Assessing parallel urban dynamics through local-scale morphological and activity indicators in German and Spanish cities

It is of interest to know the relative importance of horizontal and vertical correlation in pi-electron systems. A method has been developed for including both kinds of correlation simultaneously. Vertically correlated atomic orbitals are formed by combining 2pπ and 3dσ orbitals. These are then used as a basis for alternant molecular orbitals. Calculations to minimize the ground-state energy of ethylene indicate that horizontal correlation accounts for almost all of the energy improvement. This is due to the fact that the increase in kinetic energy associated with vertical correlation is greater than that for horizontal correlation.

Hierarchical multi-label text classification (HMTC) deals with the challenging task where an instance can be assigned to multiple hierarchically structured categories at the same time. The majority of prior studies either focus on reducing the HMTC task into a flat multi-label problem ignoring the vertical category correlations or exploiting the dependencies across different hierarchical levels without considering the horizontal correlations among categories at the same level, which inevitably leads to fundamental information loss. In this paper, we propose a novel HMTC framework that considers both vertical and horizontal category correlations. Specifically, we first design a loosely coupled graph convolutional neural network as the representation extractor to obtain representations for words, documents, and, more importantly, level-wise representations for categories, which are not considered in previous works. Then, the learned category representations are adopted to capture the vertical dependencies among levels of category hierarchy and model the horizontal correlations. Finally, based on the document embeddings and category embeddings, we design a hybrid algorithm to predict the categories of the entire hierarchical structure. Extensive experiments conducted on real-world HMTC datasets validate the effectiveness of the proposed framework with significant improvements over the baselines.

Farmers engaging in irrigated agriculture are subject to externalities stemming from their neighbours’ operational decisions on water use. Because of the nature of water flow, these externalities are more evident between neighbours in an upstream–downstream relationship rather than in horizontal proximity. In order to accommodate this characteristic of irrigated agriculture into the spatial analysis, this paper proposes a novel specification of the spatial weight matrix for spatial regressions, in which each observation’s vertical location within the whole sample is taken into account. The proposed concept is applied to a land use regression using the authors’ original remote sensing data from southwestern China, with four different matrix formations: symmetric contiguous, symmetric distance-based, asymmetric contiguous, and asymmetric distance-based. The results show that inclusion of vertical spatial correlation can potentially enhance the explanatory power of the model, especially when the weight linking horizontal correlation and vertical correlation is appropriately specified. In the present case, the importance of vertical correlation is shown to be approximately one-tenth of horizontal correlation.

The performance of active sonar detection systems is seriously affected by the reverberation at the bottom of the waveguide in shallow water. In order to improve the performance of active sonar detection, it is necessary to understand the horizontal correlation of shallow-water bottom reverberation in active towed-array processing technology. However, the current research on the spatial correlation of reverberation is mainly based on vertical correlation, little work has been done on the horizontal correlation characteristics of long-distance seabed reverberation, and there is no support from sea test data. In this paper, the coupled mode reverberation model is applied to the horizontal correlation, and is studied according to the receiving position, time, and frequency. The simulation results show that, for the long-range bottom reverberation, the lateral correlation is greater than the longitudinal correlation in the horizontal space. By introducing the adiabatic mode solution, the mathematical model of horizontal correlation in the range-dependent waveguide with depth is derived. The numerical results show that the influence of the seabed dip angle on the horizontal correlation should be considered and that the horizontal correlation is affected obviously by the propagation effects of the sloped sea floor. Finally, the experimental data processing and analysis are given and verify the correctness of the algorithm.

Characteristics of depositional environment and evolution of Upper Cretaceous Mishrif Formation, Halfaya Oil field, Iraq based on sedimentary microfacies analysis

Acoustic signals from one point are transmitted everywhere without distortion, and signals received at any two points are related in an infinite homogeneous medium. But in the actual ocean, because of the sound absorption, the random fluctuation of the sea water medium, its boundary in the time and space and the inhomogeneous medium, the change of the spatial correlation of the sound field in the ocean will be huge. At present, there are many researches about horizontal longitudinal correlation of sound field, but the research on vertical correlation of sound field is less. In this paper, the vertical correlation coefficients of shallow sea sound field are calculated and analyzed by using the measured vertical array data, and compared with the simulation results. Some meaningful results are obtained.

Analysis of correlation of 2DPalmHash Code and orientation range suitable for transposition

This study investigates the interplay between urban morphology, vegetation, and thermal environments by integrating mobile air temperature (AT) measurements with satellite-derived land surface temperature (LST). The case study is the city of Bologna (Italy). Correlation analysis revealed strong multicollinearity among morphological indicators, with building density and floor area ratio nearly collinear, while vegetation cover (PV) remained the most independent predictor. A composite urban density indicator (CUDI), derived through principal component analysis, was introduced to address redundancy among morphological metrics. Ordinary least squares regressions demonstrated significant associations, with PV exerting a pronounced cooling effect and CUDI amplifying both AT and LST. Model diagnostics confirmed statistical robustness, though residual spatial autocorrelation necessitated spatial regression approaches. Spatial lag models (SLMs) substantially improved explanatory power, highlighting spatial spillovers and neighborhood effects as central to understanding urban heat dynamics. Comparative analysis with spatial error models reinforced the dominance of SLM in capturing localized dependencies. Despite limitations in spatial coverage, temporal scope, and indicator transferability, findings emphasize the critical roles of vegetation and urban compactness in shaping thermal environments. This work underscores the necessity of integrating greening strategies with urban form management for effective heat mitigation and provides a methodological framework for analyzing urban heat islands through multi-source thermal and morphological data.

Estimation of spatial correlations should be an integral part of objective analysis of geophysical variables. However, a statistical assessment of spatial correlations has been absent from studies of objective analysis of snow depth since its debut over 2 decades ago. We show a method for computing regional spatial correlations of observed snow depth and the daily snow depth increment and fitting them to correlation functions to estimate the correlation scale parameters. Both horizontal and vertical distance correlations are computed from station observations over a well sampled part of North America. The vertical and horizontal distance correlations are fitted to exponential functions using the least square method to estimate the correlation scale parameters including the amplitude, which represents short distance correlation. Our assessment suggests a large horizontal e-folding correlation scale for both the observed snow depth and the daily increment, with implications for improving predictions in poorly monitored areas with relatively flat topography. Over mountainous terrain, vertical e-folding correlation scale for observed snow depth is much smaller than that for the daily snow depth increment and for the snow depth increment used in operational snow analyses. That means that optimal interpolation-based analysis of the increments may be more accurate than the interpolation of snow depth data.

A comparison of the spatial distribution of vadose zone water in forested and agricultural floodplains a century after harvest

Lentigo maligna (LM) is a subtype of in situ melanoma that classically presents in elderly patients as a slowly growing lesion on sun‐exposed areas that may evolve to invasive melanoma. Line‐field confocal optical coherence tomography (LC‐OCT) is a new non‐invasive technique for a real‐time, vertical, and horizontal skin imaging with high resolution close to conventional histopathology. We present the LC‐OCT features of an LM of the nose in a 49‐year‐old white man along with their horizontal and vertical histopathological correlations. LC‐OCT was able to detect in vivo, in both horizontal and vertical imaging, the main microscopic features typical of LM by showing, in the epidermis and around the hair follicles, the presence of large, bright roundish, or dendritic atypical cells, with evident nuclei, corresponding to atypical melanocytes with a tendency toward folliculotropism. A strong correspondence between LC‐OCT images and vertical and horizontal histopathological sections was observed. Our study, although limited to a single case, is indicative of the great potential of LC‐OCT to improve the non‐invasive diagnosis of LM.

Machine learning is widely used in malware detection systems as a core component. However, machine learning algorithm is based on the assumption that the underlying malware concept is stable for training and testing. The assumption is vulnerable to well-crafted concept drift attacks, such as mimicry attacks, gradient descent attacks, poisoning attacks and so on. This paper proposes an ensemble learning system which combines vertical and horizontal correlation learning models. The significant diversity among vertical and horizontal correlation models increases the difficulty of concept drift attacks. And average p-value assessment is applied to fortify the system to be sensitive to hidden concept drift. The experiment results show that the hybrid system could actively recognize the concept drift among different Miuref variants.

Side-channel attacks are a serious threat for physical implementations of public key cryptosystems and notably for the RSA. Side-channel leakages can be explored from unprotected cryptodevices and several power or electromagnetic traces are collected in order to construct (vertical) differential side-channel attacks. On exponentiations, the so-called horizontal correlation attacks originally proposed by Walter in “Sliding windows succumbs to big mac attack” (Cryptographic hardware and embedded systems, 2001) and improved by Clavier et al. in “Horizontal correlation analysis on exponentiation” (ICICS, 2010) demonstrated to be efficient even in the presence of strong countermeasures like the exponent and message blinding. In particular, a single trace is sufficient to recover the secret if the modular exponentiation features long integer multiplications. In this paper, we consider the application of vertical and horizontal correlation attacks on residue number systems (RNS)-based approaches. The montgomery multiplication, which is widely adopted in the finite ring of an exponentiation, has different construction details in the RNS domain. Experiments are conducted on hardware (parallel) and software (sequential) and leakage models for known and masked inputs are constructed for the regular and SPA-protected Montgomery ladder algorithm.

Dose urban landscape pattern affect CO2 emission efficiency? Empirical evidence from megacities in China

A method is described for creating a two-dimensional field of random samples with the assumed marginal pdf and specified horizontal and vertical correlation coefficients. The presented method is based on transforming the set of random features with an appropriate prepared correlation matrix, according to given schemes. A field of random samples with log-normal marginal pdf and desired horizontal and vertical correlation was created as an example of using the presented algorithm.