Regularity of optimal sets for some functional involving eigenvalues of an operator in divergence form

In this paper we consider minimizers of the functional$\min \{{\lambda }_1(\mathrm{\Omega })+\cdots +{\lambda }_k(\mathrm{\Omega })+\mathrm{\Lambda }|\mathrm{\Omega }|,:\mathrm{\Omega }\subset D\text{ open}\}$ where $D\subset {\mathbb{R}}^d$ is a bounded open set and where $0<{\lambda }_1(\mathrm{\Omega })\le \cdots \le {\lambda }_k(\mathrm{\Omega })$ are the first k eigenvalues on Ω of an operator in divergence form with Dirichlet boundary condition and with Hölder continuous coefficients. We prove that the optimal sets ${\mathrm{\Omega }}^{⁎}$ have finite perimeter and that their free boundary $\partial {\mathrm{\Omega }}^{⁎}\cap D$ is composed of a regular part, which is locally the graph of a $C^{1,\alpha }$-regular function, and a singular part, which is empty if $d<d^{⁎}$, discrete if $d=d^{⁎}$ and of Hausdorff dimension at most $d-d^{⁎}$ if $d>d^{⁎}$, for some $d^{⁎}\in \{5,6,7\}$.     

The impact of insecticides containing deltamethrin and cyfluthrin on the composition of surface compounds in the larvae,females and males of Tenebrio molitor

This paper presents the effect of insecticides on the composition of the surface compounds of one of the most harmful insects, Tenebrio molitor, by analysis using GC–MS. As a result of the use of insecticides, the composition of the chemical compounds on the surface of insects changes, depending on the insecticides used. The most numerous groups of the marked compounds were fatty acids, alkanes, esters and sterols. The content of the identified compounds in the larvae increased at both 24 and 48 h after the application of insecticides, in comparison with the control samples. The content of identified compounds in the samples taken from the females increased 24, 48 and 72 h after the application of insecticides in comparison with the control samples. By contrast, in samples prepared from males, the content of identified compounds decreased 24 h after the application of insecticides, compared with the control samples. The highest content of chemical compounds was for fatty acids and alkanes after the use of insecticides. The content of fatty acids after the application of the insecticide with deltamethrin was 62.1 ± 3.3–466.9 ± 5.9 μg/g, and after the application of the insecticide with cyfluthrin was 49.9 ± 1.9–458.3 ± 4.2 μg/g. However, the content of alkanes after the use of deltamethrin was 115.6 ± 4.2–4672.0 ± 32.1 μg/g, and after the use of cyfluthrin was 189.4 ± 3.8–3975.0 ± 10.2 μg/g.     

Portable single-sided NMR measurements at variable temperatures: Implementation of a thermo-controlled device and application to the heating of bread dough

A temperature control unit was implemented to vary the temperature of samples studied on a commercial Mobile Universal Surface Explorer nuclear magnetic resonance (MOUSE-NMR) apparatus. The device was miniaturized to fit the maximum MOUSE sampling depth (25 mm). It was constituted by a sample holder sandwiched between two heat exchangers placed below and above the sample. Air was chosen as the fluid to control the temperature at the bottom of the sample, at the interface between the NMR probe and the sample holder, in order to gain space. The upper surface of the sample was regulated by the circulation of water inside a second heat exchanger placed above the sample holder. The feasibility of using such a device was demonstrated first on pure water and then on several samples of bread dough with different water contents. For this, T1 relaxation times were measured at various temperatures and depths and were then compared with those acquired with a conventional compact closed-magnet spectrometer. Discussion of results was based on biochemical transformations in bread dough (starch gelatinization and gluten heat denaturation). It was demonstrated that, within a certain water level range, and because of the low magnetic field strength of the MOUSE, a linear relationship could be established between T1 relaxation times and the local temperature in the dough sample.     

Optimization of heterogeneous Catalyst-assisted fatty acid methyl esters biodiesel production from Soybean oil with different Machine learning methods

There is a growing attention to the bio and renewable energies due to fast depletion of fossil fuels as well as the global warming problem. Here, we developed a modeling and simulation method by means of artificial intelligence (AI) for prediction of the bioenergy production from vegetable bean oil. AI methods are well known for prediction of complex and nonlinear process. Three distinct Adaptive Boosted models including Huber regression, LASSO, and Support Vector Regression (SVR) as well as artificial neural network (ANN) were applied in this study to predict actual yield of Fatty acid methyl esters (FAME) production. All boosted utilizing the Adaptive boosting algorithm. The important influencing parameters on the biodiesel production such as the catalyst loading (CAO/Ag, wt%) and methanol to oil (Soybean oil) molar ratio were selected as the input variables of models while the yield of FAME production was selected as output. Model hyper-parameters were tuned to maintain generality while improving prediction accuracy. The models were evaluated using three distinct metrics Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R². Error rates of 8.16780E-01, 4.43895E-01, 2.06692E + 00, and 3.92713 E-01 were obtained with the MAE metric for boosted Huber, SVR, LASSO and ANN models. On the other hand, the RMSE error of these models were about 1.092E-02, 1.015E-02, 2.669E-02, and 1.01174E-02, respectively. Finally, the R-square score were calculated for boosted Huber, boosted SVR, and boosted LASSO as 0.976, 0.990, 0.872, and 0.99702, respectively. Therefore, it can be concluded that although the boosted SVR and ANN models were better models for prediction of process efficiency in terms of error, but all algorithms had high accuracy. The optimum yield of 83.77% and 81.60% for biodiesel production were observed at optimum operating values from boosted SVR and ANN models, respectively.     

Identification of metabolic markers in patients with type 2 Diabetes by Ultrafast gas chromatography coupled to electronic nose. A pilot study

Metabolomics is a potential tool for the discovery of new biomarkers in the early diagnosis of diseases. An ultra-fast gas chromatography system equipped to an electronic nose detector (FGC eNose) was used to identify the metabolomic profile of Volatile Organic Compounds (VOCs) in type 2 diabetes (T2D) urine from Mexican population. A cross-sectional, comparative, and clinical study with translational approach was performed. We recruited twenty T2D patients and twenty-one healthy subjects. Urine samples were taken and analyzed by FGC eNose. Eighty-eight compounds were identified through Kovats's indexes. A natural variation of 30% between the metabolites, expressed by study groups, was observed in Principal Component 1 and 2 with a significant difference (p < 0.001). The model, performed through a Canonical Analysis of Principal coordinated (CAP), allowed a correct classification of 84.6% between healthy and T2D patients, with a 15.4% error. The metabolites 2-propenal, 2-propanol, butane- 2,3-dione and 2-methylpropanal, were increased in patients with T2D, and they were strongly correlated with discrimination between clinically healthy people and T2D patients. This study identified metabolites in urine through FGC eNose that can be used as biomarkers in the identification of T2D patients. However, more studies are needed for its implementation in clinical practice.     

Accurate prediction of norbornadiene cycle enthalpies by DLPNO-CCSD(T1)/CBS method

The DLPNO-CCSD(T₁)/CBS method combined with simple reactions containing small reference species leads to an improvement in the accuracy of theoretically evaluated enthalpies of formation of medium-sized polyalicyclic hydrocarbons when compared with the widely used composite approach. The efficiency of the DLPNO-CCSD(T₁)/CBS method is most vividly demonstrated by comparing with the results of G4 calculations for adamantane. The most important factor in choosing appropriate working reaction is the same number of species on both sides of the equation. Among these reactions, the reactions with small enthalpy change usually provide a better cancellation of errors. The DLPNO-CCSD(T₁)/CBS method was used to calculate the enthalpies of formation of compounds belonging to the norbornadiene cycle (norbornadiene, quadricyclane, norbornene, nortricyclane, and norbornane). The most reliable experimental enthalpies of formation are recommended for these compounds by comparing calculated values with conflicting experimental data.     

15-deoxy-isoxeniolide-A,new diterpenoid from a bornean soft coral,Xenia sp.

A new xenicane diterpenoid, 15-deoxy-isoxeniolide-A (1) along with four known compounds 9-deoxy-isoxeniolide-A (2), isoxeniolide-A (3), xeniolide-A (4) and coraxeniolide-B (5) were isolated from the Bornean soft coral Xenia sp. The structures of these metabolites were elucidated on the basis of spectral analysis, NMR and HRESIMS. Compound 5 showed cytotoxic activity against ATL cell line, S1T.     

On actions of Drinfel'd doubles on finite dimensional algebras

Let q be an nth root of unity for $n>2$ and let $T_n(q)$ be the Taft (Hopf) algebra of dimension $n^2$. In 2001, Susan Montgomery and Hans-Jürgen Schneider classified all non-trivial $T_n(q)$-module algebra structures on an n-dimensional associative algebra A. They further showed that each such module structure extends uniquely to make A a module algebra over the Drinfel'd double of $T_n(q)$. We explore what it is about the Taft algebras that leads to this uniqueness, by examining actions of (the Drinfel'd double of) Hopf algebras H “close” to the Taft algebras on finite-dimensional algebras analogous to A above. Such Hopf algebras H include the Sweedler (Hopf) algebra of dimension 4, bosonizations of quantum linear spaces, and the Frobenius–Lusztig kernel $u_q({\mathfrak{sl}}_2)$.     

Water and oil signal assignment in low-moisture mozzarella as determined by time-domain NMR T2 relaxometry

A time-domain ¹H nuclear magnetic resonance relaxometry method was elaborated for the rapid microstructural characterization of mozzarella cheese. For this purpose, there is a strong need to know how the experimentally determined T₂ relaxation time distribution can be related to specific constituents in mozzarella. In this study, a detailed investigation is offered for fresh and aged low-moisture mozzarella cheese, often applied as a pizza cheese, by application of both a conventional Carr–Purcell–Meiboom–Gill (CPMG) sequence and a free-induction decay CPMG (FID-CPMG) sequence. The relaxation behavior was further elucidated by addition of deuterium oxide and by mild heat treatment of samples. The relaxation times of water protons in mozzarella were found to range from a few microseconds to some tens of milliseconds (in aged mozzarella) or to about hundred milliseconds (in fresh mozzarella). The upper limit of the T₂ distribution can even be extended to the seconds range upon releasing water protons from the mozzarella matrix using a mild heat treatment or upon addition of deuterated water. Both stimuli also provided evidence for the absorption of water into the cheese matrix. The potential release and uptake of water demonstrated that mozzarella acts as a very dynamic system during production and storage. The detected differences in the behavior of the water fraction between fresh and aged low-moisture mozzarella might be utilized to study the influence of either production and/or storage conditions on the cheese ripening process.