Hitachi (United States)

For the social implementation of delivery services by uncrewed aerial vehicles, safety of flight path and punctuality are important. The author aims to develop a guidance algorithm that satisfies these requirements as a part of the uncrewed air traffic management system. In this paper, the algorithm using a new potential field method based on reverse search approach in spacetime, is proposed. The 3D (2D space and time) simulation of 54 scenarios is performed, and the result verifies that the proposed algorithm generates flight plans for five drones to arrive simultaneously without conflict in just one time and reduces the number of attempts significantly compared to the conventional method of 53 attempts.

The increasing digitisation of power systems has introduced new cyber-physical threats, particularly False Data Injection Attacks (FDIAs) targeting critical components such as smart power transformers. This paper presents a scalable anomaly detection framework leveraging Temporal Convolutional Networks (TCNs) integrated with the Predictive Confidence Level Approach (PCL) to identify stealthy FDIAs in highdimensional power grid environments. Building on models validated in the IEEE 14-bus system, the study extends the approach to the IEEE 39bus New England system, incorporating diverse attack and fault scenarios generated through Real Time Digital Simulations (RTDS). The proposed method maintains high detection accuracy 99% while managing increased data volume and complexity. Case studies on load-induced thermal stress, OLTC misoperation, harmonic interference, and cyber manipulation of phase shifting transformers and point-on-wave (POW) controlled shunt reactors reveal the model's applicability across transformer types and operational conditions. The results validate the robustness of the detection scheme and highlight key vulnerabilities in digital substation assets requiring attention in future cyber resilience frameworks.

As the global data center market is growing, the demand for utility-scale solar PV coupled with Battery Energy Storage Systems (BESS) is increasing. This study compares ACcoupled and DC-coupled solar PV systems with BESS as energy hubs for powering data centers. The analysis is conducted from the perspective of the energy hub developer, focusing on the Power Purchase Agreement (PPA) prices offered to data center operators. The DC-coupled configuration offers advantages by reducing transformer CAPEX due to smaller size requirements, higher efficiency, and enhancing economic feasibility. The findings evaluate the impact of solar PV investment, BESS investment, and internal rate of return management to offer competitive PPA prices, ensuring the economic viability of renewable energy solutions for data centers.

This paper proposes a novel approach to automate the conversion of power system dynamic models between different formats using Large Language Models (LLMs). By leveraging the ability of LLMs to understand and process textual information, we aim to streamline the complex and time-consuming process of converting models between different software platforms. This automation will facilitate the integration of model parameters from commercial software like PSS/E into open-source platforms like Dynawo, enabling researchers and engineers to access and utilize a broader range of tools for power system analysis and design. The proposed architecture, which employs the Meta Llama 3.1-405B LLM, has the potential to significantly improve the efficiency and accuracy of model conversion, ultimately contributing to the advancement of power system modeling and analysis tools. The method was validated on the IEEE 14-bus system, and an exact conversion was achieved, with an average error in voltage trajectories of 0.31%, which can be attributed to the different solvers of PSS/E and Dynawo, and not the AI assisted conversion methodology proposed in the paper.

A fundamental study on a 3D wind-condition-estimation technology using a hybrid method of preliminary analysis and sensing for establishing a three-dimensional (3D) wind-condition-estimation system for enabling automatic takeoff and landing of drones and air-mobility aircraft was started. A prototype of a 3D wind-condition-estimation system with the minimum necessary sensors by combining mode extraction was constructed, which is a characteristic of the flow field, and automatic optimization technology for sensor placement in accordance with the characteristics of the flow-field data and verified its effect on hypothetical complex terrain. As a result of reconstructing the velocity distribution of the training data with the optimal sensor arrangement, the reconstructed data reproduced the velocity distribution equivalent to the true value, and the effectiveness of the data-construction method based on the optimal sensor arrangement was confirmed.

Lymphoma, the most predominant neoplastic disorder, is divided into Hodgkin and Non-Hodgkin Lymphoma classifications. Immunotherapeutic modalities have emerged as essential methodologies in combating lymphoid malignancies. Chimeric Antigen Receptor (CAR) T cells exhibit promising responses in chemotherapy-resistant B-cell non-Hodgkin lymphoma cases. This comprehensive review delineates the advancement of CAR-T cell therapy as an immunotherapeutic instrument, the selection of lymphoma antigens for CAR-T cell targeting, and the conceptualization, synthesis, and deployment of CAR-T cells. Furthermore, it encompasses the advantages and disadvantages of CAR-T cell therapy and the prospective horizons of CAR-T cells from a computational research perspective. In order to improve the design and functionality of artificial CARs, there is a need for TCR recognition investigation, followed by the implementation of a quality surveillance methodology. Various lymphoma antigens are amenable to CAR-T cell targeting, such as CD19, CD20, CD22, CD30, the kappa light chain, and ROR1. A notable merit of CAR-T cell therapy is the augmentation of the immune system's capacity to generate tumoricidal activity in patients exhibiting chemotherapy-resistant lymphoma. Nevertheless, it also introduces manufacturing impediments that are laborious, technologically demanding, and financially burdensome. Physical, physicochemical, and physiological limitations further exacerbate the challenge of treating solid neoplasms with CAR-T cells. While the efficacy and safety of CAR-T cell immunotherapy remain subjects of fervent investigation, the promise of this cutting-edge technology offers valuable insights for the future evolution of lymphoma treatment management approaches. Moreover, CAR-T cell therapies potentially benefit patients, motivating regulatory bodies to foster international collaboration.

Abstract Here we offer an approach for being reasonably sure that finite element determinations of stress concentration factors are accurate enough to be included in engineering handbooks. The approach has two contributors. The first consists of analyzing a stress concentration on a sequence of systematically refined meshes until the error estimates of ASME have that sufficient accuracy has been achieved. The second consists of constructing a test problem with an exact and somewhat higher value of its stress concentration factor, then analyzing this test problem with the same sequence of meshes and showing that, in fact, sufficient accuracy has been achieved. In combination, these two means of verification are applied to a series of U-notches in a plate under tension. Together they show that it is reasonable to regard finite element values of stress concentration factors on the finest meshes as being accurate to three significant figures. Given this level of accuracy, it is then also reasonable to use the approach to verify other existing stress concentration factors and resolve any discrepancies between them, as well as to verify new stress concentration factors.

BackgroundNon-invasive, prompt, and proper detection tools for kidney graft injuries (KGIs) are awaited to ensure graft longevity. We screened diagnostic biomarkers for KGIs following kidney transplantation using extracellular vesicles (EVs; exosomes and microvesicles) from the urine samples of patients.MethodsOne hundred and twenty-seven kidney recipients at 11 Japanese institutions were enrolled in this study; urine samples were obtained prior to protocol/episode biopsies. EVs were isolated from urine samples, and EV RNA markers were assayed using quantitative reverse transcription polymerase chain reaction. Diagnostic performance of EV RNA markers and diagnostic formulas comprising these markers were evaluated by comparison with the corresponding pathological diagnoses.ResultsEV CXCL9, CXCL10, and UMOD were elevated in T-cell-mediated rejection samples compared with other KGI samples, while SPNS2 was elevated in chronic antibody-mediated rejection (cABMR) samples. A diagnostic formula developed through Sparse Logistic Regression analysis using EV RNA markers allowed us to accurately (with an area under the receiver operator characteristic curve [AUC] of 0.875) distinguish cABMR from other KGI samples. EV B4GALT1 and SPNS2 were also elevated in cABMR, and a diagnostic formula using these markers was able to distinguish between cABMR and chronic calcineurin toxicity accurately (AUC 0.886). In interstitial fibrosis and tubular atrophy (IFTA) urine samples and those with high Banff chronicity score sums (BChS), POTEM levels may reflect disease severity, and diagnostic formulas using POTEM detected IFTA (AUC 0.830) and high BChS (AUC 0.850).ConclusionsKGIs could be diagnosed with urinary EV mRNA analysis with relatively high accuracy.