A smart university campus (SUC) is a cyber-physical system that integrates advanced technologies, such as the Internet of Things, artificial intelligence, smart sensors, and cloud services, to improve educational, research, and administrative processes. The fusion of intelligent physical and cyber elements creates complex cybersecurity challenges for SUCs. This study proposes a fast and practical method for integrating an attack-defense graph approach with online control to enhance the cybersecurity of SUCs. The proposed approach involves three stages. First, the most vulnerable intrusion path in the attack graph, representing the shortest (minimum time) attack path in the SUC, is determined. In the second stage, countermeasures are applied to defend the most vulnerable assets. Finally, the third stage serves as a control layer that implements regular preventive and corrective control/monitoring measures to ensure the proper functioning and effectiveness of deployed defenses in the face of their unexpected failures and violations. The proposed polynomial method was tested and validated using the analysis of a real ransomware attack on Maastricht University in 2019, confirming its effectiveness and applicability.

Cyber-physical supply chains (CPSCs) integrate physical assets with cyber technologies to form connected and intelligent ecosystems. The increasing frequency and sophistication of cyber-attacks on CPSCs have made defence against these threats increasingly complex. A key strategy in this direction is to identify vulnerable attack paths to detect the most endangered components in the CPSC, thereby allowing the development of more effective and attack-resistant countermeasures. We begin by presenting generalised attack graphs with AND, OR, and QUORUM nodes (so-called AOQ graphs), using them to model and counter attacks on CPSC. Then, we develop an efficient path-extraction method that exploits supply chains' structural dynamics and reconfigurability to find vulnerable attack paths in AOQ graphs. Finally, we reconstruct the most vulnerable paths by hardening components and deploying an innovative honeypot technology - this is a decoy system that imitates assets to distract attackers. Unlike known methods, the proposed methodology automatically extracts the most vulnerable attack paths from the AOQ graphs in polynomial time without human intervention and provides proactive defence against intrusions. This approach has been tested on experimental benchmark problems and a real post-harvest supply chain and has demonstrated its superiority over previously known pathfinding algorithms.

Popular open source frameworks are used today to automatically combat attack threats in DCCNs. However, they cannot visualize logical conditions well enough in representing network protocol vulnerabilities in attacks such as ARP poisoning, DNS spoofing, and SYN flooding, and they do not support advanced types of communication, such as wireless, vehicular, and tethering communications on networks. To address the above shortcomings, we present an AND/OR attack graph-based security model for DCCNs, which is capable of modeling multiple attack types (e.g., ARP poisoning, spoofing, man-in-the-middle, and many others). A novel efficient algorithm is proposed to extract the most vulnerable attack path from the AND/OR graph. Unlike previous heuristic pathfinding algorithms, the proposed algorithm runs in polynomial time and efficiently handles positive-length cycles. We demonstrate the effectiveness of the proposed model and the new algorithm on several testbed problems implementing a network architecture of IT and industrial components.

This paper addresses the problem of information extraction using an AI-powered chatbot. The problem concerns searching and extracting relevant information from large databases in response to a human user's query. Expanding the traditional discrete search problem well known in operations research, this problem introduces two players; the first player-an AI chatbot such as ChatGPT 4.0-sequentially scans available datasets to find an appropriate answer to a given query, while the second-a human user-conducts a dialogue with the chatbot and evaluates its answers in each round of the dialogue. The goal of an AI-powered chatbot is to provide maximally useful and accurate information. During a natural language conversation between a human user and an AI, the human user can modify and refine queries until s/he is satisfied with the chatbot's output. We analyze two key characteristics of human-AI interaction: search reliability and efficiency. Search reliability is defined as the ability of a robot to understand user queries and provide correct answers; it is measured by the frequency (probability) of correct answers. Search efficiency of a chatbot indicates how accurate and relevant the information returned by the chatbot is; it is measured by the satisfaction level a human user receives for a correct answer. An AI chatbot must perform a sequence of scans over the given databases and continue searching until the human user declares, in some round, that the target has been found. Assuming that the chatbot is not completely reliable, each database may have to be scanned infinitely often; in this case, the objective of the problem is to determine a search policy for finding the optimal sequence of chatbot scans that maximizes the expected user satisfaction over an infinite time horizon. Along with these results, we found a counterintuitive relationship between AI chatbot reliability and search performance: under sufficiently general conditions, a less reliable AI chatbot may have higher expected search efficiency; this phenomenon aligns with other well-known "more-for-less" paradoxes. Finally, we discussed the underlying mechanism of this paradox.

A robotic wheelchair can be considered a primary means of transportation for people with limited mobility, allowing them to reach their destination safely and quickly. Using an effective pathfinding algorithm in combination with artificial intelligence-based devices installed on a wheelchair can significantly improve navigation. The purpose of this paper is twofold: (a) to develop an effective pathfinding algorithm, called D-PRM, that can be applied in obstacle-rich environment and (b) to create a mathematical decision-support tool for selecting AI-based devices to be installed on a wheelchair. To achieve these goals, we construct an efficient pathfinding algorithm that combines the strengths of the Probabilistic Roadmap (PRM) and A* methods and formulate a mixed integer mathematical programming model for AI-based device selection. Extensive computational experiments validate the performance of the proposed algorithm.

We consider the problem of assigning a fleet of aircraft of different types to a given periodic flight schedule aiming to maximize fuel economy for a predetermined fleet flight schedule. The challenge is to select the most profitable aircraft type for each flight and then determine the required number of aircraft and the number of deadhead flights to maximize fuel economy. We split this general problem into two successive optimization problems. The first one, the aircraft-type assignment problem, is to select the most profitable aircraft type for each flight; the second one is the aircraft routing problem. One way to solve the first problem, which we adopt in this paper, is to reduce it to a parametric minimum-cost network flow problem. To solve it, we propose a new Newton-type algorithm that finds good solutions for large-scale problems. Another focus of the paper is on efficient solution of the aircraft routing problem for a single aircraft type. The goal is to maximize fuel economy, taking into account both the required number of aircraft and the number of deadhead flights. We transform it into a special fractional assignment problem and solve using a new version of the Newton-type algorithm that is adapted for the aircraft fleet routing problem and runs in O(n4) time, where n is the number of flights.

Unmanned aerial vehicles (UAVs, drones) are not just a technological achievement based on modern ideas of artificial intelligence; they also provide a sustainable solution for green technologies in logistics, transport, and material handling. In particular, using battery-powered UAVs to transport products can significantly decrease energy and fuel expenses, reduce environmental pollution, and improve the efficiency of clean technologies through improved energy-saving efficiency. We consider the problem of maximizing the average environmental benefit of a fleet of drones given a periodic schedule of tasks performed by the fleet of vehicles. To solve the problem efficiently, we formulate it as an optimization problem on an infinite periodic graph and reduce it to a special type of parametric assignment problem. We exactly solve the problem under consideration in O(n3) time, where n is the number of flights performed by UAVs.

This paper studies the security issues for cyber-physical systems, aimed at countering potential malicious cyber-attacks. The main focus is on solving the problem of extracting the most vulnerable attack path in a known attack graph, where an attack path is a sequence of steps that an attacker can take to compromise the underlying network. Determining an attacker's possible attack path is critical to cyber defenders as it helps identify threats, harden the network, and thwart attacker's intentions. We formulate this problem as a path-finding optimization problem with logical constraints represented by AND and OR nodes. We propose a new Dijkstra-type algorithm that combines elements from Dijkstra's shortest path algorithm and the critical path method. Although the path extraction problem is generally NP-hard, for the studied special case, the proposed algorithm determines the optimal attack path in polynomial time, O(nm), where n is the number of nodes and m is the number of edges in the attack graph. To our knowledge this is the first exact polynomial algorithm that can solve the path extraction problem for different attack graphs, both cycle-containing and cycle-free. Computational experiments with real and synthetic data have shown that the proposed algorithm consistently and quickly finds optimal solutions to the problem.

High-throughput screening systems are robotic cells that automatically scan and analyze thousands of biochemical samples and reagents in real time. The problem under consideration is to find an optimal cyclic schedule of robot moves that ensures maximum cell performance. To address this issue, we proposed a new efficient version of the parametric PERT/CPM project management method that works in conjunction with a combinatorial subalgorithm capable of rejecting unfeasible schedules. The main result obtained is that the new fast PERT/CPM method finds optimal robust schedules for solving large size problems in strongly polynomial time, which cannot be achieved using existing algorithms.

Cloud manufacturing is a rapidly growing trend in modern manufacturing, which has transformed the traditional operations and value chain structure of enterprises. It is crucial to develop a rational and effective trading mechanism of cloud manufacturing resources to meet the ever-increasing demands in this new environment. This paper proposes a sequential auction-based paradigm for the trading of manufacturing resources. The main challenge of the design of the paradigm is to determine the dynamic lot size of resources allocated to each auction considering the uncertainty of arriving demands. To achieve this, we first develop a competitive game model to identify optimal bidding strategies of arrived customers and estimate the expected revenue for each round with the given lot size. Secondly, we construct a Markov decision process (MDP) model to characterize the dynamics of the arrival of stochastic demand and the inventory transition of the manufacturing resources in sequential auctions. Lastly, we leverage a data-driven approach by integrating machine learning with an offline deep reinforcement learning (RL) approach. Specifically, we employ a long short-term memory (LSTM) model to predict forthcoming demands in the environment and develop the deep Q-network (DQN) learning algorithm to optimize lot-sizing policy by interacting with the well-learned LSTM environment model. Our simulation experiments validate the effectiveness of our approach and some management insights are given.

This paper studies the security issues for cyber–physical systems, aimed at countering potential malicious cyber-attacks. The main focus is on solving the problem of extracting the most vulnerable attack path in a known attack graph, where an attack path is a sequence of steps that an attacker can take to compromise the underlying network. Determining an attacker’s possible attack path is critical to cyber defenders as it helps identify threats, harden the network, and thwart attacker’s intentions. We formulate this problem as a path-finding optimization problem with logical constraints represented by AND and OR nodes. We propose a new Dijkstra-type algorithm that combines elements from Dijkstra’s shortest path algorithm and the critical path method. Although the path extraction problem is generally NP-hard, for the studied special case, the proposed algorithm determines the optimal attack path in polynomial time, O(nm), where n is the number of nodes and m is the number of edges in the attack graph. To our knowledge this is the first exact polynomial algorithm that can solve the path extraction problem for different attack graphs, both cycle-containing and cycle-free. Computational experiments with real and synthetic data have shown that the proposed algorithm consistently and quickly finds optimal solutions to the problem.

This paper studies the security issues for cyber–physical systems, aimed at countering potential malicious cyber-attacks. The main focus is on solving the problem of extracting the most vulnerable attack path in a known attack graph, where an attack path is a sequence of steps that an attacker can take to compromise the underlying network. Determining an attacker’s possible attack path is critical to cyber defenders as it helps identify threats, harden the network, and thwart attacker’s intentions. We formulate this problem as a path-finding optimization problem with logical constraints represented by AND and OR nodes. We propose a new Dijkstra-type algorithm that combines elements from Dijkstra’s shortest path algorithm and the critical path method. Although the path extraction problem is generally NP-hard, for the studied special case, the proposed algorithm determines the optimal attack path in polynomial time, O(nm), where n is the number of nodes and m is the number of edges in the attack graph. To our knowledge this is the first exact polynomial algorithm that can solve the path extraction problem for different attack graphs, both cycle-containing and cycle-free. Computational experiments with real and synthetic data have shown that the proposed algorithm consistently and quickly finds optimal solutions to the problem.

This work addresses the problem of scheduling multiple hoists (robots), which arises on real automated electroplating lines. Several hoists operate on a shared track and should not collide with each other. The processing time for each operation is preset in interval form. The problem simultaneously takes into account two criteria: maximum processing quality and maximum line productivity. To do this, we estimate the dependence of the duration of operations on the level of their quality and, in accordance with these estimates, we represent the processing times in the form of fuzzy numbers with triangular membership functions. The novelty of this study is two-fold. First, we propose a new mathematical model based on the use of fuzzy numbers and fuzzy operations on them, which is designed to deal with complex galvanic lines served by multiple hoists. The second new feature is a new combinatorial method developed for scheduling multiple hoists. For this, we modify and refine the so-called “method of prohibited intervals”, which has been actively used in scheduling theory in recent decades to solve problems, but until now was known only for the case of a single hoist. Thus, the second novelty of this work is that it is actually the first attempt to use the powerful technique of fuzzy set theory to solve a practical problem of scheduling multiple hoists. Computational experiments on real and randomly generated instances show that the new algorithm can successfully solve real problems that arise in practice.

Social media networks highly influence on a broad range of global social life, especially in the context of a pandemic. We developed a mathematical model with a computational tool, called EMIT (Epidemic and Media Impact Tool), to detect and control pandemic waves, using mainly topics of relevance on social media networks and pandemic spread. Using EMIT, we analyzed health-related communications on social media networks for early prediction, detection, and control of an outbreak. EMIT is an artificial intelligence-based tool supporting health communication and policy makers decisions. Thus, EMIT, based on historical data, social media trends and disease spread, offers an predictive estimation of the influence of public health interventions such as social media-based communication campaigns. We have validated the EMIT mathematical model on real world data combining COVID-19 pandemic data in the US and social media data from Twitter. EMIT demonstrated a high level of performance in predicting the next epidemiological wave (AUC = 0.909, F1 = 0.899).

BackgroundDiscussions of health issues on social media are a crucial information source reflecting real-world responses regarding events and opinions. They are often important in public health care, since these are influencing pathways that affect vaccination decision-making by hesitant individuals. Artificial intelligence methodologies based on internet search engine queries have been suggested to detect disease outbreaks and population behavior. Among social media, Twitter is a common platform of choice to search and share opinions and (mis)information about health care issues, including vaccination and vaccines.ObjectiveOur primary objective was to support the design and implementation of future eHealth strategies and interventions on social media to increase the quality of targeted communication campaigns and therefore increase influenza vaccination rates. Our goal was to define an artificial intelligence–based approach to elucidate how threads in Twitter on influenza vaccination changed during the COVID-19 pandemic. Such findings may support adapted vaccination campaigns and could be generalized to other health-related mass communications.MethodsThe study comprised the following 5 stages: (1) collecting tweets from Twitter related to influenza, vaccines, and vaccination in the United States; (2) data cleansing and storage using machine learning techniques; (3) identifying terms, hashtags, and topics related to influenza, vaccines, and vaccination; (4) building a dynamic folksonomy of the previously defined vocabulary (terms and topics) to support the understanding of its trends; and (5) labeling and evaluating the folksonomy.ResultsWe collected and analyzed 2,782,720 tweets of 420,617 unique users between December 30, 2019, and April 30, 2021. These tweets were in English, were from the United States, and included at least one of the following terms: “flu,” “influenza,” “vaccination,” “vaccine,” and “vaxx.” We noticed that the prevalence of the terms vaccine and vaccination increased over 2020, and that “flu” and “covid” occurrences were inversely correlated as “flu” disappeared over time from the tweets. By combining word embedding and clustering, we then identified a folksonomy built around the following 3 topics dominating the content of the collected tweets: “health and medicine (biological and clinical aspects),” “protection and responsibility,” and “politics.” By analyzing terms frequently appearing together, we noticed that the tweets were related mainly to COVID-19 pandemic events.ConclusionsThis study focused initially on vaccination against influenza and moved to vaccination against COVID-19. Infoveillance supported by machine learning on Twitter and other social media about topics related to vaccines and vaccination against communicable diseases and their trends can lead to the design of personalized messages encouraging targeted subpopulations’ engagement in vaccination. A greater likelihood that a targeted population receives a personalized message is associated with higher response, engagement, and proactiveness of the target population for the vaccination process.

Background: Vaccines are one of the most important achievements of modern medicine. However, their acceptance is only partial, with vaccine hesitancy and refusal representing a major health threat. Influenza vaccines have low compliance since repeated, annual vaccination is required. Influenza vaccines stimulate discussions both in the real world and online. Social media is currently a significant source of health and medical information. Elucidating the association between social media engagement and influenza vaccination is important and may be applicable to other vaccines, including ones against COVID-19. Objective: The goal of this study is to characterize profiles of social media engagement regarding the influenza vaccine and their association with knowledge and compliance in order to support improvement of future web-associated vaccination campaigns. Methods: A weblink to an online survey in Hebrew was disseminated over social media and messaging platforms. The survey answers were collected during April 2020. Anonymous and volunteer participants aged 21 years and over answered 30 questions related to sociodemographics; social media usage; influenza- and vaccine-related knowledge and behavior; health-related information searching, its reliability, and its influence; and COVID-19-related information searching. A univariate descriptive data analysis was performed, followed by multivariate analysis via building a decision tree to define the most important attributes associated with vaccination compliance. Results: A total of 213 subjects responded to the survey, of whom 207 were included in the analysis; the majority of the respondents were female, were aged 21 to 40 years, had 1 to 2 children, lived in central Israel, were secular Israeli natives, had higher education, and had a salary close to the national average. Most respondents (128/207, 61.8%) were not vaccinated against influenza in 2019 and used social media. Participants that used social media were younger, secular, and living in high-density agglomerations and had lower influenza vaccination rates. The perceived influence and reliability of the information on social media about COVID-19 were generally similar to those perceptions about influenza. Conclusions: Using social media is negatively linked to compliance with seasonal influenza vaccination in this study. A high proportion of noncompliant individuals can lead to increased consumption of health care services and can, therefore, overload these health services. This is particularly crucial with a concomitant outbreak, such as COVID-19. Health care professionals should use improved and targeted health communication campaigns with the aid of experts in social media. Targeted communication, based on sociodemographic factors and personalized social media usage, might increase influenza vaccination rates and compliance with other vaccines as well.

Scheduling theory is a major field in operations research and discrete applied mathematics. This paper focuses on several recent developments in scheduling theory and a broad range of new applications – from multiagent scheduling to robots in communication networks. The survey presents a personal view on current trends, critical issues, strengths and limitations of this advantageous field.