Neighbor Effects Research Articles

CNN 기반 위성 이미지를 활용한 북한의 도시 인구추정

North Korea does not officially disclose population data through formal procedures. However, information related to North Korea's population can be verified through KOSTAT or internet searches. Nevertheless, these data are based on population surveys conducted with the support of the United Nations Population Fund (UNFPA) in 1993 and 2008, and are known to be of questionable reliability. In this study, as an initial attempt to estimate the current population of North Korea, we utilized daytime satellite images from 2023 to estimate the populations of Pyongyang and Kaesong at the grid level. The research resulted in the development of a gridded population estimation model based on a Convolutional Neural Network (CNN). The model was trained on CNN using data from South Korea and aimed to be applied to North Korea. It employed the VGG16 model, a representative algorithm of CNN, for transfer learning. To account for seasonal differences in the images from daytime satellite data, a U-net was used for image adjustment. Furthermore, neighboring effects were incorporated to enhance the model's performance. The model fitting results indicated that the populations of the four major metropolitan cities in South Korea were estimated without significant differences from actual populations. The populations of Pyongyang and Kaesong in North Korea were also estimated to be similar to the 2008 census data, demonstrating satisfactory results.

Effect of next-nearest neighbor hopping on the single-particle excitations at finite temperature

In the half-filled one-orbital Hubbard model on a square lattice, we study the effect of next-nearest neighbor hopping on the single-particle spectral function at finite temperature using an exact-diagonalization + Monte-Carlo based approach to the simulation process. We find that the pseudogap-like dip, existing in the density of states in between the Néel temperature T_NTN and a relatively higher temperature scale T^*T*, is accompanied with a significant asymmetry in the hole- and particle-excitation energy along the high-symmetry directions as well as along the normal-state Fermi surface. On moving from (\pi/2, \pi/2π/2,π/2) toward (\pi, 0)(π,0) along the normal state Fermi surface, the hole-excitation energy increases, a behavior remarkably similar to what is observed in the dd-wave state and pseudogap phase of high-T_cTc cuprates, whereas the particle-excitation energy decreases. The quasiparticle peak height is the largest near (\pi/2, \pi/2π/2,π/2) whereas it is the smallest near (\pi, 0)(π,0). These spectral features survive beyond T_NTN. The temperature window T_N ≲ T ≲ T^*TN≲T≲T* shrinks with an increase in the next-nearest neighbor hopping, which indicates that the next-nearest neighbor hopping may not be supportive to the pseudogap-like features.

Plant trait dissimilarity increases competitive interactions among co‐occurring plants

Abstract Plant–plant interactions, often studied in the context of plant traits, are considered crucial assembly mechanisms for plant communities and offer insights into how neighbours affect growth. Three key questions regarding these interactions have theoretical importance but limited empirical support: (1) does similarity in plant traits enhance competitive interactions among species, (2) is the “competitive environment” determined through plant traits of the resident species, and (3) do greater species diversity and niche use result in increased competition among co‐occurring species. Here we use a simple experimental design to directly test these foundational questions. In a mesocosm experiment using native grassland fragments, we investigated our questions simultaneously by manipulating trait relationships among resident and colonizing plants. To assess the net impact of neighbours on plant growth, we measured the degree of suppression experienced by focal plants compared to growth in the absence of neighbours. We further tested whether trait‐function relationships are context‐dependent by modifying available resources. Contrary to the limiting similarity hypothesis, we found that reduced overall dissimilarity mitigated neighbour‐induced growth suppression, with more negative effects occurring among dissimilar neighbours. The influence of trait dissimilarity depended on nutrient context and was stronger under increased resource availability. We found little support for the idea that specific plant traits are “functional” in creating competitive environments: no community traits (community weighted means) were associated with net neighbour effects. In contrast, resource supply significantly affected species interactions, with increased resources exacerbating negative neighbour effects on plant growth. We found that plant trait dissimilarity does function in competition but is contingent on resource context. However, trait values were not inherently competitive: traits may have physiological functions but did not function in focal plants' competitive response. We provide some of the limited data directly testing the core mechanisms of community assembly. We encourage such direct experimental tests as they are essential to building an understanding of ecological processes. Read the free Plain Language Summary for this article on the Journal blog.

Spin modulation of single Fe atoms with thiolate‐poisoned Pd nanoclusters for highly efficient ammonia synthesis

AbstractNitrate reduction toward ammonia is pivotal in energy and environmental domains but faces challenges in achieving simultaneous high selectivity and activity. Manipulating interactions between intermediates and metal species is deemed effective for catalytic enhancement. In this study, a uniformly configured Fe1‐N4 single‐atom catalyst with four‐coordination was synthesized. Subsequent deposition of thiol‐poisoned Pd nanoclusters provided a platform to investigate the neighboring effect of Pd nanoclusters, excluding activity influences from Pd catalysts. Through this neighbor engineering, the Fe 3d electron spin configuration shifted from low spin to medium spin (MS). Experimental and theoretical analyses affirmed that MS Fe species optimized nitrate reduction by enhancing the *NO desorption process. This spin‐modulated Fe single‐atom catalyst demonstrated superior activity (392.16 mmol−1 gcat−1 h−1, ~40 mol−1 gFe−1 h−1) and a Faraday efficiency of 98.6% at −0.5 V vs. RHE, surpassing reported values. This study presents a promising strategy for nitrogen‐chemistry transition metal catalysts.

Study of Covariates’ Effects in the Presence of Neighbor Effects : An Informative Review

With reference to a Gauss-Markov Model, Analysis of Covariance (ANCOVA) is a standard exercise in the study of differential treatment effects in the presence of covariates. Again in the presence of ‘Neighbor Effects’, we carry out necessary data analysis in a routine manner. In this paper we present a review of this area of research, encompassing both covariates’ effects and neighbor effects. International Journal of Statistical Sciences, Vol.24(1), March, 2024, pp 65-73

Some New Generators to Produce Minimal Circular Strongly Balanced Generalized Neighbor Designs of Class-I

Minimal circular strongly balanced neighbor designs control the neighbor effects economically but these designs can only be constructed for v odd. For v even, their alternates are minimal circular strongly balanced generalized neighbor designs of class-I (MCSBGNDs-I) which are the designs in which (i) each treatment appears exactly once with itself as neighbors, (ii) v/2 unordered pairs appear twice as neighbors while the remaining ones appear once. On the basis of constructors developed by Munir et al. (2023), some new generators are developed in this article to generate cyclic shifts for efficient MCSBGNDs-I in blocks of (i) equal sizes, (ii) two different sizes, and (iii) three different sizes for m (mod 4) ≡ 2 & 3 with v even and k = 4l, k = 4l+2, k (odd) > 3, k (mod) ≡ 1 & k (mod) ≡ 3. Efficiency of neighbor effects and of Separability show that our proposed generators produce designs which control the neighbor effects efficiently as well as estimate the direct effects and neighbor effects independently.

Modeling Spatial Regimes With Smooth Transitions

In this paper, we introduce a new way of modeling spatial regimes using smooth transitions. We propose an autoregressive spatial lag model where a logistic function captures structural variation in the spatial lag parameter. In the regime-switching spatial lag model with smooth transitions, the effect of the spatial neighbors depends on the transition variable that governs the regime switch. An LM test for detecting nonlinearity is derived, and a simulation study, where the properties of the test are investigated, is conducted. The test shows good power in relatively small samples with moderate deviation from linearity. An empirical application is included, where data on median house prices from the Boston area is used to explore the spatial dependence between census tracts. A smooth shift between regimes governed by the variable property tax rate is found. The smooth spatial lag model performs slightly better than the regular spatial lag model in terms of model fit and capturing spatial dependence.

Regional factors associated with smart factory adoption in South Korea

This study investigates the relationship between regional characteristics and smart factory adoption in South Korea by analyzing smart factory data. Our findings reveal that agglomeration economies and regional occupational structures significantly influence smart factory adoption. Moreover, we identified a neighboring effect, where the presence of existing smart factories within a region fosters further adoption, underscoring the impact of local industrial ecosystems on the diffusion of smart technologies. Our findings show that the impacts of agglomeration economies, occupational structures, and neighboring effects on smart factory adoption vary across different technology levels of smart factories and types of regions, suggesting a complex interplay between regional dynamics and technological advancement in the adoption process.

Direct Machine Learning Predictions of C3 Pathways

AbstractThe C3 pathways of CO2 reduction reaction (CO2RR) lead to the generation of high‐value‐added chemicals for broad industrial applications, which are still challenging for current electrocatalysis. Only limited electrocatalysts have been reported with the ability to achieve C3 products while the corresponding reaction mechanisms are highly unclear. To overcome such challenges, the first‐principle machine learning (FPML) technique on graphdiyne‐based atomic catalysts (GDY‐ACs) is introduced to directly predict the reaction trends for the key C─C─C coupling processes and the conversions to different C3 products for the first time. All the prediction results are obtained only based on the learning dataset constructed by density functional theory (DFT) calculation results for C1 and C2 pathways, offering an efficient approach to screen promising electrocatalyst candidates for varied C3 products. More importantly, the ML predictions not only reveal the significant role of the neighboring effect and the small–large integrated cycle mechanisms but also supply important insights into the C─C─C coupling processes for understanding the competitive reactions among C1 to C3 pathways. This work has offered an advanced breakthrough for the complicated CO2RR processes, accelerating the future design of novel ACs for C3 products with high efficiency and selectivity.

Climate anomalies and neighbourhood crowding interact in shaping tree growth in old‐growth and selectively logged tropical forests

Abstract Climate extremes and biotic interactions at the neighbourhood scale affect tropical forest dynamics with long‐term consequences for biodiversity, global carbon cycling and climate change mitigation. However, forest disturbance may change crowding intensity, and thus the relative contribution of climate extremes and neighbourhood interactions on tree growth, thereby influencing tropical forest resistance and resilience to climate change. Here, we aim to evaluate the separate and interactive effects of climate and neighbours on tree growth in old‐growth and disturbed tropical forests. We used 30 years of growth measurements for over 300 tropical tree species from 15 forest plots in French Guiana to investigate the separate and interactive effects of climate anomalies (in solar radiation, maximum temperature, vapour pressure deficit and climatic water deficit) and neighbourhood crowding on individual tree growth. Contrasting old‐growth and selectively logged forests, we also examined how disturbance history affects tree growth sensitivity to climate and neighbours. Finally, for the most abundant 100 species, we evaluated the role of 12 functional traits pertaining to water relations, light and carbon use in mediating tree growth sensitivity to climate anomalies, neighbourhood crowding and their interactions. Climate anomalies tied to heat and drought stress and neighbourhood crowding independently reduced tree growth, and showed positive interactive effects which attenuated their separate effects on tree growth. Their separate and interactive effects were stronger in disturbed than undisturbed forests. Fast‐growing species (i.e. higher intrinsic growth rates) were more abundant in disturbed forests and more sensitive to climate anomalies and neighbourhood crowding. Traits related to water relations, light and carbon use captured species sensitivities to different climate anomalies and neighbourhood crowding levels but were weak predictors of their interactions. Synthesis: Our results demonstrate that climate anomalies and neighbourhood crowding can interact to shape tropical tree growth, suggesting that considering the biotic context may improve predictions of tropical forest dynamics facing altered climate regimes. Furthermore, species traits can capture tree growth sensitivity to the separate effects of climate and neighbours, suggesting that better representing leading functional dimensions in tropical tree strategies offers a promising way towards a better understanding of the underlying ecological mechanisms that govern tropical forest dynamics.

Exploiting Hierarchical Interactions for Protein Surface Learning.

Predicting interactions between proteins is one of the most important yet challenging problems in structural bioinformatics. Intrinsically, potential function sites in protein surfaces are determined by both geometric and chemical features. However, existing works only consider handcrafted or individually learned chemical features from the atom type and extract geometric features independently. Here, we identify two key properties of effective protein surface learning: 1) relationship among atoms: atoms are linked with each other by covalent bonds to form biomolecules instead of appearing alone, leading to the significance of modeling the relationship among atoms in chemical feature learning. 2) hierarchical feature interaction: the neighboring residue effect validates the significance of hierarchical feature interaction among atoms and between surface points and atoms (or residues). In this paper, we present a principled framework based on deep learning techniques, namely Hierarchical Chemical and Geometric Feature Interaction Network (HCGNet), for protein surface analysis by bridging chemical and geometric features with hierarchical interactions. Extensive experiments demonstrate that our method outperforms the prior state-of-the-art method by 2.3% in site prediction task and 3.2 available at https://github.com/lyqun/HCGNet.

Multimanifold Bistructured Low Rank Representation of hyperspectral images

Hyperspectral images (HSIs) are obtained by imaging surface materials using a spectral imager. In practical applications, the hyperspectral remote sensing images are inevitably contaminated by noise, leading to errors in image processing results. Gaussian noise, Poisson noise, and stripe noise are common in HSIs. This article presents an HSI denoising model based on the low rank representation of multi manifold double constructions. Introducing multiple manifolds into the field of hyperspectral image denoising for the first time, comprehensively obtaining complex information on hyperspectral surfaces. We extended the graph-regularized low-rank representation model (GLRR) by using maximum data variance to construct global information. GLRR obtains local information, and the parallel effects of nearest neighbor and maximum variance graphs take into account both global and local consistency in the denoising process. Our model is not only applicable to the case of a single manifold, but also to the case of multiple manifolds, especially in the case of complex ground target images with multiple manifold spectra. Use the LADMAP iterative algorithm to iteratively solve the proposed algorithm. Avoid introducing auxiliary variables to reduce runtime costs and algorithm complexity. Both on simulated and real datasets, the runtime is relatively fast. We experiments on both real and simulated datasets, as well as real datasets, to verify that our method has good denoising performance when Gaussian, Poisson, and stripe noise occur simultaneously.

A CODE model bridging crowding in sparse and dense displays

Visual crowding is arguably the strongest limitation imposed on extrafoveal vision, and is a relatively well-understood phenomenon. However, most investigations and theories are based on sparse displays consisting of a target and at most a handful of flanker objects. Recent findings suggest that the laws thought to govern crowding may not hold for densely cluttered displays, and that grouping and nearest neighbour effects may be more important. Here we present a computational model that accounts for crowding effects in both sparse and dense displays. The model is an adaptation and extension of an earlier model that has previously successfully accounted for spatial clustering, numerosity and object-based attention phenomena. Our model combines grouping by proximity and similarity with a nearest neighbour rule, and defines crowding as the extent to which target and flankers fail to segment. We show that when the model is optimized for explaining crowding phenomena in classic, sparse displays, it also does a good job in capturing novel crowding patterns in dense displays, in both existing and new data sets. The model thus ties together different principles governing crowding, specifically Bouma’s law, grouping, and nearest neighbour similarity effects.

Ortho-Boronic Acid Carbonyl Compounds and Their Applications in Chemical Biology.

Iminoboronates and diazaborines are related classes of compounds that feature an imine ortho to an arylboronic acid (iminoboronate) or a hydrazone that cyclizes with an ortho arylboronic acid (diazaborine). Rather than acting as independent chemical motifs, the arylboronic acid impacts the rate of imine/hydrazone formation, hydrolysis, and exchange with competing nucleophiles. Increasing evidence has shown that the imine/hydrazone functionality also impacts arylboronic acid reactivity toward diols and reactive oxygen and nitrogen species (ROS/RNS). Untangling the communication between C=N linked functionalities and arylboronic acids has revealed a powerful and tunable motif for bioconjugation chemistries and other applications in chemical biology. Here, we survey the applications of iminoboronates and diazaborines in these fields with an eye toward understanding their utility as a function of neighboring group effects.

Ion mobility in gas and liquid phases: How much orthogonality is obtained in capillary electrophoresis-ion mobility-mass spectrometry?

Ion mobility-mass spectrometry (IM-MS) is an ever-evolving tool to separate ions in the gas phase according to electrophoretic mobility with subsequent mass determination. CE is rarely coupled to IM-MS, possibly due to similar separation mechanisms based on electrophoretic mobility. Here, we investigate the orthogonality of CE and ion mobility (IM) by analyzing a complex peptide mixture (tryptic digest of HeLa proteins) with trapped ion mobility mass spectrometry (TIMS-MS). Using the nanoCEasy interface, excellent sensitivity was achieved by identifying thousands of peptides and achieving a peak capacity of 7500 (CE: 203-323 in a 150cm long capillary, IM: 27-31). Plotting CE versus mass and CE versus (inverse) mobility, a clear grouping in curved striped patterns is observed according to the charge-to-size and mass-to-charge ratios. The peptide charge in the acidic background electrolyte can be estimated from the number of basic amino acids, with a few exceptions where neighboring effects reduce the positive charge. A surprisingly high orthogonality of CE and IM is observed, which is obviously caused by solvation effects leading to different charges and sizes in the liquid phase compared to the gas phase. A high orthogonality of CE and ion mobility is expected to be observed for other peptide samples as well as other substance classes, making CE-IM-MS a promising tool for various applications.

Inhibitory and facilitative effects of lexical neighbors in spoken word recognition: The role of language experience

AbstractThe study evaluated whether the direction (inhibitory or facilitative) of the phonological neighborhood density effect in English spoken word recognition was modulated by the relative strength of competitor activation (neighborhood type) in two groups of English-dominant learners of Spanish who differed in language experience. Classroom learners and heritage learners of Spanish identified spoken English words from dense (e.g., BEAR) and sparse (e.g., BOAT) phonological neighborhoods presented in moderate noise. The phonological neighborhood was separately manipulated at word onset (cohort) and word offset (rhyme). Classroom learners were overall slower in recognizing spoken words from denser neighborhoods. Strongly active (onset) neighbors exerted inhibitory effects in both classroom and heritage learners. Critically, weakly active (offset) neighbors exerted inhibitory effects in classroom learners but facilitative effects in heritage learners. The results suggest that the activation of both within and cross-language neighbors should be considered in determining the direction of neighbor effects in bilingual lexical processing.

Positive and negative plant−plant interactions influence seedling establishment at both high and low elevations

Deciphering how plants interact with each other across environmental gradients is important to understand plant community assembly, as well as potential future plant responses to environmental change. Plant−plant interactions are expected to shift from predominantly negative (i.e. competition) to predominantly positive (i.e. facilitation) along gradients of environmental severity. However, most experiments examine the net effects of interactions by growing plants in either the presence or absence of neighbours, thereby neglecting the interplay of both negative and positive effects acting simultaneously within communities. To partially unravel these effects, we tested how the seedling establishment of 10 mountain grassland plants varied in the presence versus absence of plant communities at two sites along an elevation gradient. We created a third experimental treatment (using plastic plant mats to mimic surrounding vegetation) that retained the main hypothesised benefits of plant neighbours (microsite amelioration), while reducing a key negative effect (competition for soil resources). In contrast to our expectations, we found evidence for net positive effects of vegetation at the low elevation site, and net negative effects at the high elevation site. Interestingly, the negative effects of plant neighbours at high elevation were driven by high establishment rates of low elevation grasses in bare soil plots. At both sites, establishment rates were highest in artificial vegetation (after excluding two low elevation grasses at the high elevation site), indicating that positive effects of above-ground vegetation are partially offset by their negative effects. Our results demonstrate that both competition and facilitation act jointly to affect community structure across environmental gradients, while emphasising that competition can be strong also at higher elevations in temperate mountain regions. Consequently, plant−plant interactions are likely to influence the establishment of new, and persistence of resident, species in mountain plant communities as environments change.

Effect of neighboring transitions on the transformation between electromagnetically induced transparency and two-photon absorption

We report the effect of neighboring transitions on the transformation between electromagnetically induced transparency (EIT) and two-photon absorption (TPA) in an open multi-level ladder type system of 87Rb atom, the EIT peak exhibits split and asymmetric line feature owing to a neighboring transition line with a stronger relative hyperfine transition strength factor. By comparing the signals of the 5S1/2−5P3/2−5D3/2,5/2 transitions when increasing the power of the coupling laser, it can be found that avoiding neighboring effects due to Doppler broadening in a real atom, a lower decay rate of the excited state and a higher hyperfine transition strength ratio between the coupling and probe lasers are the crucial factors that enhance the transformation efficiency between TPA and EIT and the resolution of the switched spectra. Detailed theoretical calculations, considering the crucial factors, are consistent with the experimental results.

Role of inter-site Coulomb interaction on the thermodynamic and ground state properties within the [formula omitted] model

In this work, we report the effect of nearest neighbor (NN) inter-site Coulomb interaction (V) with a certain finite value of on-site Coulomb repulsion (U) and NN antiferromagnetic exchange interaction (J) in the context of high-Tc cuprates. An exact diagonalization method has been applied in a 2D t−J−U model extended by NN inter-site Coulomb interaction in the tilted square cluster near optimal doping. The antiferromagnetic tendency is suppressed by the inter-site e−e repulsion. The inter-site Coulomb repulsion produces an effective on-site attraction between electrons, resulting in the creation of doublons against the on-site Coulomb repulsion. The computation of binding energy demonstrates the binding of holes for small U/t, resulting from the interplay between U and V. Superconductivity arises in a limited window of V depending on U. However, the effective dominance of either U or V results in the annihilation of these hole pairs. The presence of two peaks in specific heat is caused by two Coulomb interactions. The development of doublons as V increases leads to a more ordered state, lowering entropy. The thermodynamic properties also show that the t−J−U−V model acts as an attractive Hubbard model for U<V. However, a pure attractive t−J−U model cannot explain these phenomenologies.

Spatial Spillover Effect of Water Environment Pollution Control in Basins—Based on Environmental Regulations

The basin economy is essential in China’s high-quality development, which brings prodigious economic and social benefits. However, high industrialization and urbanization led to a significant escalation in water pollution within basins. The achievement of synergistic control of water environment pollution at the basin scale has emerged as a primary goal for governmental departments in developing environmental policies. This study constructs a “four-in-one” basin’s water environment regulation system comprising four categories: total, quality, project, and governance. Further, the spatial spillover effects of water environment pollution control in 27 Chinese provincial-level administrative regions spanning key river basins were analyzed. This study aims to provide evidence from China on the transfer of water pollution in basins worldwide. The results indicate the following conclusions: (1) A consistent upward trend in the environmental regulations and water environment pollution indexes across all provinces in the basin. Compared to 2006, in 2018, the mean value of the basin environmental regulation index was 0.21, which is an increase of 23.5%, and the mean value of the water environmental pollution index was 0.83, which is an increase of 29.7%. (2) Whether in the basin’s upper, middle, or lower reaches, the relationship between environmental regulations and water pollution follows a “U”-shaped pattern, with thresholds of 0.318, 0.331, and 0.390, respectively. (3) Under the neighbor weight matrix and water flow distance weight matrix, the impact coefficient of the water environment pollution index on the surrounding areas is significantly positive, implying that the implementation of local environmental policies will radiate to the neighboring areas in the short term and bring positive governance effects. (4) Regarding the time-lag effect, it is observed that the lag term associated with the water environment pollution index exhibits a statistically significant positive relationship. This finding suggests that the pollution of the water environment within the basin follows a cumulative and continuous pattern. (5) It is noted that long-term environmental regulation measures do not contribute favorably to the amelioration of water pollution in the neighboring regions. This implies the presence of a characteristic neighboring avoidance effect.