Control design for trajectory tracking of multi-rotor aerial vehicles (MAVs) represents a challenging task due to the under-actuated property, highly nonlinear and cross-coupled dynamics, modeling errors, parametric uncertainties and external disturbances. This paper presents the design of the first order sliding mode control (FOSMC) algorithm for trajectory tracking of the octo-rotor unmanned aerial vehicle (UAV) in the presence of various disturbances. The highly nonlinear octo-rotor UAV dynamics is considered via the generalized framework for MAVs modeling. The stability analysis of the closed-loop system is presented using the Lyapunov based approach. The developed FOSMC exhibits finite-time convergence of the octo-rotor trajec-tories to the sliding manifold and the asymptotic stability of the equilibrium in the presence of vanishing disturbances. Simulation studies show a superior tracking performance and robustness properties of the FOSMC in comparison with the concurrent techniques for trajectory tracking of the octo-rotor UAV in the presence of internal and external disturbances.

This paper provides an overview of the influential parameters for the power circuit breaker condition assessment based on the vibration fingerprint. By creating the feature subsets based on the domain of computation originating from the vibration fingerprint, the features are firstly ranked by four features ranking algorithms. To confirm the ranked feature contribution to the classification performance, 11 different machine learning classifiers are trained. The training of the classifier is performed on the complete feature set where afterward the same classifiers are trained with the subset of the features ordered by the ranking algorithms. The ranking and the classifier performance yield the concept of kurtosis in the time and frequency domain as a highly promising feature for binary classification which credibly reflects the circuit breaker's mechanical condition.

Due to the significant growth in the number of devices, the range of services it provides, and strict air conditioning requirements, the telecommunications infrastructure is becoming an increasingly important electricity consumer. The efficiency of the power supply system and the power quality are significant challenges in the design and maintenance of telecommunications infrastructure elements. In such systems, power electronic converters play an indispensable role. This paper discusses the results of power quality measurements for supply systems of telecommunications devices. The power supply systems of telecommunications devices with different power converters were analyzed. Also, the power supply of a mobile telephony base station at a remote location was considered, with special reference to the reaction of battery storage in the event of a power outage. Obtained results demonstrate that it is necessary to treat such consumers with special care and take measures to limit their emission of current harmonics.

The paper presents an algorithm for determining the optimal connection location and power of a photovoltaic plant in a distribution network. The proposed algorithm is based on the use of the fuzzy logic and power flow calculation method. The fuzzy logic is used for the selection of candidate buses for the photovoltaic plant connection, while load flow analysis is used for the verification of voltage conditions and power losses in the distribution network. For each of the candidate buses photovoltaic plant of a certain power range was considered. The practical application of the considered algorithm was demonstrated on a part of Sarajevo's 10 kV distribution network.

This paper presents the use of the Hilbert-Huang Transform (HHT) to identify low-frequency electromechanical oscillatory modes, their characteristics, and damping. As these oscillations can have varying features, locations, and impacts on power systems, identifying and monitoring them is crucial for the monitoring, protection, and control of modern power systems. The Hilbert-Huang transform (HHT) is a technique used to analyze nonlinear and non-stationary time series data. It involves breaking down the data into components using Empirical Mode Decomposition (EMD), which generates components with varying amplitudes and frequencies. The EMD process includes an inner loop called sifting, which produces an Intrinsic Mode Function (IMF) until the signal reaches a mean value of zero or a maximum number of iterations. The obtained IMF is a characteristic function of a fundamental oscillation that is symmetrical around the abscissa. The dominant oscillatory mode's frequency can be determined by applying the Hilbert transformation to the first IMF, and the damping ratio and damping can be calculated by fitting a least square line to the logarithmic instantaneous amplitude of the first IMF. To demonstrate the efficacy of the methodology, three case studies are examined. The first case involves generating a synthetic signal to simulate a load angle change with a defined frequency and damping. In the second case, a small disturbance in mechanical power change in the Single Machine System is simulated. The third case simulates a three-phase short circuit on the transmission line using the IEEE 39 bus test system. The results are compared to modal analysis conducted in DigSilent PowerFactory software. The application of HHT yielded satisfactory and promising results in identifying the dominant mode's oscillation frequency and damping.

This paper presents KF-RRT algorithm: a novel approach to path planning for robotic manipulators in dynamic environments. It is based on a modified RRT algorithm combined with Kalman filtering technique. RRT modification implies two aspects. The first one is related to continuous update of struc-ture/ordering within the tree to accommodate for online execution of the algorithm. The second one relies on forest-based replanning by combining connected components. On the other hand, Kalman filter is used to track/predict the motion of obstacles. Virtually augmented obstacles influence the growth of trees, which yields the improved safety margin of the resulting motion. KF-RRT is validated within a simulation study, where it is compared to comneting algorithms,

The main focus of this study is early-stage flame detection, where the number of flame pixels in the image is very scarce. To address this challenge, a custom-made dataset was created specifically for early-stage flame detection, encom-passing challenging environmental conditions. The DeepLabv3+ architecture with ResNet-50 backbone was employed for training and weighted cross-entropy was used to effectively handle the imbalanced nature of the dataset. As a result, the model achieved a mean Intersection over Union (mIoU) value of 0.7519, demonstrating robust performance in challenging conditions. The model exhibited accurate flame pixel detection and flame shape identification in images with low flame content but high smoke levels. Additionally, the model performed well in night-time conditions, accurately identifying flame regions and shapes. An important aspect of the model's performance was its ability to correctly identify images with no flames, thereby reducing false alarms and making it suitable for UAV-based flame detection tasks.

With the advancements of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL), it is now possible to greatly speed up the processes of predicting certain anomalies and prevent unforeseen situations and disasters. One example of such an environmental disaster is the problem of early-stage flame segmentation. It is not only important to create a model capable of pattern recognition with high accuracy but also to optimize it for real-time execution. In this paper, we demonstrate the capabilities of Deeplabv3+ for early-stage flame segmentation on a custom-made dataset with challenging conditions, and near real-time execution with the adoption of the Open VINO toolkit. Acceleration of the inference process in the range of 70.46% to 93.46% is achieved, while the speed of the inference process when using the GPU with FP16 precision is increased by almost 2 times when compared to FP32 precision. The impact of our findings is significant, as early-stage flame segmentation is a critical component of disaster prevention in environmental settings. Our results demonstrate the potential of using the OpenVINO toolkit for the acceleration of the inference process.

The prediction of the dynamics of High-Speed Craft (HSC) with prismatic hulls is commonly performed by designers using semi-empirical formulations based on Savitsky’s classic method. However, the accuracy of this prediction decreases with the presence of warp, when the deadrise of the hull change along its length, which is typical for small passenger ferries, even when considering the effective deadrise and trim angle concept proposed by Savitsky in 2012. The present work assessed the dynamics of three planing warped hulls and one prismatic monohull developed by the University of Naples, using a morphing grid approach implemented in OpenFOAM to capture the motion of the vessel. Numerical results on resistance, wetted area, dynamic trim angle, wall shear stress, and pressure distribution were compared with the method proposed by Savitsky, and previously published results where possible. Results suggested that it is possible to improve Savitsky prediction by changing the location where the equivalent deadrise angle is evaluated. This single modification will allow to extend the application of Savitsky method for a wider range of warp rates.

AIM To critically evaluate the reporting quality of a random sample of animal studies within the field of endodontics against the Preferred Reporting Items for Animal Studies in Endodontics (PRIASE) 2021 checklist and to investigate the association between the quality of reporting and several characteristics of the selected studies. METHODOLOGY Fifty animal studies related to endodontics were randomly selected from the PubMed database with publication dates from January 2017 to December 2021. For each study, a score of '1' was given when the item of the PRIASE 2021 checklist was fully reported, whereas a score of '0' was given when an item was not reported; when the item was inadequately or partially reported, a score of '0.5' was given. Based on the overall scores allocated to each manuscript, they were allocated into three categories of reporting quality: low, moderate, and high. Associations between study characteristics and reporting quality scores were also analysed. Descriptive statistics, and Fisher's exact tests were used to describe the data and determine associations. The probability value of 0.05 was selected as the level of statistical significance. RESULTS Based on the overall scores, four (8%) and 46 (92%) of the animal studies evaluated were categorised as 'High' and 'Moderate' reporting quality, respectively. A number of items were adequately reported in all studies related to background (Item 4a), relevance of methods/results (7a) and interpretation of images (11e), whereas only one item related to changes in protocol (6d) was not reported in any. No associations were confirmed between reporting quality scores and number of authors, origin of the corresponding author, journal of publication (endodontic specialty versus non- specialty), impact factor or year of publication. CONCLUSIONS Animal studies published in the specialty of endodontics were mostly of 'moderate' quality in terms of the quality of reporting. Adherence to the PRIASE 2021 guidelines will enhance the reporting of animal studies in the expectation that all future publications will be high-quality.

Road infrastructure management is an extremely important task of traffic engineering. For the purpose of efficient management, it is necessary to determine the efficiency of the traffic flow through PAE 85%, AADT and other exploitation parameters on the one hand, and the number of different types of traffic accidents on the other. In this paper, a novel TrIT2F (trapezoidal interval type-2 fuzzy) PIPRECIA (pivot pairwise relative criteria importance assessment)-TrIT2F MARCOS (measurement of alternatives and ranking according to compromise solution) was developed in order to, in a defined set of 14 road segments, identify the most efficient one for data related to light goods vehicles. Through this the aims and contributions of the study can be manifested. The evaluation was carried out on the basis of seven criteria with weights obtained using the TrIT2F PIPRECIA, while the final results were presented through the TrIT2F MARCOS method. To average part of the input data, the Dombi and Bonferroni operators have been applied. The final results of the applied TrIT2F PIPRECIA-TrIT2F MARCOS model show the following ranking of road segments, according to which Vrhovi–Šešlije M-I-103 with a gradient of −1.00 represents the best solution: A5 > A8 > A2 > A1 > A4 > A3 > A6 > A12 > A13 = A14 > A11 > A7 > A9 > A10. In addition, the validation of the obtained results was conducted by changing the values of the four most important criteria and changing the size of the decision matrix. Tests have shown great stability of the developed TrIT2F PIPRECIA-TrIT2F MARCOS model.

To maximize the impact of precision medicine approaches, it is critical to accurately identify genetic variants in cancer-associated genes with functional consequences. Yet, our knowledge of rare variants conferring clinically relevant phenotypes and the mechanisms through which they act remains highly limited. A tumor suppressor gene exemplifying the challenge of variant interpretation is VHL. VHL encodes an E3 ubiquitin ligase that regulates the cellular response to hypoxia. Germline pathogenic variants in VHL predispose patients to tumors including clear cell renal cell carcinoma (ccRCC) and pheochromocytoma, and somatic VHL mutations are frequently observed in sporadic renal cancer. Here, we optimize and apply Saturation Genome Editing (SGE) to assay nearly all possible single nucleotide variants (SNVs) across VHL’s coding sequence. To delineate mechanisms, we quantify mRNA dosage effects over time and compare effects in isogenic cell lines. Function scores for 2,268 VHL SNVs identify a core set of pathogenic alleles driving ccRCC with perfect accuracy, inform differential risk across tumor types, and reveal novel mechanisms by which variants impact function. These results have immediate utility for classifying VHL variants encountered in both germline testing and tumor profiling and illustrate how precise functional measurements can resolve pleiotropic and dosage-dependent genotype-phenotype relationships across complete genes.

The automotive industry requires ultra-reliable low-latency connectivity for its vehicles, and as such, it is one of the promising customers of 5G ecosystems and their orchestrated network infrastructure. In particular, Multi-Access Edge Computing (MEC) provides moving vehicles with localized low-latency access to service instances. However, given the mobility of vehicles, and various resource demand patterns at the distributed MEC nodes, challenges such as fast reconfiguration of the distributed deployment according to mobility pattern and associated service and resource demand need to be mitigated. In this paper, we present the orchestrated edges platform, which is a solution for orchestrating distributed edges in complex cross-border network environments, tailored to Connected, Cooperative, and Automated Mobility (CCAM) use cases within a 5G ecosystem. The proposed solution enables collaboration between orchestrators that belong to different tiers, and various federated edge domains, with the goal to enable service continuity for vehicles traversing cross-border corridors. The paper presents the prototype that we built for the H2020 5G-CARMEN trials, including the validation of the orchestration design choices, followed by the promising results that span both orchestration (orchestration latency) and application performance-related metrics (client-to-edge and edge-to-edge service data plane latencies).

In this paper, we report on teachers’ and principals’ shared perceptions regarding beliefs, rules, trust, and encouragement of new initiatives. Collectively, these are aspects of leadership for learning (LFL) describing an overall shared climate in schools. We demonstrate how these perceptions on school climate differ across teachers and principals within and across countries. Moreover, we report how different perceptions of school climate are associated with leadership style. We analyze data from 37 countries that participated in the last cycle of the Teaching and Learning International Survey (TALIS) in 2018. To build the measurement model, we employ multigroup multilevel confirmatory factor analysis, whereas multivariate linear regression is used to inspect associations. Overall, principals and teachers differ in their views of school climate. In the majority of the countries, principals report stronger school climate than teachers. We further confirm these perceptual differences between teachers and principals by separately studying the relationships between teacher perceived school climate and principal perceived school climate with relevant leadership variables. In the entire sample, we find that principals’ perceptions of school climate are more strongly and consistently associated with leadership in schools. This relationship is particularly stable for distributed leadership. In the entire sample, leadership styles are weakly positively correlated with teacher perceptions of school climate too; however, this association is less pronounced and less stable within individual countries. The analyses conducted within countries revealed that the distributed leadership rather than instructional leadership shapes teachers’ perceptions of school climate. More discussion is presented on the need for alignment between different perceptions of school climate and leadership styles in the overall organizational quality.

ABSTRACT High-risk Human Papillomaviruses (HPVs) and Epstein – Barr virus (EBV) are present and involved in several types of human carcinomas, including cervical and, head and neck cancers. Nevertheless, their presence and association in the pathogenesis of colorectal cancer is still nascent. The current study explored the association between the high-risk HPVs and EBV and tumor phenotype in colorectal cancers (CRCs) in the Qatari population. We found that high-risk HPVs and EBV are present in 69/100 and 21/100 cases, respectively. Additionally, 17% of the cases showed a copresence of high-risk HPVs and EBV, with a significant correlation only between the HPV45 subtype and EBV (p = .004). While the copresence did not significantly associate with clinicopathological characteristics, we identified that coinfection with more than two subtypes of HPV is a strong predictor of advanced stage CRC, and the confounding effect of the copresence of EBV in such cases strengthens this association. Our results indicate that high-risk HPVs and EBV can co-present in human CRCs in the Qatari population where they could plausibly play a specific role in human colorectal carcinogenesis. However, future studies are essential to confirm their copresence and synergistic role in developing CRCs.