New and emerging forms of data and technologies: literature and bibliometric review

Singular Value Decomposition-Driven Non-negative Matrix Factorization with Application to Identify the Association Patterns of Sarcoma Recurrence

Sarcomas are malignant tumors from mesenchymal tissue and are characterized by their complexity and diversity. The high recurrence rate making it important to understand the mechanisms behind their recurrence and to develop personalized treatments and drugs. However, previous studies on the association patterns of multi-modal data on sarcoma recurrence have overlooked the fact that genes do not act independently, but rather function within signaling pathways. Therefore, this study collected 290 whole solid images, 869 gene and 1387 pathway data of over 260 sarcoma samples from UCSC and TCGA to identify the association patterns of gene-pathway-cell related to sarcoma recurrences. Meanwhile, considering that most multi-modal data fusion methods based on the joint non-negative matrix factorization (NMF) model led to poor experimental repeatability due to random initialization of factorization parameters, the study proposed the singular value decomposition (SVD)-driven joint NMF model by applying the SVD method to calculate initialized weight and coefficient matrices to achieve the reproducibility of the results. The results of the experimental comparison indicated that the SVD algorithm enhances the performance of the joint NMF algorithm. Furthermore, the representative module indicated a significant relationship between genes in pathways and image features. Multi-level analysis provided valuable insights into the connections between biological processes, cellular features, and sarcoma recurrence. In addition, potential biomarkers were uncovered, while various mechanisms of sarcoma recurrence were identified from an imaging genetic perspective. Overall, the SVD-NMF model affords a novel perspective on combining multi-omics data to explore the association related to sarcoma recurrence.

Individual Responsibility Around Deepfakes: It's No Laughing Matter

The objectives of this research were to examine the contextual factors that impact individual's interpretation of their responsibility in the context of Deepfake videos. Using a test/retest methodology, a total of 1,023 respondents participated in a Deepfake survey instrument which measured perceptions of individual responsibility with respect to Deepfakes, individual concern with Deepfakes, and humorous perception of Deepfakes. The results of the study found that individual responsibility is negatively related to individual concern, indicating the externalization of responsibility for difficult to detect fake online videos designed to be convincing. Further, humorous perception and age impact the participants perception of individual responsibility. Younger participants were more likely to find Deepfakes humorous and this increased their perception of their own responsibility, potentially exposing them to greater harm from malicious Deepfakes.

scFED: Clustering Identifying Cell Types of scRNA-Seq Data Based on Feature Engineering Denoising

Recently developed single-cell RNA-seq (scRNA-seq) technology has given researchers the chance to investigate single-cell level of disease development. Clustering is one of the most essential strategies for analyzing scRNA-seq data. Choosing high-quality feature sets can significantly enhance the outcomes of single-cell clustering and classification. But computationally burdensome and highly expressed genes cannot afford a stabilized and predictive feature set for technical reasons. In this study, we introduce scFED, a feature-engineered gene selection framework. scFED identifies prospective feature sets to eliminate the noise fluctuation. And fuse them with existing knowledge from the tissue-specific cellular taxonomy reference database (CellMatch) to avoid the influence of subjective factors. Then present a reconstruction approach for noise reduction and crucial information amplification. We apply scFED on four genuine single-cell datasets and compare it with other techniques. According to the results, scFED improves clustering, decreases dimension of the scRNA-seq data, improves cell type identification when combined with clustering algorithms, and has higher performance than other methods. Therefore, scFED offers certain benefits in scRNA-seq data gene selection.

Two-Stage Model-Based Predicting PV Generation with the Conjugation of IoT Sensor Data

This paper proposes a novel short-term photovoltaic voltage (PV) prediction scheme using IoT sensor data with the two-stage neural network model. It is efficient to use environmental data provided by the meteorological agency to predict future PV generation. However, such environmental data represent the average value of the wide area, and there is a limitation in detecting environmental changes in the specific area where the solar panel is installed. In order to solve such issues, it is essential to establish IoT sensor data to detect environmental changes in the specific area. However, most conventional research focuses only on the efficiency of IoT sensor data without taking into account the timing of data acquisition from the sensors. In real-world scenarios, IoT sensor data is not available precisely when needed for predictions. Therefore, it is necessary to predict the IoT data first and then use it to forecast PV generation. In this paper, we propose a two-stage model to achieve high-accuracy prediction results. In the first stage, we use predicted environmental data to access IoT sensor data in the desired future time point. In the second stage, the predicted IoT sensors and environmental data are used to predict PV generation. Here, we determine the appropriate prediction scheme at each stage by analyzing the model characteristics to increase prediction accuracy. In addition, we show that the proposed prediction scheme could increase prediction accuracy by more than 12% compared to the baseline scheme that only uses a meteorological agency to predict PV generation.

Emerging Technologies for 6G Communication Networks: Machine Learning Approaches

The fifth generation achieved tremendous success, which brings high hopes for the next generation, as evidenced by the sixth generation (6G) key performance indicators, which include ultra-reliable low latency communication (URLLC), extremely high data rate, high energy and spectral efficiency, ultra-dense connectivity, integrated sensing and communication, and secure communication. Emerging technologies such as intelligent reflecting surface (IRS), unmanned aerial vehicles (UAVs), non-orthogonal multiple access (NOMA), and others have the ability to provide communications for massive users, high overhead, and computational complexity. This will address concerns over the outrageous 6G requirements. However, optimizing system functionality with these new technologies was found to be hard for conventional mathematical solutions. Therefore, using the ML algorithm and its derivatives could be the right solution. The present study aims to offer a thorough and organized overview of the various machine learning (ML), deep learning (DL), and reinforcement learning (RL) algorithms concerning the emerging 6G technologies. This study is motivated by the fact that there is a lack of research on the significance of these algorithms in this specific context. This study examines the potential of ML algorithms and their derivatives in optimizing emerging technologies to align with the visions and requirements of the 6G network. It is crucial in ushering in a new era of communication marked by substantial advancements and requires grand improvement. This study highlights potential challenges for wireless communications in 6G networks and suggests insights into possible ML algorithms and their derivatives as possible solutions. Finally, the survey concludes that integrating Ml algorithms and emerging technologies will play a vital role in developing 6G networks.

Dual-Layer Index for Efficient Traceability Query of Food Supply Chain Based on Blockchain

Blockchain techniques have been introduced to achieve decentralized and transparent traceability systems, which are critical components of food supply chains. Academia and industry have tried to enhance the efficiency of blockchain-based food supply chain traceability queries. However, the cost of traceability queries remains high. In this paper, we propose a dual-layer index structure for optimizing traceability queries in blockchain, which consists of an external and an internal index. The dual-layer index structure accelerates the external block jump and internal transaction search while preserving the original characteristics of the blockchain. We establish an experimental environment by modeling the blockchain storage module for extensive simulation experiments. The results show that although the dual-layer index structure introduces a little extra storage and construction time, it significantly improves the efficiency of traceability queries. Specifically, the dual-layer index improves the traceability query rate by seven to eight times compared with that of the original blockchain.

How containment policy and medical service impact COVID-19 transmission: A cross-national comparison among China, the USA, and Sweden

As COVID-19 shows a heterogeneous spreading process globally, investigating factors associated with COVID-19 spreading among different countries will provide information for containment strategy and medical service decisions. A significant challenge for analyzing how these factors impact COVID-19 transmission is assessing key epidemiological parameters and how they change under different containment strategies across different nations. This paper builds a COVID-19 spread simulation model to estimate the core COVID-19 epidemiological parameters. Then, the correlation between these core COVID-19 epidemiological parameters and the times of publicly announced interventions is analyzed, including three typical countries, China (strictly containment), the USA (moderately control), and Sweden (loose control). Results show that the recovery rate leads to a distinct COVID-19 transmission process in the three countries, as all three countries finally have similar and close to zero spreading rates in the third period of COVID-19 transmission. Then, an epidemic fundamental diagram between COVID-19 "active infections" and "current patients" is discovered, which could plan a country's COVID-19 medical capacity and containment strategies when combined with the COVID-19 spreading simulation model. Based on that, the hypothetical policies are proved effectively, which will give support for future infectious diseases.

Model of Threats to the Integrity and Availability of Information Processed in Cyberspace

Depending on their motivation, offenders have different goals, and disclosure of information is not always such a goal. It often happens that the purpose of the offender is to disrupt the normal operation of the system. This can be achieved both by acting directly on the information and by acting on the elements of the system. Actions of this kind lead to a violation of integrity and availability, but not confidentiality. It follows that the process of forming a threat model for the integrity and availability of information differs from a similar process for confidentiality threats. The purpose of this study is to develop an information integrity threat model that focuses on threats disrupting the normal operation of the system. The research methodology is based on the methods of system analysis, graph theory, discrete mathematics, and automata theory. As a result of the research, we proposed a model of threats to the integrity and availability of information. The proposed threat model differs from analogues by a high level of abstraction without reference to the subject area and identification of threats to the availability of information as a subset of threats to the integrity of the information transmission channel.