Miracle-dz/UniFAR-FTUs huggingface dataset:
Low-Power And High Performance Of An Optimized FinFET Based 8T SRAM Cell Design

The development of the nanotechnology leads to the shrinking of the size of the transistors to nanometer region. However, there are a lot of challenges due to size scaling of the transistors such as short channel effects (SCEs) and threshold voltage roll-off issues. Fin-Type Field Effect Transistor (FinFET) is another alternative technology to solve the issues of the conventional MOSFET and increase the performance of the Static Random Access Memory (SRAM) circuit design. FinFET based SRAMs are faster and more reliable which are often used as memory cache for high speed operation. However, 6T SRAM cell suffers from access transistor sizing conflict resulting in a trade-off between read and write stability. This paper presents an investigation of the stability performance in retention, read and write mode of 22nm FinFET based 8T SRAM cell. The performance comparison of 22nm FinFET based 6T and 8 $T$ SRAMs were made. The simulation of the SRAM model are carried out in GTS Framework TCAD tool based on 22nm technology. In 8 $T$ SRAM cell, two n-FinFETs are added to the conventional 6 $T$ SRAM cell which will be controlled by the Read Word Line (RWL) to isolate the read and write operation path for better read stability. FinFET based 8T SRAM cell gives better performance in Static Noise Margin (SNM) and power consumption than 6T SRAM cells. The simulation results affirms the proposed FinFET based 8T SRAM improved read static noise margin by 166.67% and power consumption by 76.13% as compared to the FinFET based 6T SRAM.
Fiber Optic Refractive Index Distributed Multi-Sensors by Scattering-Level Multiplexing With MgO Nanoparticle-Doped Fibers

In this work, we present the architecture of a multiplexed refractive index (RI) sensing system based on the interrogation of Rayleigh backscattering. The RI sensors are fabricated by fiber wet-etching of a high-scattering MgO nanoparticle-doped fiber, without the need for a reflector or plasmonic element. Interrogation is performed by means of optical backscatter reflectometry (OBR), which allows a detection with a millimeter-level spatial resolution. Multiplexing consists of a simultaneous scan of multiple fibers, achieved by means of scattering-level multiplexing (SLMux) concept, which uses the backscattered power level in each location as a diversity element. The sensors fabricated have sensitivity in the order of 0.473-0.568 nm/RIU (in one sensing point) and have been simultaneously detected together with a distributed temperature sensing element for multi-parameter measurement. An experimental setup has been prepared to demonstrate the capability of each sensing region to operate without cross-talk, while operating multi-fiber detection.
Research on Optimized Energy Scheduling of Rural Microgrid

This paper introduces a new rural microgrid model, including residents and agricultural greenhouses. Based on the new model framework, the precise energy scheduling of a rural microgrid is realized by means of load classification and load forecasting. Moreover, we also adopt a new energy-storage mode, cloud energy storage (CES), as the shared energy-storage unit of rural microgrid, and analyze the service and operation mechanism of CES in detail. The shared storage characteristic and adjustable storage capacity of CES are helpful for the precise management of power dispatching. At the same time, in order to accurately implement energy scheduling, we fully consider the load characteristics of rural areas and divide the load into residential load and agricultural load. Then the extreme gradient boosting (XGBoost) algorithm is used to predict the short-term power consumption of the two types of load respectively, which can effectively alleviate the uncertainty of load power consumption and improve the accuracy of scheduling. Finally, an illustrative example of rural energy scheduling is given. The example studies the impact of energy-storage capacity on the cost of the scheduling scheme, and designs a power-dispatching scheme based on load forecasting, which accurately solves the energy charging and discharging planning and grid energy trading planning.
The study on the relationship between polymorphism of MMP-9 and gastric cancer

OBJECTIVE To investigate the relationship between polymorphism of MMP-9 and gastric cancer, and to further clarify the association between MMP -9 and susceptibility of gastric caner in individuals of Man ethnicity in the city of Chengde, Hebei province. METHODS A total of 98 patients with gastric cancer and 100 healthy controls were prospectively examined. 5ml venous blood was drawn and peripheral leukocytes were separated by hypotonic hemolysis , phenol/chloroform methods. -1562C/T polymorphism was assayed by polymerase chain reaction-restriction fragment length polymorphism (PCR- RFLP). All the data was analyzed by SPSS 11.5 (SPSS Company, Chicago, Illinois, USA). P value less than 0.05 was considered statistically significant. R x C Chi Square was used to analyze the difference of genotype frequency and distribution between patient group and control group. The relative risk of incidence in different genotypes was described with odds ratio (OR) and 95% confidence interval (CI). RESULTS (1) MMP-9 gene PCR products digested electrophoresis showed fragments and PCR products were sequenced consistent with the results. (2) The frequency of polymorphic C and T allele in -1562C/T was obviously different between patients with gastric cancer and normal controls; the genotype frequencies of C/C, C/T, T/T were evidently different between patients with gastric cancer and normal controls. As compared to patients with the genotype of C/C, the incidence of gastric cancer was remarkably increased in those with C/T + T/T genotypes (OR = 0.532, 95%CI = 0.298-0.948). (3) MMP-9 genotype distribution was correlated with TNM classification and lymph node metastasis, but not with tumor differentiation and invasion. CONCLUSION (1) The susceptibility of gastric cancer in individual of Man ethnicity in the city of Chengde is associated with reaction-restriction fragment length polymorphism in the region of -1562 (C→T) in the promoter of MMP-9. (2) The genotype of MMP is correlated with TNM classification and lymph node metastasis, and plays an important role in the progression and metastasis of gastric cancer.
Carbon Price Forecasting Based on Multi-Resolution Singular Value Decomposition and Extreme Learning Machine Optimized by the Moth–Flame Optimization Algorithm Considering Energy and Economic Factors

Carbon price forecasting is significant to both policy makers and market participants. However, since the complex characteristics of carbon prices are affected by many factors, it may be hard for a single prediction model to obtain high-precision results. As a consequence, a new hybrid model based on multi-resolution singular value decomposition (MRSVD) and the extreme learning machine (ELM) optimized by moth–flame optimization (MFO) is proposed for carbon price prediction. First, through the augmented Dickey–Fuller test (ADF), cointegration test and Granger causality test, the external factors of the carbon price, which includes energy and economic factors, are selected in turn. To select the internal factors of the carbon price, the carbon price series are decomposed by MRSVD, and the lags are determined by partial autocorrelation function (PACF). MFO is then used for the optimization of ELM parameters, and external and internal factors are input to the MFO-ELM. Finally, to test the capability and effectiveness of the proposed model, MRSVD-MFO-ELM and its comparison models are used for carbon price forecast in the European Union (EU) and China, respectively. The results show that the performance of the model is significantly better than other models.
A Survey of Initial and Factors of Homosexual Intercourse Among Men Who Have Sex With Men

Objective To investigateKG*2the factors associated with first homosexual intercourse among men who have sex with men(MSM). Methods From November,2006 to February,2007,541 MSM were investigated in Beijing.The questionnaire included birth date,ethnicity,education level,registered permanent residence,the object who was the first sexual intercourse with,sexual orientation and how the first homosexual intercourse happened.Results Among 541 MSM,the average incidence rate from birth to first homosexual intercourse was 4.57 per person-years(95% CI=4.19-4.96)with median age 21.0 years(95% CI=21.0-22.0DK). In the multivariate Cox proportion hazard regression model,late birth date(1970-1980 HR,2.68;95% CI=1.83-3.94.1980 HR=6.17;95% CI=4.11-9.28),minority(HR,1.44;95% CI=1.06-1.96),first sex with men(HR,2.18;95% CI=1.76-2.68),voluntary initial sexual with men(HR,0.65;95% CI=0.50-0.85) were the factors independently associated with the first homosexual intercourse.Conclusion Minority,first sex with men and voluntary initial sexual with men increased incidence rate from birth to initial homosexual intercourse men among MSM,and HIV intervention should be paid more attention to adolescent MSM.
A Unified Analytical Transregional MOSFET Model for Nanoscale CMOS Digital Technologies

For IC designers, power has always been the main design constraint. Near threshold (moderate inversion) computing is a promising technique to manage power and energy requirements. A modeling framework specific to moderate inversion is developed in literature known as Transregional Mosfet Model (TRM). This paper presents an extension of TRM model by considering the lateral and vertical field dependent mobility of carriers that make it suitable for circuit design at supply voltages not restricted to near threshold voltage. The model proposed is the unified model applicable in all operating regions (weak, moderate, and strong) and all saturation levels from a long channel with negligible effect of velocity saturation to a short channel having extreme velocity saturation. Further, it has been shown that the proposed drain current model can be reduced to unified interpolated expression of EKV model for long channel MOSFET.
Novel Empirical Model for Calculation of Core Losses in Permanent Magnet Machines

This article presents a simple model for the calculation of core losses in permanent magnet machines based on the total flux linkage of the stator winding. In reality, the total core losses will vary as a function of permanent magnet field and the combination of <inline-formula><tex-math notation="LaTeX">$d-q$</tex-math></inline-formula> current components even in the case when different <inline-formula><tex-math notation="LaTeX">$d-q$</tex-math></inline-formula> currents produce the same amount of stator winding flux linkage at a given speed. The classical model with constant equivalent core loss resistance predicts constant core losses for constant flux linkage which leads to incorrect results. The model presented in this paper expands the classical core loss resistance based model with a novel additional term which corrects the classical model and calculates an additional core loss component as a function of <inline-formula><tex-math notation="LaTeX">$d$</tex-math></inline-formula> axis current, total flux linkage of the stator winding, and two constant parameters. The accuracy of the model is confirmed by finite element simulations and core loss measurements for the cases of an interior permanent magnet traction motor and an industrial surface permanent magnet motor (only simulations).
A Modular Battery Voltage-Balancing System Using a Series-Connected Topology

A new modular topology and control method is presented for balancing the voltages of a series-connected string of battery cells. The proposed topology has fewer components compared to similar methods and is characterized as “cell to string to cell” category since charge transfer occurs between all cells during a cycle: this increases the speed of the charge equalization process. Two battery cells are connected to a bridge module and bridge modules are connected in series through transformer tertiary windings. This series connection causes all bridges to have the same effect on each other and makes the system operation and analysis more predictable. As waveforms and transferred charge can be predicted accurately through analysis, the equalization time is also optimized through a design procedure. The easy-to-implement distributed controller described requires only one voltage sensor per two battery cells.
Incidence and influencing factors of simple obesity among presohool children in Hefei urbanareas

Objective:To explore the incidence and influencing factors of simple obesity among children aged 3～6 in Hefei urban areas. Methods:With a stratified sampling method, 1 731 preschool children were selected in 8 kindergartens. Their height, weight, dieting behaviors, family income, their parent' nutrition knowledge were investigated. Adopting weight-for-height to judge whether a child was obese. Results:The incidence of simple obesity in preschool children in Hefei urban areas was 7.05 %. Multi-factor analysis indicated that snack food not affecting dinner, not playing when eating, enjoying fatness, eating more meat and eggs, eating more western-style food were risk factors of simple obesity in children.Conclusion: The incidence of simple obesity in preschool children in Hefei urban areas is higher, and measures should be taken to prevent it.
Implementation of Data Abstraction Layer Using Kafka on SEMAR Platform for Air Quality Monitoring

Urbanization and fast-growing industries causing air quality in urban areas to be bad and even tend to be dangerous. In addition, the largest percentage of energy emissions come from the transportation sector, specifically on road transportation. Therefore, the need for a quality detection system that is capable of distributing and displaying large data information in real-time cannot be resolved by the system currently used by the government. This research offers a solution to the implementation of data abstraction in cloud computing which is built using the concept microservice architecture and integrated with mobile-based sensors to detect air quality in real-time. This solution consists of integrated cloud computing services using Smart Environment Monitoring and Analytical in Real-time (SEMAR) and Vehicles as Mobile Sensor Networks (VaaMSN) to detecting air quality. SEMAR was built with microservice references consist of data abstraction, communication, data analytical with business analytics proccess, data storage with Big data service and also real-time visualization in maps, chart, and table through dasboard website. Through the experiments that we did show that the microservice of data abstraction layer can be installed at the SEMAR stage indicating that the average delay in sending information is around 0.09 ms (90μs), this indicates that the system can be said to be real-time. With specific and real-time locations in data visualization, the government can use this method as an new alternative method of air quality.
Image processing–based real‐time displacement monitoring methods using smart devices

Displacement measurement is one of the most important methods for structural health monitoring. However, because of high cost and heavy on‐site installation, conducting displacement measurement using conventional sensors is not easy in practice. In this case, with improvements in image sensors in smart devices, it seems possible to measure displacement using image processing methods. In this study, smart device–based bridge displacement monitoring system was developed. Images captured by the image sensor are processed in real time to obtain the feature‐responsive displacement. First, shaking table tests based on sine wave loading were conducted so that the reliable domain for frequency and amplitude measurement for different smart devices were identified by comparing with the reference‐accurate displacement sensor. Then, on‐site application and stability of the proposed system were demonstrated through 1‐day field measurement on a real in‐service bridge. Comparison of the displacement due to traffic and temperature using smart devices with accurate displacement sensor shows significant potential of the proposed approach.
Experimental Study of Reinforcement Learning in Mobile Robots Through Spiking Architecture of Thalamo-Cortico-Thalamic Circuitry of Mammalian Brain

SUMMARY In this paper, the behavioral learning of robots through spiking neural networks is studied in which the architecture of the network is based on the thalamo-cortico-thalamic circuitry of the mammalian brain. According to a variety of neurons, the Izhikevich model of single neuron is used for the representation of neuronal behaviors. One thousand and ninety spiking neurons are considered in the network. The spiking model of the proposed architecture is derived and prepared for the learning problem of robots. The reinforcement learning algorithm is based on spike-timing-dependent plasticity and dopamine release as a reward. It results in strengthening the synaptic weights of the neurons that are involved in the robot’s proper performance. Sensory and motor neurons are placed in the thalamus and cortical module, respectively. The inputs of thalamo-cortico-thalamic circuitry are the signals related to distance of the target from robot, and the outputs are the velocities of actuators. The target attraction task is used as an example to validate the proposed method in which dopamine is released when the robot catches the target. Some simulation studies, as well as experimental implementation, are done on a mobile robot named Tabrizbot. Experimental studies illustrate that after successful learning, the meantime of catching target is decreased by about 36%. These prove that through the proposed method, thalamo-cortical structure could be trained successfully to learn to perform various robotic tasks.
New tool for stock investment risk management

Purpose The purpose of this paper is to propose a new tool for stock investment risk management through studying stocks with what kind of characteristics can be predicted by individual investor behavior. Design/methodology/approach Based on comment data of individual stock from the Snowball, a thermal optimal path method is employed to analyze the lead–lag relationship between investor attention (IA) and the stock price. And machine learning algorithms, including SVM and BP neural network, are used to predict the prices of certain kind of stock. Findings It turns out that the lead–lag relationships between IA and the stock price change dynamically. Forecasting based on investor behavior is more accurate only when the IA of the stock is stably leading its price change most of the time. Research limitations/implications One limitation of this paper is that it studies China’s stock market only; however, different conclusions could be drawn for other financial markets or mature stock markets. Practical implications As for the implications, the new tool could improve the prediction accuracy of the model, thus have practical significance for stock selection and dynamic portfolio management. Originality/value This paper is one of the first few research works that introduce individual investor data into portfolio risk management. The new tool put forward in this study can capture the dynamic interplay between IA and stock price change, which help investors identify and control the risk of their portfolios.
A New Method for Detecting Architectural Distortion in Mammograms by NonSubsampled Contourlet Transform and Improved PCNN

Breast cancer is the leading cause of cancer death in women, and early detection can reduce mortality. Architectural distortion (AD) is a feature of clinical manifestations for breast cancer, however, due to its complex structure and low detection accuracy, which cause a high mortality of breast cancer. In order to improve the accuracy of AD detection and reduce the mortality of breast cancer, this paper proposes a new method by combining the non-subsampled contourlet transform (NSCT) with the improved pulse coupled neural network (PCNN). Firstly, the top–bottom hat transformation and the exponential transformation are employed to enhance the image. Secondly, the NSCT is employed to expand the overall contrast of the mammograms and filter out the noise. Finally, the improved PCNN by the maximum inter-class variance threshold selection method is employed to complete the AD detection. This proposed approach is tested on the public and authoritative database—Digital Database for Screening Mammography (DDSM). The specificity of the method is 98.73%, the accuracy is 93.16%, and the F1-score is 79.80%, and the area under curve (AUC) of the receiver operating characteristic (ROC) curve is 0.93, these results clearly demonstrate that the proposed method is comparable with those methods in recent literatures. This proposed method is simple, furthermore it can achieve high accuracy and help doctors to perform computer-aided detection of AD effectively.
Designing for Revitalization of Communities through New Business Models for Traditional Arts and Crafts

This paper discusses the importance and opportunities of design in helping to revitalize rural and marginalized communities where traditional arts and craft businesses often reside. It argues that traditional arts and craft businesses need to build a symbiotic relationship with the local communities to ensure the survival and thriving of both. It aims to demonstrate that designers should and can work traditional arts and craft business to better tell their stories and pursue new business opportunities with the goal to achieve sustainable development of these businesses and their communities. Through ethnography research and participatory design process, the paper presents the research team’s collaborations with a few traditional arts and crafts business owners in three communities and presents a few design concepts resulted from this process. It provides a list of suggestions based on the above analysis for designers who are interested in working as the link between business and traditional arts and design.
A Generic Block-Level Error Confinement Technique for Memory Based on Principal Component Analysis

Nanoscale CMOS technology has encountered severe reliability issues especially in on-chip memory. Conventional word-level error resilience techniques such as Error Correcting Codes (ECC) suffer from high physical overhead and inability to correct increasingly reported multiple bit flip errors. On the other hands, state-of-the-art applications such as image processing and machine learning loosen the requirement on the levels of data protection, which result in dedicated techniques of approximated fault tolerance. In this work, we introduce a novel error protection scheme for memory, based on feature extraction through Principal Component Analysis and the modular-wise technique to segment the data before PCA. The extracted features can be protected by replacing the fault vector with the averaged confinement vectors. This approach confines the errors with either single or multi-bit flips for generic data blocks, whilst achieving significant savings on execution time and memory usage compared to traditional ECC techniques. Experimental results of image processing demonstrate that the proposed technique results in a reconstructed image with PSNR over 30 dB, while robust against both single bit and multiple bit flip errors, with reduced memory storage to just 22.4% compared to the conventional ECC-based technique.