Design and Assessment of an Electric Vehicle Powertrain Model Based on Real-World Driving and Charging Cycles

In this paper, an advanced analytical model for an electric vehicle (EV) powertrain has been developed to illustrate the vehicular dynamics by combining electrical and mechanical models in the analysis. This study is based on a Nissan Leaf EV. In the electrical system, the powertrain has various components including a battery pack, a battery management system, a dc/dc converter, a dc/ac inverter, a permanent magnet synchronous motor, and a control system. In the mechanical system, it consists of power transmissions, axial shaft, and vehicle wheels. Furthermore, the driving performance of the Nissan Leaf is studied through the real-world driving tests and simulation tests in MATLAB/Simulink. In the analytical model, the vehicular dynamics is evaluated against changes in the vehicle velocity and acceleration, state of charge of the battery, and the motor power. Finally, a number of EVs involved in the power dispatch is studied. The greenhouse gas emissions of the EV are analyzed according to various energy power and driving features, and compared with the conventional internal combustion engine vehicle. In this case, Nissan Leaf is a pure EV. For a given drive cycle, Nissan Leaf can reduce CO2 emissions by 70%, depending on the way electricity is generated and duty cycles

AdaTT: Adaptive Task-to-Task Fusion Network for Multitask Learning in Recommendations

Multi-task learning (MTL) aims at enhancing the performance and efficiency of machine learning models by training them on multiple tasks simultaneously. However, MTL research faces two challenges: 1) modeling the relationships between tasks to effectively share knowledge between them, and 2) jointly learning task-specific and shared knowledge. In this paper, we present a novel model Adaptive Task-to-Task Fusion Network (AdaTT) to address both challenges. AdaTT is a deep fusion network built with task specific and optional shared fusion units at multiple levels. By leveraging a residual mechanism and gating mechanism for task-to-task fusion, these units adaptively learn shared knowledge and task specific knowledge. To evaluate the performance of AdaTT, we conduct experiments on a public benchmark and an industrial recommendation dataset using various task groups. Results demonstrate AdaTT can significantly outperform existing state-of-the-art baselines

Let Models Speak Ciphers: Multiagent Debate through Embeddings

Discussion and debate among Large Language Models (LLMs) have gained considerable attention due to their potential to enhance the reasoning ability of LLMs. Although natural language is an obvious choice for communication due to LLM's language understanding capability, the token sampling step needed when generating natural language poses a potential risk of information loss, as it uses only one token to represent the model's belief across the entire vocabulary. In this paper, we introduce a communication regime named CIPHER (Communicative Inter-Model Protocol Through Embedding Representation) to address this issue. Specifically, we remove the token sampling step from LLMs and let them communicate their beliefs across the vocabulary through the expectation of the raw transformer output embeddings. Remarkably, by deviating from natural language, CIPHER offers an advantage of encoding a broader spectrum of information without any modification to the model weights. While the state-of-the-art LLM debate methods using natural language outperforms traditional inference by a margin of 1.5-8%, our experiment results show that CIPHER debate further extends this lead by 1-3.5% across five reasoning tasks and multiple open-source LLMs of varying sizes. This showcases the superiority and robustness of embeddings as an alternative "language" for communication among LLMs

The correlation between multimodal radiomics and pathology about thermal ablation lesion of rabbit lung VX2 tumor

ObjectiveTo explore the correlation of CT-MRI pathology with lung tumor ablation lesions by comparing CT, MRI, and pathological performance of rabbit lung VX2 tumor after thermal ablation.MethodsThermal ablation including microwave ablation (MWA) and radiofrequency ablation (RFA) was carried out in 12 experimental rabbits with lung VX2 tumors under CT guidance. CT and MRI performance was observed immediately after ablation, and then the rabbits were killed and pathologically examined. The maximum diameter of tumors on CT before ablation, the central hypointense area on T2-weighted image (T2WI) after ablation, and the central hyperintense area on T1-weighted image (T1WI) after ablation and pathological necrosis were measured. Simultaneously, the maximum diameter of ground-glass opacity (GGO) around the lesion on CT after ablation, the surrounding hyperintense area on T2WI after ablation, the surrounding isointense area on T1WI after ablation, and the pathological ablation area were measured, and then the results were compared and analyzed.ResultsAblation zones showed GGO surrounding the original lesion on CT, with a central hypointense and peripheral hyperintense zone on T2WI as well as a central hyperintense and peripheral isointense zone on T1WI. There was statistical significance in the comparison of the maximum diameter of the tumor before ablation with a central hyperintense zone on T1WI after ablation and pathological necrosis. There was also statistical significance in the comparison of the maximum diameter of GGO around the lesion on CT with the surrounding hyperintense zone on T2WI and isointense on T1WI after ablation and pathological ablation zone. There was only one residual tumor abutting the vessel in the RFA group.ConclusionsMRI manifestations of thermal ablation of VX2 tumors in rabbit lungs have certain characteristics with a strong pathological association. CT combined with MRI multimodal radiomics is expected to provide an effective new method for clinical evaluation of the immediate efficacy of thermal ablation of lung tumors

Electrical and magnetic properties of antiferromagnetic semiconductor MnSi2N4 monolayer

Two-dimensional antiferromagnetic semiconductors have triggered significant attention due to their unique physical properties and broad application. Based on first-principles calculations, a novel two-dimensional (2D) antiferromagnetic material MnSi2N4 monolayer is predicted. The calculation results show that the two-dimensional MnSi2N4 prefers an antiferromagnetic state with a small band gap of 0.26 eV. MnSi2N4 has strong antiferromagnetic coupling which can be effectively tuned under strain. Interestingly, the MnSi2N4 monolayer exhibits a half-metallic ferromagnetic properties under an external magnetic field, in which the spin-up electronic state displays a metallic property, while the spin-down electronic state exhibits a semiconducting characteristic. Therefore, 100% spin polarization can be achieved. Two-dimensional MnSi2N4 monolayer has potential application in the field of high-density information storage and spintronic devices

MoNuSAC2020:A Multi-Organ Nuclei Segmentation and Classification Challenge

Detecting various types of cells in and around the tumor matrix holds a special significance in characterizing the tumor micro-environment for cancer prognostication and research. Automating the tasks of detecting, segmenting, and classifying nuclei can free up the pathologists' time for higher value tasks and reduce errors due to fatigue and subjectivity. To encourage the computer vision research community to develop and test algorithms for these tasks, we prepared a large and diverse dataset of nucleus boundary annotations and class labels. The dataset has over 46,000 nuclei from 37 hospitals, 71 patients, four organs, and four nucleus types. We also organized a challenge around this dataset as a satellite event at the International Symposium on Biomedical Imaging (ISBI) in April 2020. The challenge saw a wide participation from across the world, and the top methods were able to match inter-human concordance for the challenge metric. In this paper, we summarize the dataset and the key findings of the challenge, including the commonalities and differences between the methods developed by various participants. We have released the MoNuSAC2020 dataset to the public

DC Networks on the Distribution Level – New Trend or Vision?

"DC networks on Distribution Level – are they a new trend or a Vision?" That is the question that has focused the efforts of the Working Group the last two years, and whose consideration is summarized in this report. This report represents the first phase evaluation of this topic and is focused primarily on medium (MVDC) and low voltage (LVDC) level applications

Targeted therapies in the treatment of advanced hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is the most common liver cancer and the third leading cause of cancer death. It has been a major worldwide health problem with more new cases being diagnosed each year. The current available therapies for patients with advanced HCC are extremely limited. Therefore, it is of great clinical interests to develop more effective therapies for systemic treatment of advanced HCC. Several promising target-based drugs have been tested in a number of clinical trials. One breakthrough of these efforts is the approved clinical use of sorafenib in patients with advanced HCC. Targeted therapies are becoming an attractive option for the treatment of advanced HCC. In this review, we summarize the most recent progress in clinical targeted treatments of advanced HCC