Miracle-dz/UniFAR-FTUs huggingface dataset:
A Game Theory Based Clustering Scheme (GCS) for 5G-based Smart Healthcare

5G technology will play a significant role in the next-generation smart healthcare network. D2D communication, one the key enabling technology of 5G, makes use of devices in the vicinity for resources utilization, reducing latency, improving data rates and increasing system capacity. The integration of multi-hop and cellular networks not only delivers reliability and Quality of Service (QoS) but also make the network adaptable and flexible. In next-generation multi-hop cellular D2D networks, clustering and routing are presents significant challenges. Therefore, if the clustering and routing decisions are not appropriately implemented, multi-hop network performance can be worse as compared to the traditional network. This is because clustering and routing play a significant role to manage network fragmentation and dynamic network topology, which are not present in a traditional cellular network. In this paper, we propose a clustering scheme based on game theory (i.e., mixed strategy) to select optimal cluster heads (CHs) and transmit data from a cluster head (CH) to base station (BS). The simulation results show that our proposed scheme is better than LEACH protocol in terms of a network lifetime and energy consumption. We used MATLAB environment to simulate our proposed scheme and compared with LEACH protocol.
Phase Shift Based Level Sensing using two guided Wave Mode T (0, 1) and F(1,1) on a thin Waveguide

This paper reports a novel ultrasonic guided wave-based waveguide sensor for liquid level measurement which is critical for process controls in industries. A thin stainless wire with a varying cross-section (cylinder and rectangular end) is selected as the waveguide sensor in which all three fundamental wavemode L (0,1), T (0,1), and F (1,1) are excited and received by a single shear transducer. We validated the suitability of this waveguide for continuous level measurement experiments in the laboratory conditions. Level measurement experiments were carried out in inviscid fluid (water) and the fluid level is monitored/extracted by tracking the change in time of flight ((δTOF) of the excited torsional wavemode T(0,1) and flexural wavemode F(1,1) in the waveguide that is directly related to the fluid level in the probed media. The presented technique is suitable for enhanced level measurement using two wavemodes simultaneously. The versatility of this technique enables its application for remote measurements in a wide range of applications Keywords: Guided wave, Waveguide, Sensor, Level Measurement
Optimal Scattering Devices Location for Multi-User MIMO Beamforming

This paper investigates optimal locations for scattering devices in a multiple-input multiple-output (MIMO) channel using transmitter (Tx) beamforming techniques. It aims to find best locations of the active scatters in a MIMO system so that the radiated power at a desired receiver antenna element is maximized while total radiated power is minimized. The Tx beamforming for multiple orthogonal beams (OB) uses beam shaping techniques under multiple specified performance constraints; not only to control radiated power density peak at a desired receiving (Rx) element at a specified level, say 0 dBm/m2, but also to suppress radiated power density levels to below -40dBm/m2 at other undesired receive (Rx) antenna elements. A line-of-sight (LOS) channel model is developed, and optimized active scatter locations are selected for MIMO systems. Composite transfer functions (CTF) based on channel state information (CSI) are built with CVX optimization in beam-shaping in the Tx array with multiple elements. It is demonstrated by selecting the location of scatters, not only the radiated power at a desired receiver antenna element is maximized while total radiated power is minimized, but also multiple OB beams are generated with active scatters for frequency reuse by multiple users.
The Sub-Threshold Leakage Reduction Techniques in CMOS Circuits

Minimizing the leakage power has become one of the major concerns in low-voltage, low-power and high performance applications in VLSI involving CMOS circuits. The first part of this paper describes the need for low power and drawbacks in technology scaling and power components. The second part describes the various sources of leakage power and various techniques involved in reducing the same without affecting the performance. Various power reduction techniques can be employed at process and circuit level which includes Transistor stacking, Multiple threshold (MTCMOS), Dynamic threshold, Dual threshold, Variable threshold (VTCMOS), Pin-reordering, Supply voltage scaling & stacking techniques.
Versatile fabrication and integration method of optical oxygen sensors in organ-on-chips

Oxygen concentration is an essential parameter in in vitro cell cultures as it affects cell viability, cellular activity, cell differentiation, and can be a measure for cellular response. Organ-on-chip (OoC) devices provide an in vitro cell culture method, aiming to mimic the in vivo situation better than conventional cell culture methods. Oxygen control in these OoC devices is very meaningful especially for organs with inherently large oxygen concentration gradients, such as the gut. Here, we show a versatile fabrication method for optical oxygen sensor patches based on platinum tetrakis (pentafluorophenyl) porphyrin (PtTFPP) in polystyrene (PS). The PtTFPP/PS sensors show a linear Stern Volmer calibration plot. The proposed fabrication method allows abundant design freedom regarding both the sensor patches (size and shape) and integration (site and number of patches) in a typical two-channel OoC device.
Methylation: An Ineluctable Biochemical and Physiological Process Essential to the Transmission of Life

Methylation is a universal biochemical process which covalently adds methyl groups to a variety of molecular targets. It plays a critical role in two major global regulatory mechanisms, epigenetic modifications and imprinting, via methyl tagging on histones and DNA. During reproduction, the two genomes that unite to create a new individual are complementary but not equivalent. Methylation determines the complementary regulatory characteristics of male and female genomes. DNA methylation is executed by methyltransferases that transfer a methyl group from S-adenosylmethionine, the universal methyl donor, to cytosine residues of CG (also designated CpG). Histones are methylated mainly on lysine and arginine residues. The methylation processes regulate the main steps in reproductive physiology: gametogenesis, and early and late embryo development. A focus will be made on the impact of assisted reproductive technology and on the impact of endocrine disruptors (EDCs) via generation of oxidative stress.
Knockdown of Forkhead box A1 suppresses the tumorigenesis and progression of human colon cancer cells through regulating the phosphatase and tensin homolog/Akt pathway

Background This study aimed to evaluate the role and the underlying mechanisms of Forkhead box A1 (encoded by FOXA1) in colon cancer. Methods We analyzed FOXA1 mRNA and protein expression in colon cancer tissues and cell lines. We also silenced FOXA1 expression in HCT116 and SW480 cells to evaluate the effects on cell proliferation, cell cycle, migration, and invasion by using MTT, colony formation, flow cytometry, and the Transwell assay, respectively. Results FOXA1 immunostaining was higher in colon cancer tissues than adjacent healthy tissues. FOXA1 mRNA and protein expression was significantly increased in human colon cancer cells compared with a normal colonic cell line. FOXA1 expression was also significantly higher in colorectal cancer tissues from TCGA data sets and was associated with worse prognosis in the R2 database. FOXA1 expression was negatively correlated with the extent of its methylation, and its knockdown reduced proliferation, migration, and invasion, and induced G2/M phase arrest in HCT116 and SW480 cells by suppressing the phosphatase and tensin homolog/Akt signaling pathway and inhibiting epithelial–mesenchymal transition. Conclusion FOXA1 may act as an oncogene in colon cancer tumorigenesis and development.
Current and New Biomarkers for Early Detection, Prognostic Stratification, and Management of Gallbladder Cancer Patients

Simple Summary Gallbladder cancer is the sixth cause of death related to digestive tract tumors, with high mortality due to delayed diagnosis at advanced stages of the disease. Moreover, treatment options for advanced gallbladder cancer usually rely on cytotoxic chemotherapy, which is frequently ineffective. Since complete surgical removal at early stages represents the best chance for curative treatment, there is an urgent need for the discovery of effective biomarkers to assess individual risk and early detection of the disease. Equally important is the development of predictive markers for adequate selection of systemic therapies to improve patient prognosis, both in the adjuvant and palliative settings. In the following review, we summarize current and newly examined biomarkers and discuss their potential utility in the management of gallbladder cancer patients. Abstract Gallbladder cancer (GBC) is an aggressive disease that shows evident geographic variation and is characterized by a poor prognosis, mainly due to the late diagnosis and ineffective treatment. Genetic variants associated with GBC susceptibility, including polymorphisms within the toll-like receptors TLR2 and TLR4, the cytochrome P450 1A1 (CYP1A1), and the ATP-binding cassette (ABC) transporter ABCG8 genes, represent promising biomarkers for the stratification of patients at higher risk of GBC; thus, showing potential to prioritize cholecystectomy, particularly considering that early diagnosis is difficult due to the absence of specific signs and symptoms. Similarly, our better understanding of the gallbladder carcinogenic processes has led to identify several cellular and molecular events that may influence patient management, including HER2 aberrations, high tumor mutational burden, microsatellite instability, among others. Despite these reports on interesting and promising markers for risk assessment, diagnosis, and prognosis; there is an unmet need for reliable and validated biomarkers that can improve the management of GBC patients and support clinical decision-making. This review article examines the most potentially significant biomarkers of susceptibility, diagnosis, prognosis, and therapy selection for GBC patients, highlighting the need to find and validate existing and new molecular biomarkers to improve patient outcomes.
Meta-Information driven Modeling of Services for Experimentable Digital Twins

Experimental Digital Twins (EDT) provide a comprehensive view on the behavior of Cyber-Physical Systems (CPS). This is achieved by simulation technology and the application of virtual environments to develop and test desired functionality, including models, data structures, services and communication capabilities. This paper outlines a meta-information driven method of describing services provided by a CPS for consumption by peers via any means of communication. In addition, a set of application rules is presented, which handle cases such as service composition and the interaction of CPS components in distributed systems. We present the fundamental meta-information system that facilitates the specification process by providing the necessary type information for the dynamic object construction and automated (de-)serialization of data passed between provider and consumer.
Brainwave Classification Using Covariance-Based Data Augmentation

A brain–machine interface (BMI) is a technology that controls machines via brainwaves. In BMI, the performance of brainwave analysis is very important for achieving machine control that reflects the user’s intention. One of the main obstacles in this analysis is an insufficient amount of data points because long-term brain signal experiments tend to reduce data quality. Data augmentation methods can be used to overcome this limitation. Recently, several neural network-based data augmentation methods have been developed. However, those methods have several limitations; first, they require considerable computation time because a very large number of parameters must be obtained. Moreover, the neural network based method can suffer from unstable training, which results in quality degradation of artificial data. To address these problems, this paper introduces a method that generates an artificial dataset which has correlation of feature similar to the original dataset. Specifically, after decomposing the covariance matrix for the features into a lower triangular matrix, an artificial dataset can be generated by multiplying the lower triangular matrix by random variables. This method is computationally fast, and the augmentation is stable. When the brainwave data were augmented using this method, classification performance was improved by 1.08%–6.72%. This method focuses on mean, correlation, and not taking into account the other statistical parameters. Since it rapidly generates a large dataset, it can also be useful in other applications.
On Wilks' Theorem for Generalized Likelihood Ratio Test Performance of Cooperative Radar-Communications

The analysis of the generalized likelihood ratio test (GLRT) radar receiver when the radar is coexisting with a cooperative in-band communication (comm.) system is challenged by the discrete nature of the comm. symbols. While the asymptotic performance of the GLRT can be well-approximated by using the Fisher information matrix (FIM) for the Cramér-Rao bound (CRB) via Wilks' theorem, this only applies when the underlying data distribution for each hypothesis is continuous in the parameters. Thus, to accommodate the discrete nature of the comm. symbols an adaptation to Wilks' theorem is proposed in this work that uses the constrained CRB (CCRB) in place of the usual unconstrained CRB.
The Security Policies Optimization Problem for Composite Information Systems

The problem of optimizing the security policy for the composite information system is formulated. Subject-object model for information system is used. Combining different types of security policies is formalized. The target function for the optimization task is recorded. The optimization problem for combining two discretionary security policies is solved. The case of combining two mandatory security policies is studied. The main problems of optimization the composite security policy are formulated. +50 CHMBOJIOB!
Real-Time Optimal Operation Control of Micro Energy Grid Coupling With Electricity- Thermal -Gas Considering Prosumer Characteristics

Under the increasingly stringent environmental regulations, the installed capacity of distributed renewable energy is increasing rapidly. How to fully absorb renewable energy without affecting power grid security and ensuring power quality is the key problem to be solved. The rapid development of energy production, conversion and storage equipment of prosumer makes the operation of micro energy grid possible. Therefore, according to the energy characteristics of different prosumer, this article divides the micro energy grid into two types: micro energy grid of electricity selling and micro energy grid of energy supplying. The constrained nonlinear optimization method is used to solve the optimization problem of this high-dimensional nonlinear system with time delay. In this article, the comparative study of optimal operation control between the two kinds of micro energy grid is carried out. In order to deal with the uncertainty of distributed renewable energy output and load forecasting deviation, this article proposes a real time adaptive dynamic optimization control strategy based on deep learning. The strategy uses deep learning technology to pretrain the action network, so as to learn the optimal operation behavior of micro energy grid. Finally, the online simulation of micro energy grid for consecutive days is used to verify the correctness and real-time performance of the algorithm.
An Enhanced Multi-Modal Recommendation Based on Alternate Training With Knowledge Graph Representation

Deep network recommendation is a cutting-edge topic in current recommendation system research, which as a combination of recommendation systems and deep learning theory can effectively improve recommendation accuracy. In a real recommendation scenario, all the effective information in a data set should be extracted, both explicit and implicit, because the comprehensive degree of information is proportional to the recommendation performance. This article proposes an enhanced multi-modal recommendation based on alternate training with knowledge graph representation (SI-MKR) based on the MKR deep learning recommendation model. Our framework is an enhanced recommendation system based on knowledge graph representation, using valuable external knowledge as multi-modal information. The SI-MKR model solves the problem of ignoring the diversity of data types in the multi-modal knowledge-based recommendation system, which adds user and item attribute information from a knowledge graph as an enhancement recommendation multi-tasking training. By analysing the content of the item and user attributes, the SI-MKR model classifies the attributes of the items and users, processes the text type attributes and multi-value type attributes separately for feature extraction, and other types of attributes are used as inputs to the knowledge graph embedding unit. In addition, the knowledge graph data form a triplet unit, thus continuing the knowledge graph data training process. The feature extraction unit of the knowledge graph and the recommended unit are connected through the cross-compression unit for alternate training. During the deep learning framework training process, the recommendation system’s item has a potential correlation with the head entity in the knowledge graph which embodies the idea of multi-tasking. Through extensive experiments on real-world datasets, we demonstrate that SI-MKR achieves substantial gains in movie recommendation over advanced model baselines. Even user-item interactions are sparse, SI-MKR maintains better performance than the MKR model.
Static and Dynamic Event-Triggered Mechanisms for Distributed Secondary Control of Inverters in Low-Voltage Islanded Microgrids

Due to the high resistance/reactance (R/X) ratio of a low-voltage microgrid (LVMG), virtual complex impedance-based $P-\dot {V}/Q-\omega $ droop control is adopted in this article as the primary control (PC) technique for stabilizing the system. A distributed event-triggered restoration mechanism (ETSM) is proposed as the secondary control (SC) technique to restore the output-voltage frequency and improve power sharing accuracy. The proposed ETSM ensures that neighboring communication happens only at some discrete instants when a predefined event-triggering condition (ETC) is fulfilled. In general, the design of the ETC is the crucial challenge of an event-triggered mechanism (ETM). Thus, in this article, a static ETM (SETM) is proposed as the ETC at first, where two static parameters are utilized to reduce the triggering frequency. Bounded stability is ensured under the SETM, which means that the output-voltage frequency is restored to the vicinity of its nominal value, and close to fair utilization of the distributed generators (DGs) is achieved. To further improve the power sharing accuracy and accelerate the regulation process, a dynamic ETM (DETM) is then introduced. In the DETM, two dynamic parameters that converge to zero in the steady state are designed, which promises asymptotic stability of the system. Besides, Zeno behavior is excluded in both mechanisms. An LVMG consisting of four DGs is constructed in MATLAB/Simulink to illustrate the effectiveness of the proposed methods, and the simulations correspond with our theoretical analysis.
Intelligence Defect Detection of the Water Wall in Boiler System Based on CNN

As an important part to ensure the normal operation of the boiler, the defect detection of water walls based on the internal surface information of water walls has been paid more and more attention. But the traditional manual detection method is time-consuming and laborious with the low efficiency. Therefore, an intelligence detection method for detecting water wall defects in boiler systems based on the convolutional neural network (CNN) is proposed. The CNN has the characteristics of local connection, weight sharing, pooling and multi-layer structure, which can effectively extract local features of the water wall to build the intelligence defect detection model. Finally, the proposed method is used in the defect detection of the water wall of an actual thermal power plant. The experimental result has proved the effectiveness of the proposed method, And the recall rate and identification speed of the proposed method can meet the requirements of intelligent detection of the water wall defects.
Dynamic organization of cortical actin filaments during the ooplasmic segregation of ascidian Ciona eggs

Spatial reorganization of cytoplasm in zygotic cells is critically important for establishing the body plans of many animal species. In ascidian zygotes, maternal determinants (mRNAs) are first transported to the vegetal pole a few minutes after fertilization and then to the future posterior side of the zygotes in a later phase of cytoplasmic reorganization, before the first cell division. Here, by using a novel fluorescence polarization microscope that reports the position and the orientation of fluorescently labeled proteins in living cells, we mapped the local alignments and the time-dependent changes of cortical actin networks in Ciona eggs. The initial cytoplasmic reorganization started with the contraction of vegetal hemisphere approximately 20 s after the fertilization-induced [Ca2+] increase. Timing of the vegetal contraction was consistent with the emergence of highly aligned actin filaments at the cell cortex of the vegetal hemisphere, which ran perpendicular to the animal–vegetal axis. We propose that the cytoplasmic reorganization is initiated by the local contraction of laterally aligned cortical actomyosin in the vegetal hemisphere, which in turn generates the directional movement of cytoplasm within the whole egg.