Simulation Study of Tremor Suppression and Experiment of Energy Harvesting with Piezoelectric Materials

The objective of this research is to develop a wearable device that could harvest waste mechanical energy of the human hand movement and utilize this energy to suppress wrist tremors. Piezoelectric material is used to measure the hand movement signals, and the signal of wrist tremor is filtered to be utilized to suppress the tremor. In order to conduct the experiment of energy harvesting and tremor suppression, an experimental rig was fabricated. Two types of piezoelectric materials, PVDF (polyvinylidene fluoride) films and MFC (macro fiber composite) films, are used to harvest mechanical energy and used as actuators to suppress hand tremors. However, due to some shortages of the materials, these two types of materials are not used as actuators to suppress the wrist tremors. Thus, we use Matlab Simulink to simulate the tremor suppression with AVC (active vibration control) algorithm

Orthrus: A Framework for Implementing Efficient Collective I/O in Multi-core Clusters

Abstract. Optimization of access patterns using collective I/O imposes the overhead of exchanging data between processes. In a multi-core-based cluster the costs of inter-node and intra-node data communication are vastly different, and heterogeneity in the efficiency of data exchange poses both a challenge and an opportunity for implementing efficient collective I/O. The opportunity is to effectively exploit fast intra-node communication. We propose to improve communication locality for greater data exchange efficiency. However, such an effort is at odds with improving access locality for I/O efficiency, which can also be critical to collective-I/O performance. To address this issue we propose a framework, Orthrus, that can accommodate multiple collective-I/O implementations, each optimized for some performance aspects, and dynamically select the best performing one accordingly to current workload and system patterns. We have implemented Orthrus in the ROMIO library. Our experimental results with representative MPI-IO benchmarks on both a small dedicated cluster and a large production HPC system show that Orthrus can significantly improve collective I/O performance under various workloads and system scenarios.

B7-H4 Pathway in Islet Transplantation and β

Type 1 diabetes (T1D) is a chronic autoimmune disease and characterized by absolute insulin deficiency. β-cell replacement by islet cell transplantation has been established as a feasible treatment option for T1D. The two main obstacles after islet transplantation are alloreactive T-cell-mediated graft rejection and recurrence of autoimmune diabetes mellitus in recipients. T cells play a central role in determining the outcome of both autoimmune responses and allograft survival. B7-H4, a newly identified B7 homolog, plays a key role in maintaining T-cell homeostasis by reducing T-cell proliferation and cytokine production. The relationship between B7-H4 and allograft survival/autoimmunity has been investigated recently in both islet transplantation and the nonobese diabetic (NOD) mouse models. B7-H4 protects allograft survival and generates donor-specific tolerance. It also prevents the development of autoimmune diabetes. More importantly, B7-H4 plays an indispensable role in alloimmunity in the absence of the classic CD28/CTLA-4 : B7 pathway, suggesting a synergistic/additive effect with other agents such as CTLA-4 on inhibition of unwanted immune responses

B7-H4 Treatment of T Cells Inhibits ERK, JNK, p38, and AKT Activation

B7-H4 is a newly identified B7 homolog that plays an important role in maintaining T-cell homeostasis by inhibiting T-cell proliferation and lymphokine-secretion. In this study, we investigated the signal transduction pathways inhibited by B7-H4 engagement in mouse T cells. We found that treatment of CD3+ T cells with a B7-H4.Ig fusion protein inhibits anti-CD3 elicited T-cell receptor (TCR)/CD28 signaling events, including phosphorylation of the MAP kinases, ERK, p38, and JNK. B7-H4.Ig treatment also inhibited the phosphorylation of AKT kinase and impaired its kinase activity as assessed by the phosphorylation of its endogenous substrate GSK-3. Expression of IL-2 is also reduced by B7-H4. In contrast, the phosphorylation state of the TCR proximal tyrosine kinases ZAP70 and lymphocyte-specific protein tyrosine kinase (LCK) are not affected by B7-H4 ligation. These results indicate that B7-H4 inhibits T-cell proliferation and IL-2 production through interfering with activation of ERK, JNK, and AKT, but not of ZAP70 or LCK

Forecasting the Potential Number of Influenza-like Illness Cases by Fusing Internet Public Opinion

As influenza viruses mutate rapidly, a prediction model for potential outbreaks of influenza-like illnesses helps detect the spread of the illnesses in real time. In order to create a better prediction model, in this study, in addition to using the traditional hydrological and atmospheric data, features, such as popular search keywords on Google Trends, public holiday information, population density, air quality indices, and the numbers of COVID-19 confirmed cases, were also used to train the model in this research. Furthermore, Random Forest and XGBoost were combined and used in the proposed prediction model to increase the prediction accuracy. The training data used in this research were the historical data taken from 2016 to 2021. In our experiments, different combinations of features were tested. The results show that features, such as popular search keywords on Google Trends, the numbers of COVID-19 confirmed cases, and air quality indices can improve the outcome of the prediction model. The evaluation results showed that the error rate between the predicted results and the actual number of influenza-like cases form Week 15 to Week 18 fell to less than 5%. The outbreak of COVID-19 in Taiwan began in Week 19 and resulted in a sharp rise in the number of clinic or hospital visits by patients of influenza-like illnesses. After that, from Week 21 to Week 26, the error rate between the predicted and actual numbers of influenza-like cases in the later period dropped down to 13%. It can be confirmed from the actual experimental results in this research that the use of the ensemble learning prediction model proposed in this research can accurately predict the trend of influenza-like cases

Forecasting the Potential Number of Influenza-like Illness Cases by Fusing Internet Public Opinion

As influenza viruses mutate rapidly, a prediction model for potential outbreaks of influenza-like illnesses helps detect the spread of the illnesses in real time. In order to create a better prediction model, in this study, in addition to using the traditional hydrological and atmospheric data, features, such as popular search keywords on Google Trends, public holiday information, population density, air quality indices, and the numbers of COVID-19 confirmed cases, were also used to train the model in this research. Furthermore, Random Forest and XGBoost were combined and used in the proposed prediction model to increase the prediction accuracy. The training data used in this research were the historical data taken from 2016 to 2021. In our experiments, different combinations of features were tested. The results show that features, such as popular search keywords on Google Trends, the numbers of COVID-19 confirmed cases, and air quality indices can improve the outcome of the prediction model. The evaluation results showed that the error rate between the predicted results and the actual number of influenza-like cases form Week 15 to Week 18 fell to less than 5%. The outbreak of COVID-19 in Taiwan began in Week 19 and resulted in a sharp rise in the number of clinic or hospital visits by patients of influenza-like illnesses. After that, from Week 21 to Week 26, the error rate between the predicted and actual numbers of influenza-like cases in the later period dropped down to 13%. It can be confirmed from the actual experimental results in this research that the use of the ensemble learning prediction model proposed in this research can accurately predict the trend of influenza-like cases

BFLP: An Adaptive Federated Learning Framework for Internet of Vehicles

Applications of Internet of Vehicles (IoV) make the life of human beings more intelligent and convenient. However, in the present, there are some problems in IoV, such as data silos and poor privacy preservation. To address the challenges in IoV, we propose a blockchain-based federated learning pool (BFLP) framework. BFLP allows the models to be trained without sharing raw data, and it can choose the most suitable federated learning method according to actual application scenarios. Considering the poor computing power of vehicle systems, we construct a lightweight encryption algorithm called CPC to protect privacy. To verify the proposed framework, we conducted experiments in obstacle-avoiding and traffic forecast scenarios. The results show that the proposed framework can effectively protect the user's privacy, and it is more stable and efficient compared with traditional machine learning technique. Also, we compare the CPC algorithm with other encryption algorithms. And the results show that its calculation cost is much lower compared to other symmetric encryption algorithms

Bibliometric analysis on the progress of immunotherapy in renal cell carcinoma from 2003-2022

The incidence and mortality rates of renal cell carcinoma (RCC) have been increasing annually due to obesity and environmental pollution. Although immunotherapy of RCC has been studied for decades, few comprehensive bibliometric analyses exist on the treatment. Therefore, the purpose of this bibliometric analysis was to identify scientific achievements of the global research on RCC immunotherapy from 2003 to 2022 and discuss research trends. Data were retrieved from the Clarivate Web of Science Core Collection using a set retrieval strategy. The Bibliometrics tool Cite Space 6.2 R2 (Chaomei Chen, Drexel University) was used to analyze 4,841 articles. The USA had the most publications (n = 1,864); Harvard University was identified as the leading institution (n = 264); and Dr. Toni K. Choueiri, was the most productive researcher in the field (n = 55). Keyword analysis showed that nivolumab, immune checkpoint inhibitors, tumor microenvironment, everolimus, cabozantinib, resistance, pembrolizumab and ipilimumab were the main hotspots and frontier directions of RCC. By analyzing the results of bibliometrics, national and international researchers can better understand the current research status of RCC immunotherapy and identify new directions for future research. However, the analysis also identified pockets of insularity, highlighting a need for greater collaboration and cooperation among researchers to advance the field of RCC immunotherapy