In the current context of emerging several types of database systems (relational and non-relational), choosing the type and database system for storing large amounts of data in today’s big data applications has become an important challenge. In this paper, we aimed to provide a comparative evaluation of two popular open-source database management systems (DBMSs): MySQL as a relational DBMS and, more recently, as a non-relational DBMS, and CouchDB as a non-relational DBMS. This comparison was based on performance evaluation of CRUD (CREATE, READ, UPDATE, DELETE) operations for different amounts of data to show how these two databases could be modeled and used in an application and highlight the differences in the response time and complexity. The main objective of the paper was to make a comparative analysis of the impact that each specific DBMS has on application performance when carrying out CRUD requests. To perform the analysis and to ensure the consistency of tests, two similar applications were developed in Java, one using MySQL and the other one using CouchDB database; these applications were further used to evaluate the time responses for each database technology on the same CRUD operations on the database. Finally, a comprehensive discussion based on the results of the analysis was performed that centered on the results obtained and several conclusions were revealed. Advantages and drawbacks for each DBMS are outlined to support a decision for choosing a specific type of DBMS that could be used in a big data application.

At the time being knowledge-based systems are used in almost all life aspects. The main reason for trying to use knowledge-based systems in real-time control is to reduce cognitive load on users (overload), their application proving to be important when conventional techniques have failed or are not sufficiently effective [1]. The development of automated diagnosis techniques and systems can help also to minimize downtime and maintain efficient output. This paper presents some issues of applying knowledge-based systems to real-time control systems. It describes and analyzes the main issues concerning the real-time domain and provides possible solutions, such as a set of requirements that a real-time knowledge-based system must satisfy. The paper proposes a possible architecture for applying knowledge-based techniques in real-time control systems. Finally, a way of employing knowledge-based techniques for extending the existing automatic control and monitoring system for the geothermal plant from the University of Oradea is presented.

Background: Hemodialysis (HD) patients are a highly vulnerable population with elevated mortality driven by comorbidities and dialysis-specific factors. While most studies focused on intra-pandemic outcomes, long-term effects remain underexplored. We aimed to evaluate 5-year mortality and the impact of COVID-19 vaccination in chronic HD patients. Methods: A retrospective study was conducted on 211 HD patients monitored between 2020 and 2024. Outcomes included overall and cardiovascular mortality, risk factors in COVID-19-positive patients, and vaccination impact. Logistic regression identified independent predictors. Results: The cohort had a mean age of 65.6 ± 13.3 years, with 55.9% males and mean dialysis vintage of 6.9 ± 5.5 years. Overall mortality reached 53.6%, while 38.4% were vaccinated. Predictors of all-cause mortality included age (OR = 1.078, p < 0.001), BMI (OR = 0.868, p < 0.001), hemoglobin (OR = 0.581, p < 0.001), phosphorus (OR = 1.351, p = 0.025), dialysis adequacy (OR = 0.138, p = 0.013), and ischemic cardiopathy (OR = 0.327, p = 0.009). In COVID-19-positive patients, mortality was associated with age (OR = 1.069, p = 0.002), low hemoglobin (OR = 0.642, p = 0.014), BMI (OR = 0.885, p = 0.009), CRP (OR = 1.015, p < 0.001), and coronary artery disease (OR = 5.68, p < 0.001). Cardiovascular disease was the leading cause of death (44.6% in COVID-19-positive vs. 73.3% in negatives, p = 0.006). Vaccination significantly reduced COVID-19-related mortality (OR = 0.023, p = 0.005) but did not influence overall or non-COVID mortality. Conclusions: Five-year mortality in HD patients remained high, mainly cardiovascular, and was strongly influenced by age, BMI, hemoglobin, dialysis adequacy, and comorbidities. COVID-19 vaccination substantially reduced COVID-related mortality but did not alter all-cause outcomes. These findings support vaccination and careful risk stratification in HD populations for future pandemics.

For real-time applications, task scheduling is a problem of paramount importance. Several scheduling algorithms were proposed in the literature, starting from static scheduling or cyclic executives which provide very deterministic yet inflexible behaviour, to the so called best-effort scheduling, which facilitates maximum run-time flexibility but allows only probabilistic predictions of run-time performance presenting a non-predictable and nondeterministic solution. Between these two extremes lies fixed priority scheduling algorithms, such as Rate Monotonic, that is not so efficient for real-time purposes but exhibits a predictable approach because scheduling is doing offline and guarantees regarding process deadlines could be obtained using appropriate analysis methods. This paper investigates the use of Rate Monotonic algorithm by making adjustments in order to make it more suitable for real-time applications. The factors that motivate the interest for fixed priority scheduling algorithms such Rate Monotonic when doing with real-time systems lies in its associated analysis that could be oriented in two directions: schedulability analysis and analysis of process interactions. The analyzing process is carried out using a previously implemented framework that allows modelling, simulation and schedulability analysis for a set of real-time system tasks, and some of the results obtained are presented.

In today’s world communication is relying heavily on electronic means, both for voice and other native data. All these communication sessions leave behind journaling information by the very nature of the underlying services. This information is both sensitive with respect to user’s rights and important for law enforcement purposes, so proper storage and retrieval techniques have to be taken into consideration. The paper discusses such techniques in relation with recent EU recommendations and suggests some methods for achieving good performance while preserving the required security levels.