Space-time dynamics of the dengue epidemic in Brazil, 2024: an insight for decision making.

BACKGROUND: Dengue is a vector-borne viral infection caused by the dengue virus transmitted to humans primarily by Aedes aegypti. The year 2024 has been a historic year for dengue in Brazil, with the highest number of probable cases ever registered. Herein, we analyze the temporal trend and spatio-temporal dynamics of dengue cases in Brazil during the first nine epidemiological weeks (EW) of 2024. METHODS: This is an ecological study, including all probable cases of dengue in Brazil during the period, carried out in two steps: time series analysis to assess the temporal trend and spatial analysis to identify high-risk clusters. RESULTS: 1,345,801 probable cases of dengue were reported. The regions with the highest increasing trend were the Northeast with an average epidemiologic week percent change (AEPC) of 52.4 (95% CI: 45.5-59.7; p < 0.001) and the South with 35.9 (95% CI: 27.7-44.5; p < 0.001). There was a statistically significant increasing trend in all states, except Acre (AEPC = -4.1; 95% CI: -16.3-10; p = 0.55), Amapá (AEPC = 1.3; 95% CI: -16.2-22.3; p = 0.9) and Espírito Santo (AEPC = 8.9; 95% CI: -15.7-40.6; p = 0.5). The retrospective space-time analysis showed a cluster within the Northeast, Central-West and Southeast regions, with a radius of 515.3 km, in which 1,267 municipalities and 525,324 of the cases were concentrated (RR = 6.3; p < 0.001). Regarding the spatial variation of the temporal trend, 21 risk areas were found, all of them located in Southeast or Central-West states. The area with the highest relative risk was Minas Gerais state, where 5,748 cases were concentrated (RR = 8.1; p < 0.001). Finally, a purely spatial analysis revealed 25 clusters, the one with the highest relative risk being composed of two municipalities in Acre (RR = 6.9; p < 0.001). CONCLUSIONS: We described a detailed temporal-spatial analysis of dengue cases in the first EWs of 2024 in Brazil, which were mainly concentrated in the Southeast and Central-West regions. Overall, it is recommended that governments adopt public policies to control the the vector population in high-risk areas, as well as to prevent the spread of dengue fever to other areas of Brazil.

Cross-border surveillance of sheep and goat rabies in Brazil.

Since Brazil has the largest territory in South America and borders 10 other countries, rabies control is strategic to prevent cross-border spread. However, prevention and control of rabies in small ruminants is neglected. The present study evaluated the spatiotemporal distribution and temporal trends of rabies in small ruminants in Brazil between 2005 and 2023. Official data on rabies case notifications and the population density of goats and sheep from the Brazilian states were used. Descriptive epidemiology and temporal analyses of high-risk clusters and trends were conducted. Rabies cases were reported in all states, except for the Federal District, Roraima, Amapá, Amazonas, and Rondônia. In sheep, 174 cases were reported, with an emphasis on Paraná (25.29 %), being highest. There were 64 cases in goats, with a highlight on Bahia (37.50 %) and Maranhão (18.75 %). However, Espírito Santo presented the highest incidence risk (IR) for goats and sheep. The highest peaks in IR occurred in 2005, 2006 and 2013. A temporal trend of decreasing goat cases was observed in northeastern Brazil from 2005 to 2023. In sheep, the Northeast region showed a downward trend in rabies cases. Three high-risk clusters were identified: the primary cluster for goats occurred in 2006, and for sheep, between 2005 and 2013. Rabies in small ruminants occurs across all Brazilian regions, with high-risk areas in the Northeast, Southeast, and South, as well as a risk of cross-border transmission. These findings support animal health authorities in strengthening rabies control for small ruminants and reducing the risk of transboundary spread.

Information-Theoretic Modeling of Categorical Spatiotemporal GIS Data.

An information-theoretic data mining method is employed to analyze categorical spatiotemporal Geographic Information System land use data. Reconstructability Analysis (RA) is a maximum-entropy-based data modeling methodology that works exclusively with discrete data such as those in the National Land Cover Database (NLCD). The NLCD is organized into a spatial (raster) grid and data are available in a consistent format for every five years from 2001 to 2021. An NLCD tool reports how much change occurred for each category of land use; for the study area examined, the most dynamic class is Evergreen Forest (EFO), so the presence or absence of EFO in 2021 was chosen as the dependent variable that our data modeling attempts to predict. RA predicts the outcome with approximately 80% accuracy using a sparse set of cells from a spacetime data cube consisting of neighboring lagged-time cells. When the predicting cells are all Shrubs and Grasses, there is a high probability for a 2021 state of EFO, while when the predicting cells are all EFO, there is a high probability that the 2021 state will not be EFO. These findings are interpreted as detecting forest clear-cut cycles that show up in the data and explain why this class is so dynamic. This study introduces a new approach to analyzing GIS categorical data and expands the range of applications that this entropy-based methodology can successfully model.

Space-time cluster analysis and maximum entropy modeling of Peste des petits ruminants (PPR) in Türkiye.

Peste des petits ruminants (PPR) is an economically important highly serious transboundary disease that mainly occurs in small ruminants such as sheep and goats. The aim of this study was to identify the probability of risk and and space-time clusters of Peste des Petits Ruminants (PPR) in Türkiye. The occurrence of PPR in Türkiye from 2017 to 2019 was investigated in this study using spatial analysis based on geographic information system (GIS). Between these dates, it was determined that 337 outbreaks and 18,467 cases. The highest number of outbreaks were detected in the Central Anatolia region. It was determined that PPR is seen more intensely in sheep compared to goats in Türkiye. In this study, 34 environmental variables (19 bioclimatic, 12 precipitation, altitude and small livestock density variables) were used to explore the environmental influences on PPR outbreak by maximum entropy modeling (Maxent). The clusters of PPR in Türkiye were identified using the retrospective space-time scan data that were computed using the space-time permutation model. A PPR prediction model was created using data on PPR outbreaks combination with environmental variables. Nineteen significant (p < 0.001) space-time clusters were determined. It was discovered that the variables altitude, sheep density, precipitation in june, and average temperature in the warmest season made important contributions to the model and the PPR outbreak may be strongly related with these variables. In this study, PPR in Türkiye has been characterized significantly spatio-temporal and enviromental factors. In this context, the disease pattern and obtained these findings will contribute to policymakers in the prevention and control of the disease.

Error Estimates and Adaptivity of the Space-Time Discontinuous Galerkin Method for Solving the Richards Equation.

We present a higher-order space-time adaptive method for the numerical solution of the Richards equation that describes a flow motion through variably saturated media. The discretization is based on the space-time discontinuous Galerkin method, which provides high stability and accuracy and can naturally handle varying meshes. We derive reliable and efficient a posteriori error estimates in the residual-based norm. The estimates use well-balanced spatial and temporal flux reconstructions which are constructed locally over space-time elements or space-time patches. The accuracy of the estimates is verified by numerical experiments. Moreover, we develop the hp-adaptive method and demonstrate its efficiency and usefulness on a practically relevant example.

Writing direction influences the spatial representations of past- and future-tense forms: Evidence from eye tracking.

The present study tests the hypothesis that the directionality of reading habits (left-to-right or right-to-left) impacts individuals' representation of nonspatial events. Using the blank screen paradigm, we examine whether eye movements reflect culture-specific spatial biases in processing temporal information, specifically, grammatical tense in Russian and Hebrew. Sixty-two native speakers of Russian (a language with a left-to-right reading and writing system) and 62 native speakers of Hebrew (a language with a right-to-left reading and writing system) listened to verbs in the past or future tense while their spontaneous gaze positions were recorded. Following the verb, a visual spatial probe appeared in one of the five locations of the screen, and participants responded manually to indicate its position. While participants' response latencies to the spatial probe revealed no significant effects, their gaze positions along the horizontal axis for past- and future-tensed verbs aligned with the reading and writing direction in their language. These results provide novel evidence that eye movements during auditory processing of grammatical tense are influenced by culturally specific reading and writing conventions, shifting leftward or rightward on the horizontal plane depending on the stimuli's time reference (past or future) and the participants' language (Russian or Hebrew). This spatial bias indicates a common underlying cognitive mechanism that uses spatial dimensions to represent temporal constructs.

Geospatial modelling of ambient air pollutants and chronic obstructive pulmonary diseases at regional scale in Pakistan.

Pakistan is among the South Asian countries mostly vulnerable to the negative health impacts of air pollution. In this context, the study aimed to analyze the spatiotemporal patterns of chronic obstructive pulmonary disease (COPD) incidence and its relationship with air pollutants including aerosol absorbing index (AAI), carbon monoxide, sulfur dioxide (SO2), and nitrogen dioxide. Spatial scan statistics were employed to identify temporal, spatial, and spatiotemporal clusters of COPD. Generalized linear regression (GLR) and random forest (RF) models were utilized to evaluate the linear and non-linear relationships between COPD and air pollutants for the years 2019 and 2020. The findings revealed three spatial clusters of COPD in the eastern and central regions, with a high-risk spatiotemporal cluster in the east. The GLR identified a weak linear relationship between the COPD and air pollutants with R2 = 0.1 and weak autocorrelation with Moran's index = -0.09. The spatial outcome of RF model provided more accurate COPD predictions with improved R2 of 0.8 and 0.9 in the respective years and a very low Moran's I = -0.02 showing a random residual distribution. The RF findings also suggested AAI and SO2 to be the most contributing predictors for the year 2019 and 2020. Hence, the strong association of COPD clusters with some air pollutants highlight the urgency of comprehensive measures to combat air pollution in the region to avoid future health risks.

Analysis of spatial-temporal evolution and investment model of industrial cross-regional investment network-taking the Pearl River Delta urban agglomeration as an example.

Cross-regional investment behavior plays a pivotal role in enterprise development and regional economic growth. Nonetheless, existing investment network analyses often adopt a broad perspective, most studies tend to invest in network models and focus on a particular industry, and pay less attention to investment models in different industries. Therefore, this paper will combine the dual perspectives of space and network to observe the spatial and temporal evolution characteristics of enterprise investment and its investment model. Additionally, it delves into the unique features of investment networks within various industries at the district and county levels. The results show that enterprise investment agglomeration exhibits a development pattern characterized by points, bands, and surfaces, with a notable distance attenuation trend in investment distances. The overall structure of the industry investment network showcases characteristics of being multi-centered and multi-nodal. The investment models across different industries can be categorized into four distinct types. The research conclusion is of great significance to optimize multi-scale intercity investment, realize industrial gradient transfer and promote the coordinated development of industry.

Congenital and acquired hypothyroidism: Temporal and spatial trends in France from 2014 to 2019.

PURPOSE: To assess the incidence of congenital hypothyroidism (CH) and acquired hypothyroidism (AH) between 2014 and 2019 in continental France. METHODS: New cases of CH and AH were identified using the French National Health Data System (Système Nationale des Données de Santé, SNDS). Temporal trends were studied using linear regression models. Spatial distributions were studied using Moran's global index (I) and the statistical method and local indicators of spatial association. RESULTS: The incidence of permanent CH in females increased by 8.9 % per year (2014: 36.9 [31.1-43.7] per 100,000 birth-years vs. 2019: 51 [43.9-59.3] per 100,000 birth-years, p < 0.01). The incidence of AH decreased between 2014 and 2019 for both females (2014: 535.7 [533.2-538.2] per 100,000 person-years vs 2019: 335.5 [333.6-337.4] per 100,000 person-years, p < 0.01) and males (2014: 197.5 [195.9-199] per 100,000 person-years vs 2019: 141.7 [140.4-142.9] per 100,000 person-years, p < 0.01). The incidence of hypothyroidism was high in the Nord-Pas-De-Calais and Lorraine regions (CH and AH). CONCLUSIONS: The incidence of permanent CH in females has increased over time. AH incidence decreased. It seems necessary to investigate environmental factors in the disparity of incidence distribution.

Age-related changes in time perception: Effects of immersive virtual reality and spatial location of stimuli.

The perception of time is subject to various environmental influences and exhibits changes across the lifespan. Studies on time perception have often been conducted using abstract stimuli and artificial scenarios, and recent claims for more naturalistic paradigms and realistic stimuli pose the question as to whether immersive virtual reality set-ups differently affect the timing abilities of older versus younger adults. Here, we tested the hypotheses that naturalistic 3D stimuli presented in immersive virtual reality (as opposed to abstract 2D stimuli presented on a computer screen) and the spatial location of those stimuli (left vs. right) affect the perceived time point of their occurrence. Our results demonstrate that a naturalistic presentation of stimuli leads to a bias towards earlier time points in younger, but not older participants. Furthermore, this bias was associated with lower scores of memory capacity. Contrary to our hypothesis that right-sided stimuli are perceived as later than left-sided stimuli, no spatial influences on temporal processing were observed. These results show that older and younger adults are differently affected by an increase in the realism and the immersiveness of experimental paradigms, and highlight the importance of task design in studies on human time perception.

Multisource information fusion method for vegetable disease detection.

Automated detection and identification of vegetable diseases can enhance vegetable quality and increase profits. Images of greenhouse-grown vegetable diseases often feature complex backgrounds, a diverse array of diseases, and subtle symptomatic differences. Previous studies have grappled with accurately pinpointing lesion positions and quantifying infection degrees, resulting in overall low recognition rates. To tackle the challenges posed by insufficient validation datasets and low detection and recognition rates, this study capitalizes on the geographical advantage of Shouguang, renowned as the "Vegetable Town," to establish a self-built vegetable base for data collection and validation experiments. Concentrating on a broad spectrum of fruit and vegetable crops afflicted with various diseases, we conducted on-site collection of greenhouse disease images, compiled a large-scale dataset, and introduced the Space-Time Fusion Attention Network (STFAN). STFAN integrates multi-source information on vegetable disease occurrences, bolstering the model's resilience. Additionally, we proposed the Multilayer Encoder-Decoder Feature Fusion Network (MEDFFN) to counteract feature disappearance in deep convolutional blocks, complemented by the Boundary Structure Loss function to guide the model in acquiring more detailed and accurate boundary information. By devising a detection and recognition model that extracts high-resolution feature representations from multiple sources, precise disease detection and identification were achieved. This study offers technical backing for the holistic prevention and control of vegetable diseases, thereby advancing smart agriculture. Results indicate that, on our self-built VDGE dataset, compared to YOLOv7-tiny, YOLOv8n, and YOLOv9, the proposed model (Multisource Information Fusion Method for Vegetable Disease Detection, MIFV) has improved mAP by 3.43%, 3.02%, and 2.15%, respectively, showcasing significant performance advantages. The MIFV model parameters stand at 39.07 M, with a computational complexity of 108.92 GFLOPS, highlighting outstanding real-time performance and detection accuracy compared to mainstream algorithms. This research suggests that the proposed MIFV model can swiftly and accurately detect and identify vegetable diseases in greenhouse environments at a reduced cost.

Stress guides in generic static mechanical metamaterials.

The confinement of waves within a waveguide can enable directional transmission of signals, which has found wide applications in communication, imaging, and signal isolation. Extending this concept to static systems, where material deformation is piled up along a spatial trajectory, remains elusive due to the sensitivity of localized deformation to structural defects and impurities. Here, we propose a general framework to characterize localized static deformation responses in two-dimensional generic static mechanical metamaterials, by exploiting the duality between space in static systems and time in one-dimensional non-reciprocal wave systems. An internal time-reverse symmetry is developed by the space-time duality. Upon breaking this symmetry, quasi-static load-induced deformation can be guided to travel along a designated path, thereby realizing a stress guide. A combination of time-reverse and inversion symmetries discloses the parity-time symmetry inherent in static systems, which can be leveraged to achieve directional deformation shielding. The tailorable stress guides can find applications in various scenarios, ranging from stress shielding and energy harvesting in structural tasks to information processing in mechanical computing devices.

Leveraging Experience Sampling/Ecological Momentary Assessment for Sociological Investigations of Everyday Life.

Experience sampling (ES) - also referred to as ecological momentary assessment (EMA) - is a data collection method that involves asking study participants to report on their thoughts, feelings, behaviors, activities, and environments in (or near) real time. ES/EMA is typically administered using an intensive longitudinal design (repeated assessments within and across days). Although use of ES/EMA is widespread in psychology and health sciences, uptake of the method among sociologists has been limited. We argue that ES/EMA offers key advantages for the investigation of sociologically relevant phenomena, particularly in light of recent disciplinary emphasis on investigating the everyday mechanisms through which social structures and micro (individual and relational) processes are mutually constitutive. We describe extant and potential research applications illustrating advantages of ES/EMA regarding enhanced validity, disentangling short-term dynamics, and the potential for linkage with spatially and temporally referenced data sources. We also consider methodological challenges facing sociological research using ES/EMA.

Spatiotemporal Changes and Influencing Factors of Hand, Foot, and Mouth Disease in Guangzhou, China, From 2013 to 2022: Retrospective Analysis.

Background: In the past 10 years, the number of hand, foot, and mouth disease (HFMD) cases reported in Guangzhou, China, has averaged about 60,000 per year. It is necessary to conduct an in-depth analysis to understand the epidemiological pattern and related influencing factors of HFMD in this region. Objective: This study aims to describe the epidemiological characteristics and spatiotemporal distribution of HFMD cases in Guangzhou from 2013 to 2022 and explore the relationship between sociodemographic factors and HFMD incidence. Methods: The data of HFMD cases in Guangzhou come from the Infectious Disease Information Management System of the Guangzhou Center for Disease Control and Prevention. Spatial analysis and space-time scan statistics were used to visualize the spatiotemporal distribution of HFMD cases. Multifactor ordinary minimum regression model, geographically weighted regression, and geographically and temporally weighted regression were used to analyze the influencing factors, including population, economy, education, and medical care. Results: From 2013 to 2022, a total of 599,353 HFMD cases were reported in Guangzhou, with an average annual incidence rate of 403.62/100,000. Children aged 5 years and younger accounted for 93.64% (561,218/599,353) of all cases. HFMD cases showed obvious bimodal distribution characteristics, with the peak period from May to July and the secondary peak period from August to October. HFMDs in Guangzhou exhibited a spatial aggregation trend, with the central urban area showing a pattern of low-low aggregation and the peripheral urban area demonstrating high-high aggregation. High-risk areas showed a dynamic trend of shifting from the west to the east of peripheral urban areas, with coverage first increasing and then decreasing. The geographically and temporally weighted regression model results indicated that population density (ß=-0.016) and average annual income of employees (ß=-0.007) were protective factors for HFMD incidence, while the average number of students in each primary school (ß=1.416) and kindergarten (ß=0.412) was a risk factor. Conclusions: HFMD cases in Guangzhou were mainly infants and young children, and there were obvious differences in time and space. HFMD is highly prevalent in summer and autumn, and peripheral urban areas were identified as high-risk areas. Improving the economic level of peripheral urban areas and reducing the number of students in preschool education institutions are key strategies to controlling HFMD.

[Effects of Landscape Pattern on Water Quality at Different Spatial and Temporal Scales in Wuding River Basin and Yanhe River Basin].

To explore the relationship between land use and water quality in basins with different land use patterns at different spatial and temporal scales, the Wuding River Basin and Yanhe River Basin were taken as research objects. Based on land use data in 2020 and water quality monitoring data during two periods, the redundancy analysis method was adopted to quantitatively explore the impact of land use on water quality at multiple scales. The results showed that： ① The main land use types in the two basins were cultivated land and grassland, and the difference was mainly in the proportion of bare land and forest land. ② The water quality in spring was better than that in autumn, and the water quality in the middle and lower reaches was worse. ③ The interpretation rate of land use on the riparian scale was the highest in the two basins. ④ The effect of land use on water quality was more significant in the Wuding River Basin in autumn than in spring, whereas the Yanhe River Basin showed the opposite trend. ⑤ Different land uses had different impacts on water quality. Bare land, cultivated land, and Shannon diversity index （SHDI） in the Wuding River Basin had significant impacts on water quality, whereas grassland, cultivated land, artificial surface, patch density （PD）, and SHDI were significant in the Yanhe River Basin. Cultivated land and artificial surfaces in the Wuding River Basin had a negative impact on water quality. Grassland and bare land had a negative correlation with most chemical indicators. Artificial surfaces and grasslands in the Yanhe River Basin had a negative impact on water quality, whereas forest land had a significant purification effect. The research results provide important information for sustainable land use and multi-scale landscape planning, which can be used to improve water quality.

The effect of temperature on the rate of oxygen evolution reaction during ferrate(VI) synthesis by anodic dissolution of iron in highly alkaline media.

This study investigates the effect of temperature on the rate of the oxygen evolution reaction (OER) during the electrochemical production of ferrate(VI) through anodic iron dissolution. We employed a membrane-divided electrochemical cell with a galvanostatically operated three-electrode setup. During the experiments, we recorded the anode potential at various temperatures and monitored temperature variations over time. Simultaneously, we measured the rates of ferrate(VI) formation and the oxygen evolution reaction. The latter, considered a parasitic reaction, competes with ferrate synthesis. By quantifying the extent to which the OER consumed the applied charge, we discovered that the OER rate decreases with temperature. Specifically, at 25 °C and 168 Am-2, the OER consumes more than double the charge of the produced ferrate, at higher temperatures the rate sensibly decays and with it the consumed charge by the OER. The specific energy required for ferrate(VI) production decreases as temperatures increase, aligning well with current efficiency and space-time yield values within the same temperature range.

Spatial-temporal variations of sediment transport rate and driving factors in Shule River Basin, northwest China.

The sediment content and transport rate of rivers are crucial indicators reflecting soil erosion, water quality, and water resource management in a region. Studying changes in river sediment transport rates within a basin is essential for evaluating water quality, restoring water ecosystems, and implementing soil and water conservation measures. This study focused on the Shule River Basin and utilized various methods such as moving average, cumulative anomaly, Mann-Kendall mutation test, Mann-Kendall (M-K) trend test, Sen's slope estimation, Correlation analysis, wavelet analysis, R/S analysis, ARCGIS10.7 interpolation, non-uniformity coefficient, and concentration to analyze data from hydrologic stations at Changmapu (CMP), Panjiazhuang (PJZ), and Dangchengwan (DCW). The research examined the temporal and spatial characteristics of sediment transport rates and identified key driving factors. Findings revealed significant increases in annual sediment transport rates at CMP and PJZ by 12.227 and 4.318 kg/s (10a)-1, respectively, while DCW experienced a decrease of 0.677 kg/s (10a)-1. The sediment transport rate of the three stations had a sudden change around 1994. The average annual sediment transport rates displayed distinct cycles, with CMP, PJZ, and DCW showing cycles of 51a, 53a, and 29a respectively. Additionally, while CMP and PJZ exhibited a continuous upward trend in sediment transport rates, DCW showed a consistent decline. The annual average sediment transport rates of CMP, PJZ, and DCW were 1305.43 kg/s, 810.06 kg/s, and 247.80 kg/s, respectively. These research findings contribute to enhancing the comprehension of sediment dynamics in the arid region of northwest China and offer a theoretical basis for the restoration and management of ecological environments in similar areas in the future.

The relationship between space and time perception: A registered replication of Casasanto and Boroditsky (2008).

Everything in our environment moves through both space and time, and to effectively act we must be aware of both spatial and temporal elements in relation to our own bodies. Thus, perceptions of space and time have an intimate relationship. Walsh's a theory of magnitude (ATOM) suggests that space and time perception rely on a general magnitude system and their relationship should be roughly symmetrical. Alternatively, metaphor theory, which is based on the philosophical work of Lakoff and Johnson, argues that we represent time using a spatial metaphor and thus the relationship should be asymmetrical (with space influencing time more than time influences space). A compelling line of evidence for metaphor theory comes from the work of Casasanto & Boroditsky. Cognition, 106(2), 579-593. (2008) who experimentally demonstrated this asymmetric effect. However, in our previous unpublished online replication attempt of this work, we found a roughly symmetrical relationship between space and time, more in line with the theoretical predictions of ATOM. Given this, we performed a registered replication of Casasanto & Boroditsky. Cognition, 106(2), 579-593. (2008) in both an online and laboratory environment.

Prediction and model evaluation for space-time data.

Evaluation metrics for prediction error, model selection and model averaging on space-time data are understudied and poorly understood. The absence of independent replication makes prediction ambiguous as a concept and renders evaluation procedures developed for independent data inappropriate for most space-time prediction problems. Motivated by air pollution data collected during California wildfires in 2008, this manuscript attempts a formalization of the true prediction error associated with spatial interpolation. We investigate a variety of cross-validation (CV) procedures employing both simulations and case studies to provide insight into the nature of the estimand targeted by alternative data partition strategies. Consistent with recent best practice, we find that location-based cross-validation is appropriate for estimating spatial interpolation error as in our analysis of the California wildfire data. Interestingly, commonly held notions of bias-variance trade-off of CV fold size do not trivially apply to dependent data, and we recommend leave-one-location-out (LOLO) CV as the preferred prediction error metric for spatial interpolation.

Fully Breaking Entanglement of Multiple Harmonics for Space- and Frequency-Division Multiplexing Wireless Applications via Space-Time-Coding Metasurface.

Harmonic generation and utilization are significant topics in nonlinear science. Although the progress in the microwave region has been expedited by the development of time-modulated metasurfaces, one major issue of these devices is the strong entanglement of multiple harmonics, leading to criticism of their use in frequency-division multiplexing (FDM) applications. Previous studies have attempted to overcome this limitation, but they suffer from designing complexity or insufficient controlling capability. Here a new space-time-coding metasurface (STCM) is proposed to independently and precisely synthesize not only the phases but also the amplitudes of various harmonics. This promising feature is successfully demonstrated in wireless space- and frequency-division multiplexing experiments, where modulated and unmodulated signals are simultaneously transmitted via different harmonics using a shared STCM. To illustrate the advantages, binary frequency shift keying (BFSK) and quadrature phase shift keying (QPSK) modulation schemes are respectively implemented. Behind the intriguing functionality, the mechanism of the space-time coding strategy and the analytical designing method are elaborated, which are validated numerically and experimentally. It is believed that the achievements can potentially propel the time-vary metasurfaces in the next-generation wireless applications.