Human aflatoxin exposure in utero and through breast milk, infant formulas, cereals, and cereal-based foods has been linked to various health consequences, including adverse birth outcomes, impaired growth and development, immune system suppression, and hepatic dysfunction. Recent evidence suggests that especially infants and children are more susceptible to aflatoxins due to their lower body weight, lowered capacity to detoxify harmful substances, more restrictive diet, immature metabolism and elimination, and faster rates of growth and development.

Summary
It is essential for both food safety and infant and child health that aflatoxins in cereal and cereal-based products are precisely detected, detoxified, and managed.
Improving the Industrial Efficiency of Recycling Aluminum Alloy Chips Using Friction Stir Extrusion: Thin Wires Production Process
Authors: Buffa Gianluca , Campanella Davide , Adnan Muhammad , La Commare Umberto , Ingarao Giuseppe , Fratini Livan 
Published: January 2024

AbstractFriction stir extrusion is one of the most promising solid-state chip recycling techniques because of its relative simplicity and high efficiency. One of the most straightforward applications for the process is the production of recycled wires to be utilized as filler material in welding or welding-based additive manufacturing processes, in order to create an industrial symbiosis link, fostering a circular economy and enhancing the technology readiness level of the process. The scalability of the process to the thin wires needed for such applications has not been investigated so far. In this paper, an experimental and numerical analysis was developed. A dedicated numerical model was first validated and then used to design the tool geometry. The effect of tool rotation and tool force on both “standard” mechanical properties, as Ultimate Tensile Strength and microhardness, and specific properties for the envisaged application, as the wrapping around reels with different radii, was investigated. The numerical model results were used to explain the influence of the process parameters on the material flow as well as on the distribution of the primary field variables, namely temperature, strain, and strain rate. Finally, the energy demand was measured, and the specific energy consumption (SEC) was evaluated. It was found that a conical shoulder surface favors the conditions for effective solid bonding. Low values of the extrusion force have detrimental effects on the wires properties as they result either in insufficient strain, or hot cracking defects. High values of extrusion force results in lower SEC, unlocking the potential of the process as symbiotic link enabler.
DRL-based dependent task offloading with delay-energy tradeoff in medical image edge computing
Authors: Liu Qi , Tian Zhao , Wang Ning , Lin Yusong 
Published: January 2024

AbstractTask offloading solves the problem that the computing resources of terminal devices in hospitals are limited by offloading massive radiomics-based medical image diagnosis model (RIDM) tasks to edge servers (ESs). However, sequential offloading decision-making is NP-hard. Representing the dependencies of tasks and developing collaborative computing between ESs have become challenges. In addition, model-free deep reinforcement learning (DRL) has poor sample efficiency and brittleness to hyperparameters. To address these challenges, we propose a distributed collaborative dependent task offloading strategy based on DRL (DCDO-DRL). The objective is to maximize the utility of RIDM tasks, which is a weighted sum of the delay and energy consumption generated by execution. The dependencies of the RIDM task are modeled as a directed acyclic graph (DAG). The sequence prediction of the S2S neural network is adopted to represent the offloading decision process within the DAG. Next, a distributed collaborative processing algorithm is designed on the edge layer to further improve run efficiency. Finally, the DCDO-DRL strategy follows the discrete soft actor-critic method to improve the robustness of the S2S neural network. The numerical results prove the convergence and statistical superiority of the DCDO-DRL strategy. Compared with other algorithms, the DCDO-DRL strategy improves the execution utility of the RIDM task by at least 23.07, 12.77, and 8.51% in the three scenarios.
Evolutionary stability strategies of tacit collusion in supervised container terminals
Authors: Liu Zongtuan , Dong Gang , Shang Shaopeng 
Published: January 2024

AbstractTo investigate the long-term effects of the Port Authority’s supervision strategies on the container terminal’s collusion strategies, this study constructs a two-sided evolutionary game model consists of the Port Authority and a container terminal based on evolutionary game theory which usually examine a game’s long-term trends. Under the premise that the container terminal’s net profits from collusion strategy are greater than those from non-collusion strategy, the stability analysis demonstrates that the container terminal tend to choose collusion strategy, and the Port Authority tends to choose lax supervision strategy when the fine received by the container terminal is less than the Port Authority’s cost; the container terminal tends to choose collusion strategy, and the Port Authority tends to choose strict supervision strategy when the fine received by the container terminal is greater than the Port Authority’s cost and the sum of the fine and subsidy is less than the difference in the container terminal’s profits from collusion strategies. Finally, the numerical simulation results confirm the robustness of the stability analysis. These results can guide and benefit relevant stakeholders in the sustainable development of the container shipping supply chain.
Analysing Instrumented Justification: Unveiling Student’s Tool Use and Conceptual Understanding in the Prediction and Justification of Dynamic Behaviours
Authors: Gregersen Rikke Maagaard 
Published: January 2024

AbstractThe study advances the instrumental approach to mathematics education (Drijvers et al., 2013; Trouche, 2003), aiming to elucidate the interplay between students’ reasoning competency, conceptual knowledge and tool utilisation in dynamic digital geometry and algebra environments. The dynamic properties of these environments pose a nuanced predicament, as the outsourcing of translation between visual and algebraic representations raises concerns regarding students’ conceptual development and reasoning competency. To mitigate this issue, a prediction task is proposed, focusing on the dynamic behaviour of variable points in GeoGebra. I introduce a comprehensive framework adapting Toulmin’s argumentation model into the instrumental approach, emphasising processes of justification. This is complemented by the application of components of Vergnaud’s (1998) scheme concerning generative and epistemic ways to approach how students’ conceptual knowledge has played a part in these processes. Through a case study of a student pair solving a prediction task, I explore the links between instrumented justification, students’ mathematical reasoning competency and conceptual understanding, and how students’ use of GeoGebra tools is intertwined with their justification processes. The analysis reveals the intricate interplay between data production and interpretation, and it is grounded in inference drawn regarding students’ implied theorems about concepts, dynamic behaviour and progression in terms of techniques. The results indicate that the progression of technique is driven by the experience of the inefficiency of techniques and artefacts related to the goal of justification. Essentially, the framework links students’ reasoning competency to their use of tools and conceptual knowledge, as well as demonstrates that predicting dynamic behaviour can enhance knowledge-based justification.
Prediction of Cross-Section-Dependent ADI Microstructures by Experimental Heat Treatment Simulation
Authors: Lachart P. , Tonn B. , Stolz E. , Esderts A. , Masendorf R. 
Published: January 2024

AbstractHeavy section Austempered Ductile Iron (ADI) components may contain pearlitic or a mixed microstructure (mixture of pearlite and ausferrite) in their centres besides ausferrite. As a result, mechanical properties are not homogeneously distributed across the thicknesses of the components. The oversizing of the components therefore leads to unnecessary material expenses in foundries. In order to avoid the complex manufacturing process of heavy section ADI components and subsequent investigations, preliminary tests were performed with smaller test samples. Here, temperature-time curves were detected during heat treatment at different depths. Material samples of the customised composition were heat treated in a dilatometer under the same heat treatment conditions according to different sections of the cast component. A variation of the quenching rates was conducted to investigate the influence on the mixed microstructure formation and ratio of pearlitic and ausferritic microstructures. A comparison of the conventionally heat-treated and dilatometer produced microstructures proved the successful application for heavy section parts and enables microstructure predictions with small samples only. In this way, there is no requirement of expensive tests with high-mass components.
Optimizing germination performance of Lamiaceae family seeds: insights from research
Authors: Ekren Sıdıka , Gökçöl Adem , Yıldırım Keskinoğlu Aliye , Paylan İsmail Can 
Published: January 2024

AbstractThe Lamiaceae family comprises herbaceous to woody plants that can thrive in a wide range of habitats and are cosmopolitan in distribution. In Turkey, there are 45 genera and 586 species belonging to the Lamiaceae family. Among these species, 239 taxa are endemic, making it the third-largest family in Turkey in terms of endemic individuals. In this study, seeds of three species from the Lamiaceae family, Izmir oregano (Origanum onitesL.), French lavender (Lavandula stoechasL.), and basil (Ocimum basilicumL.), were used as test materials. The research aimed to overcome heterogeneous and low germination issues observed in these seeds, which were determined to be seed-related through preliminary studies, and to achieve the highest germination percentage and average germination time. Various pre-sowing treatments were applied to improve the germination performance and quality of Izmir oregano, French lavender, and basil seeds. For this purpose, the effects of treatments including control, KNO3, GA3, polymer coating, priming, priming + polymer coating, priming + polymer coating + KNO3, and priming + polymer coating + GA3 on seed germination rate and average germination days were investigated in comparison to the control. Prior to the treatments, molecular analyses were conducted on all seed samples to detect viral agents. As a result of the research, the highest germination percentage of 94% was found in Izmir oregano with the priming + polymer coating + KNO3 treatment. In contrast, for French lavender and basil seeds, the germination performance with a 92% and 94% germination rate, respectively, was achieved with the priming + polymer coating + GA3 treatment. Molecular analyses conducted on seed samples did not detect any viral agents.
Effect of pediatric drugs on the color stability of dental restorative materials currently used in pediatric dentistry
Authors: Çapan Belen Şirinoğlu , Birant Sinem 
Published: January 2024

AbstractThe purpose of this in vitro study was to assess the color stability of esthetic restorative materials after immersion in different pediatric drugs. Three different restorative materials namely composite resin, alkasite and high-viscosity glass ionomer cement(HVGIC) were used to prepare total 96 (32 from each) disc-shaped specimens(2mmx5mm). A spectrophotometer was used to record the color of each sample after sample preparation and 7 days following the staining technique. After 1-week period, ΔE ∗ values were calculated. The average color changes(ΔE) for all drugs/restorative materials ranged from 1.81 to 8.08. The most prominent alteration was found in Cough syrup-Alkasite(8.08 ± 2.62) and the least one was found in Cough syrup-HVGIC(1.81 ± 0.74) pairwise. The color change observed in Cough syrup-Alkasite group was statistically significantly higher than in Cough syrup-HVGIC (p < 0.05). Alkasite showed greater color alteration with all drugs compared to other materials and HVGIC showed better color stability with all drug formulations.
One-Pot Biopreparation of Trimetallic ZnO–MgO–CuO Nanoparticles: Enhanced Cytotoxicity, Antibacterial Activities and Molecular Docking Studies
Authors: Orshiso Temesgen Achamo , Zereffa Enyew Amare , Murthy H. C. Ananda , Demissie Taye B. , Ghotekar Suresh , Pagar Khanderao , Pardeshi Onkar 
Published: January 2024

AbstractNowadays, metal oxide nanoparticles (MO NPs) are powerful tools for biological applications due to their distinctive features. Moreover, the biological efficacy of multimetallic NPs is more fascinating because of their structural modifications and synergistic effects. This study utilized the one-pot green route to fabricate trimetallic ZnO-MgO-CuO (ZMC) NPs employing a greener reducing agent from Artemisiaabyssinica leaf extract (AALE). The crystal structure, size, compositions, shapes, and external topology of ZMC NPs were characterized by Fourier transform infrared (FTIR), UV–Visible (UV–vis), X-ray diffraction (XRD), scanning electron microscopy (SEM), energy-dispersive X-ray (EDX), and transmission electron microscopy combined with selected area electron diffraction (TEM/HRTEM-SAED). The outcomes suggested that the bio-prepared ZMC NPs are highly crystalline and have hexagonal structures lattice with monoclinic symmetry and spherical morphology with average crystalline and particle sizes of 14.67 and 15.13 nm, respectively. Using MTT assay, the bio-prepared ZMC NPs demonstrated high inhibition percentage (94.37 ± 0.14 at 250 mg/mL) with an IC50 value of 24.83 mg/mL for MCF-7 cell lines. The in-vitro antibacterial potential of ZMC NPs has been evaluated against four bacterial (Gram-positive and Gram-negative) strains and has demonstrated the highest inhibition zone (35 ± 0.03 mm) against the S. aureus strain and the lowest inhibition zone (31 ± 0.11) against the E. coli strain. Moreover, ZMC NPs have also shown strong molecular binding interactions with amino acids of estrogen receptor (ERα), S. aureus, and E. coli with binding energies of − 9.85, − 12.31, and − 6.04 kcal/mole, respectively.
Graphical Abstract
Thomson Effect on an Initially Stressed Diffusive Magneto-thermoelastic Medium via Dual-Phase-Lag Model
Authors: Eraki Ebtesam E. M. , Fathy Rania A. , Othman Mohamed I. A. 
Published: January 2024

Abstract
Objective
This study investigates the influence of the Thomson effect on the behavior of a diffusive magneto-thermoelastic medium with initial stress and the dual-phase-lag (DPL) model.

Methods
The normal mode analysis is utilized for solving the problem. The copper material was chosen for numerical assessments. The results are presented graphically for various physical quantities.

Results
A comparison is made between the DPL model and the Lord and Shulman (L-S) theory, both in the absence and presence of the Thomson effect parameter as well as at two different values for the phase lag of heat flux.

Conclusions
The findings provide insights into the impact of the Thomson effect on the behavior of the magneto thermoelastic medium, highlighting the differences between the DPL model and the L-S theory in different scenarios. This type of work has many applications in rock mechanics, geophysics, and petroleum industries. This work may be helpful for those researchers who are working in material science, smart materials, and new material designers.
Emerging Methods for the Evaluation of Sensory Quality of Food: Technology at Service
Authors: Rodrigues Sandra S. Q. , Dias Luís G. , Teixeira Alfredo 
Published: January 2024

Abstract
Purpose of Review
Sensory evaluation holds vital significance in the food sector. Typically, humans conduct sensory analysis. Humans, being the ultimate consumers, assess food traits effectively. However, human judgment is influenced by various factors. Hence, countering subjectivity is crucial for objective evaluation while retaining hedonic insights.

Recent Findings
Food’s sensory assessment primarily employs humans. Various techniques differentiate, depict, or rank food. Modern sensory tools, aiming to enhance objectivity and reliability, are emerging to supplement or supplant human assessment. This advance can bolster quality, consistency, and safety by mimicking human senses such as smell, taste, and vision, mitigating risks tied to human assessors.

Summary
This paper provides a review about sensory analysis of food using technological methodologies. A review of different technological tools to analyze sensory characteristics of food, as well as a discussion of how those technological tools can relate to humans’ perception of food is presented.
Predicting Gross Domestic Product (GDP) using a PC-LSTM-RNN model in urban profiling areas
Authors: Shams Mahmoud Y. , Tarek Zahraa , El-kenawy El-Sayed M. , Eid Marwa M. , Elshewey Ahmed M. 
Published: January 2024

AbstractGross Domestic Product (GDP) is significant for measuring the strength of national and global economies in urban profiling areas. GDP is significant because it provides information on the size and performance of an economy. The real GDP growth rate is frequently used to indicate the economy’s health. This paper proposes a new model called Pearson Correlation-Long Short-Term Memory-Recurrent Neural Network (PC-LSTM-RNN) for predicting GDP in urban profiling areas. Pearson correlation is used to select the important features strongly correlated with the target feature. This study employs two separate datasets, denoted as Dataset A and Dataset B. Dataset A comprises 227 instances and 20 features, with 70% utilized for training and 30% for testing purposes. On the other hand, Dataset B consists of 61 instances and 4 features, encompassing historical GDP growth data for India from 1961 to 2021. To enhance GDP prediction performance, we implement a parameter transfer approach, fine-tuning the parameters learned from Dataset A on Dataset B. Moreover, in this study, a preprocessing stage that includes median imputation and data normalization is performed. Mean Square Error, Mean Absolute Error, Root Mean Square Error, Mean Absolute Percentage Error, Median Absolute Error, and determination coefficient (R2) evaluation metrics are utilized in this study to demonstrate the performance of the proposed model. The experimental results demonstrated that the proposed model gave better results than other regression models used in this study. Also, the results show that the proposed model achieved the highest results for R2, with 99.99%. This paper addresses a critical research gap in the domain of GDP prediction through artificial intelligence (AI) algorithms. While acknowledging the widespread application of such algorithms in forecasting GDP, the proposed model introduces distinctive advantages over existing approaches. Using PC-LSTM-RNN which achieves high R2 with minimum error rates.