Numerous Processes Research Articles

Optimizing FDM process parameters: predictive insights through taguchi, regression, and neural networks

Fused deposition modelling (FDM) is a popular additive manufacturing process used for rapid prototyping and the production of complex geometries. Despite its popularity, FDM’s susceptibility to variations in numerous process parameters can significantly impact the quality, design, functionality, and mechanical properties of 3D printed parts. This study explores thirteen FDM process parameters and their influence on the mechanical properties of polylactic acid (PLA) polymer, encompassing surface roughness, warpage, tensile and bending strength, elongation at break, deformation, and microhardness. The optimum parameters were identified alongside key contributors by applying the Taguchi method, signal-to-noise ratios, and analysis of variances (ANOVA). Notably, specific FDM parameters significantly affect the surface profile, with layer thickness contributing 32.65% and fan speed contributing 8.59% to the observed variations. Similarly, warping values show notable influence from nozzle temperature (29.53%), wall thickness (16.74%), layer thickness (16.56%), and retraction distance (12.80%). Tensile strength is primarily determined by wall thickness (31.83%), followed by infill percentage (26.73%) and infill pattern (16.18%). Elongation at break predominantly correlates with wall thickness (44.82%), with a supplementary contribution from nozzle temperature (10.90%). Microhardness lacks a dominant parameter. Bending strength variations primarily arise from layer thickness (38%), wall thickness (37.6%), and infill percentage (9.17%). Deformation tendencies are influenced by layer thickness (19.20%), print speed (11.37%), wall thickness, and fan speed (10.9% each). The optimized dataset of FDM process parameters was then employed in two prediction models: multiple-regression and artificial neural network (ANN). Evaluation based on the correlation coefficient (R2) and root mean squared error (RMSE) indicates that the ANN model outperforms the multiple-regression approach. The results indicate that precise control of FDM parameters, coupled with ANN predictions, facilitates the fabrication of 3D printed parts with the desired mechanical characteristics.

Rainfall Potential and Consequences on Structural Soil Degradation of the Most Important Agricultural Region of Mexico

This study investigates the historical variability in annual average precipitation in the northwest region of Mexico, aiming to evaluate the cumulative impact of precipitation on soil degradation and associated risks posed by rainfall. Despite being known as “The Agricultural Heart of Mexico”, the region’s soil has experienced significant damage to its granulometric structure due to unpredictable rainfall patterns attributed to climate change. Sixteen historical series of average annual rainfall were analyzed as stationary stochastic processes for spectral analysis. The results revealed exponential decay curves in each radial spectrum, indicating a linear relationship between frequency and amplitude. These curves identified initial impulses correlated with moments of severity for structural damages caused by rainfall-induced degradation. The degradation process, exacerbated by water stress, accelerates, as evidenced by maps illustrating approximately 75% soil damage. In the context of climate change and the uncertainty surrounding soil responses to extreme meteorological events, understanding this phenomenon becomes crucial. Recognizing the dynamic nature of soil responses to environmental stressors is essential for effective soil management. Emphasizing the need to employ numerical processes tailored to new environmental considerations related to observed soil damages is crucial for sustainable soil management practices in any region.

Are early-type galaxies quenched by present-day environment? A study of dwarfs in the Fornax Cluster.

Galaxies undergo numerous transformative processes throughout their lifetimes that ultimately lead to the expulsion of gas and the cessation of star-forming activity. This phenomenon is commonly known as quenching, and in this study, we delve into the possibility that this process is caused by the environmental processes associated with the surrounding cluster. To this end, we used the results of our previous paper ---where we analyzed dwarf galaxies in the survey together with massive galaxies from the survey--- to compute the quenching time of each galaxy and compare it with the infall time into the cluster. Using as an approximation of the quenching time and deriving the infall time from phase-space models, we determined the probability of the quenching being produced by the local environment of galaxies. Our results reveal a relation between galaxy mass and quenching probability. Massive galaxies, down to exhibit a low, almost zero probability of quenching, suggesting their independence of environmental effects. As we move into the mass regime of dwarf galaxies, the probability increases with decreasing mass, highlighting their sensitivity to environmental quenching. For dwarfs, 36pm 9<!PCT!> of our observational data are consistent with this hypothesis, challenging the idea that the present-day cluster, Fornax, is the primary driver of quenching in the low-mass galaxies of our sample with stellar mass from to To further investigate the importance of environmental processes, we compared these results with cosmological simulations, selecting galaxies under similar conditions to our observational sample. Remarkably, the simulated sample shows lower quenching probabilities as we move down in mass, and barely 5pm 1<!PCT!> of galaxies meet the quenching criteria. This discrepancy between observations and simulations underlines the fact that the modelling of quenching is still in its infancy. In general, the number of observed galaxies quenched by their environment is lower than expected, which suggests that preprocessing plays a larger role in galaxy evolution. Ultimately, our results highlight the need for higher-quality simulations and refinement of galaxy formation and evolution models.

CLASSIFICATION, ADVANTAGES AND DISADVANTAGES OF COMPUTER MODELING METHODS AND THEIR APPLICATION IN THE PREDICTION OF MULTIPLE PROCESSES

The development of the chemical industry in recent years requires the development of economical and environmentally friendly technological systems during production. In order to solve all possible problems, work is underway to optimize the production process as a whole, to develop waste-free or low-waste technologies. The most important condition for this is modeling and optimization of chemical production. By applying computer modeling, it is possible to predict the properties of many compounds, as well as numerous processes. This creates conditions for pre-assessment of existing risks and finding alternative ways by obtaining simulations of processes that require expensive resources and are accompanied by the separation of harmful waste. The article contains information about computer models in general, their superior and deficient properties, as well as the classification of computer modeling methods, modern literature on the application of these models in chemistry was reviewed

Mechanistic Insights into the Copper‐Catalyzed Cross‐Dehydrogenative Allylic Alkynylation Reaction

Mechanistic Insights into the Copper‐Catalyzed Cross‐Dehydrogenative Allylic Alkynylation Reaction

Imperfections for the calculation of steel structures by the finite element method. Part 1

It is well known that imperfections are always present in structural elements. Imperfections can significantly affect the behavior and bearing capacity of steel structures, especially in the case of stability-related tasks. Therefore, inconsistencies must be taken into account in the loadbearing capacity model and their correct application (setting the shape and value) is a key point in the numerical analysis process. In recent decades, much attention has been paid in the domestic scientific space to updating imperfection models for use in numerical models, including taking into account modern more accurate manufacturing and installation technologies for steel structures. The purpose of this study is an analytical review and analysis of scientific research and technical literature, followed by synthesis and elaboration of recommendations on imperfections in relation to the calculation of steel structures using computer modeling technology, including the finite element method. The results of the study contain instructions on how to set the shapes and values of imperfections for different groups of imperfections. The article consists of two parts. The first part is devoted to the study of geometric imperfections, residual stresses and rules for the combination of imperfections, the second part of the article is devoted to equivalent imperfections.

Machine Learning Classification Algorithms for Traffic Stops—A Comparative Study

The application of machine learning algorithms across various fields is gaining momentum, and the results increasingly emphasize the need for further testing and implementation. This is driven by the potential to streamline and expedite numerous processes. In this paper, we have employed five algorithms: KNN, Decision Tree, Random Forest, Logistic Regression, and Naive Bayes, and these algorithms have been tested in three large datasets. On average, their performance ranges from a minimum of 80% to a maximum of 90%. Data preprocessing has been completed, and concurrently, we have implemented the SMOTE algorithm to address the challenge of unbalanced data in this research. Simultaneously, the Naïve Bayes algorithm yields the most favorable results of Accuracy, Precision, Recall, and F1 Score, for the “is_arrested” class. Furthermore, to assess the performance of each algorithm, we employed metrics including Accuracy, Precision, Recall, and F1 Score. These metrics allowed us to decide which algorithm achieved the most effective classification.

Oligonucleotide-Based Photoaffinity Probes: Chemical Tools and Applications for Protein Labeling.

A variety of proteins interact with DNA and RNA, including polymerases, histones, ribosomes, transcription factors, and repair enzymes. However, the transient non-covalent nature of these interactions poses challenges for analysis. Introducing a covalent bond between proteins and DNA via photochemical activation of a photosensitive functional group introduced onto nucleic acids offers a means to stabilize these often weak interactions without significantly altering the binding interface. Consequently, photoactivatable oligonucleotides are powerful tools for investigating nucleic acid-protein interactions involved in numerous biological and pathological processes. In this review, we provide a comprehensive overview of the chemical tools developed so far and the different strategies used for incorporating the most commonly used photoreactive reagents into oligonucleotides probes or nucleic acids. Furthermore, we illustrate their application with several examples including protein binding site mapping, identification of protein binding partners, and in cell studies.

Multiple Cofactor Engineering Strategies to Enhance Pyridoxine Production in Escherichia coli

Pyridoxine, also known as vitamin B6, is an essential cofactor in numerous cellular processes. Its importance in various applications has led to a growing interest in optimizing its production through microbial biosynthesis. However, an imbalance in the net production of NADH disrupts intracellular cofactor levels, thereby limiting the efficient synthesis of pyridoxine. In our study, we focused on multiple cofactor engineering strategies, including the enzyme design involved in NAD+-dependent enzymes and NAD+ regeneration through the introduction of heterologous NADH oxidase (Nox) coupled with the reduction in NADH production during glycolysis. Finally, the engineered E. coli achieved a pyridoxine titer of 676 mg/L in a shake flask within 48 h by enhancing the driving force. Overall, the multiple cofactor engineering strategies utilized in this study serve as a reference for enhancing the efficient biosynthesis of other target products.

An assessment of numerical and geometrical quality of bases on surface fitting on Powell–Sabin triangulations

It is well known that the problem of fitting a dataset by using a spline surface minimizing an energy functional can be carried out by solving a linear system. Such a linear system strongly depends on the underlying functional space and, particularly, on the basis considered. Some papers in the literature study the numerical behavior and processing of the above-mentioned linear systems in specific cases. The bases that have local support and constitute a partition of unity have been shown to be interesting in the frame of geometric problems. In this work, we investigate the numerical effects of considering these bases in the quadratic Powell–Sabin spline space. Specifically, we present a direct approach to explore different preconditioning strategies and assess whether the already known ‘good’ bases also possess favorable numerical properties. Additionally, we introduce an inverse optimization approach based on a nonlinear optimization model to identify new bases that exhibit both good geometric and numerical properties.

Probing Electronic Structure Changes in Cobalt Oxalate Anode for Lithium‐Ion Batteries

AbstractTransition metal‐based materials exhibit a broad range of catalytic activities in numerous electrochemical processes. Their displayed catalytic functions rely essentially on their distinctive electronic structures, changes of which play pivotal roles in dictating reaction dynamics within many electrochemical devices. Nevertheless, accurately probing electronic structure changes, especially in real‐time, of active materials remains a formidable challenge. In this work, a viable approach to achieve it within a CoC2O4/Li model system by employing a combined microscopic is demonstrated, spectroscopic analysis, plus a unique operando magnetometry technique. The findings reveal that upon completion of the conventional conversion of CoC2O4, a surface‐dominated capacitance emerges at the Co/Li2C2O4 interface owing to the injection of spin‐polarized electrons. Subsequently, the decomposition of Li2C2O4 proceeds through the releasing of the spin‐polarized electrons from Co, which, therefore serve as a catalyst leading to further discharge products. Such real‐time monitoring of electron transfer is realized by in situ monitoring electronic structure changes of Co, manifested by its intriguing magnetization alterations during the process. This work highlights a novel characterization tool that provides a solid explanation for the commonly observed large capacities in this type of materials and sheds light on design rules and selection guidance of materials for high‐performance electrochemical energy storage systems.

Application of artificial intelligence in mine ventilation: a brief review.

In recent years, there has been a notable integration of artificial intelligence (AI) technologies into mine ventilation systems. A mine ventilation network presents a complex system with numerous interconnected processes, some of which pose challenges for deterministic simulation methods. The utilization of machine learning techniques and evolutionary algorithms offers a promising avenue to address these complexities, resulting in enhanced monitoring and control of air parameter distribution within the ventilation network. These methods facilitate the timely identification of resistance faults and enable prompt calculation of ventilation parameters during emergency scenarios, such as underground explosions and fires. Furthermore, evolutionary algorithms play a crucial role in the advancement of methods for visual analysis of ventilation systems. However, it is essential to acknowledge that the current utilization of AI technologies in mine ventilation is limited and does not encompass the full spectrum of challenging-to-formalize problems. Promising areas for AI application include analyzing changes in air distribution caused by unaccounted thermal draft and gas pressure, as well as developing novel approaches for calculating shock losses. Moreover, the application of AI technologies in optimizing large-scale mine ventilation networks remains an unresolved issue. Addressing these challenges holds significant potential for enhancing safety and efficiency in mine ventilation systems.

Numerical analysis and process optimization of electron beam melting for superalloys

This study aims to determine the optimal parameters for the electron beam melting process by employing a modified transient three-dimensional thermal model. The validity of the thermal model is confirmed through experimental results. Moreover, the proposed method establishes mathematical relationships between the resulting molten pool temperature and process parameters. A broader range of process parameters for a new Ni–Co-based superalloy was achieved by investigating the effects of scanning velocities (10–100 mm/s), scanning radius (20–60 mm), and melting mass (1500–3100 g) on temperature at various electron beam power levels (10–30 kW). Regression models were established between scanning velocities-temperature, and scanning radius-temperature under different electron beam power levels. These models laid the foundation for real-time automatic control of molten pool temperature. Significantly, orthogonal experimental design and response surface method are employed to conduct a multi-objective optimization based on sixteen experiments and three parameters. The results of multi-objective optimization indicate that with a smelting mass of 1.5 kg, an electron beam power of 11 kW, a scanning radius of 60 mm, and a scanning velocity of 75 mm/s, the energy consumption is 34.95 kW h, and the theoretical mass loss of Ni element is 0.002 kg. The desirability is 91.2 %.

Treg-Derived Extracellular Vesicles: Roles in Diseases and Theranostics.

Regulatory T cells (Tregs), a subset of CD4+ T cells, are indispensable in maintaining immune self-tolerance and have been utilized in various diseases. Treg-derived extracellular vesicles (Treg-EVs) have been discovered to play an important role in the mechanism of Treg functions. As cell-derived membranous particles, EVs carry multiple bioactive substances that possess tremendous potential for theranostics. Treg-EVs are involved in numerous physiological and pathological processes, carrying proteins and miRNAs inherited from the parental cells. To comprehensively understand the function of Treg-EVs, here we reviewed the classification of Treg-EVs, the active molecules in Treg-EVs, their various applications in diseases, and the existing challenges for Treg-EVs based theranostics. This Review aims to clarify the feasibility and potential of Treg-EVs in diseases and theranostics, facilitating further research and application of Treg-EVs.

Lighting the way: how the Vibrio fischeri model microbe reveals the complexity of Earth's "simplest" life forms.

Vibrio (Aliivibrio) fischeri's initial rise to fame derived from its alluring production of blue-green light. Subsequent studies to probe the mechanisms underlying this bioluminescence helped the field discover the phenomenon now known as quorum sensing. Orthologs of quorum-sensing regulators (i.e., LuxR and LuxI) originally identified in V. fischeri were subsequently uncovered in a plethora of bacterial species, and analogous pathways were found in yet others. Over the past three decades, the study of this microbe has greatly expanded to probe the unique role of V. fischeri as the exclusive symbiont of the light organ of the Hawaiian bobtail squid, Euprymna scolopes. Buoyed by this optically amenable host and by persistent and insightful researchers who have applied novel and cross-disciplinary approaches, V. fischeri has developed into a robust model for microbe-host associations. It has contributed to our understanding of how bacteria experience and respond to specific, often fluxing environmental conditions and the mechanisms by which bacteria impact the development of their host. It has also deepened our understanding of numerous microbial processes such as motility and chemotaxis, biofilm formation and dispersal, and bacterial competition, and of the relevance of specific bacterial genes in the context of colonizing an animal host. Parallels in these processes between this symbiont and bacteria studied as pathogens are readily apparent, demonstrating functional conservation across diverse associations and permitting a reinterpretation of "pathogenesis." Collectively, these advances built a foundation for microbiome studies and have positioned V. fischeri to continue to expand the frontiers of our understanding of the microbial world inside animals.

Melatonin alleviates myocardial dysfunction through inhibition of endothelial-to-mesenchymal transition via the NF-κB pathway.

Endothelial-to-mesenchymal transition (EndMT) is a complex biological process of cellular transdifferentiation by which endothelial cells (ECs) lose their characteristics and acquire mesenchymal properties, leading to cardiovascular remodeling and complications in the adult cardiovascular diseases environment. Melatonin is involved in numerous physiological and pathological processes, including aging, and has anti-inflammatory and antioxidant activities. This molecule is an effective therapeutic candidate for preventing oxidative stress, regulating endothelial function, and maintaining the EndMT balance to provide cardiovascular protection. Although recent studies have documented improved cardiac function by melatonin, the mechanism of action of melatonin on EndMT remains unclear. The present study investigated the effects of melatonin on induced EndMT by transforming growth factor-β2/interleukin-1β in both in vivo and in vitro models. The results revealed that melatonin reduced the migratory ability and reactive oxygen species levels of the cells and ameliorated mitochondrial dysfunction in vitro. Our findings indicate that melatonin prevents endothelial dysfunction and inhibits EndMT by activating related pathways, including nuclear factor kappa B and Smad. We also demonstrated that this molecule plays a crucial role in restoring cardiac function by regulating the EndMT process in the ischemic myocardial condition, both in vessel organoids and myocardial infarction (MI) animal models. In conclusion, melatonin is a promising agent that attenuates EC dysfunction and ameliorates cardiac damage compromising the EndMT process after MI.

A Na+ channel receptor of FMRFamide in the cephalopod Sepiella japonica: Identification, characterisation, and expression profiling during different stages of gonadal development

FMRFamide, a member of the neuropeptide family, is involved in numerous physiological processes. FMRFamide-activated sodium channels (FaNaCs) are a family of non-voltage-gated, amiloride-sensitive, Na+-selective channels triggered by the neuropeptide FMRFamide. In the present study, the full-length cDNA of the FaNaC receptor of Sepiella japonica (SjFaNaC) was cloned. The cDNA of SjFaNaC was 3004 bp long with an open reading frame (ORF) of 1812 bp, encoding 603 amino acid residues with no signal peptide at the N-terminus. Sequence analysis indicated that SjFaNaC shared a high identity with other cephalopods FaNaCs and formed a sister clade with bivalves. The protein structure was predicted using SWISS-MODEL with AcFaNaC as the template. Quantitative real-time PCR (qRT-PCR) revealed that SjFaNaC transcripts were highly expressed in both female and male reproductive organs, as well as in the optic lobe and brain of the central nervous system (CNS). Results of in situ hybridisation (ISH) showed that SjFaNaC mRNA was mainly distributed in the medulla and deep retina of the optic lobe and in both the supraesophageal and subesophageal masses of the brain. Subcellular localisation indicated that the SjFaNaC protein was localised intracellularly and on the cell surface of HEK293T cells. In summary, these findings may lay the foundation for future exploration of the functions of SjFaNaC in cephalopods.

Characterization of polysulfides (Sx2−) in seawater euxinic conditions by electroanalytical methods

Polysulfides (Sx2−) are important reduced sulfur species (RSS) that play a role in numerous environmental processes. A sound analytical method for the measurement of Sx2− in euxinic waters is lacking. In this work, differential pulse voltammetry (DPV) at the Hg electrode was used to measure the presence of Sx2− in a model seawater solution and an euxinic marine lake (Rogoznica Lake - RL Croatia), in which the concentration of RSS, mainly HS−, varies between 100 and 4000 μM. In the DPV, an adsorption phenomenon associated with Sx2− reduction on Hg produces a characteristic current minimum at −1.0 V (vs. Ag/AgCl), the magnitude of which is proportional to the concentration of polysulfidic sulfur.The DPV current minima were recorded in the model solution K2Sx NaCl/NaHCO3 (pH ~ 8.2) in a concentration range from 10 to 100 μM of polysulfidic sulfur. Total RSS was measured by cyclic voltammetry, and sampled DC voltammetry showed the ratio between HS− and S0 within the Sx2−. Using the same methodology, the presence of Sx2− below the chemocline enriched by photoptrophic sulfur bacteria was measured in the euxinic layer of RL at concentrations of up to 70 μM of polysulfidic sulfur. The results suggest that euxinic RL samples can be considered as polydisperse solutions of S0 or of S-rich compounds that can change their physicochemical properties and speciation during sample manipulation. These changes can be detected by electrochemistry. Atomic force microscopy proved the release of bacterial cellular S0 during the acidification and purging step in the electrochemical measurements, which contributed to the voltammetric RSS signal.

MLm5C: A high-precision human RNA 5-methylcytosine sites predictor based on a combination of hybrid machine learning models

RNA modification serves as a pivotal component in numerous biological processes. Among the prevalent modifications, 5-methylcytosine (m5C) significantly influences mRNA export, translation efficiency and cell differentiation and are also associated with human diseases, including Alzheimer’s disease, autoimmune disease, cancer, and cardiovascular diseases. Identification of m5C is critically responsible for understanding the RNA modification mechanisms and the epigenetic regulation of associated diseases. However, the large-scale experimental identification of m5C present significant challenges due to labor intensity and time requirements. Several computational tools, using machine learning, have been developed to supplement experimental methods, but identifying these sites lack accuracy and efficiency. In this study, we introduce a new predictor, MLm5C, for precise prediction of m5C sites using sequence data. Briefly, we evaluated eleven RNA sequence-derived features with four basic machine learning algorithms to generate baseline models. From these 44 models, we ranked them based on their performance and subsequently stacked the Top 20 baseline models as the best model, named MLm5C. The MLm5C outperformed the-state-of-the-art predictors. Notably, the optimization of the sequence length surrounding the modification sites significantly improved the prediction performance. MLm5C is an invaluable tool in accelerating the detection of m5C sites within the human genome, thereby facilitating in the characterization of their roles in post-transcriptional regulation.

Accounting for nuclear and mito genome in dairy cattle breeding - a simulation study

Mitochondria play a significant role in numerous cellular processes through proteins encoded by both the nuclear genome (nDNA) and mito genome (mDNA), and increasing evidence shows that traits of interest might be affected by mito-nuclear interactions. While the variation in nDNA is influenced by mutations and recombination of parental genomes, the variation in mDNA is solely driven by mutations. In addition, mDNA is inherited in a haploid form, from the dam. Cattle populations show significant variation in mDNA between and within breeds. Past research suggests that variation in mDNA accounts for 1–5% of the phenotypic variation in dairy traits. Here we simulated a dairy cattle breeding program to assess the impact of accounting for mDNA variation in pedigree-based and genome-based genetic evaluations on the accuracy of estimated breeding values for mDNA and nDNA components. We also examined the impact of alternative definitions of breeding values on genetic gain, including nDNA and mDNA components that both impact phenotype expression, but mDNA is inherited only maternally. We found that accounting for mDNA variation increased accuracy between +0.01 and +0.03 for different categories of animals, especially for young bulls (+0.03) and females without genotype data (between +0.01 and +0.03). Different scenarios of modeling and breeding value definition impacted genetic gain. The standard approach of ignoring mDNA variation achieved competitive genetic gain. Modeling, but not selecting on mDNA expectedly reduced genetic gain, while optimal use of mDNA variation recovered the genetic gain.