Miracle-dz/UniFAR-FTUs huggingface dataset:
Functional Nmr Imaging Of The Human Brain

ce Abstracl Knowledge of regional hemodynamics has widespread application for both physiological research and clinical assessment because of the well-established interrelationship between physiological function, energy metabolism, and localized blood supply. External monitoring of the transit of either intravascular or diffusible tracers through the brain has been used for years to measure cerebral hemodynamics. Following bolus administration, the concentration of agent in an area of tissue at a given time is the convolution integral of the input function and the clearance function. Tracer kinetic analysis of these concentration-time data can yield both flow and volume information. In normal brain, lanthanide chelates are confined to the intravascular space by an intact blood-brain barrier. Because cerebral transit times are on the order of seconds, a highspeed imaging technique is needed to resolve the passage of an intravascular agent through the capillary bed. The advent of ultrafast NMR imaging has removed spatial-temporal limitations, allowing for real-time contrast enhanced movies of the transit of contrast agent through the brain. We report our results using the "Instascan" method (l), which provides a complete 2-D image from a single radio frequency excitation. Preliminary data in selected patients (including stroke, tumor, and neurodegenerative disorders) and normal volunteers are presented.
Radical Region Based CNN for Offline Handwritten Chinese Character Recognition

In recent years, deep learning based methods have been widely used in handwritten Chinese character recognition (HCCR) and greatly improved the recognition accuracy. However, most of the current methods simply employ famous networks like GoogleNet without fully embedding the specific features of Chinese characters. Taking structural characteristics into consideration, we propose a radical region network structure to represent the radical region information (For example left, right, top and bottom radical regions). In our study, the character feature is represented as global feature while the radical region feature is represented as local feature. The multi-supervised training method is also used to learn two kinds of feature at the same time. Experiment results show the proposed methods improve recognition accuracy of current models. The performance of the best model has been raised to 97.42% on ICDAR 2013 offline HCCR competition database which achieves the state-of-the-art result as we know.
The research on customer classification of B2C platform based on k-means algorithm

Unlike the traditional customer relationship management, e-commerce can not only retain transaction data, but also collect other customer information, such as browsing data, comments, and preferences. In order to solve the problem of B2C e-commerce customer classification, K-means algorithm was selected as the clustering classification method, and an improved RFM value model was proposed to add indicators that meet the e-commerce domain characteristics. The customer group in the B2C environment is well classified.
Mechanical fault diagnosis method for OLTC based on MPE and K-means++ algorithm

On-load tap-changer (OLTC) is easy to fail because of the frequency switching operation. The vibration signals produced by the operation process show obvious nonlinear characteristics, but most of the existing methods of extracting OLTC running feature are based on the linear theory, and there are some limitations. In order to realize the mechanical fault diagnosis of OLTC under the nonlinear vibration signal, a kind of OLTC mechanical fault diagnosis method based on the multiscale permutation entropy (MPE) nonlinear analysis method is proposed. Firstly, the vibration signals are collected in the different mechanical faults of the OLTC in the laboratory. Secondly, the Cao's method is used to determine the MPE parameters and the MPE characteristic of the mechanical failure state of the OLTC is extracted. Finally, the K-means++ algorithm is used to identify the mechanical state of the OLTC and compares it with the clustering effect of the traditional K-means algorithm. The results show that Mechanical fault diagnosis method for OLTC based on MPE and K-means++ algorithm can effectively extract the mechanical fault characteristics of OLTC, and can correctly identify the OLTC mechanical fault, and its diagnosis effect is better than the traditional K-means algorithm, and the stability is high.
Benchmarking Multi-target Regression Methods

Machine learning methods for multi-target regression (MTR) rely on the hypothesis that an inter-target correlation can improve predictive performance. In the last years, many MTR methods were developed, but there are still questions about how their performances are influenced by the datasets characteristics such as linearity, number of targets, and inter-correlation complexity. Aiming at contributing to the understanding of the relationship between the dataset properties and MTR methods, we generated 33 synthetic datasets with controlled characteristics and tested their performance with single-target and six MTR methods. The results showed that MTR methods were able to improve performance even in datasets whose targets were not linearly correlated among them, but the predictive improvement differed among the combinations of method/regressor according to the dataset composition.
Review of Measured Photon Detection Efficiencies of Microchannel Plates

In this paper, we examine the history of detection efficiency measurements of photons (soft to hard X-rays and beyond) with microchannel plates (MCPs). We investigate the detection efficiency as a function of photon energy over a wide energy range, from a few hundred eVs up to 20 MeV. We also investigate detection efficiency as a function of incident angle onto the MCP. We interpreted the published efficiency data measured with a variety of MCPs and the use of MCP coatings for enhancement of X-ray quantum detection efficiency, and discuss theoretical expectations based on the main photon interactions with matter: the photoelectric effect, Compton scattering, and pair production. Contributing to the published literature at the highest end of the energy range, we also discuss our photon detection efficiency experiments from 2.5 to 20 MeV and theoretical implications.
Power Quality Assessment of Grid Connected Photovoltaic System on Power Factor

This paper describes prediction of power factor (PF) on Grid Connected Photovoltaic (GCPV) power systems towards Malaysia climate variations (solar irradiance) by using Mathematical model. The mathematical model was developed by using a phasor diagram under three different cases. Each case presents different possibilities of GCPV system current flow and power factor characteristic towards solar irradiance condition. Each mathematical equation was derived from a phasor diagram which utilize solar irradiance as the input and power factor as the output. The verification on the accuracy of the proposed model is conducted based on real data measurement on the GCPV system which located at Green Energy Research Center (GERC), University of Technology MARA (UiTM) Shah Alam, Malaysia. Based on the comparative study and statistical approach, it shows a good agreement between actual and prediction data. Besides, the development of the proposed model provides an effective method for prediction mainly on the power factor characteristic of GCPV system and feasible in time under the same accuracy.
Research on Personalized Recommendation of Products based on Concept Similarity of Node Depth

With the development of product diversification, the characteristics of users' demands nowadays reflect diversity and difficult to locate accurately. Nowadays, simple recommendation does not solve the contradiction between product diversity and user needs, and this contradiction has an increasing trend. Technology of personalized recommendation will be the key to solve the contradiction. By analyzing the attribute structure of the products, the attribute is divided into three different layers: content structure, operation and function, and the representation method of tree structure is designed. The structure model can express the attribute structure of products clearly.
An Undeniable Signature Scheme based on Quasi-Dyadic Codes

Undeniable signature is a kind of signature primitive where the verification step is an interaction between the verifier and the signer. Code-based cryptography is an alternative of post quantum cryptography, which permits fast encryption operation. In this paper, we introduce a quasi-dyadic code based undeniable signature. The verification protocol for our scheme is a 5-pass zero-knowledge protocol derived from our previous identification protocol. We also analyze the security of completeness, soundnes, zero-knowledge, invisibility, unforgeability and nontransferability of the scheme. And the performance of our scheme shows the signature lengths for different security level, and that the communication costs in the verification step is less than the costs in the compared scheme.
Lassie Come-Home

Joe Carraclough is devastated when the closure of the coalmines at Greenall Bridge in Yorkshire forces his father to sell their beloved, pure-bred collie dog, Lassie, to the Duke of Rudling. But when Lassie turns up again to wait for Joe at the school gate, his father insists they return her to the not-toogentle care of Hynes, the Duke's kennel man. The Duke takes Lassie away to the north of Scotland, but she feels the separation so keenly she begins an epic 400-mile journey back home. But, if she survives, will this prevent the Duke from coming to claim her once more?
The Exploration of the Stress with a Width Crack on Semi-infinite Plane Boundary

This paper discusses a static problem of elastic semi-infinite plane with a width crack. We put forward the boundary condition by the force analysis of the actual physical model, and establish the integral equation on the boundary crack L°, + L1 by using the D.I. Sherman method, which is based on the relevant elastic mechanics knowledge. Then by inverse transformation the original problem is reduced to the singular integral equations on crack L1, which is discreted by using the Gauss formula afterwards, to get the numerical solution of it. Finally, we use MATLAB to simulate the numerical solution to get the trend chart of the crack displacement, which is affected by external force and crack inclination angle. It intuitively shows the change rule of crack and all parameters.
Neue Standards in der Therapie des fortgeschrittenen hepatozellulären Karzinoms

A New Standard Therapy for Advanced Hepatocellular Carcinoma. In > 80 % of HCC cases, the etiology is cirrhosis. Viral hepatitis B and C infections, hemochromatosis, alcoholic/non-alcoholic liver diseases and aflatoxin are major risk factors for liver cirrhosis. In patients with Child A and B liver cirrhosis, screening by ultrasound should be performed once every 6 months, but screening with AFP levels is still controversial. The use of imaging techniques such as USG and spiral computed tomography/magnetic resonance imaging gives us the opportunity for early diagnosis of HCC. If a mass appears in the liver during the follow-up of a cirrhotic patient it is sufficient for the diagnosis of HCC without any need for further diagnostic procedures such as biopsy (NCCN guideline). The choice of therapy (resection, liver transplantation, percutaneous ablation therapy, arterial embolization, systemic therapy) for HCC depends on the liver function of the patient as well as on the tumor stage. When diagnosed, < 20 % of HCC are curatively resectable, so other curative treatment options are very important. For advanced tumors of the liver, newly developed systemic therapies begin to have ever increasing importance. The targeted oncologic therapy is an important option because it is better tolerated. Here the primary goal of therapy should be the prolongation of progression-free survival (PFS) and overall survival with a better quality of life. The SHARP trial is the first phase-III trial that shows a significant improvement in the overall survival of HCC patients undergoing systemic therapy. The results of this trial led to the approval of sorafenib in the systemic treatment of advanced HCC, first in the EU and then in the US, now in many other countries around the world. The Pacific-Asia Study has shown a prolonged survival with sorafenib compared with placebo in Asian patients with HCC as well. J Gastroenterol Hepatol Erkr 2010; 8 (3): 26–31.
Stochastic Dual Dynamic Programming for Operation of DER Aggregators Under Multi-Dimensional Uncertainty

The operation of aggregators of distributed energy resources (DER) is highly complex, since it entails the optimal coordination of a diverse portfolio of DER under multiple sources of uncertainty. The large number of possible stochastic realizations that arise can lead to complex operational models that become problematic in real-time market environments. Previous stochastic programming approaches resort to two-stage uncertainty models and scenario reduction techniques to preserve the tractability of the problem. However, two-stage models cannot fully capture the evolution of uncertain processes and the a priori scenario selection can lead to suboptimal decisions. In this context, this paper develops a novel stochastic dual dynamic programming approach which does not require discretization of either the state space or the uncertain variables and can be efficiently applied to a multi-stage uncertainty model. Temporal dependencies of the uncertain variables as well as dependencies among different uncertain variables can be captured through the integration of any linear multidimensional stochastic model, and it is showcased for a p-order vector autoregressive model. The proposed approach is compared against a traditional scenario-tree-based approach through a Monte-Carlo validation process, and is demonstrated to achieve a better trade-off between solution efficiency and computational effort.
Predictive Control for Relative Performance Management

This paper examines the implementation of Model Predictive Control (MPC) for relative performance management in a shared resources system. Finite Control Set MPC as a model-based control is integrated in a scheme of differentiated control for a multi classes virtualized software system. A dynamic model is estimated in block-oriented form with nonlinear compensation feedback system. The performance objective is to maintain response time on the reference levels or subject to the degree of priority between the user classes. Experiments are conducted in a two-classes of virtualized software system for several performance differentiation scenarios. The performance of Integral FCSMPC is evaluated in comparison with Proportional Integral (PI) control. The results show that predictive control framework provides significant improvement in predictability and disturbance rejection procedure. Therefore, Integral FCS-MPC outperforms PI controller in terms of maintaining the stability of relative performance objectives.
Breeding Data Service Platform Based on the New Architecture of Cloud Technology

With the rise of information and communication technologies such as big data and Internet of things, traditional breeding data management methods have been unable to cope with the demand of large-scale breeding. Aiming at the present situation, a new breeding data service structure based on cloud technology is put forward, and a breeding data service platform is constructed. The platform with the Django Web framework, Python modeling language for the back-end operation, MongoDB for the database, uses cloud storage data center model and the user-oriented multi-level service structure, providing data storage and data analysis services for breeders. The platform provides the feasible architecture scheme for the breeding units to carry out the corresponding service, realizes the sharing of breeding data resources, and advances the process of Internet + agricultural informatization construction.
Mathematical Modeling of Semicircular Linear Motor Based on Vector Potential With Landen's Transformation

A semicircular linear motor that realizes a motion along circumference of the circle has been developed. To settle the optimal design of this motor by mathematical analysis, the theoretical equation of motor performance has to be clarified. In this paper, the mathematical modeling of semicircular linear motor is carried out from the viewpoint of Lorentz force and magnetic flux density derived by vector potential. By comparing calculated, analyzed, and measured magnetic flux density and thrust force, the validity of derived mathematical model is confirmed. In addition, the cause of difference among three results is also discussed.
Parallel Structure of All Digital Timing Synchronization and Realization of FPGA

This paper focuses on the parallel implementation of high-speed and broadband transmission system, and proposes a low-complexity parallel structure, which is the integration of frequency domain timing synchronization and sample points shift. The study is on the basis of AVR Algorithm, which is able to satisfy different modulations. Different from the traditional feedback type all digital timing synchronization structure, the paper divides feedback timing adjustment into integer and fractional parts. The Algorithm of time domain sampling adjustment and frequency domain timing correction is designed and implemented on FPGA platform, and the simulation result verifies that the performance of this algorithm has 1dB gap compared with theoretical value in high-order modulation.
Control Strategy for a Three-Phase DC-AC Converter with an Output LCLC Filter that Feeds an Unknown Load Current Source

This paper presents a control strategy for a three-phase DC-AC converter with an output LCLC filter that feeds an unknown current load. The main purpose is to improve the performance of the system under sudden changes in the load while the output voltage follows a reference giving by the designer. To obtain a quick response, a feedforward and PR controller is proposed, which includes a compensation that estimates the current load, its first derivative and two states of the filter. To get this estimation, virtual sensors are used. With this, the use of four sensors is avoided. Simulation results are presented to show the performance of the proposed controller
Project IAAP: An Overview on Optimizing Business Process in Smart Enterprises

The growing rhythm of the modern economy forces companies to constantly introduce new solutions, resulting in innovating the market. Nowadays, the vast majority of businesses are determined to implement product, technical, technological and organizational innovation. Enterprises are focused on creating value for the customer, who is becoming more aware and demanding in terms of increased customer requirements relating to lead time delivery services, product availability and reliability. The newest solutions such as Internet of Things, Big Data and Industry 4.0 create opportunities to meet the needs and to contribute to the development of logistics and supply chains management. In this paper we analyse the possibility of utilizing Business Process Management techniques, Standard Methodology, Artificial Intelligence techniques (AI) and Decision Support Systems (DSS) in the management of business processes for supporting business processes optimization with a centred approach at supply chains under the usage of the business process lifecycle.
The Degradation Depth of Non-grain Oriented Electrical Steel Sheets of Electric Machines Due to Mechanical and Laser Cutting: A State-of-the-Art Review

Literature agrees that cutting may damage the electromagnetic properties of electrical steel sheets, notably along the cut edge. However, statements on the depth of the deterioration vary. An analysis of the various results presented would allow for a better consideration of the material degradation in the modeling of electromagnetic properties of the cut steel sheet in electric machines. To this aim, this paper analyzes and summarizes the deterioration depths presented in the literature. It not only provides an overview of the quantitative results, but discusses these in the context of the respective measurement method, since every technique used only provides an image of some of the aspects of the cut material affected by the cutting process.
Research on the Coordinated Control of the Integrated Wind-PV-ES Microgrid System

Considering the characteristics of intermittent and fluctuating characteristics such as wind power, photovoltaic and other new energy generation, the introduction of large capacity energy storage system is an important way to increase the proportion of new energy generation. As an important way to realize the distributed power supply scale utilization, micro grid provides a new idea for the new energy accepted by power grid. However, due to the diverse forms and performance of distributed generation in micro grid, the coordinated control of distributed generation is very important to fully play the potential advantages of micro grid. In this context, the power characteristics of wind power and photovoltaic are analyzed. Based on the natural complementarity between them and the characteristics of real-time charging and discharging of energy storage, a micro grid system combining wind power, photovoltaic and energy storage is constructed. At the same time, the coordinated control of distributed generation is studied.
Halstead Metric for Quality Measurement of Various Version of Statcato

Halstead metric is one of software quality measurement technique. Some studies call it as Primitive and/or Classic Software Metric, although some researchers find vagueness of it. This paper utilizing Halstead metric to measuring the quality of various versions of Statcato software. Measurements are performed in each class of each version of Statcato software. Python programming used to facilitate this. This paper find that in terms of Halstead metric, this software seems stable during its lifecycle. So, the quality of the newer versions of Statcato are better in terms of stability of Halstead metrics than the earlier version, although its feature is increasing.
Optimal Energy-Efficient for OFDM-Based Cognitive Relay Networks with Sensing Errors

In this paper, the energy efficiency maximization problem in OFDM-based cognitive relay networks with incomplete spectrum sensing is investigated. Firstly, a secondary relay transmission system model is established where the interference limitation, the incompleteness of spectrum sensing, and the maximum transmit power are considered. The joint optimization energy efficient model of spectrum sensing and transmission problem is mixed-integer nonlinear programming problem in the NP problem, it is difficult to find the optimal solution directly. Therefore, the fractional programming is used to solve the problem to reduce the complexity of the algorithm by convex optimization. Simulation results show the effectiveness of the proposed algorithms.
Scaling beyond the 65 nm node with FinFET-DGCMOS

Exponential growth in leakage power density with physical scaling is driving ULSI technology towards innovative device architectures. Double-gate CMOS (DGCMOS), achieved through use of the Delta Device (D.Hisamoto et al, IEDM 1989, p.833-836), or FinFET (X.Huang et al, IEDM 1999, p.67-70), provides both a tactical solution to the gate-leakage challenge and a strategic scaling advantage. FinFET fabrication is very close to that of the conventional CMOS process, with only minor disruptions, yielding the potential for a rapid deployment to manufacturing. Planar circuit designs have been converted to FinFET-DGCMOS without disruption to physical area.