Sound Scattering Layers Within and Beyond the Seychelles-Chagos Thermocline Ridge in the Southwest Indian Ocean

In global oceans, ubiquitous and persistent sound scattering layers (SL) are frequently detected with echosounders. The southwest Indian Ocean has a unique feature, a region of significant upwelling known as the Seychelles-Chagos Thermocline Ridge (SCTR), which affects sea surface temperature and marine ecosystems. Despite their importance, sound SL within and beyond the SCTR are poorly understood. This study aimed to compare the characteristics of the sound SL within and beyond the SCTR in connection with environmental properties, and dominant zooplankton. To this end, the region north of the 12°S latitude in the survey area was defined as SCTR, and the region south of 12°S was defined as non-SCTR. The results indicated contrasting oceanographic properties based on the depth layers between SCTR and non-SCTR regions. Distribution dynamics of the sound SL differed between the two regions. In particular, the diel vertical migration pattern, acoustic scattering values, metrics, and positional properties of acoustic scatterers showed two distinct features. In addition, the density of zooplankton sampled was higher in SCTR than in the non-SCTR region. This is the first study to present bioacoustic and hydrographic water properties within and beyond the SCTR in the southwest Indian Ocean.

Spectral distribution dynamics across different attentional priority states

Anticipatory covert spatial attention improves performance on tests of visual detection and discrimination, and shifts are accompanied by decreases and increases of alpha-band power at EEG electrodes corresponding to the attended and unattended location, respectively. Although the increase at the unattended location is often interpreted as an active mechanism (e.g., inhibiting processing at the unattended location), most experiments cannot rule out the alternative possibility that it is a secondary consequence of selection elsewhere. To adjudicate between these accounts, we designed a Posner-style cuing task in which male and female human participants made orientation judgments of targets appearing at one of four locations: up, down, right, or left. Critically, trials were blocked such that within a block the locations along one meridian alternated in status between attended and unattended, and targets never appeared at the other two, making them irrelevant. Analyses of the concurrently measured EEG signal were carried out on traditional narrowband alpha (8-14 Hz), as well as on two components resulting from the decomposition of this signal: periodic alpha; and the slope of the aperiodic 1/f-like component. Although data from right-left blocks replicated the familiar pattern of lateralized asymmetry in narrowband alpha power, with neither alpha signal could we find evidence for any difference in the time course at unattended versus irrelevant locations, an outcome consistent with the secondary-consequence interpretation of attention-related dynamics in the alpha band. Additionally, 1/f slope was lower at attended and unattended locations, relative to irrelevant, suggesting a tonic adjustment of physiological state.

Global Industrial Development: Insights From the Distribution Dynamics Approach for the Post Covid Era

The spread of COVID-19 has significantly dampened global economic activity and has also wreaked havoc on the industrial sector. Understanding the disparity and convergence of global industrial outputs is important in assessing the effectiveness of concurrent development policies. This study investigates the spatial distribution of global industrial output to unveil the disparity in industrial development and the feasibility of achieving convergence over time. Stochastic kernel analyses are carried out for national regimes to study the overall pattern of industrialization for all the countries in the world. Countries are then classified into different groups to further analyse the geographical and income effects on industrial development. The results show that disparity between the Global North and the Global South will enlarge further in the future. Industrial development in the Global North will continue to prosper, while the industrial output in many countries in the Global South just cannot reach the global average.

Measurement, Distribution Characteristics and Regional Differences Analysis of Eco-efficiency Development in Tobacco Planting Zone

The positive development of ecological efficiency is of great significance for the high quality synchronization development of economy and ecological environment. In order to study the temporal and spatial distribution dynamics and regional differences of the ecological efficiency of tobacco planting zone in China, the Super-SBM model was used to calculate the ecological efficiency of provincial tobacco planting zone in China from 2005 to 2019 from economic perspective, and the kernel density function, Gini coefficient, σ convergence and Markvo transition matrix were used to analyze the dynamics of spatial distribution and regional differences in this paper. The research results show that: (1) China's overall ecological efficiency exhibits a U-shaped curve, i.e., decreased first and then increased. (2) The overall difference in eco-efficiency of tobacco planting zone is large, which is mainly originated from the inter-regional differences and hypervariable density. (3)The eco-efficiency development of tobacco planting zone in China does not have the characteristics of convergence, resulting in differences in spatial distribution. (4) The national and regional eco-efficiency of tobacco planting zone has the greatest transfer probability from year t to year t+1 in the same level of eco-efficiency.

The parallel convergence of income and educational achievements: joint distribution dynamics

Human capital is an important factor of economic growth. Previous studies show that convergence patterns of income and education differ on a regional level. The purpose of this article is to verify whether there is statistical relationship between the patterns of educational achievements convergence and income convergence processes in Poland on the regional and local level. The paper describes and applies an innovative way for the formal verification of similarities in convergence patterns on the basis of transition matrices and kernel density estimation. The analysis is performed on Polish data for educational achievements (lagged exam results of 15-year old pupils) and income per capita worker on the regional and local level for the period 2003–2015. Despite the occurrence of (weak) convergence for both phenomena, each has a different course. Therefore, the processes of the income convergence and educational achievements convergence in Poland were not parallel.

Spatiotemporal Correlation Feature Spaces to Support Anomaly Detection in Water Distribution Networks

Monitoring disruptions to water distribution dynamics are essential to detect leakages, signal fraudlent and deviant consumptions, amongst other events of interest. State-of-the-art methods to detect anomalous behavior from flowarate and pressure signal show limited degrees of success as they generally neglect the simultaneously rich spatial and temporal content of signals produced by the multiple sensors placed at different locations of a water distribution network (WDN). This work shows that it is possible to (1) describe the dynamics of a WDN through spatiotemporal correlation analysis of pressure and volumetric flowrate sensors, and (2) analyze disruptions on the expected correlation to detect burst leakage dynamics and additional deviant phenomena. Results gathered from Portuguese WDNs reveal that the proposed shift from raw signal views into correlation-based views offers a simplistic and more robust means to handle the irregularity of consumption patterns and the heterogeneity of leakage profiles (both in terms of burst volume and location). We further show that the disruption caused by leakages can be detected shortly after the burst, highlighting the actionability of the proposed correlation-based principles for anomaly detection in heterogeneous and georeferenced time series. The computational approach is provided as an open-source tool available at GitHub.