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A numerical model describing a magnonic active ring oscillator (MARO) based on a microscopic spin-wave delay line is proposed. The model considers excitation, propagation, and reception of the magnetostatic surface waves in a yttrium iron garnet (YIG) magnetic film with a thickness in the nanometer range. The waves are excited and received with a microscopic coplanar antenna. We employed the model to analyze the influence of the YIG-film thickness and the distance between the antennas on the MARO performance characteristics. We showed that an increase in the delay time inserted by the delay line reduces the phase noise of the MARO and increases the auto-oscillation threshold. In addition, we found a relation between the auto-oscillation threshold, the thickness of the YIG film, and the distance between the antennas. The relation helps design miniature MAROs and suggests a way to reduce the phase noise of the device. The model predicts a phase noise level of −115 dBc/Hz at a 10 kHz offset from an oscillation frequency in the vicinity of 5 GHz for the MARO based on a 100 nm-thick YIG film and 56 μm of distance between the coplanar nano-antennas of the YIG-film based delay line. We believe that this is a clear way forward to microminiaturize the time-delay feedback microwave auto-oscillators. A further reduction in the phase noise down to −125 dBc/Hz at a 10 kHz offset is found in a model of cascaded connection of several microscopic spin-wave delay lines. [ABSTRACT FROM AUTHOR]

Abstract Background Existing software for comparison of species delimitation models do not provide a (true) metric or distance functions between species delimitation models, nor a way to compare these models in terms of relative clustering differences along a lattice of partitions. Results Piikun is a Python package for analyzing and visualizing species delimitation models in an information theoretic framework that, in addition to classic measures of information such as the entropy and mutual information [1], provides for the calculation of the Variation of Information (VI) criterion [2], a true metric or distance function for species delimitation models that is aligned with the lattice of partitions. Conclusions Piikun is available under the MIT license from its public repository ( https://github.com/jeetsukumaran/piikun ), and can be installed locally using the Python package manager ‘pip‘.

Background: Existing software for comparison of species delimitation models do not provide a (true) metric or distance functions between species delimitation models, nor a way to compare these models in terms of relative clustering differences along a lattice of partitions. Results: Piikun is a Python package for analyzing and visualizing species delimitation models in an information theoretic framework that, in addition to classic measures of information such as the entropy and mutual information [1], provides for the calculation of the Variation of Information (VI) criterion [2], a true metric or distance function for species delimitation models that is aligned with the lattice of partitions. Conclusions: Piikun is available under the MIT license from its public repository (https://github.com/jeetsukumaran/piikun), and can be installed locally using the Python package manager 'pip'. [ABSTRACT FROM AUTHOR]

The article deals with the definitions of the distance, divergence, and similarity characteristics between two finite fuzzy measures, which are generalizations of the same definitions between two finite probability distributions. As is known, a fuzzy measure can be uniquely represented by the so-called its associated probability class (APC). The idea of generalization is that new definitions of distance, divergence, and similarity between fuzzy measures are reduced to the definitions of distance, divergence, and similarity between the APCs of fuzzy measures. These definitions are based on the concept of distance generator. The proof of the correctness of generalizations is provided. Constructed distance, similarity, and divergence relations can be used in such applied problems as: determining the difference between Dempster-Shafer belief structures; Constructions of collaborative filtering similarity relations; non-additive and interactive parameters of machine learning in phase space metrics definition, object clustering, classification and other tasks. In this work, a new concept is used in the fuzzy measure identification problem for a certain multi-attribute decision-making (MADM) environment. For this, a conditional optimization problem with one objective function representing the distance, divergence or similarity index is formulated. Numerical examples are discussed and a comparative analysis of the obtained results is presented. [ABSTRACT FROM AUTHOR]

Extended simple regions have been the focus of recent developments in philosophical logic, metaphysics, and philosophy of physics. However, only a handful of works provides a rigorous characterization of an extended simple region. In particular, a recent paper in this journal defends a definition based on an extrinsic notion of least distance. Call it the Least Distance proposal. This paper provides the first assessment of it. It argues that Least Distance faces difficulties and drawbacks. The paper then goes on to suggest a different proposal, the Diameter proposal that is able to handle such drawbacks and difficulties. [ABSTRACT FROM AUTHOR]

Extreme mass ratio inspirals (EMRIs) are one of the primary targets for the recently adopted millihertz gravitational-wave observatory LISA. Some previous studies have argued that a fraction of all EMRIs form in matter-rich environments, and can potentially explain the dozens of soft X-ray band (⁠|$\sim 10^{-1}\,\rm keV$|⁠), low-frequency (⁠|$\sim 0.1$| mHz) periodic phenomena known as quasi-periodic eruptions (QPEs) and quasi-periodic oscillations (QPOs). Here, using a representative EMRI population retrofitted with cutoffs on LISA-band SNRs and luminosity distances to account for the sensitivity of current instruments, we estimate the mean frequency band in which QPEs and QPOs originating from detectable LISA EMRIs may be emitting an X-ray signal 'today' (i.e. in 2024) to be |$0.46 \pm 0.22$| mHz. We also model the well-known QPO source, RE J1034+396, which falls in this frequency band, as an EMRI assuming its primary black hole mass to be |$10^6{-}10^7 \,{\rm M}_\odot$|⁠. Through a prior-predictive analysis, we estimate the orbiting compact object's mass to be |$46^{+ 10}_{-40} \,{\rm M}_\odot$| and the source's LISA-band SNR as |$\approx 14$|⁠ , highlighting it as a candidate multimessenger EMRI target. We also highlight the role of current and near-future X-ray and UV observatories in enabling multimessenger observations of EMRIs in conjunction with LISA, and conclude with a discussion of caveats of the current analysis, such as the exclusion of eccentricity and inclination from the model, and the measurability of subsolar mass compact object EMRIs. [ABSTRACT FROM AUTHOR]

The shortest distance between the first $ n $ iterates of a typical point can be quantified with a log rule for some dynamical systems admitting Gibbs measures. We show this in two settings. For topologically mixing Markov shifts with at most countably infinite alphabet admitting a Gibbs measure with respect to a locally Hölder potential, we prove the asymptotic length of the longest common substring for a typical point converges and the limit depends on the Rényi entropy. For interval maps with a Gibbs-Markov structure, we prove a similar rule relating the correlation dimension of Gibbs measures with the shortest distance between two iterates in the orbit generated by a typical point. [ABSTRACT FROM AUTHOR]

To overcome the defects of large rigidity and the low load of magnetic particles in the magnetic component of an electromagnetic generator, Nd2Fe14B magnetic powder was used as the magnetic material, weft-knitted hollow webbing as the flexible matrix, and waterborne polyurethane as the packaging layer to prepare magnetic yarns, which were then woven into magnetic fabrics. A magnetoelectric generation system and magnetoelectric clothing generator based on magnetic fabric and a conductive coil were prepared, and their electrical output performance was tested. The results show that the load of magnetic particles in magnetic yarn is up to 182.80 mg/cm, and the magnetic intensity of the magnetic fabric reaches 38 mT. The electrical output performance of the magnetoelectric generation system improves with increasing the content of magnetic particles, the number of turns of the conductive coil, the speed of relative movement, and reducing the vertical working distance. The voltage and current generated by the magnetoelectric clothing generator reach 2.42 V and 6.26 mA respectively, and the maximum output power density can reach 439.94 mW/m2. This electromagnetic generator based on magnetic fabric is expected to be applied in the field of wearable self-powered devices. [ABSTRACT FROM AUTHOR]

Standardized indicators of α- and β-diversity (𝒚′𝒌𝒎) calculated by DNA microsatellites (STR) in samples of bulls of Red Scandinavian (RED, n = 29) and Holstein (HOL, n = 45) breeds (each with three sub-samples) were analyzed using one-two- and multivariate statistics methods. The data represented two 11×7 matrices: objects – 11 STR loci (Eth3, Inra23, Tgla227, Tgla126, Tgla122, Sps115, Eth225, Tgla53, Bm2113, Bm1824, Eth10; No.1-11), variables – three indicators of α-diversity (number of alleles and effective alleles per locus, heterozygosity) and four – β-diversity (indices: fixation by Nei, differentiation by Meirmans-Hedrick, Jost and Shannon-Sherwin). ANOVA, using a fixed-type model, revealed a statistically significant (pvalue< 0.02) effect on the variability of 𝒚′𝒌𝒎 factors «breed» (2 %), «locus» (36.7 %) and their interaction (15.6 %). According to the mixed-type model (the «locus» factor as random), only the interaction effect was statistically significant (25.8 %, pvalue< 0.0001). The probability of a noncoincidence between the numbers of a randomly selected pair of loci from the RED and HOL samples was 31 %. The average Euclidean distance between the two samples, calculated by analogical loci, was 37.8±5.35 %. The Mantel correlation between the matrices of paired interlocus distances in RED and in HOL samples was 0.257±0.130 (pvalue = 0.056). The ordinations of loci and their grouping (structuring) in the space of the two main components of the REL sample and the HOL sample differed (Procrust test: m2 = 0.994, m12 = 0.747, pperm = 0.164, 𝒓𝟐𝑷𝒓𝒐𝒄 = 0.253). Estimates of the distance between samples based on the profiles of the α- and β-diversity of loci did not contradict, in general, the genetic distances calculated by allelic frequencies (29–37 %). To analyze the covariance (commonality) of multivariate RED and HOL sample data, a two-block partial least squares (2B-PLS) method was used. The integrated latent variables (LV) maximized the total square of covariance («squared covar» = 14.3 %), in which 83 % accounted for the first LV with max «weights» in terms of α-diversity (aLV). The second LV accounted for 16.7 % with max «weights» in terms of β-diversity (βLV). The linear relationship between RED and HOL samples for aLV was 0.717 (pvalue = 0.013), for βLV – 0.395 (pvalue = 0.229), averaged – 0.56 (pvalue = 0.025). The commonality (co-dispersion) of the two samples for aLV and βLV was estimated at 25,0–32.5 %. 2B-PLS analysis based on reduced data (only for α-diversity) showed a max «squared covar» of 0.393, in which 99.9 % accounted for the first LV (LV1). According to LV1, the linear relationship between RED and HOL samples was estimated at 0.659 (pvalue = 0.0253), the co-dispersion was 43.4 % (according to aLV it was 51.4 %). The ordinations of loci in the coordinate space of the RED and HOL samples for the complete (αLV) and reduced (LV1) datasets had a good match (Procrust test: m2 = 0.0742, m12 = 0.0728, pperm = 0.001, 𝒓𝟐𝑷𝒓𝒐𝒄 = 0.927). In the structure of the inter-sample covariance, «clumps» of loci with a bootstrap probability of [grouping] 50, 75 and 100 % were distinguished. It can be assumed that the RED and HOL samples had some consistency (congruence) in terms of the α-diversity of the loci of the same name. The extension of the «multivariate» approach to descriptive statistics of α-diversity of 7 breeds of dairy cattle and 11 breeds of pigs showed a fairly good correspondence of the results (differentiation index, PCA-ordination) with those obtained using «traditional» methods (pperm of matching ordinations 0.054 and 0.004). The approaches and methods considered expand the possibilities of population-genetic [and breeding-zootechnical] studies in which multidimensional data sets are the norm, not the exception.

Using detailed data on company visits by Chinese mutual funds, we provide direct evidence of mutual fund information acquisition activities and the consequent informational advantages mutual funds establish in local firms. Mutual funds are more likely to visit local and nearby firms both in and outside of their portfolios, but the ease of travel between fund and firm locations can substantially alleviate geographic distance constraints. Company visits by mutual funds are strongly associated with both fund trading activities and fund trading performance. Our results show that geographic constraints and costly information acquisition amplify information asymmetry in financial markets. [ABSTRACT FROM AUTHOR]

The authors investigate how the geographical distance between online users is associated with electronic word-of-mouth (eWOM) effectiveness. Their research leverages variation in the visibility of eWOM messages on the social media platform of Twitter to address the issue of correlated user behaviors and preferences. The study shows that the likelihood that followers who are exposed to users' WOM subsequently make purchases increases with followers' geographic proximity to the users. The authors propose social identification as a potential mechanism for why geographical distance still matters online in eWOM: because consumers may form a sense of social identity based on their physical location, information regarding the spatial proximity of users could trigger online social identification with others. The findings are robust to alternative methods and specifications, such as further controlling for latent user homophily by incorporating user characteristics and embeddings based on advanced machine-learning and deep-learning models and a corpus of 140 million messages. The authors also rule out several alternative explanations. The findings have important implications for platform design, content curation, and seeding and targeting strategies. [ABSTRACT FROM AUTHOR]

Absolute distance measurements based on optical frequency combs (OFCs) have greatly promoted advances in both science and technology, owing to the high precision, large non-ambiguity range (NAR), and a high update rate. However, cyclic error, which is extremely difficult to eliminate, reduces the linearity of measurement results. In this study, we quantitatively investigated the impact of cyclic error on absolute distance measurement using OFCs based on two types of interferometry: synthetic wavelength interferometry and single-wavelength interferometry. The numerical calculations indicate that selecting a suitable reference path length can minimize the impact of cyclic error when combining the two types of interferometry. Recommendations for selecting an appropriate synthetic wavelength to address the tradeoff between achieving a large NAR and minimizing the risk of failure when combining the two methods are provided. The results of this study are applicable not only in absolute distance measurements but also in other applications based on OFCs, such as surface profile, vibration analysis, etc. [ABSTRACT FROM AUTHOR]

Copyright of GeSec: Revista de Gestao e Secretariado is the property of Sindicato das Secretarias e Secretarios do Estado de Sao Paulo (SINSESP) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In approximate query searching (AQS), the given query point ( q ¯ ′ ) can be seen as a noise ( η ¯ ) corrupted version of one of the points ( q ¯ ) in the existing database X , i.e., q ¯ ′ = q ¯ + η ¯ . Thus deciding on an appropriate distance d that would return the correct match ( q ¯ ) entails that the chosen distance should be aware of the type of distribution of the noise. In this work, we study the suitability of Minkowski-type distances in AQS when the q ¯ is afflicted by both white and coloured noises to different extent. To this end, we employ a simple similarity search based scoring algorithm proposed in François et al. (ESANN 2005, 13th European Symposium on Artificial Neural Networks, Bruges, Belgium, April 27–29, 2005, Proceedings, pp 339–344, 2005). Our study reveals an interesting interplay of the following 3D's in the quest for an appropriate distance: Dimensionality and Domain geometry of the data and the type of noise Distribution and has led us to explore this problem from a basic geometric perspective. Our main contribution herein is the proposal of a novel index called the Relative Contained Volume (RCV) that helps explain the performance of the considered distances. [ABSTRACT FROM AUTHOR]

In this paper, we study the distribution of parallelograms and rhombi in a given set in the plane over arbitrary finite fields F q 2. As an application, we improve the recent result due to Fitzpatrick, Iosevich, McDonald, and Wyman on the Vapnik–Chervonenkis dimension of the induced distance graph. [ABSTRACT FROM AUTHOR]

A study to determine the detection efficiency and location accuracy of the Worldwide Lightning Location Network (WWLLN) over Spain is presented by comparing data with those of the Meteorological Spanish Agency (AEMET), taken as a ground truth. The WWLLN operates a planetary distributed network of stations which detect lightning signals at a planetary scale. Very high currents from lightning strokes radiate strong Very Low Frequency (VLF) signals in the band 6–22 kHz, which are detected up to 10,000 km by the WWLLN stations. Two WWLLN stations operate in the Iberian Peninsula since 2012, which are supported by other stations at distances below 4000 km. The stations in the Iberian Peninsula are at a distance of around 800 km. This is a short distance in comparison with the typical distance between WWLLN stations in other areas, which is around 5.000–15.000 km. The WWLLN stations locate the time and position of the lightning stroke detected by triangulation, similarly as Global Positioning Systems do. Distances to each station are obtained by means of the time of arrival of the signal to the corresponding stations. A lightning detection is considered as a valid one when at least five stations detect it with a time and space coincidence with AEMET data of 0.5 s and 20 km, respectively. A study of the WWLLN performance for the whole area of Spain is carried out, obtaining that the detection efficiency of WWLLN is around 38 % with a location accuracy between 2 and 3 km. The efficiency for high energy strokes is considerable higher. The results obtained for Spain are better than those obtained in previous studies in other areas of the World, which may be caused by the high density of stations in the Spanish region and its surroundings. A study for two reduced regions with different geographic features is also considered to assess the possible influence of the different typology of storms on the network features. Finally, an application of the WWLLN data for three major storms in 2020, 2021 and 2022 in the Mediterranean area of Spain demonstrates that the WWLLN is well suited for tracking the time evolution of adverse meteorological phenomena. [ABSTRACT FROM AUTHOR]

In the post-epidemic era, the restart of China's inbound tourism is imminent. However, there are gaps in our current understanding of how distance perception dynamically affects inbound tourism in China. In order to understand the past patterns of inbound tourism in China, we mapped the data of 61 countries of origin from 2004 to 2018 into a dynamic expanding gravity model to understand the effects of cultural distance, institutional distance, geographical distance, and economic distance on inbound tourism in China and revealed the dynamic interaction mechanism of non-economic distance perception on inbound tourism in China. Our research results show that cultural distance has a positive impact on China's inbound tourism, while institutional distance has a negative impact. The significant finding is that the dynamic interaction of the above two kinds of perceived distance can still have a positive impact on China's inbound tourism. Its practical significance is that it can counteract the influence of institutional distance by strengthening the cultural distance. Generally speaking, geographical distance and institutional distance restrict China's inbound tourism flow, while cultural distance, economic distance, and interactive perceptual distance promote China's inbound tourism flow. [ABSTRACT FROM AUTHOR]

The tip of the red giant branch (TRGB) based distance method in the I band is one of the most efficient and precise techniques for measuring distances to nearby galaxies (D ≲ 15 Mpc). The TRGB in the near-infrared (NIR) is 1–2 mag brighter relative to the I band, and has the potential to expand the range over which distance measurements to nearby galaxies are feasible. Using Hubble Space Telescope (HST) imaging of 12 fields in eight nearby galaxies, we determine color-based corrections and zero-points of the TRGB in the Wide Field Camera 3 IR (WFC3/IR) F110W and F160W filters. First, we measure TRGB distances in the I band equivalent Advanced Camera System (ACS) F814W filter from resolved stellar populations with the HST. The TRGB in the ACS F814W filter is used for our distance anchor and to place the WFC3/IR magnitudes on an absolute scale. We then determine the color dependence (a proxy for metallicity/age) and zero-point of the NIR TRGB from photometry of WFC3/IR fields that overlap with the ACS fields. The new calibration is accurate to ∼1% in distance relative to the F814W TRGB. Validating the accuracy of the calibrations, we find that the distance modulus for each field using the NIR TRGB calibration agrees with the distance modulus of the same field as determined from the F814W TRGB. This is a JWST preparatory program, and the work done here will directly inform our approach to calibrating the TRGB in JWST NIRCam and NIRISS photometric filters. [ABSTRACT FROM AUTHOR]

In this article, using the formulas derived in (Nenkov 2019) and (Nenkov 2022) for the distances from some remarkable points in the quadrilateral to its vertices and to the midpoints of its sides and diagonals, formulas for the distances from these remarkable points to the intersection of the diagonals and to the intersections of the pairs of opposite sides were obtained. Formulas are also derived for the distances from another remarkable point to the vertices of the quadrilateral, as well as to the remarkable points discussed in (Nenkov 2019). Using the obtained formulas for these distances, a number of interesting inequalities in the quadrilateral are proved. [ABSTRACT FROM AUTHOR]

Lead telluride nanostructures were obtained on silicon substrates by thermal evaporation in vacuum. Growth occurred at three different distances between the evaporation source and the substrate. The distances between the evaporator and the evaporation source were 5 cm; 7.5 cm and 10 cm. Structural characteristics were studied using XRD, SEM, EDX, AFM analyses. These methods provided information about the crystal structure, morphology, microstructure and elemental composition of the material. X-ray diffraction analysis showed that thin films of lead telluride obtained by thermal evaporation in vacuum have a cubic crystal structure. This experimental work was carried out to determine the effect of distance on the structure of lead telluride (PbTe). During the experiment, the optimal modes for the formation of lead telluride (PbTe) nanostructures were determined, which was equal to d = 10 cm. It was found that lead telluride (PbTe) nanostructures are formed at this distance. [ABSTRACT FROM AUTHOR]

Secure hamming distance computation is an important primitive of secure multiparty computation, enabling two parties to jointly compute the hamming distance without revealing any private information of each party. Secure hamming distance computation has a wide application in private similarity determination fields, such as secure biometric identification and error detection. In this paper, we present a quantum protocol for secure hamming distance computation based on single photons and local unitary operations, ensuring easy implementation with present technologies. Our protocol guarantees fairness between the two parties and provides higher security compared to classical protocols, leveraging the physical principles of quantum mechanics. [ABSTRACT FROM AUTHOR]

Distance and direction play crucial roles in modeling the spatial nonstationarity relationship. Because Euclidean distance ignores the effect of direction, several modified geographically weighted regression (GWR) attempts have been made to model anisotropic relationships using various non-Euclidean distance metrics. These methods, however, adopt uniform parameters to define the non-Euclidean metrics over the whole study area, neglecting the varying numerical features existing in different regions. As a result, they fail to accurately depict spatial anisotropic relationships between variables. To address this issue, we propose a novel method called gradient-based geographically weighted regression (GGWR) that integrates the gradient of spatial relationships into GWR. Additionally, we introduce an l0-norm regularization technique to achieve the parameter estimation of GGWR. Both simulated and actual data sets were used to validate the proposed method, and the experimental results demonstrate that the gradient field of the spatial relationship obtained by GGWR can effectively characterize the direction and intensity of variable relationships at various locations. Moreover, GGWR outperforms other models, including GWR, directional geographically weighted regression, and Minkowski distance-based geographically weighted regression, in terms of fitting accuracy, coefficient estimation accuracy, and interpretation of coefficient symbols. These findings indicate that the GGWR can be a valuable tool for modeling spatial anisotropic relationships by leveraging the spatial relationship gradient field. [ABSTRACT FROM AUTHOR]

In this paper, according to the element discretization characteristic of the geometric model, a complex function theory based transformation boundary element method is proposed to perform the steady-state thermal analysis of plate structures. The numerical implementation of the proposed method can be attributed to a numerical integration problem in element analysis, which is mainly divided into the following two steps: firstly, a bidirectional transformation method based on distance function and complex function is proposed to remove the weak singularities of the fundamental solution in integral equation, which is suitable for the weakly singular integrals of narrow strip discrete elements; secondly, a dual sinh method based on complex poles is developed to remove the influence of the nearly singular integrals in the analysis of the plate structures. With this method, the numerical integration in the integral equation can be accurately evaluated, which ensures the accurate calculation of the thermal physical variables of the plate structures. Several numerical examples are presented and discussed to prove the accuracy and versatility of the presented method. The results show that the presented method has the superiority of dealing with plate structure problems. [ABSTRACT FROM AUTHOR]

Evidence is analyzed that indicates that the material universe, in which the Earth and Milky Way Galaxy reside, does not have a high speed net expansion or translational velocity. The velocity of planet Earth in the solar system is used to correct data carefully obtained by Albert Michelson from 1926 to 1933 for the speed of light measured directly from c = d/t for round trips of travel in differing directions on Earth. When light source and target move together in lateral motion with respect to Earth orbit velocity, synchronous aberration of light is incorporated to correct the computations. All data are then in close agreement with the intrinsic speed that light must have from its own instantaneous position in space, derived by James Maxwell. The relative velocity between a light front and a moving target depends on the velocity of the target and causes light to travel a different distance than the simple ground distance between source and target, all while light speed remains fixed at c. The close agreement of observed speed with true light speed in all cases indicates that the difference between total velocity of the Earth and its solar orbit velocity is not relatively large. It is possible that the universe of matter may exist in a rotating dynamic equilibrium state in which regional irregularity exists with overall order. • The direct measurements of light speed by Michelson are corrected for earth orbit velocity. • All light speed measured values agree to high accuracy with the Maxwell derived formula. • The total velocity of the Earth in space is not consistent with rapid motion. • The unjverse of galaxies including the Milky Way may not be expanding from a central location. • The correlation of red shifts with star distances are explained by mechanisms other than Doppler motion. [ABSTRACT FROM AUTHOR]

Copyright of Tikrit Journal of Engineering Sciences is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Ultraviolet-C (UV-C) germicidal light can effectively inactivate airborne pathogens and mitigate the transmission of infectious diseases. As the application of UV-C for disinfection gains popularity, practical estimation of UV irradiance is essential in determining the UV fluence (dose) and designing tubular UV lamp configurations for indoor air treatment. It is generally understood that the inverse square (~1/d²) law (i.e., irradiance is proportional to the inverse square of the distance) applies well to point light sources. However, there has been a recognition that the ~1/d² law does not work well for tubular light sources in the commonly defined near-field applications where the UV source is relatively close to the treated air. Therefore, practical near-field irradiation estimation is needed for designing portable air cleaners and heating, ventilation, and air conditioning (HVAC) ducts with built-in UV light bulbs. This research investigated UV-C light irradiance from tubular (L = 0.9 m) light bulbs at near distances inside an air cleaner prototype duct under three power output (1-, 4-, and 8-bulb) scenarios and conducted theoretical estimation based on a line-source irradiation model. Similarly sized visible fluorescent bulbs were used as a reference. The data were fitted on both ~1/d² and ~1/d correlation of irradiance with distance. Both measured and line source estimated data fit better (i.e., evaluated by R-square, standard errors, root mean squared errors) with the ~1/d than the ~1/d² relationship in the near distance. Although the differences between the measured and the modeled were observed, the pattern of light distribution generally follows an inverse relationship (~1/d) with distances (d) shorter than two tubular bulb lengths (d < 2L). The pattern applies to both UV and visible light tested in this study. It is recommended that the inverse (~1/d) correlation be used for near-distance estimation of light distribution, especially for disinfection purposes in air ducting for indoor air quality improvement and airborne disease mitigation. [ABSTRACT FROM AUTHOR]

The giant spiral galaxy M101 is host to the nearest recent Type Ia supernova (SN 2011fe) and thus has been extensively monitored in the near-infrared to study the late-time light curve of the SN. Leveraging this existing baseline of observations, we derive the first Mira-based distance to M101 by discovering and classifying a sample of 211 Miras with periods ranging from 240–400 days in the SN field. Combined with new Hubble Space Telescope (HST) WFC3/IR channel observations, our data set totals 11 epochs of F110W (HST YJ) and 13 epochs of F160W (HST H) data spanning ∼2900 days. We adopt absolute calibrations of the Mira period–luminosity relation based on geometric distances to the Large Magellanic Cloud and the water megamaser host galaxy NGC 4258, and find μ M101 = 29.10 ± 0.06 mag. This distance is in 1 σ agreement with most other recent Cepheid and tip of the red giant branch distance measurements to M101. Including the previous Mira SN Ia host, NGC 1559, and SN 2005df, we determine the fiducial SN Ia peak luminosity, M B 0 = − 19.27 ± 0.09 mag. With the Hubble diagram of supernovae Ia, we derive H 0 = 72.37 ± 2.97 km s−1 Mpc−1, a 4.1% measurement of H 0 using Miras. We find excellent agreement with recent Cepheid distance ladder measurements of H 0 and confirm previous indications that the local universe value of H 0 is higher than the early universe value at ∼95% confidence. Currently, the Mira-based H 0 measurement is still dominated by the statistical uncertainty in the SN Ia peak magnitude. [ABSTRACT FROM AUTHOR]

The giant spiral galaxy M101 is host to the nearest recent Type Ia supernova (SN 2011fe) and thus has been extensively monitored in the near-infrared to study the late-time light curve of the SN. Leveraging this existing baseline of observations, we derive the first Mira-based distance to M101 by discovering and classifying a sample of 211 Miras with periods ranging from 240–400 days in the SN field. Combined with new Hubble Space Telescope (HST) WFC3/IR channel observations, our data set totals 11 epochs of F110W (HST YJ) and 13 epochs of F160W (HST H) data spanning ∼2900 days. We adopt absolute calibrations of the Mira period–luminosity relation based on geometric distances to the Large Magellanic Cloud and the water megamaser host galaxy NGC 4258, and find μ M101 = 29.10 ± 0.06 mag. This distance is in 1 σ agreement with most other recent Cepheid and tip of the red giant branch distance measurements to M101. Including the previous Mira SN Ia host, NGC 1559, and SN 2005df, we determine the fiducial SN Ia peak luminosity, M B 0 = − 19.27 ± 0.09 mag. With the Hubble diagram of supernovae Ia, we derive H 0 = 72.37 ± 2.97 km s−1 Mpc−1, a 4.1% measurement of H 0 using Miras. We find excellent agreement with recent Cepheid distance ladder measurements of H 0 and confirm previous indications that the local universe value of H 0 is higher than the early universe value at ∼95% confidence. Currently, the Mira-based H 0 measurement is still dominated by the statistical uncertainty in the SN Ia peak magnitude. [ABSTRACT FROM AUTHOR]

We provide state-dependent error bounds for strongly continuous unitary representations of connected Lie groups. That is, we bound the difference of two unitaries applied to a state in terms of the energy with respect to a reference Hamiltonian associated with the representation and a left-invariant metric distance on the group. Our method works for any connected Lie group, and the metric is independent of the chosen representation. The approach also applies to projective representations and allows us to provide bounds on the energy-constrained diamond norm distance of any suitably continuous channel representation of the group. [ABSTRACT FROM AUTHOR]

This study examines the impact of urban management on the efficiency of regional innovation in China's manufacturing industry, with a specific focus on modern sustainable energy. The research takes into consideration the regional diversity in innovation development across China's three major regions and integrates common Frontier theory with the MSBM model. By employing a non-radial distance function approach, this study develops the MMSBMP model, incorporating various improved methods proposed by researchers. The Luenberger index methodology is utilized to assess the innovation efficiency of the national manufacturing industry from 2017 to 2021, enabling the identification of efficiency losses. The findings highlight significant disparities and opportunities for enhancing innovation efficiency across the three major regions, both at the common Frontier and the group Frontier. However, caution is advised due to the potential overestimation of regional Frontier efficiency values resulting from variations in technical reference datasets. Analysis of the Total Growth Rate (TGR) values reveals distinct development characteristics among the regions, with the eastern region exhibiting smaller extremes and the central and western regions displaying larger extremes. This comprehensive examination of China's manufacturing industry emphasizes the influence of urban management. By investigating the consequences of urban management practices, this research provides insights into the relationship between urban development strategies and the performance, efficiency, and sustainable growth of the manufacturing industry. The study highlights the significance of urban management in shaping regional innovation efficiency and emphasizes the continual improvement of the industry's innovation performance in the context of modern sustainable energy. [ABSTRACT FROM AUTHOR]

Recent puzzling observations, such as the H 0 tension, large-scale anisotropies, and massive disk galaxies at high redshifts, have been challenging the standard cosmological model. While one possible explanation is that the standard model is incomplete, other theories are based on the contention that the redshift model as a distance indicator might be biased. These theories can explain the recent observations, but they are challenged by the absence of a direct empirical reproducible observation that the redshift model can indeed be inconsistent. Here, I describe a simple experiment that shows that the spectra of galaxies depend on their rotational velocity relative to the rotational velocity of the Milky Way. Moreover, it shows that the redshift of galaxies that rotate in the opposite direction relative to the Milky Way is significantly smaller compared with the redshift of galaxies that rotate in the same direction relative to the Milky Way (p < 0.006). Three different datasets were used independently, each one was prepared in a different manner, and all of them showed similar redshift bias. A fourth dataset of galaxies from the Southern Galactic pole was also analyzed and shows similar results. All four datasets are publicly available. While a maximum average z difference of ∼0.012 observed with galaxies of relatively low redshift (z < 0.25) is not extreme, the bias is consistent and canpotentially lead to explanations to puzzling observations such as the H 0 tension. [ABSTRACT FROM AUTHOR]

One of the crucial steps in the multi-criteria decision analysis involves establishing the importance of criteria and determining the relationship between them. This paper proposes an extended Hellwig's method (H_EM) that utilizes entropy-based weights and Mahalanobis distance to address this issue. By incorporating the concept of entropy, weights are determined based on their information content represented by the matrix data. The Mahalanobis distance is employed to address interdependencies among criteria, contributing to the improved performance of the proposed framework. To illustrate the relevance and effectiveness of the extended H_EM method, this study utilizes it to assess the progress toward achieving Sustainable Development Goal 4 of the 2030 Agenda within the European Union countries for education in the year 2021. Performance comparison is conducted between results obtained by the extended Hellwig's method and its other variants. The results reveal a significant impact on the ranking of the EU countries in the education area, depending on the choice of distance measure (Euclidean or Mahalanobis) and the system of weights (equal or entropy-based). Overall, this study highlights the potential of the proposed method in addressing complex decision-making scenarios with interdependent criteria. [ABSTRACT FROM AUTHOR]

Motivation Protein structure comparison is pivotal for deriving homological relationships, elucidating protein functions, and understanding evolutionary developments. The burgeoning field of in-silico protein structure prediction now yields billions of models with near-experimental accuracy, necessitating sophisticated tools for discerning structural similarities among proteins, particularly when sequence similarity is limited. Results In this article, we have developed the align distance matrix with scale (ADAMS) pipeline, which synergizes the distance matrix alignment method with the scale-invariant feature transform algorithm, streamlining protein structure comparison on a proteomic scale. Utilizing a computer vision-centric strategy for contrasting disparate distance matrices, ADAMS adeptly alleviates challenges associated with proteins characterized by a high degree of structural flexibility. Our findings indicate that ADAMS achieves a level of performance and accuracy on par with Foldseek, while maintaining similar speed. Crucially, ADAMS overcomes certain limitations of Foldseek in handling structurally flexible proteins, establishing it as an efficacious tool for in-depth protein structure analysis with heightened accuracy. Availability ADAMS can be download and used as a python package from Python Package Index (PyPI): adams · PyPI. Source code and other materials are available from young55775/ADAMS-developing (github.com). An online server is available: Bseek Search Server (cryonet.ai) [ABSTRACT FROM AUTHOR]

This paper presents, for the first time, a distance metric learning algorithm for monotonic classification. Monotonic datasets arise in many real-world applications, where there exist order relations in the input and output variables, and the outputs corresponding to ordered pairs of inputs are also expected to be ordered. Monotonic classification can be addressed through several distance-based classifiers that are able to respect the monotonicity constraints of the data. The performance of distance-based classifiers can be improved with the use of distance metric learning algorithms, which are able to find the distances that best represent the similarities among each pair of data samples. However, learning a distance for monotonic data has an additional drawback: the learned distance may negatively impact the monotonic constraints of the data. In our work, we propose a new model for learning distances that does not corrupt these constraints. This methodology will also be useful in identifying and discarding non-monotonic pairs of samples that may be present in the data due to noise. The experimental analysis conducted, supported by a Bayesian statistical testing, demonstrates that the distances obtained by the proposed method can enhance the performance of several distance-based classifiers in monotonic problems. [ABSTRACT FROM AUTHOR]

The process of sorting or categorizing objects or information about these objects into clusters according to certain criteria is a fundamental procedure in data analysis. Where it is feasible to determine the distance metric for any pair of objects, the significance and reliability of the separation can be evaluated by calculating the separation/segregation power (SP) index proposed herein. The latter index is the ratio of the average inter distance to the average intra distance, independent of the scale parameter. Here, the calculated SP value is compared to its statistical distribution obtained by a simulation study for a given partition under the homogeneity null hypothesis to draw a conclusion using standard statistical procedures. The proposed concept is illustrated using three examples representing different types of objects under study. Some general considerations are given regarding the nature of the SP distribution under the null hypothesis and its dependence on the number of divisions and the amount of data within them. A detailed modus operandi (working method) for analyzing a metric data partition is also offered. [ABSTRACT FROM AUTHOR]

With the large-scale integration of distributed renewable energy into low-voltage distribution station areas, rapidly growing services put forward new requirements on transparent monitoring. In order to accurately and objectively quantify the transparency of various distribution station areas and measure the impact of different dimensional indexes on the transparency evaluation of distribution station areas, this paper proposes a confidence relative offtarget distance-based multi-dimensional transparency evaluation method. First, a multi-dimensional transparency evaluation index system with electrical and communication integration is constructed. Second, an improved gray target model combining both positive and negative target vectors is established to realize bi-directional quantitative analysis. Then, the relative off-target distance is calculated based on the endowment coefficient by leveraging both subjective and objective weights. Finally, the confidence of the relative off-target distance is calculated based on fuzzy entropy to improve the reliability of transparency evaluation. The simulation results show that this method can effectively distinguish the transparency gap between different distribution station areas, identify the single dimensional index with the greatest contribution or potential, and verify the effectiveness of the proposed method applied to the transparency evaluation of distribution station areas. [ABSTRACT FROM AUTHOR]

The Kalman filter algorithm is very important as a recursive algorithm method to optimize sensor output from physical parameter measurement systems, especially physics practicum demonstration systems. One of the distance parameter measurement demonstration systems used in Hooke’s law demonstration system is applied in physics practicum, the system has problems related to fluctuating or unstable sensor output. This research implements the Kalman filter algorithm on the Arduino IDE sketch to reduce noise that appears at the ultrasonic sensor output. The methodology used in this study includes the application of the Kalman filter algorithm to the Arduino IDE sketch with the variable value of the Kalman filter algorithm equation modified with a value of R=10, H=1, and Q=1, and returns the filtered Kalman out value. The Arduino output results are exported to Ms. Excel for further analysis and generate a filtered ultrasonic sensor output signal graph compared without using the Kalman filter. The ultrasonic sensor output noise filtration effectively reduces noise by showing a decrease in the mean squared error (MSE) value and obtaining the best performance of up to 89.23 %. The accuracy of Kalman filter filtration results can be seen from the calculation that the spring constant of filtered metal materials is smaller than the conventional measurement spring constant. Accurate and effective results with the implementation of the Kalman filter algorithm can be developed for the variation values of distance parameters and Kalman filter algorithm variables (R, Q, and H) with other value variations, especially variables that produce filtering curves close to straight lines. It was concluded that the Kalman filter algorithm was able to improve the performance of Hooke’s law prop system. [ABSTRACT FROM AUTHOR]

Near infra-red spectroscopy (NIRS) has been suggested as a rapid, cost-effective, and accurate diagnostic tool for leaf nutrient analysis that could replace more traditional laboratory diagnostics. To ease operational workflows, there would advantage in estimating nutrients using a single method, namely NIRS. This study evaluated the potential of NIRS as a diagnostic method for the measurement of key macro and micronutrients in sugarcane leaf samples. Three hundred and fifty-one sugarcane leaf samples used in quality control reference analysis in Fertiliser Advisory Service (FAS) were used for model calibration. About 35% of the samples were from growers within South Africa, while the remainder were from estates across southern and eastern Africa. Dried and milled leaf material was scanned on a Bruker MPA-NIRS instrument, and spectral pre-processing was performed. Support vector machine regression (SVMR) and partial least squares regression (PLSR) were used for calibrating the estimation models with the test validation(Tval) procedure. The results showed both the PLSR and SVMR model resulted in the best calibration for (K, Ca, Mg)(R2 >87% and the ratio of performance to inter-quartile distance(RPIQ) > 2.1). With a test validation dataset, the SVMR yielded relatively high accuracy(R2 >85% and the RPIQ > 2.5, while PLSR yielded poor accuracy. The SVMR also outperforms Zn(R2 = 0.86; and RPIQ = 2.5) for model calibration. NIR Spectrometry combined with the SVMR technique resulted in a practical option to accurately measure leaf nutrient concentrations and evaluate sugarcane mineral contents accurately. These results indicate that NIRS can replace the current analytical methods used in FAS. [ABSTRACT FROM AUTHOR]

Combining gravity with quantum theory is still a work in progress. On the one hand, classical gravity is the geometry of space-time determined by the energy–momentum tensor of matter and the resulting nonlinear equations; on the other hand, the mathematical description of a quantum system is Hilbert space with linear equations describing evolution. In this paper, various measures in Hilbert space will be presented. In general, distance measures in Hilbert space can be divided into measures determined by energy and measures determined by entropy. Entropy measures determine quasi-distance because they do not satisfy all the axioms defining distance. Finding a general rule to determine such a measure unambiguously seems to be fundamental. [ABSTRACT FROM AUTHOR]

Hellwig's method is a multi-criteria decision-making technique designed to facilitate the ranking of alternatives based on their proximity to the ideal solution. Typically, this approach calculates distances using the Euclidean norm, assuming implicitly that the considered criteria are independent. However, in real-world situations, the assumption of criteria independence is rarely met. The paper aims to propose an extension of Hellwig's method by incorporating the Mahalanobis distance. Substituting the Euclidean distance with the Mahalanobis distance has proven to be effective in handling correlations among criteria, especially in the context of asymmetrical relationships between criteria. Subsequently, we investigate the impact of the Euclidean and Mahalanobis distance measures on the several variants of Hellwig procedures, analyzing examples based on various illustrative data with 10 alternatives and 4 criteria. Additionally, we examine the influence of three normalization formulas in Hellwig's aggregation procedures. The investigation results indicate that both the distance measure and normalization formulas have some impact on the final rankings. The evaluation and ranking of alternatives using the Euclidean distance measure are influenced by the normalization formula, albeit to a limited extent. In contrast, the Mahalanobis distance-based Hellwig's method remains unaffected by the choice of normalization formulas. The study concludes that the ranking of alternatives is strongly dependent on the distance measure employed, whether it is Euclidean or Mahalanobis. The Mahalanobis distance-based Hellwig method is deemed a valuable tool for decision-makers in real-life situations. It enables the evaluation of alternatives by considering interactions between criteria, providing a more comprehensive perspective for decision-making. [ABSTRACT FROM AUTHOR]