Food Science Text Series

Identifying cyber-attacks that attempt to compromise digital systems is a critical function of intrusion detection systems (IDS). Data labeling difficulties, incorrect conclusions, and vulnerability to malicious data injections are only a few drawbacks of using machine learning algorithms for cybersecurity. To overcome these obstacles, researchers have created several network IDS models, such as the Hidden Naive Bayes Multiclass Classifier and supervised/unsupervised machine learning techniques. This study provides an updated learning strategy for artificial neural network (ANN) to address data categorization problems caused by unbalanced data. Compared to traditional approaches, the augmented ANN's 92% accuracy is a significant improvement owing to the network's increased resilience to disturbances and computational complexity, brought about by the addition of a random weight and standard scaler. Considering the ever-evolving nature of cybersecurity threats, this study introduces a revolutionary intrusion detection method.

Financial market liquidity is a popular research topic. Investor-driven research uses the turnover rate to measure liquidity and generally finds that the higher the stock turnover rate, the lower the returns. However, the traditional financial liquidity theory has been impacted by new machine-driven quantitative trading models. To explore high machine-driven liquidity and the impact of high turnover rates on returns, this study establishes a dual-market quantitative trading system, introduces a variational modal decomposition (VMD)-bidirectional gated recurrent unit (BiGRU) model for data prediction, and uses the back-end Hong Kong foreign exchange market to develop a quantitative trading strategy using the same rotating funds in the U.S. and Chinese stock markets. The experimental results show that given a principal amount of 210,000.00 CNY, the final predicted net return is 226,538.30 CNY, a net return of 107.86%, which is 40.6% higher than the net return of a single Chinese market. We conclude that, under machine-driven trading, increasing liquidity and turnover increase returns. This study provides a new perspective on liquidity theory that is useful for future financial market research and quantitative trading practices.

Higher education institutions are becoming increasingly concerned with the retention of their students. This work is motivated by the interest in predicting and reducing student dropout, and consequently in reducing the financial loss of said institutions. From the characterization of the dropout problem, and application of a knowledge discovery process, a model (ensemble) to improve dropout prediction is proposed. The ensemble model combines the results of three models: Logistic Regression, Neural Networks, and Decision Tree. As a result, the model can correctly classify 89% of the students (as enrolled and dropped) and accurately identify 98.1% dropouts. When the proposed model is compared with the ensemble method, Random Forest, the proposed model presented desirable characteristics to assist the management in proposing actions to retain students.

This study introduces a real-time data-driven battery management scheme designed to address uncertainties in load and generation forecasts, which are integral to an optimal energy storage control system. By expanding on an existing algorithm, this study resolves issues discovered during implementation and addresses previously overlooked concerns, resulting in significant enhancements in both performance and reliability. The refined real-time control scheme is integrated with a day-ahead optimization engine and forecast model, which is utilized for illustrative simulations to highlight its potential efficacy on a real site. Furthermore, a comprehensive comparison with the original formulation was conducted to cover all possible scenarios. This analysis validated the operational effectiveness of the scheme and provided a detailed evaluation of the improvements and expected behavior of the control system. Incorrect or improper adjustments to mitigate forecast uncertainties can result in suboptimal energy management, significant financial losses and penalties, and potential contract violations. The revised algorithm optimizes the operation of the battery system in real time and safeguards its state of health by limiting the charging/discharging cycles and enforcing adherence to contractual agreements. These advancements yield a reliable and efficient real-time correction algorithm for optimal site management, designed as an independent white box that can be integrated with any day-ahead optimization control system.

In metaverse, digital assets are essential to define identity, shape the virtual environment, and facilitate economic transactions. This study introduces a novel feature to the metaverse by capturing a fundamental aspect of individuals–their conversations–and transforming them into digital assets. It utilizes natural language processing and machine learning methods to extract a key sentence from user conversations and matches them with emojis that reflect their sentiments. The selected sentence, which encapsulates the essence of the user's statements, is then transformed into digital art through a generative visual model. This digital artwork is transformed into a non-Fungible Token (NFT), becoming a valuable digital asset within the blockchain ecosystem that is ideal for integration into metaverse applications. Our aim is that the management of personality traits as digital assets will foster individual uniqueness, enrich user experiences, and facilitate more personalized services and interactions with both like-minded users and non-player characters, thereby enhancing the overall user journey.

Volunteer engagement in emergency management, focusing on mitigating adverse consequences, has attracted scholarly and practitioner attention. Digital volunteering helps overcome the limitations of traditional on-site volunteering through extensive volunteering opportunities in emergency management. This study utilizes a case text analysis and interviews to investigate and categorize digital volunteer services in emergency scenarios. Based on two key dimensions—direct recipients of volunteer services and the nature of the services rendered—the study presents four types of digital volunteer services: bridging, supportive, complementary, and collaborative. Moreover, it delineates eight role archetypes digital volunteers assume in emergency response situations along with their primary service contributions. Compared to conventional on-site volunteer services, digital volunteer services offer unique advantages while facing specific challenges. Finally, this study offers recommendations in four dimensions for the robust development of digital volunteer services, contributing to more effective and sustainable emergency management practices.

This paper studies the application and the application value of intelligent emergency for emergency management in the big data environment, to fill the research gap that neglected the application value (performance) measurement of intelligent emergency, further improving the effectiveness of intelligent emergency management. Firstly, bibliometric analyses of approximately 3900 documents in intelligent emergency field are imported, and the future research trend of intelligent emergency management is cleared. Theory of STT (Socio-Technical Theory), concerning technical system and social system, is introduced. The concept of emergency management system 'technology enabling' and 'enabling value creation' are defined in accordance with the bibliometric analyses and STT. Secondly, a research framework including 'technology enabling' and 'enabling value creation' for decision-making paradigm in emergency management according to the big data environment is constructed. Furthermore, detail analyses approach from intelligent emergency 'technology enabling' to 'enabling value creation' in emergency management are proposed. Finally, earthquake disasters are taken as an example and specific analyses of the intelligent emergency enabling and enabling value creation are explored, specifically, the enabling value creation are discussed from the view of measurable indicators. The clear concept of emergency management system 'technology enabling' and 'enabling value creation' as well as the detail analyses approach from intelligent emergency 'technology enabling' to 'enabling value creation' provide theoretical basis for scholars and practitioners to evaluating the value (performance) of intelligent emergency for the first time.