Due to a complex of factors, visually impaired people are facing difficulties and increased risks during fire emergencies and evacuations from different types of buildings. Even if a lot of studies have been conducted to improve the mobility and autonomy of people with visual impairment during emergency evacuation processes, these offer only partial solutions, especially in the presence of uncertainties characteristic of fire evolution. Aiming for a more comprehensive approach to the safe evacuation of people with visual impairments, this paper proposes a support system that integrates innovative aspects related to the architecture of the application, modeling and simulation methods, and experimental realization. The system is decentralized, capable of anticipating possible fire extensions and determining, in real-time, new corresponding evacuation routes. The overall design complies with the standard norms in emergency situations. Two models, one developed in Stateflow and the other based on Delay Time Petri Nets (DTPN), were constructed to describe the dynamic behavior of the system in the presence of unexpected events that can change the initial recommended evacuation path. To test the functionality and efficiency of the proposed system, the conditions created by potential fire sources were simulated as a part of realistic scenarios. Tests were conducted with visually impaired people. Simulation and prototype testing showed that the presented system can improve evacuation times, achieving a measurable gain compared to scenarios where there is no information regarding fire evolution.

A flexible support system to offer warnings, therapies and recommendations for remote patient surveillance is presented. This system aims to improve the home healthcare assistance of patients with acute, light and medium-severe forms of diseases, which were previously diagnosed by family doctors (general practitioners) and specialists. It is used to early detect possible symptoms and signs of complications and also to adapt the treatment and to increase its efficiency. Symptoms and personal reactivity to prescribed medications (specific response to treatments) can be better monitored and interpreted. An inference mechanism based on Petri nets and Fuzzy theory was designed and experimentally implemented for diseases evolution supervising purposes. The doctor - patients scheduled or emergence communication sessions are based on Internet and mobile telephony technologies.

In recent decades, special attention has been given to the adverse effects of traffic congestion. Bike-sharing systems, as a part of the broader category of shared transportation systems, are seen as viable solutions to these problems. Even if the quality of service in bike-sharing service systems were permanently improved, there would still be some issues that needed new and more efficient solutions. One of these refers to the rebalancing operations that follow the bike depletion phenomenon that affects most stations during shorter or longer time periods. Current work develops a two-step method to perform effective rebalancing operations in bike-sharing. The core elements of the method are a fuzzy logic-controlled genetic algorithm for bike station prioritization and an inference mechanism aiming to do the assignment between the stations and trucks. The solution was tested on traffic data collected from the Citi Bike New York bike-sharing system. The proposed method shows overall superior performance compared to other algorithms that are specific to capacitated vehicle routing problems: standard genetic algorithm, ant colony optimization, Tabu search algorithm, and improved performance compared to Harris Hawks optimization for some scenarios. Since the algorithm is independent of past traffic measurements, it applies to any other potential bike-sharing system.

This paper presents a portable passwords manager which has a two-stage biometric-based access procedure. Data security using biometric methods was chosen as a variant of reduced complexity but was very effective in preventing cyber theft. The implementation of biometrics for the purpose of identification in high-security systems has become essential with the evolution of technology and the spike in identity theft. Unlike passwords or IDs, a biometric feature is an identifier that cannot be lost, stolen, or replicated, which provides biometric authentication systems with an increased level of security. During the first accessing step, the 3DPassManager portable device measures the heartbeat and uses fingerprint and iris features to realize a unique biometric-based authentication. While the specific characteristics of fingerprint and iris features are integrated to ensure that the person using the device is the rightful owner, the pulse is utilized to verify if previously acquired static images are not used. During the second accessing step, a password is generated based on fingerprint details, valid only for a small-time interval. The fingerprint is stored in a secret key with a 1024-bit length. Once access is allowed, the passwords are made available through an extension installed on the web browser. The device is the size of a cigarette pack and communicates with the PC by scanning a QR code. It is safe and was previously tested for dictionary and brute force attacks.

Docked Bike Sharing Systems often experience load imbalances among bike stations, leading to uneven distribution of bicycles and to challenges in meeting users’ demand. To address the load imbalances, many docked Bike Sharing Systems employ rebalancing vehicles that actively redistribute bicycles across stations, ensuring a more equitable distribution and enhancing the availability of bikes for users. The determination of the number of rebalancing vehicles in docked Bike Sharing Systems is typically based on various criteria, such as the size of the system, the density of stations, the expected demand patterns, and the desired level of service quality. This is a determining factor, in order to increase the efficiency of customer service at a reasonable cost. To enable a cost-effective rebalancing, we have used a cluster-based approach, due to the large scale of the Bike Sharing Systems, and our model is based on Markov Chains, given their proven effectiveness in this domain. Degrees of subsystem load at station level were used for modeling purposes. Additionally, a quantization strategy around cluster load was developed, to avoid state space explosion. This allowed the computation of the probability of transitioning from one degree of system load to another. A new method was developed to determine the fleet size, based on the identified subsystem steady state, describing the rebalancing necessity. The model evaluation was performed on traffic data collected from the Citi Bike New York Bike Sharing System. Based on the evaluation results, the model transition rates were in accordance with the expected values, indicating that the rebalancing operations are efficient from the point of view of the fulfillment of on-time arrival constraints.