Aromatic character of planar boron-based clusters revisited by ring current calculations

The planarity of small boron-based clusters is the result of an interplay between geometry, electron delocalization, covalent bonding and stability. These compounds contain two different bonding patterns involving both sigma and pi delocalized bonds, and up to now, their aromaticity has been assigned mainly using the classical (4N + 2) electron count for both types of electrons. In the present study, we reexplored the aromatic feature of different types of planar boron-based clusters making use of the ring current approach. B3(+/-), B-4(2-), B-5(+/-), B-6, B-7(-), B-8(2-), B-9(-), B-10(2-), B-11(-), B-12, B-13(+), B-14(2-) and B-16(2-) are characterized by magnetic responses to be doubly sigma and pi aromatic species in which the pi aromaticity can be predicted using the (4N + 2) electron count. The triply aromatic character of B-12 and B-13(+) is confirmed. The pi electrons of B-18(2-), B-19(-) and B-20(2-) obey the disk aromaticity rule with an electronic configuration of [1 sigma(2)1 pi(4)1 delta(4)2 sigma(2)] rather than the (4N + 2) count. The double aromaticity feature is observed for boron hydride cycles including B@B5H5+, Li7B5H5 and M@BnHnq clusters from both the (4N + 2) rule and ring current maps. The double pi and sigma aromaticity in carbon-boron planar cycles B7C-, B8C, B6C2, B9C-, B8C2 and B7C3- is in conflict with the Huckel electron count. This is also the case for the ions B11C5+/- whose ring current indicators suggest that they belong to the class of double aromaticity, in which the pi electrons obey the disk aromaticity characteristics. In many clusters, the classical electron count cannot be applied, and the magnetic responses of the electron density expressed in terms of the ring current provide us with a more consistent criterion for determining their aromatic character

A Deep Learning Approach to Denoise Optical Coherence Tomography Images of the Optic Nerve Head

Purpose: To develop a deep learning approach to de-noise optical coherence tomography (OCT) B-scans of the optic nerve head (ONH). Methods: Volume scans consisting of 97 horizontal B-scans were acquired through the center of the ONH using a commercial OCT device (Spectralis) for both eyes of 20 subjects. For each eye, single-frame (without signal averaging), and multi-frame (75x signal averaging) volume scans were obtained. A custom deep learning network was then designed and trained with 2,328 "clean B-scans" (multi-frame B-scans), and their corresponding "noisy B-scans" (clean B-scans + gaussian noise) to de-noise the single-frame B-scans. The performance of the de-noising algorithm was assessed qualitatively, and quantitatively on 1,552 B-scans using the signal to noise ratio (SNR), contrast to noise ratio (CNR), and mean structural similarity index metrics (MSSIM). Results: The proposed algorithm successfully denoised unseen single-frame OCT B-scans. The denoised B-scans were qualitatively similar to their corresponding multi-frame B-scans, with enhanced visibility of the ONH tissues. The mean SNR increased from $4.02 \pm 0.68$ dB (single-frame) to $8.14 \pm 1.03$ dB (denoised). For all the ONH tissues, the mean CNR increased from $3.50 \pm 0.56$ (single-frame) to $7.63 \pm 1.81$ (denoised). The MSSIM increased from $0.13 \pm 0.02$ (single frame) to $0.65 \pm 0.03$ (denoised) when compared with the corresponding multi-frame B-scans. Conclusions: Our deep learning algorithm can denoise a single-frame OCT B-scan of the ONH in under 20 ms, thus offering a framework to obtain superior quality OCT B-scans with reduced scanning times and minimal patient discomfort

How Effective Are Neural Networks for Fixing Security Vulnerabilities

Security vulnerability repair is a difficult task that is in dire need of automation. Two groups of techniques have shown promise: (1) large code language models (LLMs) that have been pre-trained on source code for tasks such as code completion, and (2) automated program repair (APR) techniques that use deep learning (DL) models to automatically fix software bugs. This paper is the first to study and compare Java vulnerability repair capabilities of LLMs and DL-based APR models. The contributions include that we (1) apply and evaluate five LLMs (Codex, CodeGen, CodeT5, PLBART and InCoder), four fine-tuned LLMs, and four DL-based APR techniques on two real-world Java vulnerability benchmarks (Vul4J and VJBench), (2) design code transformations to address the training and test data overlapping threat to Codex, (3) create a new Java vulnerability repair benchmark VJBench, and its transformed version VJBench-trans and (4) evaluate LLMs and APR techniques on the transformed vulnerabilities in VJBench-trans. Our findings include that (1) existing LLMs and APR models fix very few Java vulnerabilities. Codex fixes 10.2 (20.4%), the most number of vulnerabilities. (2) Fine-tuning with general APR data improves LLMs' vulnerability-fixing capabilities. (3) Our new VJBench reveals that LLMs and APR models fail to fix many Common Weakness Enumeration (CWE) types, such as CWE-325 Missing cryptographic step and CWE-444 HTTP request smuggling. (4) Codex still fixes 8.3 transformed vulnerabilities, outperforming all the other LLMs and APR models on transformed vulnerabilities. The results call for innovations to enhance automated Java vulnerability repair such as creating larger vulnerability repair training data, tuning LLMs with such data, and applying code simplification transformation to facilitate vulnerability repair.Comment: This paper has been accepted to appear in the proceedings of the 32nd ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA 2023), and to be presented at the conference, that will be held in Seattle, USA, 17-21 July 202

BxGe120/+ Clusters with x=1-4:Germanium Tubes Stabilized by Three and Four Boron Dopants

Some boron-doped germanium clusters BxGe12q with x = 1, 2, 3, and 4 and q = 0, 1 were designed as stabilized double ring tubes. While the B2Ge12 constitutes the smallest deltahedral germanium cluster, both B3Ge12+ and B4Ge12 clusters present us, for the first time, with an endohedral tubular motif in which either the B-3 or the B-4 cycle is encapsulated inside a Ge-12 hexagonal prism tube. Both B-3 and B-4 units thus satisfy a geometry requirement to create an endohedral structure within the Ge-12 double ring. Keeping their high symmetry, both B-3 and B-4 units generate delocalized bonds upon interaction with the Ge-12 tubular framework and thereby induce an aromatic character for the resulting B3Ge12+ and B4Ge12, respectively. Their aromaticity was probed by the magnetic responses of electron densities. Such a tubular aromaticity appears to greatly contribute to the high thermodynamic stability of the binary hexagonal germanium tubes

Characterization of multilayered carbon-fiber–reinforced thermoplastic composites for assembly process

The aim of this research work is to characterize the mechanical behavior of multilayered carbon-fiber–reinforced polyphenylene sulfide composites with the application to assembly process of nonrigid parts. Two anisotropic hyperelastic material models were investigated and implemented in Abaqus as a user-defined material. An inverse characterization method was applied to identify the parameters of these material models. Finite element simulations at finite strains of a flexible composite sheet were carried out. Numerical results of sheet deformation were compared with the experimental results in order to evaluate the appropriateness of the material models developed for this application

Inverse procedure for mechanical characterization of multi-layered non-rigid composite parts with applications to the assembly process

In assembly process, non-rigid parts in free-state may have different forms compared to the designed model caused by gravity load and residual stresses. For non-rigid parts made by multi-layered fiber-reinforced thermoplastic composites, this process becomes much more complex due to the nonlinear behavior of the material. This paper presented an inverse procedure for characterizing large anisotropic deformation behavior of four-layered, carbon fiber-reinforced polyphenylene sulphide, non-rigid composite parts. Mechanical responses were measured from the standard three points bending test and the surface displacements of composite plates under flexural loading test. An orthotropic hyperelastic material model was implemented as a UMAT user routine in the Abaqus/Standard to analyze the behavior of flexible fiber-reinforced thermoplastic composites. Error functions were defined by subtracting the experimental data from the numerical mechanical responses. Minimizing the error functions helps to identify the material parameters. These optimal parameters were validated for the case of an eight-layered composite material

Caractérisation du comportement d’une plaque composite de grande dimension

L’assemblage de grands panneaux aéronautiques flexibles, à composites multicouches renforcés de fibres, est très complexe en raison du comportement non linéaire du matériau. L’objectif de nos travaux de recherche est donc de caractériser le comportement mécanique des composites renforcés de fibres lors de l’inspection et de l’assemblage

Morphometric, hemodynamic, and biomechanical factors influencing blood flow and oxygen concentration in the human lamina cribrosa

Purpose: We developed a combined biomechanical and hemodynamic model of the human eye to estimate blood flow and oxygen concentration within the lamina cribrosa (LC) and rank the factors that influence LC oxygen concentration. Methods: We generated 5000 finite-element eye models with detailed microcapillary networks of the LC and computed the oxygen concentration of the lamina retinal ganglion cell axons. For each model, we varied the intraocular pressure (IOP) from 10 mm Hg to 55 mm Hg in 5-mm Hg increments, the cerebrospinal fluid pressure (13 ± 2 mm Hg), cup depth (0.2 ± 0.1 mm), scleral stiffness (±20% of the mean values), LC stiffness (0.41 ± 0.2 MPa), LC radius (1.2 ± 0.12 mm), average LC pore size (5400 ± 2400 µm2), the microcapillary arrangement (radial, isotropic, or circumferential), and perfusion pressure (50 ± 9 mm Hg). Blood flow was assumed to originate from the LC periphery and drain via the central retinal vein. Finally, we performed linear regressions to rank the influence of each factor on the LC tissue oxygen concentration. Results: LC radius and perfusion pressure were the most important factors in influencing the oxygen concentration of the LC. IOP was another important parameter, and eyes with higher IOP had higher compressive strain and slightly lower oxygen concentration. In general, superior–inferior regions of the LC had significantly lower oxygen concentration than the nasal–temporal regions, resulting in an hourglass pattern of oxygen deficiency. Conclusions: To the best of our knowledge, this study is the first to implement a comprehensive hemodynamical model of the eye that accounts for the biomechanical forces and morphological parameters of the LC. The results provide further insight into the possible relationship of biomechanical and vascular pathways leading to ischemia-induced optic neuropathy

Medical reverse engineering applications and methods

Understanding, controlling and manipulation of patient data as well as shape, geometry and structure of the biomedical objects are important for developing Biomedical Engineering (BME) applications. Medical Reverse Engineering (MRE) is aimed to use the Reverse Engineering (RE) technology to reconstruct 3D models of the anatomical structures and biomedical objects for design and manufacturing of medical products as well as BME research and development. This paper presents the state of the art applications and methods about MRE. Different concepts and methodologies are provided to understand fundamentally the MRE processes and workflow. The key MRE applications are presented, including personalised implants for bone reconstruction, dental implants and simulations, surgical tools, medical training, vision science and optometry, orthopedics, ergonomics, orthosis, prosthesis, and tissue engineering. The current challenges as well as the hardware and software for MRE application development and research are discussed