The Network information systems (NIS) 2 directive was introduced to strengthen cybersecurity obligations for operators of essential and important services. The critical rail infrastructure falls under essential services; therefore, this study examines how critical rail infrastructure operators are adopting and operationalising NIS 2 directives and identifies the sector-specific challenges that affect their compliance efforts. Using a qualitative document analysis of key European Union Agency for Cybersecurity (ENISA) reports, supported by inductive and deductive coding of NIS 2 directive and Critical Entities Resilience (CER) directive, the research explores four themes: governance fragmentation, uneven cybersecurity maturity, particularly within the Operations Technology (OT) environments, supplier dependency and supply-chain vulnerabilities, and persistent incident reporting and detection gaps. Findings indicate that while the critical rail infrastructure has made progress in adopting NIS 2, its implementation remains partial and inconsistent across Member States and actors. Governance fragmentation hinders knowledge sharing and coordinated response horizontally; variability in IT–OT maturity constrains comprehensive risk management; supply-chain complexity exposes operators to cascading vulnerabilities; and the quality and completeness of incident reporting continues to challenge full compliance with Article 23. When compared with ENISA’s technical implementation guidance, these findings reveal that NIS 2 provides a practical guide for strengthening critical rail infrastructure resilience, but significant operational, organisational, and supply-chain barriers must still be addressed. The study contributes to ongoing critical infrastructure cybersecurity discourse by answering three research questions related to NIS 2 adoption, alignment of publicly available documentation, and sector-specific compliance challenges. It provides a structured evidence base that can support regulators, operators, and suppliers in translating NIS 2 obligations into realistic, sector-appropriate practices that enhance the cybersecurity European critical rail infrastructure.

Entity-aware quality assessment may reduce false interpretations of electronic health record (EHR) data, but evidence from small, rule-aligned benchmarks cannot establish operational effectiveness. We revised H-StreamQ as a proof-of-concept framework and evaluated its laboratory component using the complete MIMIC-IV v3.1 labevents file (158,374,764 events; 313,442 patients). Ten thousand patients were sampled across laboratory-activity quintiles and split at patient level into training (6000), threshold-calibration (2000), and test (2000) groups. The independent test set contained 918,651 numeric laboratory events. Without excluding naturally alerted records, 54,788 mutually exclusive defects were introduced using subtle value shifts, unit/scale errors, mapping errors, delayed records, and patient-clustered correlated defects. Rules, a context-aware Isolation Forest, their union (Hybrid), a context-free Isolation Forest, Local Outlier Factor (LOF), and linear and radial-basis-function (RBF) One-Class support vector machines (OCSVMs) were compared at a threshold fixed by a 2.5% calibration alert budget. Patient-cluster bootstrap intervals and event-micro and patient-macro results were reported. Rules alone achieved the highest event-micro F1-score (0.637; 95% confidence interval [CI] 0.547–0.722), followed by Hybrid (0.576; 0.484–0.668) and RBF One-Class SVM (0.559; 0.433–0.670). Hybrid increased recall over rules by only 0.004 (95% CI 0.003–0.006) while reducing F1 by 0.061 and increasing the background-alert rate by 0.015. Context conditioning did not improve aggregate Isolation Forest performance. In six batch-level drift simulations, an exponentially weighted moving average (EWMA) and a fixed-window monitor detected 97–100% and 98–100% of changes, respectively, whereas a custom Hoeffding adaptive-window detector was more conservative and often missed smaller or recurrent changes. These results support H-StreamQ as an explainable research framework, not as a validated clinical or production system. Patient-macro F1, which weights every patient equally, was substantially lower than event-micro F1 for every method (rules 0.395 versus 0.637; Hybrid 0.320 versus 0.576), indicating that event-level performance is weighted towards high-activity patients. Precision and F1 are computed relative to injected synthetic labels and are not clinically adjudicated estimates. The entity-aware architecture spans patients, admissions, diagnoses, transfers, and dictionaries, but the quantitative detection benchmark evaluates the numeric laboratory component only; other entities are used for linkage and contextual attachment and are audited descriptively rather than evaluated against labels.

Big data has become a crucial tool across multiple sectors worldwide. Due to the accumulative data inundation, it is cumbrous for humans to examine the vast amount of data. Therefore, big data along with AI (Artificial Intelligence) techniques are used for solving concerns associated to data storage, accessibility of important data and other aspects of big data. However, data in general can be both structured and unstructured in nature, which often affects the quality and accuracy for data analysis. Therefore, in order to overcome these pitfalls, advancements of AI technologies and big data analytics significantly aids in commendably extracting both structured and unstructured data which ultimately increases quality of the model. Owing to these advantages, big data is used across fields and more especially in healthcare industry, as big data aids in analyzing huge amount of data and offer valuable insights in terms of disease patterns, personalized treatment and ensures in discovering new drugs. Moreover, big data also plays a huge role towards cancer detection. Therefore, this paper focuses on reviewing big data, significance of big data, synergy of AI and big data approaches in healthcare sector. Further, different case studies are discussed in the paper, in addition, applications of big data in healthcare sector is reviewed in the current study for examining the importance of big data in healthcare sector. Eventually, the challenges are identified through the analysis of existing researchers and future recommendations are provided for overcoming the gaps that are intended to create encouraging work in this area.

Optical tweezers (OT), or optical traps, are a device for manipulating microscopic objects through a focused laser beam. They are used in various fields of physical and biophysical chemistry to identify the interactions between individual molecules and measure single-molecule forces. In this work, we describe the development of a homemade optical tweezers device based on a cost-effective IR diode laser, the hardware, and, in particular, the software controlling it. It allows us to control the instrument, calibrate it, and record and process the measured data. It includes the user interface design, peripherals control, recording, A/D conversion of the detector signals, evaluation of the calibration constants, and visualization of the results. Particular stress is put on the signal filtration from noise, where several methods were tested. The calibration experiments indicate a good sensitivity of the instrument that is thus ready to be used for various single-molecule measurements.

Optical traps are devices used to micromanipulation microscopic objects using a focused laser beam in an optical microscope field of view. Trapping such objects is possible only when their refractive index is higher than the environment's. Typically, trapping experiments are carried out in aqueous solutions, exploiting the low refractive index of water. Experiments in different pure organic solvents were also reported, showing not very good dependence of the optical-trap force constant on the solvent refractive index. In this study, we carry out optical trapping experiments in mixed water:organic solvents where the organic components are dimethyl sulfoxide, ethylene glycol, and glycerol. Trends of corner frequencies measured in these mixtures follow the theoretical calculations well for all the studied systems in the whole molar-fraction range, indicating their dominant dependence on the refractive index. The conversion of the corner frequencies to the force constants of the trap is strongly influenced by the differences in viscosity throughout the molar-fraction range that emphasizes experimental errors in the regions where it is high. In addition, the force-constant and corner-frequency curves can serve as an indicator of ideality of the potential profile of the optical trap.

This paper focuses on demonstration of an enhanced model for investigating data signals features, i.e., whether the given signal has stationary or non-stationary features. The accurate detection of the features of signals is crucial for the right directions towards methodology of further preprocessing to perform data analysis of the data signal, specifically in the tasks of finding anomalies in the given signal and big data environment. A problem often encountered is the exact determination of the occurrence of stationary or non-stationary data signal features in data processing. Within this research paper, the mathematical foundations of data signal processing are described. Based on the mathematical model of the input signal processing, an improved workflow using the enhanced statistical KPSS test and autocorrelation function (graphical) analysis is demonstrated here, to confirm the accuracy and usability of selected methodology. The alternative approach described here leads to a much lower computational effort and the achievement of accurate identification of signal features in big data environment for possible deployment of A.I. or machine learning anomaly detection pipeline. The obtained dataset and model are based on the real environment and measured signals in the production process of machine tools in company Tajmac-ZPS Zlin.

Whether due to unpleasant events, injuries or illnesses, people lose the mobility of their hands. In extreme cases, amputation of the hand or hands can also occur. This paper deals with designing and fabricating an affordable transradial prosthesis using 3D printing and measuring finger positioning accuracy during a long-term test. The prosthesis’ design was inspired by the tested wire construction used in both low-cost commercial and do-it-yourself prostheses. The shape of the partial parts of the prosthesis was adapted for production using 3D printing. A high priority was also placed on using as few electronics as possible, while the used electronics also has to be affordable. Six MG995 servo motors were utilized to provide movement for the fingers, thumbs and wrist, and an Arduino Nano R3 was used to control their function. A control glove was subsequently developed to control the prosthesis, allowing accurate measurement of the angles of the finger’s distal phalanges. Their measured angle served as a reference for matching the angles on the prosthetic hand. To verify the prosthesis’s durability and the finger grip’s accuracy, a long-term test of 100,000 cycles, which repeated the western world’s finger-counting system from 0 to 5, was performed. It was determined that there is only a minor deviation from the initial finger position based on measurements of the accuracy of the finger position before and after the long-term test. Only minimal wear of functional parts after the long-term test was observed. No significant deviations from the desired finger angles were measured.

When it comes to cancer-related mortality, breast cancer is the most common and predominant kind in women; lung cancer is the most common. It ranks second overall. Global scientists have been working very hard to fight this illness for a long time. Furthermore, significant advancements have been achieved in the fields of machine learning and data mining for the extraction and synthesis of insightful knowledge, even from extremely complicated data sources. Machine learning models may carry out a number of functions, including clustering, classification, and prediction, by using the knowledge obtained from data. In this study, we examine the relationship between a dataset's several features and a diagnosis of breast cancer. We use five different variables extracted from X-ray images in the dataset to predict the existence of breast cancer using a supervised learning classification technique called Random Forest. The open-source web repository Kaggle served as the source of this dataset. We use Principal Component Analysis (PCA), a dimensionality reduction method, on the data prior to putting the Machine Learning algorithm into practice. We then assess the performance of the Machine Learning algorithm using measures like accuracy, precision, recall, F1-score, and support. Additionally, we compare the model's output with Random Forest and examine the performance metrics it produces with and without PCA analysis.

In order to improve the accuracy and dependability of results in big data applications, numerous models, programs, software, hardware, and technologies have been developed and proposed. However, picking the right technology in this situation can be difficult and time-consuming. Technical compatibility, deployment complexity, costs, effectiveness, performance, dependability, help, and potential security issues are just a few of the factors that need to be carefully considered.

Accurate segmentation of brain tumors from the magnetic resonance image (MRI) is an essential step for radionics analysis as well as finding the tumor extension is so necessary to plan the best treatment to improve the survival rate. Manually extracting sub-regions of the brain tumor from MRI is a tedious process and time-consuming, as the complex brain tumor images require extensive human expertise. In recent years, deep learning models have proved effective in medical image segmentation tasks. In brain tumor segmentation, the 3D multimodal MRI poses some challenges such as computation and memory limitations. This study aims to develop a deep learning model using 3D U-Net for brain tumor segmentation. The segmentation results on BraTS 2020 dataset show that the proposed model achieves promising performance.

This research proposes an approach to improve the performance of effort estimation based on the balancing of each group for categorical variables. The proposed model is based on function point analysis, Industry Sector, and deep learning. The Pytorch library is used to build the deep learning model with the dataset ISBSG (release 2020). The accuracy of our model is compared with that of the Adj-Effort approach. We adopt the prediction level at 0.3, Mean Absolute Error, Mean Balanced Relative Error, Mean Inverted Balanced Relative Error, and Standardised Accuracy as criteria for validation. The findings demonstrate that our proposed model outweighs the unbalanced and Adj-Effort approaches.

Today, our lives, work and relationships are interconnected and completely dependent on information technologies and communication networks. Modern society has created cyberspace as an extension of our lives, which has brought us many positive things, but also many new risks that need to be managed. Every organization, whether in the public or private sector, must have a cybersecurity management system in place to survive and prosper in this environment. However, how is the cybersecurity management system established at the state level? Is it possible to consider a state as a certain type of large and complex organization and implement a cybersecurity management system similar to those used by companies and smaller institutions? This article attempts to answer these questions by analyzing the cybersecurity management system at the level of the Czech Republic and by identifying and discussing the specifics of cybersecurity management at the state level.

The article discusses the possible effects of Directive 2022/2555 of the European Parliament (EU) and of the Council of 14 December 2022 on measures to ensure an ordinary high level of cyber security in the Union and amending Regulation (EU) No. 910/2014 and Directive (EU) 2018 /1972 and on the repeal of Directive (EU) 2016/1148 (NIS2 Directive) and subsequently to it on the Act on Cybersecurity and on the amendment of related laws (Act on cyber security), which will have to be amended concerning the NIS2 directive and their possible impacts on information security management systems (ISMS policies). The contribution aims to describe the current state of implementation of the NIS2 directive into the legal environment using the example of the Czech Republic. To outline the possible procedure of applying the implemented directive into practice and possible impacts on subjects.

The COVID-19 outbreak has been causing immense damage to global health and has put the world under tremendous pressure since early 2020. The World Health Organization (WHO) has declared in March 2020 the novel coronavirus outbreak as a global pandemic. Testing of infected patients and early recognition of positive cases is considered a critical step in the fight against COVID-19 to avoid further spreading of this epidemic. As there are no fast and accurate tools available till now for the detection of COVID-19 positive cases, the need for supporting diagnostic tools has increased. Any technological method that can provide rapid and accurate detection will be very useful to medical professionals. However, there are several methods to detect COVID-19 positive cases that are typically performed based on chest X-ray images that contain relevant information about the COVID-19 virus. This paper goal is to introduce a Detectron2 and Faster R-CNN to diagnose COVID-19 automatically from X-ray images. In addition, this study could support non-radiologists with better localization of the disease by visual bounding box.

Nowadays, lie detection based on electroencephalography (EEG) is a popular area of research. Current lie detectors can be controlled voluntarily and have several disadvantages. EEG-based lie detectors have become popular over polygraphs because human intentions cannot control them, are not based on subjective interpretation, and can therefore detect lies better. This paper's main objective was to give an overview of the scientific works on the recognition of concealed information using EEG for lie detection in response to visual stimuli of faces, as there is no existing review in this area. These were selected publications from the Web of Science (WoS) database published over the last five years. It was found that the Event-Related Potential (ERP) P300 is the most often used method for this purpose. The article contains a detailed overview of the methods used in scientific research in EEG-based lie detection using the ERP P300 component in response to known and unknown faces.

Concealed information detection is nowadays an essential part of security. Conventional lie detectors are expensive, time-consuming, and their accuracy depends on the subject. Many researchers have focused on investigating concealed information for lie detection using electroencephalography (EEG) to recognize a lie better. This work aimed to provide an overview of scientific studies on EEG-based lie detection in the context of ERP P300 during the presentation of known and unknown faces published in the last five years (2017–2022). To the best of our knowledge, there is no recent available review of the most used methods for EEG data analysis in this field. For that reason, this article was created containing the current most used methods for feature extraction and classification, protocols, and accuracy of individual approaches.

The work is aimed at the development of the experimental device to study of the motion of different colloidal objects in the combined force field composed of the optical-trapping force originating in the light-intensity gradient and the thermophoretic force caused by the temperature gradient in aqueous solvents. Polystyrene microscopic beads of submicrometer diameters are used as a simple model of the optical tweezers calibration. The methods of generating temperature gradient are under development. A dual optical trap is used as a tool for the investigation of the thermophoretic force acting on the observed particles. Also, the generated optical trap's force constant is calculated by various methods, including Fast Fourier Transform, and the influence of differences in the sample environment and light intensity is compared. Results of these experiments should contribute to the theory of the optical trapping effect, so far not understood in detail, and should open a way for developing novel methods of the investigation of thermophoretic phenomena in a water environment.

Accuracy of effort estimation is one of the necessary conditions for efficiently managing software development projects. Since the information available in the early stages of software development is insufficient, software sizing metrics are considered critical factors for effort estimation. However, there is no consistent method for converting software sizing into the corresponding effort. Previous estimation methods have not considered software productivity a critical factor in estimating effort based on software sizing. This paper proposes a software productivity model based on correction factors in the Optimizing Correction Factors method through an ensemble construction mechanism of three popular machine learning techniques. The results show that using the proposed software productivity minimizes the estimation error of the methods compared to using fixed productivity metrics.

Brain-computer interface (BCI) provides direct communication between the brain and an external device. BCI systems have become a trendy field of research in recent years. These systems can be used in a variety of applications to help both disabled and healthy people. Concerning significant BCI progress, we may assume that these systems are not very far from real-world applications. This review has taken into account current trends in BCI research. In this survey, 100 most cited articles from the WOS database were selected over the last 4 years. This survey is divided into several sectors. These sectors are Medicine, Communication and Control, Entertainment, and Other BCI applications. The application area, recording method, signal acquisition types, and countries of origin have been identified in each article. This survey provides an overview of the BCI articles published from 2016 to 2020 and their current trends and advances in different application areas.

Background: There are many studies on the effect of data clustering on the effort estimation process. Most of them are on partitioning and density-based clustering, and some use hierarchical clustering but fewer details on the linkage methods. Aim: we concentrate on the aspect of the agglomerative hierarchical clustering algorithm’s effectiveness on the accuracy of the effort estimation. Method: We used the agglomerative hierarchical clustering algorithm to group the data into clusters then performed the IFPUG FPA method for effort estimation. The ISBSG dataset was used in this study. The number of clusters is determined using the dendrogram’s cut points. Different cut points and linkage methods were employed to cluster the dataset for the comparison. The estimated results of these clusters were compared with the result from the whole dataset without clustering. Result: with the selected number of clusters, results are consistently better than without clustering with all selected evaluation criteria. Conclusion: the accuracy of the effort estimation can be significantly improved when using agglomerative hierarchical clustering.