MathWorks (United States)

SHAppE (Seismic HAzard Parameters Evaluation app) is a MATLAB-based App for time-dependent probabilistic seismic hazard analysis, designed to simplify data access, analysis and visualization workflows. Since its release in April 2025, SHAppE has been well received by researchers and educators, providing an intuitive interface for complex hazard evaluations without requiring advanced programming skills. While the initial version was fully functional, community feedback over the past year has been invaluable in refining the app. Over the past year, more than 50 issues (including bug fixes, usability improvements, and feature enhancements) were addressed, many reported directly by users. This collaborative process has led to significant upgrades, such as improved data selection workflows, expanded parameter set options, and the ability to extract the complete set of custom filters and applied parameters, further strengthening reproducibility. SHAppE also integrates with external sources like the ISC Earthquake Toolbox for MATLAB, enabling direct access to global earthquake bulletins without significant preprocessing. The community contributions have been critical to the App improvement and we encourage continued feedback to drive future development. SHAppE is freely available via: GitHub (https://github.com/mathworks/Seismic-HAzard-Parameters-Evaluation-Interface-SHAppE) and File Exchange (https://www.mathworks.com/matlabcentral/fileexchange/180879-shappe-seismic-hazard-parameters-evaluation-interface)

The speed of progress in wireless communication requires smart, adaptive antenna systems that can enhance network performance in dynamic environments. This chapter presents AI-based adaptive antenna systems for smart communication networks. By using machine learning algorithms, the antenna adjusts beamforming and polarization in real-time to optimize signal quality, decrease interference, and increase throughput. We describe the architecture, the algorithmic framework, and the experimental validation. The results show a remarkable improvement in spectral efficiency and network reliability, highlighting the superiority of AI in future adaptive antenna technology.

G New Radio (NR) aims to provide a technological solution to the growing demand for faster data rates and lower latency in mobile communications through its improved features and flexibility; however, this comes at the cost of increased design complexity when targeting hardware devices. To address this, high-level design tools such as Simulink can be used with iterative model-based design to shorten development cycles and reduce human error. This paper demonstrates this design flow through the implementation of a hardware-compatible NR Physical Uplink Shared Channel (PUSCH) model, targeting the Zynq UltraScale+ Radio Frequency System-on-Chip (RFSoC) ZCU216 development board. By interfacing with the hardware through MATLAB, the design supports multiple symbol modulation schemes, and parameters such as the number of layers, antenna ports, and selected precoding matrix can be altered. The design was tested by targeting a 10 MHz bandwidth, 60 kHz subcarrier spacing (SCS) waveform with 132 active subcarriers and met timing with 0.276 ns Worst Negative Slack (WNS) for a 245.76 MHz clock frequency, demonstrating its standard compliancy. The Cyclic Prefix Orthogonal Frequency Division Multiplexing (CP-OFDM) modulated signal generated on the board was looped back to MATLAB and verified against an existing software implementation, and the hardware usage was recorded.

Detta kapitel ger en omfattande översikt av de viktigaste datadrivna metoderna för kvalitetsbedömning av givardata som kan vara ett stöd för drift och processoptimering. Begreppet datadrivna metoder används här som ett samlingsnamn för de beräkningsmetoder och algoritmer (maskininlärning, mekanistiska modeller etc.) som på olika sätt kan användas för att tolka data. Kapitlet introducerar begreppet redundanta mätningar som ligger till grund för själva kvalitetsbedömningen i de flesta metoderna. Kapitlet går också igenom det typiska dataflödet som består av en inledande analys, förbehandling av data och metodspecifika beräkningssteg. Vart och ett av dessa tre delsteg beskrivs i tre separata avsnitt. Utöver detta så ges även riktlinjer för att välja den lämpligaste metoden och de viktigaste prestandamåtten som används för att utvärdera dem samt en förklaring av hur man samlar in metadata utifrån datadrivna valideringsmetoder.

In the modern era, Autonomous Vehicles (AV) navigation in complex urban environments has become a significant challenge due to dynamic obstacles, occlusions and localization issues in Global Positioning System (GPS) denied areas. Even though, previous researchers suggested a model using traditional Reinforcement Learning (RL) which face challenges in capturing long-range dependencies in visual scenes, modeling adaptive spatial relationships and generalizing decision policies to diverse driving scenarios. To address these challenges, a hybrid framework integrating Vision Transformer (ViT), Graph Attention Network v2 (GATv2) and Hierarchical Reinforcement Learning (HRL) is proposed for scalable decision-making which is named as ViT-GATv2-HRL. Initially the multi-modal sensor data is considered which fused into an input frame. Subsequently, semantic maps are generated using ViT that converts input frame data into scene graphs and then incorporates GATv2 to extract relational embeddings. Finally, these embeddings fed into an HRL model, where high-level policies select navigation subgoals and low-level policies compute the control commands. Hence, the proposed ViT-GATv2-HRL model captures dynamic spatial relationships, enhances visual understanding and improves navigation robustness across varied urban conditions, achieving safer and more adaptive AV behavior in real-time simulations and complex urban settings.

This study aims to compare the effectiveness and efficiency of two convolutional neural network architectures, MobileNetV2 and Xception, for automated butterfly species classification. As biodiversity monitoring gains significance, effective species identification technologies are crucial for conservation. The research utilized a dataset of 100 butterfly species with 12,594 training images and 1,000 validation and test images. Transfer learning with pre-trained ImageNet weights was implemented, and both models were enhanced with custom classification layers. Data augmentation and class weighting mitigated dataset imbalance issues. Experimental results show Xception attained 93.40% test accuracy compared to MobileNetV2's 93.20%. These high accuracy rates were achieved through effective transfer learning that preserved general feature extraction capabilities, comprehensive class balancing techniques, and carefully tailored learning rate strategies for each architecture. Despite minimal performance difference, MobileNetV2 offers significant computational efficiency advantages with 4.15M parameters compared to Xception's 25.27M, while Xception provides marginally better classification. This study contributes to entomological research and highlights trade-offs between model complexity and performance in fine-grained classification tasks, supporting implementation decisions for butterfly identification systems in practical applications.

The benefits of a fully integrated electronic medical record (EMR) system across primary and specialist care institutions have yet to be formally established. Integrating the EMR systems between primary and specialist care is the first step in building a medical neighborhood. A medical neighborhood is a set of policies and procedures implemented through integrated systems and processes that support the joint management of patient care across primary care physicians, specialist physicians, and other health care providers. This study aims to quantify the impacts of integrating the EMR systems of primary and specialist care institutions in the process of developing a medical neighborhood. The impacts are operationalized in both quantitative and qualitative measures, measuring the benefits of such an integration in 3 specific areas, namely, patient diagnosis tracking, patient care management, and patient coordination. A comprehensive, mixed methods examination was conducted using 3 different data sources (EMR consultation data, clinician survey data, and in-depth interviews). The EMR data consist of patient encounters referred to a specialist clinic from 6 primary care providers before and after integrating the EMR system into the primary and specialist care institutions. We analyzed 25,404 specialist consultation referrals to the specialist clinics by the primary care partners for a 12-month period, during which the integration of the EMR system was conducted. A cohort empirical investigation was used to identify the quantitative impacts of the EMR integration, and a follow-up survey was conducted with the clinicians 18 months post integration. The clinicians' perceptions of the integration were measured to triangulate the empirical observation from the patient encounters, and the postimplementation perception survey was analyzed to triangulate the empirical investigation of consultation instances of the earlier cohort. Concurrently, a total of 30 interviews were conducted between March 16, 2021, and July 28, 2021, with clinicians and operations staff to gather on-the-ground sentiments engendered by this integration, which further informed our quantitative findings. The integration of EMR systems between primary and specialist care institutions was associated with benefits in patient diagnosis tracking, patient care management, and patient coordination. Specifically, it was found that the integration resulted in a decrease in wait time for specialist appointments of an average of 16.5 days (P<.001). Patients were also subjected to fewer repeated procedures and tests; the number of procedures (P=.006), radiographies (P=.02), and overall bill sizes (P=.004) all decreased by between 4.08% and 39.7%, resulting in reduced health care resource wastage while maintaining similar medical outcomes (P=.37). Our study's results are among the first instances of empirical evidence to show that the integration and sharing of data between primary and specialist care institutions promote continuity in health care delivery and joint patient management in a medical neighborhood. The findings go beyond the traditional benefits of improved referral communication, as shown in prior literature.

Abstract This article examines the impact of balance of political power between farmer and intermediary members on profit and risk sharing within intermediary-led agricultural cooperatives in China. The article develops a theoretical model that demonstrates how an intermediary-led cooperative, featuring a two-stage operation with on-farm production by farmer members and value-added processing by the cooperative, allocates profits and manages risks through a sharing rule that includes procurement prices and patronage refund ratios. The results indicate that as farmers gain political influence, their profit share increases, but they also assume more risk, leading to constrained supply and reinforced cooperative monopoly power. Empirical observations from a survey of cooperatives in China, where local officials and elites commonly act as intermediaries, support the model’s findings. Elite-led cooperatives, where intermediaries hold more power, are less likely to provide patronage refunds and more likely to engage in risky, value-added businesses compared to official-led cooperatives, where farmershave stronger representation. Our findings offer insights into the sustainable growth of cooperatives in developing countries.

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;Energy management strategy is essential for HEV’s to achieve an optimum of energy consumption. With predictive energy management, taking future vehicle speed predicted from ADAS map information, in-vehicle navigation traffic flow status information, and current speed into account, one could anticipate a considerable improvement in energy-saving. The major validating approach widely adopted for energy management algorithms nowadays is real-world vehicle testing, of which the economic and time costs are relatively high. Moreover, with advanced algorithms featuring AI coming into light, putting forward higher requirement in the richness of test cases, the drawback in coverage of vehicle testing is revealed. This paper proposed a MIL/SIL testing approach for predictive energy management algorithms, providing a partial replacement to, and overcome the limitations of, vehicle testing. In the testing setup, random traffic generated by MATLAB® based on real-time traffic condition will be taken over by SUMO [&lt;span class="xref"&gt;15&lt;/span&gt;]. In simulation, a map sensor fetches required signals at pre-sampled feature points on the planned trajectory. The collected data include road slope, speed limits, traffic flow densities, average speeds, etc., within which the static data will be provided by a high-fidelity map hosted in RoadRunner, runtime information is computed from the status of target vehicles on-the-fly. This testing approach can, in algorithm validation, not only save cost, but also offer the possibility of scenario variation, therefore enriched test case base, and set the foundation for further analysis on impact of performance of different factors.&lt;/div&gt;&lt;/div&gt;