The two electron molecular bond revisited: from Bohr orbits to two-center orbitals

In this review we first discuss extension of Bohr's 1913 molecular model and show that it corresponds to the large-D limit of a dimensional scaling (D-scaling) analysis, as developed by Herschbach and coworkers. In a separate but synergetic approach to the two-electron problem, we summarize recent advances in constructing analytical models for describing the two-electron bond. The emphasis here is not maximally attainable numerical accuracy, but beyond textbook accuracy as informed by physical insights. We demonstrate how the interplay of the cusp condition, the asymptotic condition, the electron-correlation, configuration interaction, and the exact one electron two-center orbitals, can produce energy results approaching chemical accuracy. Reviews of more traditional calculational approaches, such as Hartree-Fock, are also given. The inclusion of electron correlation via Hylleraas type functions is well known to be important, but difficult to implement for more than two electrons. The use of the D-scaled Bohr model offers the tantalizing possibility of obtaining electron correlation energy in a non-traditional way.Comment: 99 pages, 29 figures, review article, to appear in Advances in Atomic, Molecular and Optical Physic

Determination of aspirin in municipal wastewaters of Nur-Sultan City, Kazakhstan

The presence of aspirin in the municipal wastewater of Nur-Sultan city, Kazakhstan, was studied in this research. Aqueous phase samples were collected before any treatment [1] and in the end of treatment process of Nur-Sultan wastewater treatment plant [2]. The study was conducted from April to December 2021. The concentrations of target compound were measured using high-pressure liquid chromatography (HPLC). The obtained results showed that the concentration of aspirin was generally higher than those reported in the literature. For instance, influent and effluent concentrations of aspirin were equal to 42.8 – 60.4 ppb and 1.4 – 6.5 ppb, respectively (October – December period). The removal of aspirin by wastewater treatment process was equal to 50 - 90.2%. Aspirin was not detected in the spring-summer period of 2021. This could be due to usage of aspirin as a medicine for the treatment and prevention of seasonal flu in the autumn-winter period by the population of the city of Nur-Sultan. Currently, our research team is working on investigation of other potential contaminants of emerging concern in municipal wastewaters of Nur-Sultan city and on treatment methods that could efficiently remove the contaminants of emerging concern

МОНИТОРИНГ ЭКОЛОГИЧЕСКОГО СОСТОЯНИЯ ПОВЕРХНОСТНЫХ ВОД ГОРОДА НУР-СУЛТАН (РЕСПУБЛИКА КАЗАХСТАН) НА ПРИМЕРЕ РЕКИ ЕСИЛЬ : Esil river case

The article presents data on monitoring the ecological state of surface waters in the city of Nur-Sultan (Republic of Kazakhstan) on the example of the Yesil River, taking into account published data. The content of oxygen, anions and cations was studied in two sections of the Yesil River - at the exit from the Vyacheslav reservoir and in the city of NurSultan under the bridge on the Triathlon Park. Samples were analyzed during the year from April 2021 to April 2022. Samples were taken monthly. Anions and cations were analyzed by ion chromatography. A systematic excess of MPC standards for the content of sodium, sulfates and chlorides was established. The content of nitrates and ammonium does not exceed the MPC. The reason is the influence of both agriculture and the utility sector of the city of Nur-Sultan

НҰР СҰЛТАН ҚАЛАСЫНДАҒЫ ЖӘНЕ ОНЫҢ ТӨҢІРІНДЕГІ (ҚАЗАҚСТАН РЕСПУБЛИКАСЫ) ҚАЛЫҚТЫҚ ЖӘНЕ ЖЕТІСТІК СУЛАРЫНДАҒЫ ДӘРІЛІК ЗАТТАРДЫҢ МОНИТОРИНГІ

The article presents the monitoring data of medicinal substances (PM) in the surface waters of the city of Nur-Sultan and its environs (Republic of Kazakhstan). The results show the presence of carbamazepine and sulfamethaxazole in surface waters. Samples were taken monthly from April to December 2021. Analysis of medicinal substances was carried out using the method of high performance liquid chromatography (HPLC). A possible reason for the presence of medicinal substances in surface waters is their entry with wastewater from the public utility sector of the city of Nur-Sultan into surface sources after treatment

Sparticle Spectra and LHC Signatures for Large Volume String Compactifications

We study the supersymmetric particle spectra and LHC collider observables for the large-volume string models with a fundamental scale of 10^{11} GeV that arise in moduli-fixed string compactifications with branes and fluxes. The presence of magnetic fluxes on the brane world volume, required for chirality, perturb the soft terms away from those previously computed in the dilute-flux limit. We use the difference in high-scale gauge couplings to estimate the magnitude of this perturbation and study the potential effects of the magnetic fluxes by generating many random spectra with the soft terms perturbed around the dilute flux limit. Even with a 40% variation in the high-scale soft terms the low-energy spectra take a clear and predictive form. The resulting spectra are broadly similar to those arising on the SPS1a slope, but more degenerate. In their minimal version the models predict the ratios of gaugino masses to be M_1 : M_2 : M_3=(1.5 - 2) : 2 : 6, different to both mSUGRA and mirage mediation. Among the scalars, the squarks tend to be lighter and the sleptons heavier than for comparable mSUGRA models. We generate 10 fb^{-1} of sample LHC data for the random spectra in order to study the range of collider phenomenology that can occur. We perform a detailed mass reconstruction on one example large-volume string model spectrum. 100 fb^{-1} of integrated luminosity is sufficient to discriminate the model from mSUGRA and aspects of the sparticle spectrum can be accurately reconstructed.Comment: 42 pages, 21 figures. Added references and discussion for section 3. Slight changes in the tex

Water-Use Data in the United States: Challenges and Future Directions

In the United States, greater attention has been given to developing water supplies and quantifying available waters than determining who uses water, how much they withdraw and consume, and how and where water use occurs. As water supplies are stressed due to an increasingly variable climate, changing land-use, and growing water needs, greater consideration of the demand side of the water balance equation is essential. Data about the spatial and temporal aspects of water use for different purposes are now critical to long-term water supply planning and resource management. We detail the current state of water-use data, the major stakeholders involved in their collection and applications, and the challenges in obtaining high-quality nationally consistent data applicable to a range of scales and purposes. Opportunities to improve access, use, and sharing of water-use data are outlined. We cast a vision for a world-class national water-use data product that is accessible, timely, and spatially detailed. Our vision will leverage the strengths of existing local, state, and federal agencies to facilitate rapid and informed decision-making, modeling, and science for water resources. To inform future decision-making regarding water supplies and uses, we must coordinate efforts to substantially improve our capacity to collect, model, and disseminate water-use data

Large-Volume Flux Compactifications: Moduli Spectrum and D3/D7 Soft Supersymmetry Breaking

We present an explicit calculation of the spectrum of a general class of string models, corresponding to Calabi-Yau flux compactifications with h_{1,2}>h_{1,1}>1 with leading perturbative and non-perturbative corrections, in which all geometric moduli are stabilised as in hep-th/0502058. The volume is exponentially large, leading to a range of string scales from the Planck mass to the TeV scale, realising for the first time the large extra dimensions scenario in string theory. We provide a general analysis of the relevance of perturbative and non-perturbative effects and the regime of validity of the effective field theory. We compute the spectrum in the moduli sector finding a hierarchy of masses depending on inverse powers of the volume. We also compute soft supersymmetry breaking terms for particles living on D3 and D7 branes. We find a hierarchy of soft terms corresponding to `volume dominated' F-term supersymmetry breaking. F-terms for Kahler moduli dominate both those for dilaton and complex structure moduli and D-terms or other de Sitter lifting terms. This is the first class of string models in which soft supersymmetry breaking terms are computed after fixing all geometric moduli. We outline several possible applications of our results, both for cosmology and phenomenology and point out the differences with the less generic KKLT vacua.Comment: 64 pages, 4 figures; v2. references added; v3. typos, reference added, matches published versio

Fiscal Policy, Private Investment and Economic Growth: Evidence from G-7 Countries

Measuring the effects of fiscal policy on economic growth is difficult, because fiscal policy variables are influenced by changes in income. This paper uses an unbalanced panel data set for G-7 countries for the period 1965-2000 that includes annual estimates of cyclically adjusted government expenditures, capital outlays, income tax revenues, indirect tax revenues, corporate tax revenues and social security tax revenues, based on definitions developed by OECD revenue statistics. The percentage share of these estimates in GDP is used to investigate the effects of fiscal policy on economic growth, and results are compared with regression results that use 5-year averages of cyclically unadjusted variables. The empirical results from both sets of regressions suggest that only taxes on household income and government expenditures have negative effects on per capita income growth. We consolidate our findings by showing that both government expenditures and income taxes have distortionary effects on private investment

Functional differences between human NKp44- and NKp44+ RORC+ innate lymphoid cells

Human RORC+ lymphoid tissue inducer cells are part of a rapidly expanding family of innate lymphoid cells (ILC) that participate in innate and adaptive immune responses as well as in lymphoid tissue (re) modeling. The assessment of a potential role for innate lymphocyte-derived cytokines in human homeostasis and disease is hampered by a poor characterization of RORC+ innate cell subsets and a lack of knowledge on the distribution of these cells in adults. Here we show that functionally distinct subsets of human RORC+ innate lymphoid cells are enriched for secretion of IL-17a or IL-22. Both subsets have an activated phenotype and can be distinguished based on the presence or absence of the natural cytotoxicity receptor NKp44. NKp44+ IL-22 producing cells are present in tonsils while NKp44- IL-17a producing cells are present in fetal developing lymph nodes. Development of human intestinal NKp44+ ILC is a programmed event that is independent of bacterial colonization and these cells colonize the fetal intestine during the first trimester. In the adult intestine, NKp44+ ILC are the main ILC subset producing IL-22. NKp44- ILC remain present throughout adulthood in peripheral non-inflamed lymph nodes as resting, non-cytokine producing cells. However, upon stimulation lymph node ILC can swiftly initiate cytokine transcription suggesting that secondary human lymphoid organs may function as a reservoir for innate lymphoid cells capable of participating in inflammatory responses

A multi-biometric iris recognition system based on a deep learning approach

YesMultimodal biometric systems have been widely applied in many real-world applications due to its ability to deal with a number of significant limitations of unimodal biometric systems, including sensitivity to noise, population coverage, intra-class variability, non-universality, and vulnerability to spoofing. In this paper, an efficient and real-time multimodal biometric system is proposed based on building deep learning representations for images of both the right and left irises of a person, and fusing the results obtained using a ranking-level fusion method. The trained deep learning system proposed is called IrisConvNet whose architecture is based on a combination of Convolutional Neural Network (CNN) and Softmax classifier to extract discriminative features from the input image without any domain knowledge where the input image represents the localized iris region and then classify it into one of N classes. In this work, a discriminative CNN training scheme based on a combination of back-propagation algorithm and mini-batch AdaGrad optimization method is proposed for weights updating and learning rate adaptation, respectively. In addition, other training strategies (e.g., dropout method, data augmentation) are also proposed in order to evaluate different CNN architectures. The performance of the proposed system is tested on three public datasets collected under different conditions: SDUMLA-HMT, CASIA-Iris- V3 Interval and IITD iris databases. The results obtained from the proposed system outperform other state-of-the-art of approaches (e.g., Wavelet transform, Scattering transform, Local Binary Pattern and PCA) by achieving a Rank-1 identification rate of 100% on all the employed databases and a recognition time less than one second per person