Complex Parameters Research Articles

Unraveling the Degradation Mechanisms of Lithium-Ion Batteries

Lithium-Ion Batteries (LIBs) usually present several degradation processes, which include their complex Solid-Electrolyte Interphase (SEI) formation process, which can result in mechanical, thermal, and chemical failures. The SEI layer is a protective layer that forms on the anode surface. The SEI layer allows the movement of lithium ions while blocking electrons, which is necessary to prevent short circuits in the battery and ensure safe operation. However, the SEI formation mechanisms reduce battery capacity and power as they consume electrolyte species, resulting in irreversible material loss. Furthermore, it is important to understand the degradation reactions of the LIBs used in Electric Vehicles (EVs), aiming to establish the battery lifespan, predict and minimise material losses, and establish an adequate time for replacement. Moreover, LIBs applied in EVs suffer from two main categories of degradation, which are, specifically, calendar degradation and cycling degradation. There are several studies about battery degradation available in the literature, including different degradation phenomena, but the degradation mechanisms of large-format LIBs have rarely been investigated. Therefore, this review aims to present a systematic review of the existing literature about LIB degradation, providing insight into the complex parameters that affect battery degradation mechanisms. Furthermore, this review has investigated the influence of time, C-rate, depth of discharge, working voltage window, thermal and mechanical stresses, and side reactions in the degradation of LIBs.

On the analytic extension of three ratios of Horn's confluent hypergeometric function $\mathrm{H}_7$

In this paper, we consider the extension of the analytic functions of two variables by special families of functions — continued fractions. In particular, we establish new symmetric domains of the analytical continuation of three ratios of Horn's confluent hypergeometric function $\mathrm{H}_7$ with certain conditions on real and complex parameters using their continued fraction representations. We use Worpitzky's theorem, the multiple parabola theorem, and a technique that extends the convergence, already known for a small domain, to a larger domain to obtain domains of convergence of continued fractions, and the PC method to prove that they are also domains of analytical continuation.

Assessing the strength of deep-sea surface ultrasoft sediments with T-bar penetration: A machine learning approach

With the expansion of marine resource utilization, geological disaster prediction, and ecological protection in deep-sea regions, accurate identification of deep-sea engineering geological environments has become paramount, highlighting the significance of understanding the mechanical characteristics of deep-sea surface ultrasoft sediments (DSUSs). However, DSUSs typically exhibit low strength, high compressibility, and significant fluidity, making sampling for indoor strength testing a challenging task. On-site testing employing a T-bar penetrometer has become increasingly prominent for assessing DSUS strength, but the complexity of influencing parameters limits the application of this valuable method. This study utilizes a validated computational fluid dynamics (CFD) approach to model the complete process of T-bar penetration into a DSUS. The aim is to understand the quantitative effects and corresponding mechanisms of various significant complex parameters, including the undrained shear strength, dimensionless penetration depth, rate effect, and interface contact condition, on the dimensionless penetration resistance coefficient. Additionally, a machine learning model based on the random forest algorithm is introduced to establish a multiparameter evaluation method for the dimensionless penetration resistance coefficient. The findings illuminate the intricate interactions and effective evaluation of these factors when evaluating DSUS strength using a T-bar penetrometer, addressing the above challenges in marine engineering geology and the environment.

Nonlinear resonant responses of a novel FGM sandwich cylindrical shell structure for supersonic flight vehicles based on 1:1 internal resonance between conjugate modes

A new design scheme for the functionally graded material (FGM) sandwich cylindrical shell structure made of ceramic-FGM-carbon fiber composites is proposed, which is geared towards supersonic flight vehicles and has high specific stiffness and high-temperature resistance. We focus on analyzing the nonlinear resonant responses of the novel FGM sandwich cylindrical shell subjected to external excitation and aerodynamic force. Firstly, the mechanical properties of the novel FGM sandwich material are calculated, and then the nonlinear dynamic model of the FGM sandwich cylindrical shell is established based on the first order shear deformation theory (FSDT) and Hamilton's principle. Due to the axisymmetric property of the cylindrical shell, a 1:1 internal resonance between the driven and companion modes always exists in the perfect cylindrical shell. Taking this into consideration, the nonlinear resonant response problems of the FGM sandwich cylindrical shell are numerically simulated by combining the Galerkin method and the pseudo-arc length continuation method. Finally, the effects of complex parameters such as external excitation, aerodynamic force, aspect ratio, gradient index, and skin-core-skin ratio on the nonlinear resonant responses of the FGM sandwich cylindrical shell are investigated.

Investigation of Fatigue and Drowsiness of Welders and Goldsmiths Based on Entropies and Complexity Parameters of EOGs: A Statistical Approach

Investigation of Fatigue and Drowsiness of Welders and Goldsmiths Based on Entropies and Complexity Parameters of EOGs: A Statistical Approach

AI-powered trustable and explainable fall detection system using transfer learning

Accidental falls pose a significant public health challenge, especially among vulnerable populations. To address this issue, comprehensive research on fall detection and rescue systems is essential. Vision-based technologies, with their promising potential, offer an effective means to detect falls. This research paper presents a cutting-edge fall detection methodology aimed at enhancing individual safety and well-being. The proposed methodology utilizes deep neural networks, leveraging their capabilities to drive advancements in fall detection. To overcome data limitations and computational efficiency concerns, this study employ transfer learning by fine-tuning pre-trained models on large-scale image datasets for fall detection. This approach significantly enhances model performance, enabling better generalization and accuracy, especially in real-time applications with constrained resources. Notably, the methodology achieved an impressive test accuracy of 98.15%. Additionally, the incorporation of Explainable Artificial Intelligence (XAI) techniques is used to ensure transparent and trustworthy decision-making in fall detection using deep learning models, especially in critical healthcare contexts for vulnerable individuals. XAI provides valuable insights into complex model architectures and parameters, enabling a deeper understanding of fall identification patterns. To evaluate the effectiveness of this approach, a rigorous experimentation was conducted using a diverse dataset containing real-world fall and non-fall scenarios. The results demonstrate substantial improvements in both accuracy and interpretability, confirming the superiority of this method over conventional fall detection approaches.

Real-time non-invasive hemoglobin prediction using deep learning-enabled smartphone imaging

BackgroundAccurate measurement of hemoglobin concentration is essential for various medical scenarios, including preoperative evaluations and determining blood loss. Traditional invasive methods are inconvenient and not suitable for rapid, point-of-care testing. Moreover, current models, due to their complex parameters, are not well-suited for mobile medical settings, which limits the ability to conduct frequent and rapid testing. This study aims to introduce a novel, compact, and efficient system that leverages deep learning and smartphone technology to accurately estimate hemoglobin levels, thereby facilitating rapid and accessible medical assessments.MethodsThe study employed a smartphone application to capture images of the eye, which were subsequently analyzed by a deep neural network trained on data from invasive blood test data. Specifically, the EGE-Unet model was utilized for eyelid segmentation, while the DHA(C3AE) model was employed for hemoglobin level prediction. The performance of the EGE-Unet was evaluated using statistical metrics including mean intersection over union (MIOU), F1 Score, accuracy, specificity, and sensitivity. The DHA(C3AE) model’s performance was assessed using mean absolute error (MAE), mean-square error (MSE), root mean square error (RMSE), and R^2.ResultsThe EGE-Unet model demonstrated robust performance in eyelid segmentation, achieving an MIOU of 0.78, an F1 Score of 0.87, an accuracy of 0.97, a specificity of 0.98, and a sensitivity of 0.86. The DHA(C3AE) model for hemoglobin level prediction yielded promising outcomes with an MAE of 1.34, an MSE of 2.85, an RMSE of 1.69, and an R^2 of 0.34. The overall size of the model is modest at 1.08 M, with a computational complexity of 0.12 FLOPs (G).ConclusionsThis system presents a groundbreaking approach that eliminates the need for supplementary devices, providing a cost-effective, swift, and accurate method for healthcare professionals to enhance treatment planning and improve patient care in perioperative environments. The proposed system has the potential to enable frequent and rapid testing of hemoglobin levels, which can be particularly beneficial in mobile medical settings.Trial RegistrationThe clinical trial was registered on the Chinese Clinical Trial Registry (No. ChiCTR2100044138) on 20/02/2021.

Exploring Fe(III) coordination and membrane interaction of a siderophore-peptide conjugate: Enhancing synergistically the antimicrobial activity

Many microbes produce siderophores, which are extremely potent weapons capable of stealing iron ions from human tissues, fluids and cells and transferring them into bacteria through their appropriate porins. We have recently designed a multi-block molecule, each block having a dedicated role. The first component is an antimicrobial peptide, whose good effectiveness against some bacterial strains was gradually improved through interactive sequence modifications. Connected to this block is a flexible bio-band, also optimized in length, which terminates in a hydroxyamide unit, a strong metal binder. Thus, the whole molecule brings together two pieces that work synergistically to fight infection. To understand if the peptide unit, although modified with a long tail, preserves the structure and therefore the antimicrobial activity, and to characterize the mechanism of interaction with bio-membrane models mimicking Gram-negative membranes, we performed a set of fluorescence-based experiments and circular dichroism studies, which further supported our design of a combination of two different entities working synergistically. The chelating activity and iron(III) binding of the peptide was confirmed by iron(III) paramagnetic NMR analyses, and through a competitive assay with ethylenediamine-tetra acetic acid by ultraviolet–visible spectroscopy. The complexation parameters, the Michaelis constant K, and the number of sites n, evaluated with spectrophotometric techniques are confirmed by Fe(III) paramagnetic NMR analyses here reported. In conclusion, we showed that the coupling of antimicrobial capabilities with iron-trapping capabilities works well in the treatment of infectious diseases caused by Gram-negative pathogens.

Covalency parameters for nine-coordinated thorium acylpyrazolone complexes along with their synthesis and crystal features

This study discusses the synthesis and characterization of three thorium complexes with a general composition of [Th(L)4(EtOH)] demonstrated a nine-coordinated geometry in acylpyrazolone chemistry, a novel finding supported by single crystal evidence. A detailed analysis of complex [Th(L2)2(DMF)] was conducted, utilizing single-crystal X-ray diffraction data and Hirshfeld surface area analysis to investigate secondary interactions, exploring the planarity and bonding characteristics of the crystals. Covalency parameters such as the Nephelauxetic ratio, Sinha parameter, bonding parameter, and angular overlap parameter were calculated from electronic spectral data, revealing positive covalency and electron delocalization across the 5f orbital for all complexes. Notably, the presence of solvent bound to the 9th coordination site of the complex suggests potential changes in covalency across different solvents, thereby indirectly impacting chemical characteristics.

Model performance evaluation of build time using geometric shape complexity and process parameters in material extrusion

Additive Manufacturing (AM) is gaining widespread interest and recognition in both academia and industry due to its numerous advantages over traditional manufacturing processes. The AM process offers two significant benefits: mass customization and the ability to achieve higher shape complexity at no cost. The present study introduces a methodology for quantifying the shape complexity of feature-based parametric models that AM can produce. This method is exclusively a quantitative approach based on the “number of inputs” required to define the feature or integral sketch. Furthermore, an Artificial Neural Network (ANN) based predictive model for estimating the AM build time has been developed and validated. Supervised Machine Learning (ML) is employed to predict printing time by considering significant design and manufacturing parameters that affect the overall process time. A performance evaluation is conducted between the developed ANN model and a statistical Multiple Linear Regression (MLR) model. The validation Computer Aided Design (CAD) samples are chosen to evaluate the ANN and MLR model's performance to predict the build time. It is observed that the ANN model outperforms the MLR model in estimating the build time of complex geometries. Regarding the efficiency of models, the ANN model achieved an R square value of 0.880 and a lower prediction error of 15.543 %. However, the MLR model showed some sensitivity to high complexity and large volume parts when predicting printing time, with a R square value of 0.96. In contrast, the ANN model is more precise in forecasting FDM build time for complex geometries and large volume parts.

Towards void-free copper filling of low-aspect-ratio heat dissipation through holes in packaging substrate with high H2SO4 concentration electroplating system

The void-free filling of high-density low aspect ratio (AR≤5) through holes (THs) in the packaging substrate is beneficial for promoting the integration, reliability, and heat dissipation efficiency of high-power electronic devices. It is a challenge to fill the low ARs THs compactly in direct current (DC) electroplating. The copper filling process relies heavily on the additives’ complex interactions and multiple process parameters. Herein, an ideal convection-sensitive electroplating solution formula — 2.34 M H2SO4, 0.24 M Cu2SO4·5H2O, 30 mg/L Cl−, 9 mg/L SH110, 5 mg/L NTBC, and 200 mg/L PEG — that was appropriate for low ARs THs filling was provided by investigate the electrochemical characteristics of additives. The critical electroplating parameters were optimized by thoroughly examining their influence on filling process, including current density 1.5 ASD, solution flow rate 2.2 L/min, and temperature 25 °C. The process window was enlarged apparently by the combination of optimal parameters for simultaneous void-free filling of THs with a large AR range of 0.95–5, dense crystal structure, and high layer quality could be achieved. Subsequently, the low ARs THs array filled by the optimized technology was implemented in the packaging substrate as the heat dissipation channel, which significantly promoted the heat dissipation efficiency.

Machine learning in drug delivery

For decades, drug delivery scientists have been performing trial-and-error experimentation to manually sample parameter spaces and optimize release profiles through rational design. To enable this approach, scientists spend much of their career learning nuanced drug-material interactions that drive system behavior. In relatively simple systems, rational design criteria allow us to fine tune release profiles and enable efficacious therapies. However, as materials and drugs become increasingly sophisticated and their interactions have non-linear and compounding effects, the field is suffering the Curse of Dimensionality which prevents us from comprehending complex structure-function relationships. In the past, we have embraced this complexity by implementing high-throughput screens to increase the probability of finding ideal compositions. However, this brute force method was inefficient and led many to abandon these fishing expeditions. Fortunately, methods in data science including artificial intelligence / machine learning (AI/ML) are providing ideal analytical tools to model this complex data and ascertain quantitative structure-function relationships. In this Oration, I speak to the potential value of data science in drug delivery with particular focus on polymeric delivery systems. Here, I do not suggest that AI/ML will simply replace mechanistic understanding of complex systems. Rather, I propose that AI/ML should be yet another useful tool in the lab to navigate complex parameter spaces. The recent hype around AI/ML is breathtaking and potentially over inflated, but the value of these methods is poised to revolutionize how we perform science. Therefore, I encourage readers to consider adopting these skills and applying data science methods to their own problems. If done successfully, I believe we will all realize a paradigm shift in our approach to drug delivery.

Counting Complexity for Reasoning in Abstract Argumentation

In this paper, we consider counting and projected model counting of extensions in abstract argumentation for various semantics, including credulous reasoning. When asking for projected counts, we are interested in counting the number of extensions of a given argumentation framework, while multiple extensions that are identical when restricted to the projected arguments count as only one projected extension. We establish classical complexity results and parameterized complexity results when the problems are parameterized by the treewidth of the undirected argumentation graph. To obtain upper bounds for counting projected extensions, we introduce novel algorithms that exploit small treewidth of the undirected argumentation graph of the input instance by dynamic programming. Our algorithms run in double or triple exponential time in the treewidth, depending on the semantics under consideration. Finally, we establish lower bounds of bounded treewidth algorithms for counting extensions and projected extension under the exponential time hypothesis (ETH).

A comprehensive metric scheme for characterizing the heterogeneity of urban thermal landscapes: A case study of 14-year evaluation in Beijing

Urbanization has affected land surface temperature (LST) significantly. The spatial average of LST is widely applied to evaluate urban heat islands (UHI), but a simple comparison of mean temperature is insufficient to evaluate the heterogeneity of LST and the effectiveness of urban heat mitigation strategies. This study took the spatial distribution of LST as the thermal landscape and firstly employed 3D landscape metrics to analyze its spatial heterogeneity. To perform collaborative analysis with UHI, the urban–rural differences of landscape metrics were defined as thermal metric islands (TMI). Significant UHI and TMI were revealed by temperature amplitude, aggregation, and complexity parameters, indicating higher temperature fluctuation and fragmentation in urban regions. Before 2007, the standard deviation of thermal surface, landscape fragmentation, and shape irregularity increased significantly, particularly in the suburbs. Later, benefiting from the Summer Olympic Games, the temperature fluctuation, fragmentation, and shape complexity decreased obviously, and UHI intensity hit the lowest. Both UHI intensities and TMI increased after 2010, and various metrics suggest that maximum temperatures, landscape fragmentation, and shape complexity declined over most of the district, even as the mean temperatures increased. This study concludes that urban temperatures have risen at a higher rate than suburban areas, but the thermal landscape composition and configuration have changed more dramatically in rural areas.

CP phase in modular flavor models and discrete Froggatt-Nielsen models

We study the large mass hierarchy and CP violation in the modular symmetric quark flavor models without fine-tuning. Mass matrices are written in terms of modular forms. Modular forms near the modular fixed points are approximately given by ϵp, where ϵ and p denote the small deviation from the fixed points and their residual charges. Thus, mass matrices have the hierarchical structures depending on the residual charges and have a possibility describing the large mass hierarchy without fine-tuning. Similar structures of mass matrices are also obtained in Froggatt-Nielsen models. Nevertheless, it seems to be difficult to induce a sufficient amount of CP violation by a single small complex parameter ϵ. To realize the large mass hierarchy as well as sizable CP violation, multimoduli are required. We show the mass matrix structures with multimoduli that are consistent with quark flavor observables including the CP phase. We also discuss the origins of the large mass hierarchy and CP violation in such mass matrix structures. Published by the American Physical Society 2024

Parameterized Complexity of Streaming Diameter and Connectivity Problems

We initiate the investigation of the parameterized complexity of Diameter and Connectivity in the streaming paradigm. On the positive end, we show that knowing a vertex cover of size k allows for algorithms in the Adjacency List (AL) streaming model whose number of passes is constant and memory is O(logn)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathcal {O}(\\log n)$$\\end{document} for any fixed k. Underlying these algorithms is a method to execute a breadth-first search in O(k)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathcal {O}(k)$$\\end{document} passes and O(klogn)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathcal {O}(k \\log n)$$\\end{document} bits of memory. On the negative end, we show that many other parameters lead to lower bounds in the AL model, where Ω(n/p)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\Omega (n/p)$$\\end{document} bits of memory is needed for any p-pass algorithm even for constant parameter values. In particular, this holds for graphs with a known modulator (deletion set) of constant size to a graph that has no induced subgraph isomorphic to a fixed graph H, for most H. For some cases, we can also show one-pass, Ω(nlogn)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\Omega (n \\log n)$$\\end{document} bits of memory lower bounds. We also prove a much stronger Ω(n2/p)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\Omega (n^2/p)$$\\end{document} lower bound for Diameter on bipartite graphs. Finally, using the insights we developed into streaming parameterized graph exploration algorithms, we show a new streaming kernelization algorithm for computing a vertex cover of size k. This yields a kernel of 2k vertices (with O(k2)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathcal {O}(k^2)$$\\end{document} edges) produced as a stream in poly(k)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ ext {poly}(k)$$\\end{document} passes and only O(klogn)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathcal {O}(k \\log n)$$\\end{document} bits of memory.

Robust Prescribed-Time ESO-Based Practical Predefined-Time SMC for Benthic AUV Trajectory-Tracking Control with Uncertainties and Environment Disturbance

The aim of this study is to address the trajectory-tracking control problem of benthic autonomous underwater vehicles (AUVs) subjected to model uncertainties and extra disturbance. In order to estimate lumped uncertainties and reconstruction speed information, this paper designs a robust prescribed-time extended state observer (RPTESO), and its prescribed time can be directly designed as an explicit parameter, without relying on the initial state of the system and complex parameter settings. In addition, an adaptive law is designed to improve the robustness of RPTSEO and reduce overshoot on the premise of ensuring convergence speed. Then, a non-singular robust practical predefined-time sliding mode control (RPPSMC) considering the hydrodynamic characteristics of AUV is designed, and the predefined time can be directly set by an explicit parameter. The RPPSMC is designed based on the lumped uncertainties estimated using RPTESO, so as to improve the control accuracy of the controller in a complex environment. Theoretical analysis and simulations demonstrated the effectiveness and superiority of the proposed method.

A NEW ALGORITHM FOR DIAGNOSING OBESITY BASED ON INDICATORS OF BODY COMPOSITION

Introduction. Body mass index (BMI), which is measured as the ratio of body weight to height squared, is one of the widely used criteria for classifying overweight and obesity. Despite its popularity, BMI is often criticized for not taking into account individual differences in body composition and fat distribution, which can lead to inaccuracies in the classification of the degree of obesity. Aim. To develop and test a somatotype diagnostic algorithm based on the integration and comprehensive analysis of fat content, skeletal muscle mass, and fat distribution. Materials and methods. The study was conducted on a group of 82 men with different indicators of BMI. A developed algorithm was used to diagnose somatotype, which included body fat index (IBF), limb muscle mass index (IASM), and waist circumference to height ratio (WHtR). The results were analyzed and classified according to the defined criteria. Results. The proposed algorithm was tested on a sample of 82 examined men, who were divided into 2 groups depending on BMI. Thanks to our algorithm, it was possible to identify prognostically unfavorable somatotypes characterized by sarcopenia with a central type of fat distribution. These are F1S1C1 and F2S1C1 somatotypes, the total number of which was 9 examined (10.96% of the sample). Approbation of the algorithm was carried out on a group of examined men, confirmed its effectiveness and ability to detect different somatotypes, taking into account complex parameters of the body. Conclusions. Our research algorithm turned out to be a useful tool for somatotype diagnosis, especially in cases where BMI may not accurately reflect the real state of health. Taking into account individual differences in body components such as fat content, skeletal muscle mass and fat distribution, we provide a more accurate classification of somatotype and the possibility of a more individualized approach to treatment and prevention.

The influence of low nitrogen doping on bacterial adhesion of sputtered a-C:H coatings

Heavily dependent on both surface and bacteria characteristics, multiple and complex parameters control bacterial adhesion to any biomaterial surface. That's why there are so many contradictory results regarding the antibacterial and adhesion inhibition properties of Diamond-Like Carbon (DLC) protective coatings, despite the well-established biocompatibility of this very broad group of materials. Aiming at Orthodontics, this study reports the modification of hydrogenated amorphous carbon coatings (a-C:H), by nitrogen incorporation upon reactive magnetron sputtering. The effect of low N content up to 10 at.% on the microstructure and bacterial adhesion was studied. Despite the progressive graphitization upon doping, the surface roughness was not significantly influenced and the water contact angles remained above 70°. The adhesion of three bacterial strains of Staphylococcus aureus, Bacillus subtilis and Pseudomonas aeruginosa was evaluated. The median colony forming units was lower on the a-C:H and a-C:H:N coatings in comparison to the bare AISI 316L medical grade stainless steel surface, regardless the bacterial strain. The best bacterial adhesion inhibition capacity was achieved for N doping of 6 at.%, without using any antibacterial agents.

Study of nonstandard interactions mediated by a scalar field at the ESSnuSB experiment

In this paper, we study scalar mediator induced nonstandard interactions (SNSIs) in the context of the ESSnuSB experiment. In particular, we study the capability of ESSnuSB to put bounds on the SNSI parameters and also study the impact of SNSIs in the measurement of the leptonic CP phase δCP. Existence of SNSIs modifies the neutrino mass matrix and this modification can be expressed in terms of three diagonal real parameters (ηee, ημμ, and ηττ) and three off-diagonal complex parameters (ηeμ, ηeτ, and ημτ). Our study shows that the upper bounds on the parameters ημμ and ηττ depend upon how Δm312 is minimized in the theory. However, this is not the case when one tries to measure the impact of SNSIs on δCP. Further, we show that the CP sensitivity of ESSnuSB can be completely lost for certain values of ηee and ημτ for which the appearance channel probability becomes independent of δCP. Published by the American Physical Society 2024