Large-scale Patterns Research Articles

Large-scale patterns set the predictability limit of extreme events in Kolmogorov flow

Events of extreme intensity in turbulent flows from atmospheric to industrial scales have a strong social and economic impact, and hence there is a need to develop models and indicators which enable their early prediction. Part of the difficulty here stems from the intrinsic sensitivity to initial conditions of turbulent flows. Despite recent progress in understanding and predicting extreme events, the question of how far in advance they can be ideally predicted (without model error and subject only to uncertainty in the initial conditions) remains open. Here we study the predictability limit of extreme dissipation bursts in the two-dimensional Kolmogorov flow by applying information-theoretic measures to massive statistical ensembles with more than $10^7$ direct numerical simulations. We find that extreme events with similar intensity and structure can exhibit disparate predictability due to different causal origins. Specifically, we show that highly predictable extreme events evolve from distinct large-scale circulation patterns. We thus suggest that understanding all the possible routes to the formation of extreme events is necessary to assess their predictability.

Deterministic Global 3D Fractal Cloud Model for Synthetic Scene Generation

This paper describes the creation of a fast, deterministic, 3D fractal cloud renderer for the AFIT Sensor and Scene Emulation Tool (ASSET). The renderer generates 3D clouds by ray marching through a volume and sampling the level-set of a fractal function. The fractal function is distorted by a displacement map, which is generated using horizontal wind data from a Global Forecast System (GFS) weather file. The vertical windspeed and relative humidity are used to mask the creation of clouds to match realistic large-scale weather patterns over the Earth. Small-scale detail is provided by the fractal functions which are tuned to match natural cloud shapes. This model is intended to run quickly, and it can run in about 700 ms per cloud type. This model generates clouds that appear to match large-scale satellite imagery, and it reproduces natural small-scale shapes. This should enable future versions of ASSET to generate scenarios where the same scene is consistently viewed from both GEO and LEO satellites from multiple perspectives.

Sparse and robust support vector machine with capped squared loss for large-scale pattern classification

Support vector machine (SVM), being considered one of the most efficient tools for classification, has received widespread attention in various fields. However, its performance is hindered when dealing with large-scale pattern classification tasks due to high memory requirements and running very slow. To address this challenge, we construct a novel sparse and robust SVM based on our newly proposed capped squared loss (named as Lcsl-SVM). To solve Lcsl-SVM, we first focus on establishing optimality theory of Lcsl-SVM via our defined proximal stationary point, which is convenient for us to efficiently characterize the Lcsl support vectors of Lcsl-SVM. We subsequently demonstrate that the Lcsl support vectors comprise merely a minor fraction of entire training data. This observation leads us to introduce the concept of the working set. Furthermore, we design a novel subspace fast algorithm with working set (named as Lcsl-ADMM) for solving Lcsl-SVM, which is proven that Lcsl-ADMM has both global convergence and relatively low computational complexity. Finally, numerical experiments show that Lcsl-ADMM has excellent performances in terms of getting the best classification accuracy, using the shortest time and presenting the smallest numbers of support vectors when solving large-scale pattern classification problems.

Recent Developments in Quantum Dot Patterning Technology for Quantum Dot Display

Colloidal quantum dot (QDs) have emerged as a crucial building block for LEDs due to their size-tunable emission wavelength, narrow spectral line width, and high quantum efficiency. Tremendous efforts have been dedicated to improving the performance of quantum dot light-emitting diodes (QLEDs) in the past decade, primarily focusing on optimization of device architectures and synthetic procedures for high quality QDs. However, despite these efforts, the commercialization of QLEDs has yet to be realized due to the absence of suitable large-scale patterning technologies for high-resolution devices., This review will focus on the development trends associated with transfer printing, photolithography, and inkjet printing, and aims to provide a brief overview of the fabricated QLED devices. The advancement of various quantum dot patterning methods will lead to the development of not only QLED devices but also solar cells, quantum communication, and quantum computers.

Development of a Length-Based Cell-State Framework Toward the Re-Creation of Large-Scale Dense Congestion Patterns

This paper presents the results and novel findings of generating a simplified version of the prevailing traffic features that existed during a major evacuation. Leveraging the underlying framework provided by the widely used cell transmission model, the desire is to reconstruct the unique characteristics of large congestion patterns that propagate under dense traffic states, where limited attempts at scaling this base model have occurred. The length-based cell-state framework presented can reproduce large spatiotemporal congestion patterns that exist, specifically from large-scale evacuations. To further simplify, the framework considers traffic state heuristics which are calibrated through oblique cumulative count and occupancy curves. As a result of this preprocessing technique, an artifact was found from the use of the cumulative curves under the lens of Newell’s two-phase traffic flow theory where three unique, separate queued regimes were identified within the fundamental diagrams. The methodology re-created a unique large-scale congestion pattern that existed during a past regional evacuation event, Hurricane Irma, the subject of this paper. To test this methodology, a large-scale congested period was analyzed, both with probe vehicle trajectory data and stationary radar detector data. Results demonstrate that traffic re-creation into state-based contours was able to be verified near a 90% level of confidence even at large spatiotemporal extents.

Predation effects on benthic communities are maximized by greater taxonomic richness and diversity in Subtropical SW Atlantic

Predation effects on benthic communities are locally influenced by biological and ecological processes, such as recruitment, competition, and habitat features. We studied three Southwestern Atlantic (SWA) subtropical reef habitats (a rhodolith bed, a coral reef, and a rocky shore) to test if the predation effects on benthic communities overwhelm the habitat-context differences. We deployed panels protected and unprotected from predators on the three habitats, retrieved the panels after 45 days and quantified the recruitment of benthic sessile organisms. Despite differences in the composition of benthic assemblages, the effect of predation was mainly driven by the reduction of the ubiquitous polychaeta Spirorbidae in the three habitats. For the rhodolith bed, which supported a higher taxonomic richness and diversity than the other habitats, predation effects were higher and also driven by the reduction of other taxa. Although we recorded a greater predation effect in more diverse reefs, we attribute this result to small scale traits intrinsic to each habitat instead large-scale oceanographic patterns. Our results add pieces into the debate about how predation affects marine habitats and the importance of small-scale understanding for direct management actions in marine conservation.

Spatiotemporal patterns of groundwater over South Korea

Understanding the large-scale spatiotemporal pattern of multi-depth groundwater levels is critical to develop water management plans and policies for sustainable ecological and social prosperity, which are still lacking. Here, we investigate three major spatiotemporal modes of groundwater levels from ∼200 groundwater monitoring stations over the southern Korean Peninsula (2009–2020), using the Cyclostationary empirical orthogonal function analysis. The first two major modes are associated with the seasonality of recharge and discharge and groundwater use during the 2016/17 drought, which explained half of the total variance. The third major mode indicated a decreasing trend of deep groundwater levels over the western Korean Peninsula, where key administrative and authority offices have been relocated via balanced national land development policies. Furthermore, at least three million Koreans over this region likely experience groundwater depletion by the 2080s. Observational evidence of emerging groundwater depletion suggests a window of opportunity for pre-emptive groundwater management plans.

The Quantitative Role of Moisture and Vertical Motion in Shaping Summer Heavy Rainfall over North China under Two Distinct Large-Scale Weather Patterns

Abstract Two distinct categories of weather patterns, denoted as Type 1 and Type 2, which show higher-than-expected frequency of summer heavy rainfall days (HRDs) over North China (NC), are selected from nine weather patterns categorized by the self-organizing map algorithm during 1979–2019. The respective HRDs over NC exhibit dissimilar characteristics, with Type 1 showing a northern distribution and Type 2 a southern distribution. The quantitative disparities in terms of moisture content and vertical motion are discussed in reactions to the synoptic-scale patterns associated with HRDs. The outcomes of a 20-day backward tracking, using the so-called Water Accounting Model-2layers, reveal noteworthy contrasts in moisture sources. Type 1 predominantly receives moisture from the western North Pacific, while Type 2 relies more on contributions from the Arabian Sea, Bay of Bengal, and Eurasia. However, the major moisture sources with grid cells contributing more than 0.01 mm show a consistent cumulative contribution of 77% for Type 1 and 80% for Type 2. The finding suggests that the discrepancy between the two types cannot be solely attributed to moisture supply. Further examination of the transverse and shearwise Q-vector components provides insights into how these distinct weather patterns influence HRDs by the alteration of vertical motion. In Type 1, an upper-level jet entrance induces a thermally direct secondary circulation that enhances vertical motion, while a baroclinic trough plays a dominant role in generating vertical motion in Type 2. Moreover, these unique configurations for each type of weather pattern are not only pre-existing but also intensified during HRDs.

Human influence on the recent weakening of storm tracks in boreal summer

Anthropogenic warming can alter large-scale circulation patterns in the atmosphere, which could have serious consequences for regional climate impacts and extreme weather. Observed thermodynamic changes in boreal extratropics have been attributed to human emissions with high confidence, but most circulation changes have not. In particular, not only that in the previous suite of climate models most models do not capture the recent boreal summer storm tracks weakening, but also a quantification of the role of human emissions in the recent storm tracks weakening has not been conducted to date. Here we use the latest suite of climate models, which are found to adequately capture the recent storm tracks weakening, and show that this weakening is attributable to anthropogenic emissions. Human emissions have resulted in more-rapid warming of the high latitudes, and the associated reduction in poleward temperature gradient has weakened the storms. The physical consistency between models and reanalyses increases our confidence in the projected weakening, which presents regional risks including hot-dry extremes in summer.

Influence of Eastern, Central and Mix El Niño on the variability of rainfall in southeastern South America

El Niño/Southern Oscillation (ENSO) plays a very important role in modulating rainfall variability in South America. Although the effects of conventional ENSO are well known, there are significant variations in rainfall patterns when sea surface temperature anomalies manifest over the different regions along the equatorial Pacific, namely, Niño 3 (East), Niño 3 and Niño 4 (Mix), and Niño 4 (Central) ENSO. This work aims to analyze the variability of precipitation in southeastern South America associated with different types of El Niño (EN). For this, composite analyzes of precipitation, winds at low and high levels, vertical velocity and geopotential height were carried out. The results indicate that the rainfall pattern associated with Eastern Pacific (EP) El Niño shows the classic features expected from EN described in previous literature, i.e., a stronger and well-defined positive precipitation anomaly over southeast South America, while Central Pacific (CP) El Niño shows weaker and even an opposite sign over the region. Mix (MX) El Niño events present a spatial pattern of precipitation anomalies similar to EP El Niño events, but with lower intensity in the study area. The heterogeneity of EN-related precipitation effects results from a change in large-scale atmospheric circulation patterns: the Walker cell in the tropics, the Rossby waves in the extratropics, and the circulation of low and high-level jet streams. EP El Niño events feature a configuration similar to the South American Low-Level Jet, the strengthening of the subtropical jet, better structured Rossby waves and the Pacific-South America pattern. These characteristics are observed in MX El Niños, but in a weaker way compared to EP El Niños. CP El Niño events, on the other hand, present a weakening and spatial displacement of the aforementioned conditions, which is reflected in negative precipitation anomalies in most seasons. Thus, more significant rainfall variations are noted between the EP El Niños and CP El Niños, with a predominance of positive and negative rainfall anomalies in the study area, respectively.

1500-year reconstruction of Circumpolar Deep Water intrusion and its impact on southern elephant seal populations in King George Island, West Antarctic Peninsula

Circumpolar Deep Water (CDW) intrusion onto the continental shelf significantly affects basal melting of ice shelves, oceanographic and ecological processes in the West Antarctic Peninsula (WAP). Understanding the changes in CDW upwelling and its impacts on marine ecology during historical periods is urgent. Here, we presented a new reconstruction for the variability in CDW intrusion onto the WAP over the past 1500 years by using the cadmium (Cd) concentrations in southern elephant seal hairs collected from King George Island. In addition, multi-proxies including the abundance of seal hairs and TOC, TN, LOI, and bio-elements in the sediments were integrated to infer historical seal populations by using the Generalized Additive Models (GAMs). The overall trend of Cd concentrations was well consistent with those of primary productivity and sea ice, reflecting the contribution of CDW on regional oceanic environment and ecology. Notably, during a period of ∼900–500 yr BP, enhanced CDW intrusion allowed for increased regional marine productivity and decreased sea ice, and ultimately promoting an increase in seal populations. Enhanced CDW intrusion was possibly driven by southward shift and intensification of the Southern Hemisphere westerly wind. These results indicated that large-scale atmospheric circulation patterns were coupled with regional oceanic circulation and ecological processes of the Southern Ocean.

Moist processes in NCUM global forecasts during the boreal summer monsoon

The objective of this study is to evaluate the performance of the National Center for Medium-Range Weather Forecasting Unified model (NCUM) global version in representing the moist processes during active and break monsoon periods over the Indian region. We employed the moist static energy (MSE) equation, a process-oriented diagnostic (POD), to examine the relationship between small-scale convection and large-scale circulation patterns in the forecast for eight monsoon seasons spanning from June to September 2015 to 2022. Results are validated by utilizing the European Center for Medium-Range Weather Forecasting Reanalysis (ERA5) data products. By employing PODs, we aimed to identify the influence of various MSE terms on rainfall evolution, particularly over the Central Indian (CI) region. We also investigated the systematic errors in certain processes that contribute to rainfall biases. Additionally, the study explored the moisture-convection relationship and compared the sensitivity of free tropospheric moisture to deep convection in the NCUM forecasts against the reanalysis data. The findings indicate weaknesses in the model's sensitivity to moisture and its ability to represent the rainfall-column water vapor (CWV) relationship highlighting areas where further improvements may be necessary to enhance the model's performance in monsoon forecasting. Despite the presence of systematic biases in the model's mean state of rainfall, such as dry biases over the Bay of Bengal and wet biases over the eastern Indian Ocean (EIO), the results obtained from the MSE diagnostics aligned with the existing understanding of monsoon dynamics. Specifically, the study emphasized the significance of horizontal advection of MSE as a leading term preceding the peak active/break conditions over the CI region. Hence, these findings not only showcase the usefulness of PODs in examining the strengths and weaknesses of numerical weather prediction (NWP) models but also serve as a verification tool for assessing model skills, leading to improved understanding and development of NWP models.

Multiscale dynamics of transient merging between western disturbances and monsoonal lows: Connections to the July 2023 flood in Himachal Pradesh

The Himachal Pradesh region experienced an unprecedented 436% more rainfall than normal from July 7th to 10th, 2023, resulting in devastating landslides, flash floods, and significant socio-economic losses. Using rainfall observations and reanalysis data, this study investigates the dynamics of the extreme precipitation event that caused the disastrous 2023 flood in Himachal Pradesh, India. Prior to the flood, moderate rainfall events saturated the mountainsides, making them more vulnerable to landslides and flash floods. Local features such as rapid convergence, vertical velocity, relative humidity, relative vorticity, and moisture flux accumulation were observed to decrease following the extreme precipitation event, indicating a short-duration cloud burst event. Further, the study reveals a large-scale circulation pattern linking the extratropic and monsoonal low, which is favorable for extreme precipitation in the Himachal Pradesh region. This pattern includes a transient merging of eastward-propagating trough in the mid-upper-level, which extends southward and a monsoon low moving westward across India. The key elements of this transient merging feature consist of a Rossby wave train structure and a blocking high west of the features. The Rossby wave breaking effect occurred downstream of the blocking high two days prior to extreme precipitation event occurrence. This allowed high potential vorticity stratospheric air to intrude the equator and troposphere, causing surface pressure change and enhanced vertical motion, thereby advecting high amount of moisture causing extreme precipitation leading to catastrophic floods. Overall, the findings of this study hold direct implications for early warning systems and disaster preparedness in the face of extreme precipitation-induced flood situations triggered by such transient merging features.

A high-resolution meshfree particle method for numerical investigation of second-order macroscopic pedestrian flow models

Recent advancements in empirical observations of human psychological responses have revealed that the governing rules of human behavioral aspects can not only be predicted with adequate deterministic precision but also forged into mathematical formulations. These modeling studies enable crisis managers with reliable simulation tools to gain insights into critical facets of crowd disasters and enhance their decision-making abilities to facilitate safe egress in emergency situations. The macroscopic scale of representation in this context is often invoked to comprehend large-scale collective pattern formation in vast built environments, owing to its computational viability. The associated governing equations are typically constructed in a system of hyperbolic conservation law form, and conducting simulations in real-life complex geometric configurations can pose computational challenges. This article puts forward a high-resolution, shock-capturing meshfree particle method in an Eulerian framework for the numerical approximation of several widely adopted macroscopic pedestrian flow models. It serves as a superior alternative to traditional mesh-based methods, eliminating the need for specific mesh structures and connectivity between them. A classical Generalized Finite Difference Method (GFDM) in conjunction with several consistency conditions on spatial derivative coefficients is adopted to obtain a non-oscillatory and fairly conservative Godunov-type discretization of the governing system. The intercell flux is evaluated using a suitable Riemann solver, and a slope-limited reconstruction procedure of the corresponding Riemann states, adapted to arbitrary point distribution, is employed for improved accuracy. Moreover, the preferred direction of human motion is determined from a local density-dependent eikonal-type equation, which is solved using a generalized version of the Fast Marching Method. A thorough numerical investigation of three well-known second-order macroscopic models, namely Payne-Whitham (PW), Aw-Rascle (AR), and Aw-Rascle-Zhang (ARZ) is carried out through two hypothetical scenarios of crowd evacuation, not only to demonstrate the accuracy and applicability of the proposed approach but also to deepen understanding of their distinctive characteristics. A comparison of the flow flux with the fundamental diagram suggests that the density profile obtained with the ARZ model is equivalent to Hughes' first-order model. In contrast, the PW and AR models have proven to effectively replicate complex, unstable pedestrian phenomena such as stop-and-go waves. Finally, Helbing's counter-intuitive idea that strategically positioned barriers upstream of a bottleneck can essentially expedite evacuation is numerically investigated across various parameter choices.

Vertical structural complexity of plant communities represents the combined effects of resource acquisition and environmental stress on the Tibetan Plateau.

The vertical structural complexity (VSC) of plant communities reflects the occupancy of spatial niches and is closely related to resource utilization and environmental adaptation. However, understanding the large-scale spatial pattern of VSC and its underlying mechanisms remains limited. Here, we systematically investigate 2013 plant communities through grid sampling on the Tibetan Plateau. VSC is quantified as the maximum plant height within a plot (Height-max), coefficient of variation of plant height (Height-var), and Shannon evenness of plant height (Height-even). Precipitation dominates the spatial variation in VSC in forests and shrublands, supporting the classic physiological tolerance hypothesis. In contrast, for alpine meadows, steppes, and desert grasslands in extreme environments, non-resource limiting factors (e.g., wide diurnal temperature ranges and strong winds) dominate VSC variation. Generally, with the shifting of climate from favorable to extreme, the effect of resource availability gradually decreases, but the effect of non-resource limiting factors gradually increases, and that the physiological tolerance hypothesis only applicable in favorable conditions. With the help of machine learning models, maps of VSC at 1-km resolution are produced for the Tibetan Plateau. Our findings and maps of VSC provide insights into macroecological studies, especially for adaptation mechanisms and model optimization.

A statewide, weather-regime based stochastic weather generator for process-based bottom-up climate risk assessments in California – Part II: Thermodynamic and dynamic climate change scenarios

This study is the second of a two-part series presenting a novel weather regime-based stochastic weather generator to support bottom-up climate vulnerability assessments of water systems in California. In Part 2 of this series, we present how the model is used to develop an ensemble of climate change scenarios based on both thermodynamic and dynamic signals of climate change. The ensemble includes a suite of 30 climate change scenarios, each consisting of 1000 years of simulated daily climate data (precipitation, maximum temperature, minimum temperature) at a 6 km resolution across the entire state of California. The 30 scenarios represent a range of plausible climate changes to temperature, average precipitation, and precipitation extremes that are reflective of thermodynamic responses of the atmosphere to warming. An additional two scenarios are also created that represent changes in the frequency of weather regimes (e.g., dynamic climate change). Results from these scenarios reveal that when the effects of anthropogenic climate change are combined with plausible realizations of natural climate variability, the severity of extremes in California is amplified significantly. In addition, recent changes in the frequency of large-scale patterns of atmospheric circulation can have impacts of similar magnitude to large (>10%) declines in average precipitation, particularly with respect to drought. The scenarios developed in this work are designed to allow water managers to systematically test the sensitivity of their water system to different combinations of climate change, so that key vulnerabilities can be discovered and then addressed through adaptation planning.

PIV experimental study on natural convective flows at high Rayleigh numbers in industrial buildings

High heat loads in industrial buildings potentially form strong natural convective flows. These flows exhibit significant unsteadiness and anisotropy, which can profoundly impact indoor air distribution. To study the flow characteristics of thermal convective flows in large indoor spaces, we built a large-scale test bench with controllable thermal boundaries. Under three different heat source intensities represented by Rayleigh numbers (Ra = 3 × 1010, 6 × 1010, and 1 × 1011) that are typical in industrial buildings, we used 2-dimensional particle image velocimetry to capture the large-scale convective airflow by splicing nine subregions, each with 800 instantaneous flow fields sampled at a frequency of 3 Hz. The results show that the convective airflow exhibited large-scale circulation patterns. The velocity near the wall gradually increased with the increased Ra number, while the velocity at the center remained low. The turbulence intensity near the wall was less than 1, while that at the center was larger than 5 and fluctuated greatly. The dominant fluctuation frequency of the airflow under the high Ra was 0.1 Hz. Through proper orthogonal decomposition, it is found that even for horizontal flows, the main transient fluctuations still exhibit an up-and-down fluctuating flow structure. Additionally, the calculation of the velocity structure function revealed that the airflow near the wall is anisotropic, while the airflow at the center is isotropic. The Reynolds number of the flow generated by vertical temperature differences exhibits a power function relationship with the Rayleigh number. This study provides a basis for the optimization of air distribution design in industrial buildings.

Analysis of recent trends and spatiotemporal changes of droughts over Serbia using high-resolution gridded data

The purpose of this study is to analyze the spatiotemporal variations in drought in Serbia over recent decades using the standardized precipitation evapotranspiration index (SPEI). The analysis is performed for time series of 1-, 3-, 6-, and 12-month SPEI indices (SPEI-01, SPEI-03, SPEI-06 and SPEI-12, respectively) calculated using the E-OBS gridded dataset with a horizontal resolution of 0.1° for the period of 1950–2022. In general, it was found that negative SPEI trends prevailed in Serbia for all months and all analyzed SPEI timescales, indicating a more frequent appearance of droughts in recent decades. Moreover, for a large majority of month-timescale combinations, the area with a negative trend was larger than the area with a positive trend. For several reasons, we focused on the SPEI for August. First, the SPEI for this month shows one of the most pronounced negative trends, which is rather consistent over different timescales and consistent in terms of the area over which the trend was found to be significant. Statistically significant negative trends of SPEI-03, −06 and − 12 for August were found over 11, 17 and 38% of the country area, respectively. To better understand the trends and changes in the SPEI for August, focusing on extreme and severe drought events, an innovative trend analysis (ITA) was applied. It was found that the occurrence of both extreme and severe SPEI drought categories has doubled over the last thirty years in comparison with the previous thirty-year period. Additionally, more frequent transitions from neutral and wet categories to dry categories were observed through an application of the Markov chain. Second, the special focus on SPEI-03, −06 and − 12 for August was motivated by the fact that we found a significant correlation between these index values and the annual maize production in Serbia, indicating that the SPEI for August can be useful for the assessment of drought impact on agriculture. To better understand interannual variability and long-term trends, this paper also explores the relations between the SPEI over Serbia and large-scale circulation patterns, together with general global and continental warming trends. Our findings indicate that drought changes over recent decades may be driven by the interplay of long-term warming and large-scale circulation patterns. In the context of climate change, especially climate projections, Serbia is situated in a region for which climate projections of mean precipitation change demonstrate weak signals and often with conflicting signs of change within multimodel ensembles. From this point of view, our results can be seen as a contribution to a better understanding of changes in extremes, in our case droughts, that can be hidden by inconclusive changes in the mean values. Finally, due to the similar situation in terms of precipitation and drought trends and changes, our results may be relevant for a wider region, and not only for Serbia.

PyCoM: a python library for large-scale analysis of residue-residue coevolution data.

Computational methods to detect correlated amino acid positions in proteins have become a valuable tool to predict intra- and inter-residue protein contacts, protein structures, and effects of mutation on protein stability and function. While there are many tools and webservers to compute coevolution scoring matrices, there is no central repository of alignments and coevolution matrices for large-scale studies and pattern detection leveraging on biological and structural annotations already available in UniProt. We present a Python library, PyCoM, which enables users to query and analyze coevolution matrices and sequence alignments of 457622 proteins, selected from UniProtKB/Swiss-Prot database (length ≤ 500 residues), from a precompiled coevolution matrix database (PyCoMdb). PyCoM facilitates the development of statistical analyses of residue coevolution patterns using filters on biological and structural annotations from UniProtKB/Swiss-Prot, with simple access to PyCoMdb for both novice and advanced users, supporting Jupyter Notebooks, Python scripts, and a web API access. The resource is open source and will help in generating data-driven computational models and methods to study and understand protein structures, stability, function, and design. PyCoM code is freely available from https://github.com/scdantu/pycom and PyCoMdb and the Jupyter Notebook tutorials are freely available from https://pycom.brunel.ac.uk.

Optimization of news dissemination push mode by intelligent edge computing technology for deep learning

The Internet era is an era of information explosion. By 2022, the global Internet users have reached more than 4 billion, and the social media users have exceeded 3 billion. People face a lot of news content every day, and it is almost impossible to get interesting information by browsing all the news content. Under this background, personalized news recommendation technology has been widely used, but it still needs to be further optimized and improved. In order to better push the news content of interest to different readers, users' satisfaction with major news websites should be further improved. This study proposes a new recommendation algorithm based on deep learning and reinforcement learning. Firstly, the RL algorithm is introduced based on deep learning. Deep learning is excellent in processing large-scale data and complex pattern recognition, but it often faces the challenge of low sample efficiency when it comes to complex decision-making and sequential tasks. While reinforcement learning (RL) emphasizes learning optimization strategies through continuous trial and error through interactive learning with the environment. Compared with deep learning, RL is more suitable for scenes that need long-term decision-making and trial-and-error learning. By feeding back the reward signal of the action, the system can better adapt to the unknown environment and complex tasks, which makes up for the relative shortcomings of deep learning in these aspects. A scenario is applied to an action to solve the sequential decision problem in the news dissemination process. In order to enable the news recommendation system to consider the dynamic changes in users' interest in news content, the Deep Deterministic Policy Gradient algorithm is applied to the news recommendation scenario. Opposing learning complements and combines Deep Q-network with the strategic network. On the basis of fully summarizing and thinking, this paper puts forward the mode of intelligent news dissemination and push. The push process of news communication information based on edge computing technology is proposed. Finally, based on Area Under Curve a Q-Leaning Area Under Curve for RL models is proposed. This indicator can measure the strengths and weaknesses of RL models efficiently and facilitates comparing models and evaluating offline experiments. The results show that the DDPG algorithm improves the click-through rate by 2.586% compared with the conventional recommendation algorithm. It shows that the algorithm designed in this paper has more obvious advantages in accurate recommendation by users. This paper effectively improves the efficiency of news dissemination by optimizing the push mode of intelligent news dissemination. In addition, the paper also deeply studies the innovative application of intelligent edge technology in news communication, which brings new ideas and practices to promote the development of news communication methods. Optimizing the push mode of intelligent news dissemination not only improves the user experience, but also provides strong support for the application of intelligent edge technology in this field, which has important practical application prospects.