Miracle-dz/UniFAR-FTUs huggingface dataset:
CDKN2A Deletion Leading to Hematogenous Metastasis of Human Gastric Carcinoma

Introduction Somatic copy number deletion (SCND) of CDKN2A gene is the most frequent event in cancer genomes. Whether CDKN2A SCND drives human cancer metastasis is far from clear. Hematogenous metastasis is the main reason of human gastric carcinoma (GC) death. Thus, prediction GC metastasis is eagerly awaited. Method GC patients (n=408) enrolled in both a cross-sectional and a prospective cohorts were analysed. CDKN2A SCND was detected with a quantitative PCR assay (P16-Light). Association of CDKN2A SCND and GC metastasis was evaluated. Effect of CDKN2A SCND by CRISPR/Cas9 on biological behaviors of cancer cells was also studied. Results CDKN2A SCND was detected in 38.9% of GCs from patients (n=234) enrolled in the cross-sectional cohort. Association analysis showed that more CDKN2A SCND was recognized in GCs with hematogenous metastasis than those without (66.7% vs. 35.7%, p=0.014). CDKN2A SCND was detected in 36.8% of baseline pN0M0 GCs from patients (n=174) enrolled in the prospective study, the relationship between CDKN2A SCND and hematogenous metastasis throughout the follow-up period (62.7 months in median) was also significant (66.7% vs. 34.6%, p=0.016). Using CDKN2A SCND as a biomarker for predicting hematogenous metastasis of GCs, the prediction sensitivity and specificity were 66.7% and 65.4%. The results of functional experiments indicated that CDKN2A SCND could obviously downregulate P53 expression that consequently inhibited the apoptosis of MGC803 GC and HEK293T cells. This may account for hematogenous metastasis of GCs by CDKN2A SCND. Conclusion CDKN2A SCND may drive GC metastasis and could be used as a predictor for hematogenous metastasis of GCs.
Models and Evolutionary Methods for Objects and Systems Clustering

As we know, clustering refers to the problems of Data Mining. The clustering problem is usually solved without a supervisor with using the features of the object or system. It is necessary to carry out clustering in various fields: agriculture, medicine, biology, etc. In this paper propose to carry out clustering using two objective functions and constructed evolutionary population methods based on genetic algorithms, evolutionary strategies and the method of deformed stars. New elements of such methods for calculation of objective function are developed, advantages of the population approach before known classical methods of clustering are shown. A comparative analysis of the proposed clustering methods effectiveness using a known sample of iris data and a sample of randomly generated data is fulfilled. The peculiarities of each proposed method application, its advantages and disadvantages are determined.
A Hybrid Secure Computation Framework for Graph Neural Networks

The Multi-party Secure Computation (MPC)-based methods for privacy-preserving Graph Neural Networks (GNNs) are still challenged by high communication overhead. Moreover, the security guarantee of most MPC-based methods can only defend against the semi-honest adversary, while a few methods which can defend against the malicious adversary will cause a further increase in communication overhead. Moreover, Software Guard Extensions (SGX), which can provide the data confidentiality and code integrity, has been considered as a novel solution to privacy-preserving GNN. Unfortunately, previous work has shown that SGX is vulnerable to side-channel attacks that deprive its confidentiality and preserve only its integrity. To solve the above problems, we propose an n-party secure computation framework for GNNs using SGX. This framework can reduce the communication overhead and improve the security guarantee without relying on the confidentiality of SGX. Specifically, both data holders and the server hold SGX. Data holders enrich the data and train the model by MPC efficiently with the assistance of the server. SGX ensures integrity, where data holders and the server must execute according to protocols, so malicious adversaries cannot deviate from the protocol to breach privacy and security. Even if the confidentiality of SGX was breached, the adversary could only access the ciphertext in MPC instead of the plaintext. We conduct experiments on public datasets to demonstrate that our framework has achieved comparable performance with traditional GNNs and perform security analysis to validate that our framework satisfies security and privacy requirements.
A New Data Aggregation Model for Intelligent Transportation System

Intelligent Transportation System is a new kind of complicated information system which includes many different wireless sensors. With the development in sensor technologies and their applications, it is important to focus on how to find the useful and real-time traffic information from the Intelligent Transportation System. Using this method of building dynamical data system model for the Intelligent Transportation System is the way to solve the data aggregation problem and minimize the number of the multi-sources data.
Myeloid-Derived Suppressor Cells: A Multifaceted Accomplice in Tumor Progression

Myeloid-derived suppressor cell (MDSC) is a heterogeneous population of immature myeloid cells, has a pivotal role in negatively regulating immune response, promoting tumor progression, creating pre-metastases niche, and weakening immunotherapy efficacy. The underlying mechanisms are complex and diverse, including immunosuppressive functions (such as inhibition of cytotoxic T cells and recruitment of regulatory T cells) and non-immunological functions (mediating stemness and promoting angiogenesis). Moreover, MDSC may predict therapeutic response as a poor prognosis biomarker among multiple tumors. Accumulating evidence indicates targeting MDSC can reverse immunosuppressive tumor microenvironment, and improve therapeutic response either single or combination with immunotherapy. This review summarizes the phenotype and definite mechanisms of MDSCs in tumor progression, and provide new insights of targeting strategies regarding to their clinical applications.
Multifactor Stress Life Model of Polyimide Film Used in Inverter-Fed Traction Motors

The inverter-fed traction motor is one of the key components in the locomotive. However, the motor insulation failures have repeatedly occurred in premature. The failure mechanism for insulation under continuous impulses is different from that under sine waves. The single stress approach in design stage and also in ageing studies offers relative simplicity, but it is inapplicable to real life operating conditions which mostly are of multi-stress type. In this paper, a novel test apparatus of insulation failure time was designed for accelerating aging test. Meanwhile, electrical breakdown experiments on magnet wire insulating polyimide film (FCR 100) used in the inter-turn insulation of JD117 inverter-fed motor was conducted. The effects of multi-stress including impulse amplitude-temperature and impulse frequency-temperature were investigated respectively. The test results under different conditions were analyzed based on statistic analysis of Weibull distribution. Finally, insulation life models under combined thermal and electrical stresses were proposed using the multi-stress approach. A fitted formula for ageing life was presented which had two independent variables of voltage and temperature. The generality of the model lies in the fact that it can be used for designing of insulation system of all electrical components containing solid insulating materials surrounded by gas. Since the model is based on the probability of insulation deterioration under the action of the stresses applied, that gives us the opportunity to calculate the life time of insulation system for given system of stresses, the materials selected and the requested probability of failureless system operation.
Network Allocation and Optimal Sizing of BESS for Resilience Enhancement on Power Distribution Systems Against Volcanic Eruption

High-impact and low-probability (HILP) events, such as natural hazards, have made power distribution systems (PDSs) more vulnerable. The ability of the PDSs to efficiently withstand HILP events while enabling a quick recovery and restoration to the normal state is interpreted as resilience. This paper proposes a novel methodology for planning and operating battery energy storage systems (BESS) to enhance resilience of PDSs following a volcanic eruption with a focus on reducing the energy not supplied (ENS) during PDS recovery. The proposed methodology includes the Monte-Carlo simulation method (MCS) and a stochastic optimization model. The effectiveness of the proposed methodology framework is implemented and validated on the IEEE 37-node test feeder. The results show that for the system under study, the ENS in the expected scenario and the worst-case scenario was reduced by 24.63 % and 30 % respectively when BESS units are installed, demonstrating substantial resilience enhancement against volcanic eruption.
Shake table tests of under-designed RC frames for the seismic retrofit of buildings - design and similitude requirements of the benchmark specimen

2010 NZSEE Conference ABSTRACT: In the past years, extensive experimental work on innovative feasible retrofit solutions has been carried out at the University of Canterbury, as part of the FRST Project 'Seismic Retrofit Solutions for NZ Multi-storey Buildings'. The experiments have consisted in quasi-static test of 2/3 scale beam column joint subassemblies representative of pre-70's reinforced concrete (RC) frame buildings, referred herein as under-designed structures (non-ductile detailing, no capacity design principles), before and after retrofit. As a dynamic validation of the seismic vulnerability of such structures and the feasibility of the developed and improved retrofit solutions, a series of 4 1/2.5 scale experimental models (two 3 storey - 2 bay frames jointed together by transverse beams and floor slabs) will be tested on the shake table, without and with a seismic retrofit intervention and without and with infill panels. The retrofit interventions will include techniques such as the Metallic Haunch, FRP layers, and Shape Memory Alloys (SMA) braces, targeting for two different performance levels: Life Safety and Damage Control. In this paper, the overall description of the dynamic tests, the development of the experimental models and the similitude requirements are described.
Aggregating Learning Agents for Microgrid Energy Scheduling During Extreme Weather Events

Efficient utilization of the microgrid generation resources (MGRs) is important especially during weather-related events. Existing single-agent and cooperative multi-agent based reinforcement learning (RL) approaches may be infeasible and computationally expensive for scheduling MGRs effectively when there are some probabilistic weather-related emergency events. In this paper, we propose a Q-learning approach with multiple local agents for a grid-connected microgrid application and show that the proposed integration is capable of scheduling MGRs efficiently for both normal and weather-related emergency events. Specifically, we utilize different local Q-learning agents to learn different microgrid events, and aggregate the learned value functions to the global agent who handles the overall microgrid energy scheduling in a probabilistic way. Numerical simulations are performed to validate the effectiveness of the proposed method. The influences of the effective utilization of the MGRs and power outage duration are discussed. Two case studies with different power outage probabilities are presented to evaluate the performance of our proposed method.
The Application of Complex Morlet Wavelet for Estimating Damping Ratio and Detecting Inter-Area Oscilation Mode on a Real Power System

The undamped oscillation is a typical power system dynamic stability problem. Without early anticipation, it might initiate a cascading failure even provoke a total blackout. In this paper, The Complex Morlet Wavelet Transform method was applied to mitigate system dynamic stability by estimating the damping ratio and detecting any inter-area oscillation that resided in a signal recorded by the Synchrophasor Measurement Unit. Before using it to evaluate real data, the method was validated by applying it to the synthesis signal with known parameters. The calculation result from Complex Morlet Wavelet Transform was compared to the Eigen Value Analysis in the Two Area Four Machine System model. The results showed a satisfactory level of accuracy. Finally, an assessment was conducted in detecting interarea oscillation mode and estimating the damping ratio in the Java-Bali interconnection system.
PRL: Standardizing Performance Monitoring Library for High-Integrity Real-Time Systems

The use of complex processors is becoming ubiquitous in High-Integrity Systems (HIS). To deal with processor’s increased complexity, Performance Monitoring Counters (PMCs) are increasingly used to reason on software behavior and provide the necessary evidence to support software certification. However, the use of PMCs in HIS is relatively recent and hence far from being standardized. As a result, software engineers are forced to resort to highly-customized, low-level programming of platform-specific PMC control registers, which is both error prone and time consuming. To cover this gap, we propose building on the PAPI library, a standardized performance monitoring solution in the mainstream domain, and develop a PMC Reading Library (PRL) for configuring and collecting traceable events while capturing HIS specific requirements and peculiarities. We instantiate PRL in a reference automotive configuration to show that PRL meets key HIS requirements: negligible footprint, limited and predictable overhead, and accuracy collecting hardware events by filtering out the impact of interrupts and context switches.
STATCOM: a new era of reactive compensation

The unbundling of the generation and transmission functions in England and Wales into separate shareholder-owned companies has inevitably resulted in far less predictability in terms of generator siting and closure. National Grid, as the sole transmission company in England and Wales, is required to plan and respond quickly to changing system patterns to maintain both security and power quality standards. This has been achieved by an extensive programme of investment in reactive compensation. To supplement the existing fixed static VAr compensators installed in the last 10-15 years, National Grid has, to date, installed a further 12 relocatable SVCs at sites in England and Wales as part of a planned programme of work to meet these changing system needs.
Cost-effective approaches for high-resolution bioimaging by time-stretched confocal microscopy at 1μm

Optical imaging based on time-stretch process has recently been proven as a powerful tool for delivering ultra-high frame rate (< 1MHz) which is not achievable by the conventional image sensors. Together with the capability of optical image amplification for overcoming the trade-off between detection sensitivity and speed, this new imaging modality is particularly valuable in high-throughput biomedical diagnostic practice, e.g. imaging flow cytometry. The ultra-high frame rate in time-stretch imaging is attained by two key enabling elements: dispersive fiber providing the time-stretch process via group-velocity-dispersion (GVD), and electronic digitizer. It is well-known that many biophotonic applications favor the spectral window of ~1μm. However, reasonably high GVD (< 0.1 ns/nm) in this range can only be achieved by using specialty single-mode fiber (SMF) at 1μm. Moreover, the ultrafast detection has to rely on the state-of- the-art digitizer with significantly wide-bandwidth and high sampling rate (e.g. <10 GHz, <40 GS/s). These stringent requirements imply the prohibitively high-cost of the system and hinder its practical use in biomedical diagnostics. We here demonstrate two cost-effective approaches for realizing time-stretch confocal microscopy at 1μm: (i) using the standard telecommunication SMF (e.g. SMF28) to act as a few-mode fiber (FMF) at 1μm for the time-stretch process, and (ii) implementing the pixel super-resolution (SR) algorithm to restore the high-resolution (HR) image when using a lower-bandwidth digitizer. By using a FMF (with a GVD of ~ 0.15ns/nm) and a modified pixel-SR algorithm, we can achieve time-stretch confocal microscopy at 1μm with cellular resolution (~ 3μm) at a frame rate 1 MHz.
An Engineering Computational Thinking Diagnostic: A Psychometric Analysis

This research-track work-in-progress paper contributes to engineering education by documenting progress in developing a new standard Engineering Computational Thinking Diagnostic to measure engineering student success in five factors of computational thinking. Over the past year, results from an initial validation attempt were used to refine diagnostic questions. A second statistical validation attempt was then completed in Spring 2021 with 191 student participants at three universities. Statistics show that all diagnostic questions had statistically significant factor loadings onto one general computational thinking factor that incorporates the five original factors of (a) Abstraction, (b) Algorithmic Thinking, (c) Decomposition, (d) Data Representation and Organization, and (e) Impact of Computing. This result was unexpected as our goal was a diagnostic that could discriminate among the five factors. A small population size caused by the virtual delivery of courses during the COVID-19 pandemic may be the explanation and a third round of validation in Fall 2021 is expected to result in a larger population given the return to face-to-face instruction. When statistical validation is completed, the diagnostic will help institutions identify students with strong entry level skills in computational thinking as well as students that require academic support. The diagnostic will inform curriculum design by demonstrating which factors are more accessible to engineering students and which factors need more time and focus in the classroom. The long-term impact of a successfully validated computational thinking diagnostic will be introductory engineering courses that better serve engineering students coming from many backgrounds. This can increase student self-efficacy, improve student retention, and improve student enculturation into the engineering profession. Currently, the diagnostic identifies general computational thinking skill
A Lightweight, Privacy-Preserving Tensor Completion Framework for Internet of Things

Machine learning enabled Internet of Things (IoT) applications (e.g., object recognition and autonomous driving) require collecting large amounts of multi-dimensional data, called tensors, at IoT devices. Due to the lower-power and distributed nature of these devices, however, many data are missing during collection (i.e., incomplete tensors), impairing the performance of IoT applications. The best practice to cope with this issue is to collect the private raw data from all devices to a centralized server to execute tensor completion algorithms, causing severe privacy concerns. This paper systematically investigates the new problem of privacy-preserving tensor completion, and designs TwilightTensor, a lightweight, privacy-preserving tensor completion framework. In TwilightTensor, each IoT device works in parallel to retrieve public shared data from a logically centralized server, uses a novel, lightweight obfuscation mechanism to individually disguise its private raw data, and sends the obfuscated raw data to the server. The server then executes tensor completion algorithms with the obfuscated tensors from all devices as input, and distributes the corresponding results back to devices. Each device then independently de-obfuscates the received results to get its own reconstructed complete tensors. We rigorously analyze the performance of TwilightTensor, implement a prototype of TwilightTensor and conduct extensive experiments using real-world datasets for different IoT applications. Results show that TwilightTensor achieves comparable recovery accuracy of state-of-the-art tensor completion algorithms, while preserving the data privacy of IoT devices.
Evaluation of road load classification for fatigue assessments

A way of modelling customer usage for fatigue life considerations is discussed. Based on a parametric vehicle independent load model, parameters are estimated using measurement data. The variation of the parameters over the measurements is studied using analysis of variance. A general road classification is considered, which is compared to the effect of more local factors such as market and driver. Considerations in the design of experiments to gather data for the models are addressed. A case study with a vehicle independent description for lateral loads is presented. The analysis technique is applied using data from a field study.
Impact of Semi-fast Charging Stations on Unbalanced Power Distribution Systems Integrated with PV Systems

Rooftop photovoltaics (PVs) and electric vehicles (EVs) have gained significant attention recently due to their key factor of decarbonising the electricity and transportation sectors. However, introducing new charging stations and increasing PV integration into power distribution systems can bring new operational challenges, including overvoltage and voltage unbalance. Therefore, this paper investigates the impact of EVs connection on an unbalanced power distribution system considering local voltage control of PV inverters. Several cases were studied using the unbalanced IEEE 37-node test feeder over a 24-hour simulation period. The unbalanced load flow calculation results argue that EVs connection can increase the severity of voltage unbalance, and PVs connection can potentially reduce the voltage unbalance issues. On the other hand, the reactive power capability of a PV system does not necessarily minimise power losses, especially at a high penetration level of PV.
Verification of the Effectiveness to Monitor Darknet across Multiple Organizations

Researchers and network operators regularly monitor unused Internet address space called the darknet to understand malicious activities on the Internet such as malware infections, DDoS, and scanning to find vulnerable systems. The purpose of this study is to demonstrate the effectiveness of darknet monitoring across multiple organizations by conducting a detailed similarity analysis. In this paper, we analyze darknet data observed in two organizations in different industries and the first octet subnet range. We compared the results of the similarity analysis between intra-organization and inter-organization calculations by dividing the address space into multiple blocks so that one organization conducts similarity analysis in an intra-organization manner. The results show that the similarity of the source hosts is lower in the inter-organization calculation than in the intra-organization calculation. In addition, we monitor more source hosts in inter-organization. Moreover, this work also reports that the results differ depending on the destination ports/protocols. From the results obtained, we clarified the effectiveness of distributing the monitoring points of the darknet across multiple organizations.