Multiscale expansion of the lattice potential KdV equation on functions of infinite slow-varyness order

We present a discrete multiscale expansion of the lattice potential Korteweg-de Vries (lpKdV) equation on functions of infinite order of slow-varyness. To do so we introduce a formal expansion of the shift operator on many lattices holding at all orders. The lowest secularity condition from the expansion of the lpKdV equation gives a nonlinear lattice equation, depending on shifts of all orders, of the form of the nonlinear Schr\"odinger (NLS) equationComment: 9 pages, submitted to Journ. Phys.

In-Chain Tunneling Through Charge-Density Wave Nanoconstrictions and Break-Junctions

We have fabricated longitudinal nanoconstrictions in the charge-density wave conductor (CDW) NbSe$_{3}$ using a focused ion beam and using a mechanically controlled break-junction technique. Conductance peaks are observed below the T$_{P1}$$=145$K and T$_{P2}$$=59$K CDW transitions, which correspond closely with previous values of the full CDW gaps $2\Delta_{1}$ and $2\Delta_{2}$ obtained from photo-emission. These results can be explained by assuming CDW-CDW tunneling in the presence of an energy gap corrugation $\epsilon_{2}$ comparable to $\Delta_{2}$, which eliminates expected peak at $\Delta_{1}+\Delta_{2}$. The nanometer length-scales our experiments imply indicate that an alternative explanation based on tunneling through back-to-back CDW-normal junctions is unlikely.Comment: 5 pages, 3 figures, submitted to physical review letter

Three water sites in upper mantle olivine and the role of titanium in the water weakening mechanism

Infrared spectroscopy on synthetic olivines has established that there are at least four different mechanisms by which hydrogen is incorporated into the crystal structure. Two mechanisms occur in the system MgO-SiO2-H2O associated with silicon and magnesium vacancies, respectively. A third mechanism is associated with trivalent cation substitution, commonly Fe3+ in natural olivine, while the fourth mechanism, which is the one most prevalent in natural olivines from the spinel-peridotite facies of the Earth’s upper mantle, is associated with Ti4+ [Berry et al., 2005]. Here first principles calculations based on density functional theory are used to derive the structure and relative energies of the two defects in the pure MgO-SiO2-H2O system, and possible hydrogen-bearing and hydrogen-free point defects in Ti4+-doped forsterite. Calculated structures are used to compare the predicted orientation of the O-H bonds with the experimentally determined polarization. The energies are used to discuss how different regimes of chemical environment, temperature (T), pressure (P), and both water content and water fugacity ( fH2O), impact on which of the different hydroxyl substitution mechanisms are thermodynamically stable. We find that given the presence of Ti impurities, the most stable mechanism involves the formation of silicon vacancies containing two protons charge balanced by a Ti4+ cation occupying an adjacent octahedral site. This mechanism leads to the water-mediated formation of silicon vacancies. As silicon is known to be the most slowly diffusing species in olivine, this provides a credible explanation of the observed water weakening effect in olivine

The impact of self-heating and SiGe strain-relaxed buffer thickness on the analog performance of strained Si nMOSFETs

The impact of the thickness of the silicon–germanium strain-relaxed buffer (SiGe SRB) on the analog performance of strained Si nMOSFETs is investigated. The negative drain conductance caused by self-heating at high power levels leads to negative self-gain which can cause anomalous circuit behavior like non-linear phase shifts. Using AC and DC measurements, it is shown that reducing the SRB thickness improves the analog design space and performance by minimizing self-heating. The range of terminal voltages that leverage positive self-gain in 0.1 μm strained Si MOSFETs fabricated on 425 nm SiGe SRBs is increased by over 100% compared with strained Si devices fabricated on conventional SiGe SRBs 4 μm thick. Strained Si nMOSFETs fabricated on thin SiGe SRBs also show 45% improvement in the self-gain compared with the Si control as well as 25% enhancement in the on-state performance compared with the strained Si nMOSFETs on the 4 μm SiGe SRB. The extracted thermal resistance is 50% lower in the strained Si device on the thin SiGe SRB corresponding to a 30% reduction in the temperature rise compared with the device fabricated on the 4 μm SiGe SRB. Comparisons between the maximum drain voltages for positive self-gain in the strained Si devices and the ITRS projections of supply-voltage scaling show that reducing the thickness of the SiGe SRB would be necessary for future technology nodes

AIMS: An Automatic Semantic Machine Learning Microservice Framework to Support Biomedical and Bioengineering Research

The fusion of machine learning and biomedical research offers novel ways to understand, diagnose, and treat various health conditions. However, the complexities of biomedical data, coupled with the intricate process of developing and deploying machine learning solutions, often pose significant challenges to researchers in these fields. Our pivotal achievement in this research is the introduction of the Automatic Semantic Machine Learning Microservice Framework (AIMS). AIMS addresses these challenges by automating various stages of the machine learning pipeline, with a particular emphasis on the ontology of machine learning services tailored for the biomedical domain. This ontology encompasses everything from task representation, service modeling, and knowledge acquisition to knowledge reasoning and the establishment of a self-supervised learning policy. Our framework has been crafted to prioritize model interpretability, integrate domain knowledge effortlessly, and handle biomedical data with efficiency. Additionally, AIMS boasts a distinctive feature: it leverages self-supervised knowledge learning through reinforcement learning techniques, paired with an ontology-based policy recording schema. This enables it to autonomously generate, fine-tune, and continually adapt to machine learning models, especially when faced with new tasks and data. Our work has two standout contributions of demonstrating that machine learning processes in the biomedical domain can be automated, while integrating a rich domain knowledge base and providing a way for machines to have a self-learning ability, ensuring they handle new tasks effectively. To showcase AIMS in action, we've highlighted its prowess in three case studies from biomedical tasks. These examples emphasize how our framework can simplify research routines, uplift the caliber of scientific exploration, and set the stage for notable advances

Demography and disorders of the French Bulldog population under primary veterinary care in the UK in 2013

Abstract Background Despite its Gallic name, the French Bulldog is a breed of both British and French origin that was first recognised by The Kennel Club in 1906. The French Bulldog has demonstrated recent rapid rises in Kennel Club registrations and is now (2017) the second most commonly registered pedigree breed in the UK. However, the breed has been reported to be predisposed to several disorders including ocular, respiratory, neurological and dermatological problems. The VetCompass™ Programme collates de-identified clinical data from primary-care veterinary practices in the UK for epidemiological research. Using VetCompass™ clinical data, this study aimed to characterise the demography and common disorders of the general population of French Bulldogs under veterinary care in the UK. Results French Bulldogs comprised 2228 (0.49%) of 445,557 study dogs under veterinary care during 2013. Annual proportional birth rates showed that the proportional ownership of French Bulldog puppies rose steeply from 0.02% of the annual birth cohort attending VetCompass™ practices in 2003 to 1.46% in 2013. The median age of the French Bulldogs overall was 1.3 years (IQR 0.6–2.5, range 0.0–13.0). The most common colours of French Bulldogs were brindle (solid or main) (32.36%) and fawn (solid or main) (29.9%). Of the 2228 French Bulldogs under veterinary care during 2013, 1612 (72.4%) had at least one disorder recorded. The most prevalent fine-level precision disorders recorded were otitis externa (14.0%, 95% CI: 12.6–15.5), diarrhoea (7.5%, 95% CI: 6.4–8.7), conjunctivitis (3.2%, 95% CI: 2.5–4.0), nails overlong (3.1%, 95% CI% 2.4–3.9) and skin fold dermatitis (3.0%, 95% CI% 2.3–3.8). The most prevalent disorder groups were cutaneous (17.9%, 95% CI: 16.3–19.6), enteropathy (16.7%, 95% CI: 15.2–18.3), aural (16.3%, 95% CI: 14.8–17.9), upper respiratory tract (12.7%, 95% CI: 11.3–14.1) and ophthalmological (10.5%, 95% CI: 9.3–11.9). Conclusions Ownership of French Bulldogs in the UK is rising steeply. This means that the disorder profiles reported in this study reflect a current young UK population and are likely to shift as this cohort ages. Otitis externa, diarrhoea and conjunctivitis were the most common disorders in French Bulldogs. Identification of health priorities based on VetCompass™ data can support evidence–based reforms to improve health and welfare within the breed

A twisted tale-radiological imaging features of COVID-19 on ¹⁸F-FDG PET/CT

The COVID-19 pandemic has had a major impact on health care systems across the globe in a short period of time. There is a growing body of evidence surrounding the findings on hybrid imaging with FDG-PET/CT, and this case highlights the importance of molecular imaging in better understanding of the biomarkers of the disease which ultimately determine the success in building a model to predict the disease severity and monitoring the response to treatment