An Improved PageRank Method based on Genetic Algorithm for Web Search

AbstractWeb search engine has become a very important tool for finding information efficiently from the massive Web data. Based on PageRank algorithm, a genetic PageRank algorithm (GPRA) is proposed. With the condition of preserving PageRank algorithm advantages, GPRA takes advantage of genetic algorithm so as to solve web search. Experimental results have shown that GPRA is superior to PageRank algorithm and genetic algorithm on performance

Wideband Beamforming for STAR-RIS-assisted THz Communications with Three-Side Beam Split

In this paper, we consider the simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-assisted THz communications with three-side beam split. Except for the beam split at the base station (BS), we analyze the double-side beam split at the STAR-RIS for the first time. To relieve the double-side beam split effect, we propose a time delayer (TD)-based fully-connected structure at the STAR-RIS. As a further advance, a low-hardware complexity and low-power consumption sub-connected structure is developed, where multiple STAR-RIS elements share one TD. Meanwhile, considering the practical scenario, we investigate a multi-STAR-RIS and multi-user communication system, and a sum rate maximization problem is formulated by jointly optimizing the hybrid analog/digital beamforming, time delays at the BS as well as the double-layer phase-shift coefficients, time delays and amplitude coefficients at the STAR-RISs. Based on this, we first allocate users for each STAR-RIS, and then derive the analog beamforming, time delays at the BS, and the double-layer phase-shift coefficients, time delays at each STAR-RIS. Next, we develop an alternative optimization algorithm to calculate the digital beamforming at the BS and amplitude coefficients at the STAR-RISs. Finally, the numerical results verify the effectiveness of the proposed schemes

Beamforming Design for the Distributed RISs-aided THz Communications with Double-Layer True Time Delays

In this paper, we investigate the reconfigurable intelligent surface (RIS)-aided terahertz (THz) communication system with the sparse radio frequency chains antenna structure at the base station (BS). To overcome the beam split of the BS, different from the conventional single-layer true-time-delay (TTD) scheme, we propose a double-layer TTD scheme that can effectively reduce the number of large-range delay devices, which involve additional insertion loss and amplification circuitry. Next, we analyze the system performance under the proposed double-layer TTD scheme. To relieve the beam split of the RIS, we consider multiple distributed RISs to replace an ultra-large size RIS. Based on this, we formulate an achievable rate maximization problem for the distributed RISs-aided THz communications via jointly optimizing the hybrid analog/digital beamforming, time delays of the double-layer TTD network and reflection coefficients of RISs. Considering the practical hardware limitation, the finite-resolution phase shift, time delay and reflection phase are constrained. To solve the formulated problem, we first design an analog beamforming scheme including optimizing phase shift and time delay based on the RISs' locations. Then, an alternatively optimization algorithm is proposed to obtain the digital beamforming and reflection coefficients based on the minimum mean square error and coordinate update techniques. Finally, simulation results show the effectiveness of the proposed scheme

Beamforming Analysis and Design for Wideband THz Reconfigurable Intelligent Surface Communications

Reconfigurable intelligent surface (RIS)-aided terahertz (THz) communications have been regarded as a promising candidate for future 6G networks because of its ultra-wide bandwidth and ultra-low power consumption. However, there exists the beam split problem, especially when the base station (BS) or RIS owns the large-scale antennas, which may lead to serious array gain loss. Therefore, in this paper, we investigate the beam split and beamforming design problems in the THz RIS communications. Specifically, we first analyze the beam split effect caused by different RIS sizes, shapes and deployments. On this basis, we apply the fully connected time delayer phase shifter hybrid beamforming architecture at the BS and deploy distributed RISs to cooperatively mitigate the beam split effect. We aim to maximize the achievable sum rate by jointly optimizing the hybrid analog/digital beamforming, time delays at the BS and reflection coefficients at the RISs. To solve the formulated problem, we first design the analog beamforming and time delays based on different RISs physical directions, and then it is transformed into an optimization problem by jointly optimizing the digital beamforming and reflection coefficients. Next, we propose an alternatively iterative optimization algorithm to deal with it. Specifically, for given the reflection coefficients, we propose an iterative algorithm based on the minimum mean square error technique to obtain the digital beamforming. After, we apply LDR and MCQT methods to transform the original problem to a QCQP, which can be solved by ADMM technique to obtain the reflection coefficients. Finally, the digital beamforming and reflection coefficients are obtained via repeating the above processes until convergence. Simulation results verify that the proposed scheme can effectively alleviate the beam split effect and improve the system capacity

Radiomic Features From Multi-Parameter MRI Combined With Clinical Parameters Predict Molecular Subgroups in Patients With Medulloblastoma

The 2016 WHO classification of central nervous system tumors has included four molecular subgroups under medulloblastoma (MB) as sonic hedgehog (SHH), wingless (WNT), Grade 3, and Group 4. We aimed to develop machine learning models for predicting MB molecular subgroups based on multi-parameter magnetic resonance imaging (MRI) radiomics, tumor locations, and clinical factors. A total of 122 MB patients were enrolled retrospectively. After selecting robust, non-redundant, and relevant features from 5,529 extracted radiomics features, a random forest model was constructed based on a training cohort (n= 92) and evaluated on a testing cohort (n= 30). By combining radiographic features and clinical parameters, two combined prediction models were also built. The subgroup can be classified using an 11-feature radiomics model with a high area under the curve (AUC) of 0.8264 for WNT and modest AUCs of 0.6683, 0.6004, and 0.6979 for SHH, Group 3, and Group 4 in the testing cohort, respectively. Incorporating location and hydrocephalus into the radiomics model resulted in improved AUCs of 0.8403 and 0.8317 for WNT and SHH, respectively. After adding gender and age, the AUCs for WNT and SHH were further improved to 0.9097 and 0.8654, while the accuracies were 70 and 86.67% for Group 3 and Group 4, respectively. Prediction performance was excellent for WNT and SHH, while that for Group 3 and Group 4 needs further improvements. Machine learning algorithms offer potentials to non-invasively predict the molecular subgroups of MB.</p

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Optimal scheduling of thermal-photovoltaic power generation system considering carbon emission

Increasing the proportion of clean energy is an inevitable trend of high-quality energy development. While enjoying the benefits of clean energy, traditional fossil energy enterprises face great challenges. In this study, coal power and photovoltaic power are selected as examples for analysis. Considering coal consumption, carbon emission, unit ramp conditions and photovoltaic abandonment, a comprehensive income model containing thermal power and photovoltaic power generation is proposed. The objective function of maximizing earnings is established and solved by genetic algorithm. The results show that the overall benefit will increase if the proportion of photovoltaic does not increase blindly. The unreasonable ratio of thermal and photovoltaic power and the imbalance of supply and consumption will lead to the decline of economic benefits and the increase of coal consumption and carbon emission. In the case of a high proportion of clean energy penetration, on the one hand, traditional power plants should be responsible for ensuring the instability of electricity output when the photovoltaic is weak, on the other hand, they need to give up some of the supply proportion when clean power is at a high output state. Therefore, while increasing the proportion of clean energy, it is necessary to improve the speed of load adjustment, the depth of peak regulation and the efficiency of low load work in traditional power plants to ensure the healthy development of new energy

AI-enabled organoids: Construction, analysis, and application

Organoids, miniature and simplified in vitro model systems that mimic the structure and function of organs, have attracted considerable interest due to their promising applications in disease modeling, drug screening, personalized medicine, and tissue engineering. Despite the substantial success in cultivating physiologically relevant organoids, challenges remain concerning the complexities of their assembly and the difficulties associated with data analysis. The advent of AI-Enabled Organoids, which interfaces with artificial intelligence (AI), holds the potential to revolutionize the field by offering novel insights and methodologies that can expedite the development and clinical application of organoids. This review succinctly delineates the fundamental concepts and mechanisms underlying AI-Enabled Organoids, summarizing the prospective applications on rapid screening of construction strategies, cost-effective extraction of multiscale image features, streamlined analysis of multi-omics data, and precise preclinical evaluation and application. We also explore the challenges and limitations of interfacing organoids with AI, and discuss the future direction of the field. Taken together, the AI-Enabled Organoids hold significant promise for advancing our understanding of organ development and disease progression, ultimately laying the groundwork for clinical application

Machine Learning-based Beamforming Design for Millimeter Wave IRS Communications with Discrete Phase Shifters

In this paper, we investigate an intelligent reflecting surface(IRS)-assisted millimeter-wave multiple-input single-output downlink wirelesscommunication system. By jointly calculating the active beamforming at the basestation and the passive beamforming at the IRS, we aim to minimize the transmitpower under the constraint of each user' signal-to-interference-plus-noiseratio. To solve this problem, we propose a low-complexity machinelearning-based cross-entropy (CE) algorithm to alternately optimize the activebeamforming and the passive beamforming. Specifically, in the alternativeiteration process, the zero-forcing (ZF) method and CE algorithm are applied toacquire the active beamforming and the passive beamforming, respectively. TheCE algorithm starts with random sampling, by the idea of distribution focusing,namely shifting the distribution towards a desired one by minimizing CE, and anear optimal reflection coefficients with adequately high probability can beobtained. In addition, we extend the original one-bit phase shift at the IRS tothe common case with high-resolution phase shift to enhance the effectivenessof the algorithms. Simulation results verify that the proposed algorithm canobtain a near optimal solution with lower computational complexity

Dynamic dispersion compensation in a 40 Gb/s single-channeled optical fiber communication system

A dynamic chromatic dispersion (CD) compensating technique for a 40 Gb/s single-channeled optical fiber communication system is demonstrated. The tunable dispersion compensator is composed of 2 × 2 optical switches, dispersion compensating fiber etc. For controlling the tunable dispersion compensator, a feedback signal is obtained by detecting the narrow band electrical power centered at 12 GHz, and the electrical power increases with the decrease of the accumulated chromatic dispersion. In experiment, the maximal response time of the system is 0.7 s. The compensating range and precision reach 81.55 ps/nm and 5.28 ps/nm respectively which can be further improved by adding the number of optical switches and reducing the length of dispersion compensating fiber. Experiments indicated that the dynamic dispersion compensating system can significantly improve the performance of 40 Gb/s optical communication systems according to the eye diagram before and after dispersion compensating