Numerical simulation and process optimization on cast steel bearing sleeve

A three-dimensional Computer Aided Design (CAD) model of the bearing sleeve casting is established by Pro/E software. The ViewCast program is utilized for studying casting processes of solidification and mould-filling in order to optimize the casting technology. Based on the solidification simulation, the casting shrinkage and porosity as well as solidification processes are forecast visually in diagrams with the help of ViewCast. The mould-filling simulation results verify whether the fluid/liquid metal fills gates and the mould smoothly. The simulation results of an initial casting scheme show that this scheme is improper. Two optimization schemes have been completed based on the filling simulation so that a modified casting technology is obtained. The simulation results of optimized schemes indicate that the metal fluid fills the mold smoothly and the shrinkage is eliminated effectively. The optimized scheme II is preferred to scheme I. Experimentally, the casting confirms that these optimized methods are very useful in reducing the casting defects and improving the product quality

MESEN: Exploit Multimodal Data to Design Unimodal Human Activity Recognition with Few Labels

Human activity recognition (HAR) will be an essential function of various emerging applications. However, HAR typically encounters challenges related to modality limitations and label scarcity, leading to an application gap between current solutions and real-world requirements. In this work, we propose MESEN, a multimodal-empowered unimodal sensing framework, to utilize unlabeled multimodal data available during the HAR model design phase for unimodal HAR enhancement during the deployment phase. From a study on the impact of supervised multimodal fusion on unimodal feature extraction, MESEN is designed to feature a multi-task mechanism during the multimodal-aided pre-training stage. With the proposed mechanism integrating cross-modal feature contrastive learning and multimodal pseudo-classification aligning, MESEN exploits unlabeled multimodal data to extract effective unimodal features for each modality. Subsequently, MESEN can adapt to downstream unimodal HAR with only a few labeled samples. Extensive experiments on eight public multimodal datasets demonstrate that MESEN achieves significant performance improvements over state-of-the-art baselines in enhancing unimodal HAR by exploiting multimodal data.Comment: Accepted to the 21th ACM Conference on Embedded Networked Sensor Systems (SenSys 2023

SOA pattern effect mitigation by neural network based pre-equalizer for 50G PON

Semiconductor optical amplifier (SOA) is widely used for power amplification in O-band, particularly for passive optical networks (PONs) which can greatly benefit its advantages of simple structure, low power consumption and integrability with photonics circuits. However, the annoying nonlinear pattern effect degrades system performance when the SOA is needed as a pre-amplifier in PONs. Conventional solutions for pattern effect mitigation are either based on optical filtering or gain clamping. They are not simple or sufficiently flexible for practical deployment. Neural network (NN) has been demonstrated for impairment compensation in optical communications thanks to its powerful nonlinear fitting ability. In this paper, for the first time, NN-based equalizer is proposed to mitigate the SOA pattern effect for 50G PON with intensity modulation and direct detection. The experimental results confirm that the NN-based equalizer can effectively mitigate the SOA nonlinear pattern effect and significantly improve the dynamic range of receiver, achieving 29-dB power budget with the FEC limit at 1e-2. Moreover, the well-trained NN model in the receiver side can be directly placed at the transmitter in the optical line terminal to pre-equalize the signal for transmission so as to simplify digital signal processing in the optical network unit

Improved ethanol electrooxidation performance by shortening Pd-Ni active site distance in Pd-Ni-P nanocatalysts.

Incorporating oxophilic metals into noble metal-based catalysts represents an emerging strategy to improve the catalytic performance of electrocatalysts in fuel cells. However, effects of the distance between the noble metal and oxophilic metal active sites on the catalytic performance have rarely been investigated. Herein, we report on ultrasmall (∼5 nm) Pd-Ni-P ternary nanoparticles for ethanol electrooxidation. The activity is improved up to 4.95 A per mgPd, which is 6.88 times higher than commercial Pd/C (0.72 A per mgPd), by shortening the distance between Pd and Ni active sites, achieved through shape transformation from Pd/Ni-P heterodimers into Pd-Ni-P nanoparticles and tuning the Ni/Pd atomic ratio to 1:1. Density functional theory calculations reveal that the improved activity and stability stems from the promoted production of free OH radicals (on Ni active sites) which facilitate the oxidative removal of carbonaceous poison and combination with CH3CO radicals on adjacent Pd active sites

100G PAM-4 PON with 34 dB Power Budget Using Joint Nonlinear Tomlinson-Harashima Precoding and Volterra Equalization

We experimentally demonstrate 100G PAM-4 passive optical network using DML-based intensity modulation and direct detection with 3-dB system bandwidth of 15 GHz in O-band. Combining nonlinear Tomlinson-Harashima precoding at the transmitter and 2nd-order Volterra at the receiver enables 34-dB power budget for PON downstream

DrHouse: An LLM-empowered Diagnostic Reasoning System through Harnessing Outcomes from Sensor Data and Expert Knowledge

Large language models (LLMs) have the potential to transform digital healthcare, as evidenced by recent advances in LLM-based virtual doctors. However, current approaches rely on patient's subjective descriptions of symptoms, causing increased misdiagnosis. Recognizing the value of daily data from smart devices, we introduce a novel LLM-based multi-turn consultation virtual doctor system, DrHouse, which incorporates three significant contributions: 1) It utilizes sensor data from smart devices in the diagnosis process, enhancing accuracy and reliability. 2) DrHouse leverages continuously updating medical databases such as Up-to-Date and PubMed to ensure our model remains at diagnostic standard's forefront. 3) DrHouse introduces a novel diagnostic algorithm that concurrently evaluates potential diseases and their likelihood, facilitating more nuanced and informed medical assessments. Through multi-turn interactions, DrHouse determines the next steps, such as accessing daily data from smart devices or requesting in-lab tests, and progressively refines its diagnoses. Evaluations on three public datasets and our self-collected datasets show that DrHouse can achieve up to an 18.8% increase in diagnosis accuracy over the state-of-the-art baselines. The results of a 32-participant user study show that 75% medical experts and 91.7% patients are willing to use DrHouse

ER81 Expression in Breast Cancers and Hyperplasia

ER81 is a transcription factor that may contribute to breast cancer; however, little known about the role of ER81 in breast carcinogenesis. To investigate the role of ER81 in breast carcinogenesis, we examined ER81 expression in IDC, DCIS, ADH, HUT, and normal breast tissues by immunohistochemical staining. We found that ER81 overexpression was detected in 25.7% (9/35) of HUT, 41.2% (7/17) of ADH, 54.5% (12/22) of DCIS, and 63.0% (51/81) of IDC. In 20 of breast cancer tissues combined with DCIS, ADH, and HUT, ER81 expression was found in 14/20 (70%) IDC. In these 14 cases all cases were ER81 positive expression in DCIS, 13 of 14 cases were positively expressed of ER81 in ADH and 8 of 14 were positive for ER81 in HUT components. A statistical significance was found between NBT and HUT (P < .05) and HUT and ADH (P < .05). Clinical-pathological features analysis of breast cancer revealed that ER81 expression was significantly associated with Her2 amplification and was negatively associated with ER and PR expression. Our results demonstrated that ER81 overexpression was present in the early stage of breast development that suggested that ER81 overexpression may play an important role in breast carcinogenesis

GesturePrint: Enabling User Identification for mmWave-based Gesture Recognition Systems

The millimeter-wave (mmWave) radar has been exploited for gesture recognition. However, existing mmWave-based gesture recognition methods cannot identify different users, which is important for ubiquitous gesture interaction in many applications. In this paper, we propose GesturePrint, which is the first to achieve gesture recognition and gesture-based user identification using a commodity mmWave radar sensor. GesturePrint features an effective pipeline that enables the gesture recognition system to identify users at a minor additional cost. By introducing an efficient signal preprocessing stage and a network architecture GesIDNet, which employs an attention-based multilevel feature fusion mechanism, GesturePrint effectively extracts unique gesture features for gesture recognition and personalized motion pattern features for user identification. We implement GesturePrint and collect data from 17 participants performing 15 gestures in a meeting room and an office, respectively. GesturePrint achieves a gesture recognition accuracy (GRA) of 98.87% with a user identification accuracy (UIA) of 99.78% in the meeting room, and 98.22% GRA with 99.26% UIA in the office. Extensive experiments on three public datasets and a new gesture dataset show GesturePrint\u27s superior performance in enabling effective user identification for gesture recognition systems.Accepted to the 44th IEEE International Conference on Distributed Computing Systems (ICDCS 2024