Explore the vast range of titles available.

Information about climate and how it responds to increased greenhouse gas concentrations depends heavily on insight gained from numerical simulations by coupled climate models. The confidence placed in quantitative estimates of the rate and magnitude of future climate change is therefore strongly related to the quality of these models. In this study, we test the realism of several generations of coupled climate models, including those used for the 1995, 2001, and 2007 reports of the Intergovernmental Panel on Climate Change (IPCC). By validating against observations of present climate, we show that the coupled models have been steadily improving over time and that the best models are converging toward a level of accuracy that is similar to observation-based analyses of the atmosphere.

Extratropical cyclones (ETCs) in East Asia are automatically detected and tracked by applying a Lagrangian tracking algorithm to the 850-hPa relative vorticity field. The ETC statistics, which are derived from ERA-Interim reanalysis dataset from 1979 to 2017, show that East Asian ETCs primarily form over Mongolia, East China, and the Kuroshio Current region with a maximum frequency of six to seven cyclones per month. Both Mongolia and East China ETCs are initiated on the leeward side of the mountains. While Mongolia ETCs downstream of the Altai–Sayan Mountains develop slowly, East China ETCs downstream of the Tibetan plateau develop rapidly as they travel across the warm ocean. Both of them show a maximum frequency and intensity in spring rather than in winter. In contrast, oceanic ETCs across the Kuroshio Current and the Kuroshio–Oyashio Extension, where sea surface temperature gradient is sharp, reach a maximum frequency in winter although their intensity is still maximum in spring. On the decadal timescale, both ETC frequency and intensity exhibit insignificant trends. Exceptions are springtime East China and summertime Mongolia ETCs whose frequencies have slightly decreased since 1979. This declining trend is consistent with the enhanced static stability in the region.

With the development of robot technology, trials adopting robots for last-mile delivery are continuing, and the final destination of last-mile delivery is further expanding into indoor environments. Unlike existing studies conducted for robot-based last-mile delivery in outdoor environments, two main issues must be solved to enable last-mile delivery in indoor environments using robots. First, it is necessary to reasonably and realistically estimate the robot travel time considering horizontal and vertical movement segments within a given building. Second, optimizing the robot routing problem based on the estimated robot travel time is necessary. In this paper, we proposed a new method to estimate the robot travel time considering robot movement characteristics and an elevator in a building. In addition, we developed a mathematical model of the robot routing problem and problem-specific heuristic based on a genetic algorithm to quickly solve the proposed mathematical model. It obtained the exact solutions when the problem size was small and near-optimal solutions in the medium- and large-sized problems (average optimality gap: 0.11% and 0.18%, respectively). Through extensive experiments assuming various building structures, it was determined that the proposed model and heuristic can quickly yield realistic solutions for indoor robot-based last-mile delivery.

In general, the demand for delivery cannot be fulfilled efficiently due to the excessive traffic in dense urban areas. Therefore, many innovative concepts for intelligent transportation of freight have recently been developed. One of these concepts relies on drone-based parcel delivery using rooftops of city buildings. To apply drone logistics system in cities, the operation design should be adequately prepared. In this regard, a mixed integer programming model for drone operation planning and a heuristic based on block stacking are newly proposed to provide solutions. Additionally, numerical experiments with three different problem sizes are conducted to check the feasibility of the proposed model and to assess the performance of the proposed heuristic. The experimental results show that the proposed model seems to be viable and that the developed heuristic provides very good operation plans in terms of the optimality gap and the computation time.

Stratospheric circulation is known to affect weather in the troposphere. Climate modelling reveals a connection between variations in the stratospheric and North Atlantic ocean circulation over the past 30 years, and demonstrates that the stratosphere is an important component of climate over multidecadal timescales. The stratosphere is connected to tropospheric weather and climate. In particular, extreme stratospheric circulation events are known to exert a dynamical feedback on the troposphere 1 . However, it is unclear whether the state of the stratosphere also affects the ocean and its circulation. A co-variability of decadal stratospheric flow variations and conditions in the North Atlantic Ocean has been suggested, but such findings are based on short simulations with only one climate model 2 . Here we assess ocean reanalysis data and find that, over the previous 30 years, the stratosphere and the Atlantic thermohaline circulation experienced low-frequency variations that were similar to each other. Using climate models, we demonstrate that this similarity is consistent with the hypothesis that variations in the sequence of stratospheric circulation anomalies, combined with the persistence of individual anomalies, significantly affect the North Atlantic Ocean. Our analyses identify a previously unknown source for decadal climate variability and suggest that simulations of deep layers of the atmosphere and the ocean are needed for realistic predictions of climate.

A sudden stratospheric warming (SSW) is often defined as zonal-mean zonal wind reversal at 10 hPa and 60°N. This simple definition has been applied not only to the reanalysis data but also to climate model output. In the present study, it is shown that the application of this definition to models can be significantly influenced by model mean biases (i.e., more frequent SSWs appear to occur in models with a weaker climatological polar vortex). To overcome this deficiency, a tendency-based definition is proposed and applied to the multimodel datasets archived for phase 5 of the Coupled Model Intercomparison Project (CMIP5). In this definition, SSW-like events are defined by sufficiently strong vortex deceleration. This approach removes a linear relationship between SSW frequency and intensity of the climatological polar vortex in the CMIP5 models. The models’ SSW frequency instead becomes significantly correlated with the climatological upward wave flux at 100 hPa, a measure of interaction between the troposphere and stratosphere. Lower stratospheric wave activity and downward propagation of stratospheric anomalies to the troposphere are also reasonably well captured. However, in both definitions, the high-top models generally exhibit more frequent SSWs than the low-top models. Moreover, a hint of more frequent SSWs in a warm climate is found in both definitions.

This study explores the evolving trends and research issues in the field of drone-assisted multimodal logistics over the past two decades. By employing various text-mining techniques on related research publications, we identify the most frequently investigated topics and research issues within this domain. Specifically, we utilize titles, abstracts, and keywords from the collected studies to perform both Latent Dirichlet Allocation techniques and Term Frequency-Inverse Document Frequency analysis, which help in identifying latent topics and the core research themes within the field. Our analysis focuses on three primary categories of drone-assisted logistics: drone–truck, drone–ship, and drone–robot systems. The study aims to uncover which latent topics have been predominantly emphasized in each category and to highlight the distinct differences in research focuses among them. Our findings reveal specific trends and gaps in the existing literature, providing a clear roadmap for future research directions in drone-assisted multimodal logistics. This targeted analysis not only enhances our understanding of the current state of the field but also identifies critical areas that require further investigation to advance the application of drones in logistics.

The rapid growth of data traffic in modern communication networks has led to the development of advanced high-capacity multiplexing methods. Orbital angular momentum (OAM)–based mode division multiplexing (MDM) offers a promising scheme by utilizing the orthogonality of helical phase modes to transmit independent data streams simultaneously. In this work, we introduce a novel adjacent mode separation method exploiting OAM’s concentric intensity characteristics for free-space optical (FSO) spatial multiplexing. This method enables the detection of each OAM channel based on its distinctive ring-shaped intensity distribution, contrary to the conventional on-axis phase flattening approach. Two spatially multiplexed signals with different modes are separated by aligning its concentric intensity ring with the active area of an avalanche photodiode (APD), effectively suppressing crosstalk from adjacent modes. Experimental measurements demonstrate that our method achieves a bit-error-rate (BER) performance not exceeding the forward error correction (FEC) threshold, 3.8×10−3, at up to 160 Mbps of data rate, while the conventional detection scheme fails beyond 5 Mbps. The analysis of the eye diagram confirms that our concentric-ring demultiplexing system achieves a high signal-to-noise ratio (SNR) and mode selectivity. These results support the feasibility of the proposed concentric intensity-based mode separation methodology for constructing compact, high-throughput OAM-multiplexed FSO links.

With the increasing day-to-day acceptance of IOT computing, the issues related to it are also getting more attention. The current IOT computing infrastructure brings some security challenges concerned with the users/customers and CSP. The users can store their confidential data at IOT storage and can access them anytime when they need. Lack of trust exists among IOT users and between IOT users and CSP. The prevention of this risk is a big research issue and it needs to be solved. There is a need for trusted IOT computing in recent times to provide trusted services. Here, we propose the integration of TPM in IOT computing to performs cryptographic operations and provide hardware-based security. In this domain, different schemes and methods have been proposed to build trust in IOT computing, but the suitable solution has not been presented by these schemes because these schemes lack in terms of some security services. A comparative study based on trusted computing schemes has also been presented in this paper along with different implementations of critical analysis. Our study is based on an overview of the main issues and summarizing the literature along with their strengths and limitations. In the end, we integrated the trusted platform module in the IOT architecture to establish the trust in IOT computing and to enhance the cybersecurity challenges and evaluated it with the help of mathematical/algorithms/graph theory/matrices and logical diagrams.

Positional information plays a crucial role in embryonic pattern formation, yet its role in tooth development remains unexplored. In this study, we investigated the regional specification of lingual and buccal dental mesenchyme during tooth development. Tooth germs at the cap stage were dissected from mouse mandibles, and their lingual and buccal mesenchymal regions were separated for bulk RNA sequencing. Gene ontology analysis revealed that odontogenesis, pattern specification, and proliferation-related genes were enriched in the lingual mesenchyme, whereas stem cell development, mesenchymal differentiation, neural crest differentiation, and regeneration-related genes were predominant in the buccal mesenchyme. Reaggregation experiments using Wnt1 creERT/+ ; R26R tdT/+ and WT mouse models demonstrated that lingual mesenchyme contributes to tooth formation, while buccal mesenchyme primarily supports surrounding tissues. Furthermore, only the lingual part of tooth germs exhibited odontogenic potential when cultured in vitro and transplanted under the kidney capsule. Bulk RNA transcriptomic analysis further validated the regional specification of the lingual and buccal mesenchyme. These findings provide novel insights into the molecular basis of positional information in tooth development and pattern formation.