Local Function Research Articles

Association of precuneus Aβ burden with default mode network function.

It remains unclear whether the local amyloid-beta (Aβ) burden in key regions within the default mode network (DMN) affects network and cognitive functions. Participants included 1002 individuals from the Chinese Preclinical Alzheimer's Disease Study cohort who underwent 18F-florbetapir positron emission tomography resting-state functional magnetic resonance imaging scanning and neuropsychological tests. The correlations between precuneus (PRC) Aβ burden, DMN function, and cognitive function were investigated. In individuals with high PRC Aβ burden, there is a bidirectional relationship between DMN local function or functional connectivity and PRC Aβ deposition across various cognitive states, which is also linked to cognitive function. Even below thePRC Aβ threshold, DMN function remains related to PRC Aβ deposition and cognitive performance. The findings reveal the critical role of PRC Aβ deposition in disrupting neural networks associated with cognitive decline and the necessity of early detection and monitoring of PRC Aβ deposition. Precuneus (PRC) Aβ burden impacts DMN function in different cognitive stages. High PRC Aβ burden is linked to early neural compensation and subsequent dysfunction. Low PRC Aβ burden correlates with neural changes before significant Aβ accumulation. Changes in DMN function and connectivity provide insights into AD progression. Early detection of regional Aβ burden can help monitor the risk of cognitive decline.

The regulatory role of tRNA-derived small RNAs in the prognosis of gastric cancer

In recent years, tRNA-derived small RNAs (tsRNAs) including tRNA-derived stress-induced RNAs (tiRNAs) and tRNA-derived fragments (tRFs), with specific structure and enriched in body fluids, have been found to have specific biological functions. In this paper, the biogenesis, classification, subcellular localization, and biological functions of tsRNAs were summarized. It has been proved that tsRNAs affected tumor cells in proliferation, apoptosis, migration and invasion, and played roles in regulating the occurrence and development of various tumors. In gastric cancer (GC), the imbalance of tsRNAs, such as tRF-33-P4R8YP9LON4VDP, tRF-17-WS7K092, tRF-23-Q99P9P9NDD and others, was closely related to the clinicopathological characteristics of GC patients. Some tsRNAs, such as tRF-23-Q99P9P9NDD, tRF-31-U5YKFN8DYDZDD, and tRF-27-FDXXE6XRK45 promoted the proliferation, migration and invasion of GC cells. Other tsRNAs, such as tRF-41-YDLBRY73W0K5KKOVD, tRF-18-79MP9PO4, and tRF-Glu-TTC-027 inhibited the proliferation, migration and invasion of GC cells. The tsRNAs played roles in the occurrence of GC were through several signaling pathways, such as phosphoinositide 3-kinase (PI3K)-AKT serine/threonine kinase (AKT), Wnt-β-Catenin, and mitogen-activated protein kinase (MAPK) pathways. These findings may provide new strategies for the diagnosis and treatment of GC.

Strong CP problem in the quantum rotor

Recent studies have claimed that the strong CP problem does not occur in QCD, proposing a new order of limits in volume and topological sectors when studying observables on the lattice. In order to shed light on this issue, we study the effect of the topological θ-term on a simple quantum mechanical rotor that allows a lattice description. The topological susceptibility and the θ-dependence of the energy spectrum are both computed using local lattice correlation functions. The sign problem is overcome by considering Taylor expansions in θ, exploiting automatic differentiation methods for Monte Carlo processes. Our findings confirm the conventional wisdom on the strong CP problem. Published by the American Physical Society 2024

Detailed phenotyping of Tbr1-2A-CreER knock-in mice demonstrates significant impacts on TBR1 protein levels and axon development.

Cre recombinase knock-in mouse lines have served as invaluable genetic tools for understanding key developmental processes altered in autism. However, insertion of exogenous DNA into the genome can have unintended effects on local gene regulation or protein function that must be carefully considered. Here, we analyze a recently generated Tbr1-2A-CreER knock-in mouse line, where a 2A-CreER cassette was inserted in-frame before the stop codon of the transcription factor gene Tbr1. Heterozygous TBR1 mutations in humans and mice are known to cause autism or autism-like behavioral phenotypes accompanied by structural brain malformations, most frequently a reduction of the anterior commissure (AC). Thus, it is critical for modified versions of Tbr1 to exhibit true wild-type-like activity. We evaluated the Tbr1-2A-CreER allele for its potential impact on Tbr1 function and complementation to Tbr1 loss-of-function alleles. In mice with one copy of the Tbr1-2A-CreER allele, we identified reduction of TBR1 protein in early postnatal cortex along with thinning of the AC, suggesting hypersensitivity of this structure to TBR1 dosage. Comparing Tbr1-2A-CreER and Tbr1-null mice to Tbr1-null complementation crosses showed reductions of TBR1 dosage ranging from 20% to 100%. Using six combinatorial genotypes, we found that moderate to severe TBR1 reductions (≥44%) were associated with cortical layer 5 expansion, while only the complete absence of TBR1 was associated with reeler-like "inverted" cortical layering. In total, these results strongly support the conclusion that Tbr1-2A-CreER is a hypomorphic allele. We advise caution when interpreting experiments using this allele, considering the sensitivity of various corticogenic processes to TBR1 dosage and the association of heterozygous TBR1 mutations with complex neurodevelopmental disorders.

GDT: Multi-agent reinforcement learning framework based on adaptive grouping dynamic topological space

In many real-world scenarios, tasks involve coordinating multiple agents, such as managing robot clusters, drone swarms, and autonomous vehicles. These tasks are commonly addressed using Multi-Agent Reinforcement Learning (MARL). However, existing MARL algorithms often lack foresight regarding the number and types of agents involved, requiring agents to generalize across various task configurations. This may lead to suboptimal performance due to underestimated action values and the selection of less effective joint policies. To address these challenges, we propose a novel multi-agent deep reinforcement learning framework, called multi-agent reinforcement learning framework based on adaptive grouping dynamic topological space (GDT). GDT utilizes a group mesh topology to interconnect the local action value functions of each agent, enabling effective coordination and knowledge sharing among agents. By computing three different interpretations of action value functions, GDT overcomes monotonicity constraints and derives more effective overall action value functions. Additionally, GDT groups agents with high similarity to facilitate parameter sharing, thereby enhancing knowledge transfer and generalization across different scenarios. Furthermore, GDT introduces a strategy regularization method for optimal exploration of multiple action spaces. This method assigns each agent an independent entropy temperature during exploration, enabling agents to efficiently explore potential actions and approximate total state values. Experimental results demonstrate that our approach, termed GDT, significantly outperforms state-of-the-art algorithms on Google Research Football (GRF) and the StarCraft Multi-Agent Challenge (SMAC). Particularly in SMAC tasks, GDT achieves a success rate of nearly 100% across almost all Hard Map and Super Hard Map scenarios. Additionally, we validate the effectiveness of our algorithm on Non-monotonic Matrix Games.

Theoretical and experimental insights of two surfactants’ effects on SnIn electrodeposited coatings from ethaline

This work uses computational methodologies to investigate the behavior of Sn2+ and In3+ ions in deep eutectic solvent (DES) based on choline chloride and ethylene glycol (1ChCl:2EG, ethaline), under different conditions, such as different temperatures, ion ratios, and in the presence and absence of hexadecyltrimethylammonium bromide (CTAB) and sodium dodecyl sulfate (SDS) surfactants. Furthermore, SnIn alloys were electrodeposited on Cu surfaces to analyze the effect of the investigated different conditions on the surface morphology and chemical composition of the electrodeposited coatings. Morphology and composition were analyzed using Scanning Electron Microscopy (SEM) and Energy Dispersive X-ray (EDS). For computational approach, twelve Molecular Dynamics (MD) simulations were conducted using the GROMACS software. After MD simulations, Quantum Theory of Atoms in Molecules (QTAIM) properties were obtained. The results presented that In3+ showed a stronger affinity for Cl− than Sn2+ but it presents weaker and less stable interactions with OA (OA = oxygen of ethylene glycol-EG), particularly at higher temperatures. Electron Density and Electron Localization Function analysis indicated that In-Cl interactions are stronger and more polarized than other interactions. The Laplacian of Electron Density suggested an intermolecular nature for all interactions. The surfactant addition altered these interactions slightly, with CTAB reducing the density of Cl− around In3+ and SDS positively interacting with In3+ at 297 K and in a Sn2+:In3+ 1:1. As SDS interacted more with In3+ ions, the electrodeposition times varied between 4965 s and 75,541 s. SEM images revealed temperature- and potential-dependent changes in film morphology, changing from globular to smooth. CTAB and SDS additions led to uniform coating in Sn2+: In3+ solutions. For the 1:1 ratio at −1.4 V, there were no significant changes (between 75 and 77 %) in the percentage of Sn deposited with the use of surfactants at 297 K. At 343 K, the surfactant-free coating exhibited 69 % Sn, CTAB facilitated the Sn deposition, increasing the percentage to 92 %. At the same time, the presence of the SDS resulted in 79 % Sn. Similar behavior was obtained for −1.0 V. For 1:4 at −1.0 V at 297 K, 35 % of Sn was observed without surfactant, 29 % with CTAB, and 33 % with SDS. At 343 K, there was an increase in Sn content for 42 %, 49 % and 60 %, indicating the positive influence of the temperature, respectively. At −1.4 V, however, there was no observed influence on the Sn percentages without surfactant. With CTAB (between 29 and 34 %) at 297 and 343 K. At these temperatures, the addition of SDS promoted a higher content of Sn, increasing to 56 % and 43 %, respectively.

Predictions for the abundance and clustering of H α emitting galaxies

ABSTRACT We predict the surface density and clustering bias of H $\alpha$ emitting galaxies for the Euclid and Nancy Grace Roman Space Telescope redshift surveys using a new calibration of the galform galaxy formation model. We generate 3000 galform models to train an ensemble of deep learning algorithms to create an emulator. We then use this emulator in a Markov Chain Monte Carlo (MCMC) parameter search of an eleven-dimensional parameter space, to find a best-fitting model to a calibration data set that includes local luminosity function data, and, for the first time, higher redshift data, namely the number counts of H $\alpha$ emitters. We discover tensions when exploring fits for the observational data when applying a heuristic weighting scheme in the MCMC framework. We find improved fits to the H $\alpha$ number counts while maintaining appropriate predictions for the local universe luminosity function. For a flux limited Euclid-like survey to a depth of $2\times 10^{-16}~\textrm {erg}^{-1}~\textrm {s}^{-1}~\textrm {cm}^{-2}$ for sources in the redshift range $0.9< z< 1.8$, we estimate 2962–4331 H $\alpha$ emission-line sources deg$^{-2}$. For a Nancy Grace Roman survey, with a flux limit of $1\times 10^{-16}~\textrm {erg}^{-1}~\textrm {s}^{-1}~\textrm {cm}^{-2}$ and a redshift range $1.0< z< 2.0$, we predict 6786–10 322 H $\alpha$ emission-line sources deg$^{-2}$.

A level-set method for fast image segmentation based on local pre-fitting and bilateral filtering

PurposeAlthough many conventional level-set approaches can be used for segmenting images containing factors such as noise and intensity inhomogeneities, they still can impact the accuracy of the results seriously. To solve this problem, a level-set method for fast image segmentation based on pre-fitting and bilateral filtering is proposed.Design/methodology/approachFirstly, an improved bilateral filter was investigated for image preprocessing. Secondly, by computing the local average intensity of the preprocessed enhanced picture, two local pre-fitting functions were defined. Thirdly, a new level-set energy functional was defined. Finally, a new distance regularized energy term based on the logarithmic and polynomial functions is proposed to evolve the level-set function in a smooth state.FindingsThe experimental results demonstrate that the proposed model has an excellent segmentation capability for images with noise and intensity inhomogeneities and has different degrees of performance improvement compared with the mainstream models.Originality/value(C1) An improved bilateral filter was investigated and integrated into the model. (C2) Proposing two local pre-fitting functions by computing the local average intensity of the preprocessed enhanced image. (C3) Proposing a new level-set energy functional. (C4) A new distance regularized energy term based on the logarithmic and polynomial functions is proposed to evolve the level set function in a smooth state. (C5) Analyzing and comparing the performance of the proposed model with other similar models.

A Dynamical Density Field That Shows the Localizability of Electrons: The Exchange-Correlation Ehrenfest Force.

A gradual but steady tide in theoretical chemistry is favoring the exploration of atomic and molecular interactions through the dynamical forces perceived and exerted by the particles of a system. By integrating the quantum mechanical force operator over all the spin and all but one of the spatial coordinates of the electrons, the Ehrenfest force density field reveals these forces directly and is separable into a classical term, related to the electric field, and a quantum mechanical correction, which we introduce and analyze for various atoms and molecules in this work. This exchange-correlation Ehrenfest force density field, Fxc(r), excludes the dominant nuclear components that shape the full Ehrenfest field, revealing information about electron sharing, pairing, and delocalization. In a manner similar, though not equal, to the electron localization function, Fxc(r) unveils covalent and core basins. Its divergence, ∇·Fxc(r), indicates the presence of electron shells in atoms and recovers the positions of lone pairs and the shell structure of ionic, polar, and covalent interactions in molecules. It also exhibits a semiquantitative match with the Laplacian of the electron density that we also explore. In alignment with the established role of exchange-correlation as nature's glue, we demonstrate that a significant number of fundamental concepts in chemical bonding can be derived from the Fxc(r) dynamical field.

Machine Learning Model for the Prediction of Hubbard U Parameters and Its Application to Fe-O Systems.

Without incurring additional computational cost, the Hubbard model can prevalently address the electron self-interaction problems of the local or semilocal exchange-correlation functions within density functional theory. However, determining the value of the Hubbard parameter, U, promptly, efficiently, and accurately has been a long-standing challenge. Here, we develop a method for predicting the Hubbard U of iron oxides by establishing a potential relationship through machine learning fitting of structural fingerprints and the U evaluated by the linear response-constrained density functional theory method. This method performs well in calculating the properties of wüstite, hematite, and magnetite, aligning with experimental measurements or more costly hybrid functional results. Using this method, we redefine the convex hulls of the Fe-O system at 0, 50, and 100 GPa; the obtained results are in good agreement with experimental observations. We also provide insights into the debates surrounding the high-pressure phases of Fe2O3 and Fe3O4.

Copper diffusion hindrance in Ti-TM (TM = W, Ru) alloys: A first-principles insight

This study utilizes DFT calculations to assess the effectiveness of Ti-TM (TM = W, Ru) alloys in obstructing copper diffusion, a pivotal factor for semiconductor device reliability. The calculated diffusion barriers for Cu in TiRu and Ti4W12 alloys are found to be 0.48 eV and 0.34 eV, respectively, significantly surpassing the 0.25 eV barrier for Cu diffusion in Si. This comparative analysis highlights the enhanced diffusion resistance of these alloys, with TiRu displaying a barrier nearly twice that of Si. An in-depth examination of the electronic structure and microstructural attributes of the alloys indicates that atomic interactions at the Cu/Ti-TM interface play a crucial role in hindering Cu diffusion. The local density of states, charge density differences, and electron localization functions were scrutinized to clarify the nature of these interactions. The research uncovers that the combined action of Ru, W, Cu, and Ti atoms leads to a synergistic strengthening of the diffusion barrier, implying a robust interatomic repulsion that restricts Cu migration. The findings contribute to a deeper fundamental understanding of Cu diffusion mechanisms in Ti-TM alloys and offer a scientific basis for the judicious selection of materials for diffusion barrier layers. The study supports the further exploration and potential application of TiRu and Ti4W12 alloys in the semiconductor industry.

Local fuzzy rough attribute reduction for large-scale mixed data with limited missing labels based on local fuzzy self information

The advent of the era of big data is accompanied by the generation of large-scale data of various types. Extracting the potential value and rules from such data has always been a challenge. Due to various external and internal factors, it is commonplace for large-scale data to exhibit the phenomenon of missing limited labels. In addressing a large-scale mixed information system with limited label missing (LSMDISLML), local neighborhood rough set model (LNRS-model) is typically employed. However, the identical neighborhood radius is often used by such model when confronted with numerical attributes, which could potentially attenuate the classification capability of the data. Local fuzzy rough set model (LFRS-model) can overcome this point. This paper studies local fuzzy rough attribute reduction for large-scale mixed data with limited missing labels based on LFRS-model via local fuzzy self information and overlap degree function. First, leveraging the statistical distribution of data as a foundation, fuzzy relations on the entire sample set are established, which has the advantage of being able to use different fuzzy similarity radii to calculate similarity, thereby adapting to different data distributions. Subsequently, the samples with missing labels are discarded as they constitute a small proportion of the entire sample set and have little impact on overall performance of dataset. The limited computing resources and storage space are focused on the sample set with complete labels (denoted as target set). Thereafter, based on the target set, local fuzzy λ-upper and lower approximations are defined, and LFRS-model is constructed. This model not only reduces processing time and sources of error in large-scale data but also improves data quality and enhances the reliability of the experimental results. Then, local fuzzy λ-self information is introduced and used to design a local fuzzy rough attribute reduction algorithm in a LSMDISLML. Furthermore, a overlap degree function is introduced to evaluate and reorder the attributes based on their importance, prioritizing the elimination of redundant attributes with high overlap and low importance from the preordered attribute set. This strategy effectively improves the efficiency of obtaining the optimal subset. Finally, a series of experiments are carried out. The experiment results demonstrate that the designed algorithm exhibits excellent performance in classification tasks and outlier detection tasks, surpassing existing four algorithms.

A multi-method study of femtosecond laser modification and ablation of amorphous hydrogenated carbon coatings

We present a study on femtosecond laser treatment of amorphous hydrogen-containing carbon coatings (a-C:H). The coatings were deposited on silicon wafers by a plasma-assisted chemical vapour deposition (PA-CVD), resulting in two different types of material with distinct properties (referred to as “absorbing” and “semi-transparent” coatings in the following). The samples were laser-treated with single fs-laser pulses (800 nm center wavelength, 35 fs pulse duration) in the ablative regime. Through a multi-method approach using topometry, Raman spectroscopy, and spectroscopic imaging ellipsometry, we can identify zones and thresholds of different fluence dependent effects and have access to the local dielectric function. The two coating materials react significantly different upon laser treatment. We determined the (non-ablative) modification threshold fluence for the absorbing coating as 3.6×10-2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$3.6\ imes {10}^{-2}$$\\end{document} Jcm−2 and its ablation threshold as 0.22 Jcm−2. The semi-transparent coating does not show such a low-fluence modification but exhibits a characteristic interference-based intra-film ablation mechanism with two distinguishable ablation thresholds at 0.25 and 0.28 Jcm−2, respectively. The combination of tailored layer materials and correlative imaging spectroscopic methods delivers new insights into the behaviour of materials when treated with ultrashort-pulse laser radiation.Graphical abstract

Fourth-order phase field modelling of brittle fracture with strong form meshless method

This study aims to find a solution for crack propagation in 2D brittle elastic material using the local radial basis function collocation method. The staggered solution of the fourth-order phase field and mechanical model is structured with polyharmonic spline shape functions augmented with polynomials. Two benchmark tests are carried out to assess the performance of the method. First, a non-cracked square plate problem is solved under tensile loading to validate the implementation by comparing the numerical and analytical solutions. The analysis shows that the iterative process converges even with a large loading step, whereas the non-iterative process requires smaller steps for convergence to the analytical solution. In the second case, a single-edge cracked square plate subjected to tensile loading is solved, and the results show a good agreement with the reference solution. The effects of the incremental loading, length scale parameter, and mesh convergence for regular and scattered nodes are demonstrated. This study presents a pioneering attempt to solve the phase field crack propagation using a strong-form meshless method. The results underline the essential role of the represented method for an accurate and efficient solution to crack propagation. It also provides valuable insights for future research towards more sophisticated material models.

A Comprehensive Journey of a Vanillic Aldehyde-Chloroaniline Schiff Base from Solvent-Free Synthesis to Electronic Structure, In Silico and In Vitro Biological Analysis

Schiff bases possess a range of unique physical, chemical and medicinal properties, which contribute to their potency as biological agents. A Schiff base was synthesized, which consisted of 4-chloro aniline and vanillin. The successful formation of the compound was confirmed by their comparable FTIR, UV–Vis, 1HNMR and [Formula: see text]CNMR spectra. The compound was analyzed using X-ray crystallography, which revealed that it crystallizes in the monoclinic system with the space group P21/c. The Hirshfeld surface analysis revealed valuable information about the intermolecular interactions present in the crystal lattice. The most common contacts observed were between hydrogen and hydrogen, as well as carbon and hydrogen. The obtained fluorescence values of 375 nm and 412 nm at excitation wavelength 322 nm strongly indicated the luminescent nature of the compound. The thermal stability of the compound was assessed through thermogravimetric analysis (TGA). DFT was used to optimize the geometry using the B3LYP/cc-pVDZ basis set. The compound exhibited an energy gap of 2.35 electron volts (eV). According to the analysis of molecular electrostatic potential, C and O atoms were found to be electrophilic. According to Mulliken charge analysis, C atoms possess both positive and negative charges, hydrogen atoms exclusively carry positive charges, and Cl, O and N atoms exclusively carry negative charges. Upon analysis of the molecule, the electron localization function discovered that the atoms of carbon, nitrogen and chlorine exhibited delocalized electron density. Also, the localized orbital locator identified the localized orbital positions in the hydrogen atoms. The reduced density gradient (RDG) maps exhibit a wide range of noncovalent interactions. In comparison to the standard amoxicillin, the synthesized compound demonstrated moderate antimicrobial efficacy against the gram-positive bacteria Klebsiella pneumoniae and the fungi Candida albicans. The compound’s concentration-dependent antioxidant and anti-inflammatory properties were demonstrated through in vitro biological evaluation using 2,2-diphenyl-1-picrylhydrazyl and human red blood cell (HRBC) methods. The compound was docked using the proteins 7BYE and 4LEB were chosen from both organisms. The lowest binding attractions obtained were -3.40 kcal/mol for 7BYE and -4.51 kcal/mol for 4LEB. To investigate the stability of the protein–ligand complex, molecular dynamic simulation was employed. The ligands in silico toxicity potentialities were analyzed using seven toxicity models and the results indicated that the ligand is biocompatible.

Quasicrystalline 30° twisted bilayer graphene: fractal patterns and electronic localization properties

The recently synthesized 30° twisted bilayer graphene (30°-TBG) systems are unique quasicrystal systems possessing dodecagonal symmetry with graphene’s relativistic properties. We employ a real-space numerical atomistic framework that respects both the dodecagonal rotational symmetry and the massless Dirac nature of the electrons to describe the local density of states of the system. The approach we employ is very efficiency for systems with very large unit cells and does not rely on periodic boundary conditions. These features allow us to address a broad class of multilayer two-dimensional crystal with incommensurate configurations, particularly TBGs. Our results reveal that the 30°-TBG electronic spectrum consist of extended states together with a set of localized wave functions. The localized states exhibit fractal patterns consistent with the quasicrystal tiling.

Metal-Metal Bonding in Tri-Actinide Clusters: A DFT Study of [An3Cl6]z (z=1-6) and [An3Cl6Cp3]z (z=-2-+3; An=Ac, Th, Pa, U, Np, Pu).

The actinide-actinide bonding in tri-actinide clusters [An₃Cl₆]z (An=Ac-Pu, z=1-6) and [An₃Cl₆Cp₃]z (z=-2-+3; Cp=(η5-C5H5)) is studied using density functional theory. We find 3-centre bonding similar to the tri-thorium cluster [{Th(η⁸-C₈H₈)(μ₃-Cl)₂}₃{K(THF)₂}₂]∞, as we previously reported (Nature 2021, 598, 72-75). The population of 3-centre molecular orbitals (3c-MOs) by zero, one or two electrons correlates with shortening of the An-An bond lengths, which also decrease with increasing actinide atomic number, consistent with the contraction of the actinide valence atomic orbitals. Mulliken analyses indicate that these 3c-MOs predominantly involve An 6d and 5 f orbitals. Various methods evidence the presence of An-An bonding in most systems with populated 3c-MOs, including bond orders (Mayer and Wiberg), quantum theory of atoms in molecules metrics (ρ, ∇2ρ, -G/V, H, delocalization indices), electron localization function, and electron density assessments. Additionally, we explore the effect of Cp ligand substitution on uranium complexes, finding that bulkier Cp ligands can induce U-U bond distortions and result in slightly longer U-U bonds. Overall, this study advances our understanding of metal-metal bonding in tri-actinide clusters, highlighting its effects on geometric and electronic structures.

The Frequency and Characteristics of Brokered Illegal Drug Sales: Reconceptualizing Illegal Drug Markets and Measuring Local Demand

Background Drug policies and interventions require an ongoing understanding of the local demand for illegal drugs. How illegal drugs are distributed to consumers is critical in the demand process. To overcome barriers to purchasing illegal drugs, people utilize their peers to purchase substances for them. These brokered drug sales allow buyers to leverage their peer networks to make purchases. For brokers, this form of exchange allows them to obtain drugs for free. This drug acquisition strategy is underappreciated in understanding how local drug market's function. Methods This paper presents findings from a brief survey on brokering collected from a random sample (N = 241) of active illegal drug injectors recruited from two syringe services programs in Ohio, USA. Results Findings indicate that 30-day and lifetime brokering behavior is common (71% and 90% respectively). Respondents who brokered in the last 30 days (n = 181) purchased drugs for an average of 7 people and from an average of 4 different sellers. Among this group, over 84% indicated that they used drugs with the person they brokered for, 49% reported often or always inflating the price to the buyer, and 27% indicated they removed some drug before returning to the buyer. Conclusion Measuring brokering offers new insight on the dynamics of drug markets, how they operate, and ways to measure their activity. The demand for illegal drugs incorporates the increased use of a drug, changes in the use of different drugs, and/or a novel drug being used. Accessing drugs via brokering accommodates these dynamics. By including brokering in drug market activity, the concept and scope of illegal drug markets transform into networks and not places.

An accelerated decentralized stochastic optimization algorithm with inexact model

This paper considers the decentralized stochastic optimization problems where each node of network has only access to the local large data samples and local functions, which are distributed to the computational nodes. We extend the centralized fast adaptive gradient method with inexact model to deal with the large scale problem in the decentralized manner. Moreover, we propose an accelerated decentralized stochastic optimization algorithm with reconstructing parameter equations and defining new approximate local functions. Further, we provide the convergence analysis of the proposed algorithm and illustrate that our algorithm can achieve both the optimal stochastic oracle complexity and communication complexity that depend on the global condition number. Finally, the numerical experiments validate the convergence results of the proposed algorithm.

Spectral scheme for atomic structure calculations in density functional theory

We present a spectral scheme for atomic structure calculations in pseudopotential Kohn-Sham density functional theory. In particular, after applying an exponential transformation of the radial coordinates, we employ global polynomial interpolation on a Chebyshev grid, with derivative operators approximated using the Chebyshev differentiation matrix, and integrations using Clenshaw-Curtis quadrature. We demonstrate the accuracy and efficiency of the scheme through spin-polarized and unpolarized calculations for representative atoms, while considering local, semilocal, and hybrid exchange-correlation functionals. In particular, we find that O(200) grid points are sufficient to achieve an accuracy of 1 microhartree in the eigenvalues for optimized norm conserving Vanderbilt pseudopotentials spanning the periodic table from atomic number Z=1 to 83.