Search Results (150,739)

Flapping-Wing Aerial Vehicles (FWAVs), which take inspiration from the flight of birds and insects, have gained increasing attention over the past decades due to advantages such as low noise, biomimicry and safety, enabled by the absence of propellers. These features make them particularly suitable for applications in natural environments and operations near humans. However, their complexity introduces significant challenges, including difficulties in take-off and landing as well as limited endurance. Perching represents a promising solution to address these limitations. By equipping these drones with a perching mechanism, they could land on branches to save energy and later exploit the altitude to resume flight without requiring human intervention. Specifically, this review focuses on perching mechanisms based on grasping. It presents designs developed for flapping-wing platforms and complements them with systems originally intended for other types of aerial robots, evaluating their applicability to FWAV applications. The purpose of this work is to provide a structured overview of the existing strategies to support the development of new, effective solutions that could enhance the use of FWAVs in real-world applications. Full article

The recent growing adversarial activity against critical systems, such as the power grid, has raised attention on the necessity of appropriate measures to manage the related risks. In this setting, our research focuses on developing tools for early detection of adversarial activities, taking into account the specificities of the energy sector. We developed a framework to design and deploy AI-based detection models, and since one cannot risk disrupting regular operation with on-site tests, we also included a testbed for evaluation and fine-tuning. In the test environment, adversarial activity that produces realistic artifacts can be injected and monitored, and evidence analyzed by the detection models. In this paper we concentrate on the emulation of attacks inside our framework: A tool called SecuriDN is used to define, through a graphical interface, the network in terms of devices, applications, and protection mechanisms. Using this information, SecuriDN produces sequences of attack steps (based on the MITRE ATT&CK project) that are interpreted and executed by software called Netsploit. A case study related to Distributed Energy Resources is presented in order to show the process stages, highlight the possibilities given by our framework, and discuss possible limitations and future improvements. Full article

Deep brain stimulation (DBS) is an evolving neurosurgical treatment, originally developed for movement disorders such as Parkinson’s disease, essential tremor, and dystonia. In recent years, it has been increasingly applied to psychiatric and cognitive disorders. This review aimed to summarize the psychological and neuroethical dimensions of DBS, with particular attention to cognitive, emotional, and personality-related outcomes. While DBS can significantly enhance quality of life, it may also lead to subtle or overt changes in cognition, affect, and self-perception, especially in patients with neuropsychiatric comorbidities. Comprehensive psychological evaluation, both pre- and post-operatively, is essential. Findings from recent trials highlight a balance of potential risks and benefits that must be communicated transparently to patients. From a neuroethical perspective, DBS raises important questions regarding personal identity and autonomy, concerns that will become increasingly relevant as the technology advances. This paper underscores the need for more systematic research and the development of personalized care protocols that address not only motor outcomes but also psychosocial well-being. Full article

Background/Objectives: Decision-making under socially evaluative stress engages a dynamic interplay between cognitive control, emotional appraisal, and motivational systems. Contemporary models of multi-level co-regulation posit that these systems operate in reciprocal modulation, redistributing processing resources to prioritise either rapid socio-emotional alignment or deliberate evaluation depending on situational demands. Methods: Adopting a neurofunctional approach, a novel dual-task protocol combining the MetaCognition–Stress Convergence Paradigm (MSCP) and the Social Stress Test Neuro-Evaluation (SST-NeuroEval), a simulated social–evaluative speech task calibrated across progressive emotional intensities, was implemented. Twenty professionals from an HR consultancy firm participated in the study, with concurrent recording of frontal-temporoparietal electroencephalography (EEG) and bespoke psychometric indices: the MetaStress-Insight Index and the TimeSense Scale. Results: Findings revealed that decision contexts with higher socio-emotional salience elicited faster, emotionally guided choices (mean RT difference emotional vs. cognitive: −220 ms, p = 0.026), accompanied by oscillatory signatures (frontal delta: F(1,19) = 13.30, p = 0.002; gamma: F(3,57) = 14.93, p ≤ 0.001) consistent with intensified socio-emotional integration and contextual reconstruction. Under evaluative stress, oscillatory activity shifted across phases, reflecting the transition from anticipatory regulation to reactive engagement, in line with models of phase-dependent stress adaptation. Across paradigms, convergences emerged between decision orientation, subjective stress, and oscillatory patterns, supporting the view that cognitive–emotional regulation operates as a coordinated, multi-level system. Conclusions: These results underscore the importance of integrating behavioural, experiential, and neural indices to characterise how individuals adaptively regulate decision-making under socially evaluative stress and highlight the potential of dual-paradigm designs for advancing theory and application in cognitive–affective neuroscience. Full article

Vibration loads during deep drilling are one of the main causes of reduced service life of drilling tools and emergency failure of downhole motors. This work investigates the adaptive operation of an original elastic element based on an open cylindrical shell used as part of a drilling shock absorber. The vibration protection device contains an adjustable radial clearance between the load-bearing shell and the rigid housing, which provides the effect of structural nonlinearity. This allows effective combination of two operating modes of the drilling shock absorber: normal mode, when the clearance does not close and the elastic element operates with increased compliance; and emergency mode, when the clearance closes and gradual load redistribution and increase in device stiffness occur. A nonconservative problem concerning the contact interaction of an elastic filler with a coaxially installed shaft and an open shell is formulated, and as the load increases, contact between the shell and the housing, installed with a radial clearance, is taken into account. Numerical finite element modeling is performed considering dry friction in contact pairs. The distributions of radial displacements, contact stresses, and equivalent stresses are examined, and deformation diagrams are presented for two loading modes. The influence of different cycle asymmetry coefficients on the formation of hysteresis loops and energy dissipation is analyzed. It is shown that with increasing load, clearance closure begins from local sectors and gradually covers almost the entire outer surface of the shell. This results in deconcentration of contact pressure between the shell and housing and reduction of peak concentrations of equivalent stresses in the open shell. The results confirm the effectiveness of the adaptive approach to designing shell shock absorbers capable of reliably withstanding emergency overloads, which is important for deep drilling where the exact range of external impacts is difficult to predict. Full article

Passive microwave remote sensing provides indispensable observations for constructing long-term snow depth records, which are critical for climatology, hydrology, and operational applications. Nevertheless, despite decades of snow depth monitoring, systematic evaluations of how inter-sensor brightness temperature differences (TBDs) propagate into retrieval uncertainties are still lacking. In this study, TBDs between DMSP-F18/SSMIS, FY-3D/MWRI, and AMSR2 sensors were quantified, and the sensitivity of seven snow depth retrieval algorithms to these discrepancies was systematically assessed. The results indicate that TBDs between SSMIS and AMSR2 are larger than those between MWRI and AMSR2, likely reflecting variations in sensor specifications such as frequency, observation angle, and overpass time. In terms of algorithm sensitivity, SPD, WESTDC, FY-3B, and FY-3D demonstrate less sensitivity across sensors, with standard deviations of snow depth differences generally below 2 cm. In contrast, the Foster algorithm exhibits pronounced sensitivity to TBDs, with standard deviations exceeding 11 cm and snow depth differences reaching over 20 cm in heavily forested regions (forest fracion >90%). This study provides guidance for SWE virtual constellation design and algorithm selection, supporting long-term, seamless, and consistent snow depth retrievals. Full article

Breast cancer remains a leading cause of global cancer burden, with marked differences in incidence across countries. While biological risk factors are well established, understanding the broader structural and sociocultural influences has been less comprehensive. In this study, we analyzed harmonized data from 183 countries (2017–2023), encompassing 33 variables and 7 subvariables related to demographics, nutrition, environment, health, and healthcare access, drawn from open-access international databases. Spearman correlation analysis identified strong positive associations between breast cancer incidence and discontinued breastfeeding, high LDL cholesterol, out-of-pocket healthcare expenditure, and educational attainment. Conversely, poor sanitation, lack of handwashing facilities, unsafe water, and certain nutritional deficiencies exhibited robust negative correlations, likely reflecting under detection and reporting limitations in lower-resource settings rather than true protective effects. These findings were further explored using multiple linear regression, which explained approximately 73% of the variance in global breast cancer incidence. The final model highlighted discontinued breastfeeding, prevalence of cocaine use, unsafe sanitation, high out-of-pocket healthcare expenditure, limited handwashing access, and high processed meat consumption as the most influential independent predictors. Receiver operating characteristic (ROC) analysis confirmed strong predictive value for discontinued breastfeeding and out-of-pocket expenditure, with sanitation and hygiene variables showing paradoxical inverse associations. Our results emphasize that breast cancer risk is shaped not only by individual behaviors and genetics, but also by larger-scale structural, socioeconomic, and environmental factors. These patterns suggest that targeted interventions addressing both lifestyle behaviors and systemic inequities—such as promoting breastfeeding, reducing financial barriers to healthcare, and strengthening public health infrastructure—could meaningfully reduce the global burden of breast cancer. In conclusion, this study underscores the importance of multisectoral, equity-focused prevention strategies. It also highlights the value of country-level ecological analyses in uncovering upstream determinants of cancer incidence and calls for further research to disentangle individual and contextual effects in cancer epidemiology. Full article

Microbial electrolysis cells (MECs) present a viable platform for sustainable hydrogen generation from organic waste, but their scalability is limited by cathode performance, cost, and durability. This study evaluates three hybrid carbon nanotube (CNT) cathodes—acid-washed CNT (AW-CNT), thin layer non-acid-washed CNT (TN-NAW-CNT), and thick layer non-acid-washed CNT (TK-NAW-CNT)—each composed of stainless-steel-supported CNTs coated with molybdenum phosphide (MoP). These were benchmarked against woven carbon cloth (WCC) under varied operational conditions. A custom multi-channel reactor operated for 341 days, testing cathode performance across applied voltages (0.7–1.2 V), buffer types (phosphate vs. bicarbonate), pH (7.0 and 8.5), buffer concentrations (10–200 mM), and substrates including acetate, lactate, and treated acid whey. CNT-based cathodes consistently showed higher current densities than WCC across most conditions with significant difference found at higher applied voltages. TK-NAW-CNT achieved peak current densities of 259 A m⁻² at 1.2 V and maintained >41 A m⁻² in real-waste conditions with no added buffer. Long-term performance losses were minimal: 4.5% (TN-NAW-CNT), 0.1% (TK-NAW-CNT), 10.8% (AW-CNT), and 6.8% (WCC). CNT cathodes showed improved performance from reduced resistance and greater electrochemical stability, while proton transfer improvements benefited all materials due to buffer type and pH conditions. These results highlight CNT-based cathodes as promising, scalable alternatives to WCC for energy-positive wastewater treatment. Full article

The sudden increase in global energy demand has prompted the integration of Artificial Intelligence and the Internet of Things into the utility grid. The synergy of Artificial Intelligence and the Internet of Things in renewable energy sources has emerged as a promising solution for the development of smart grids and a transformative catalyst that restructures centralized power systems into resilient and sustainable systems. The state-of-the-art of the Internet of Things and Artificial Intelligence is presented in this paper to support the design, planning, operation, management and optimization of renewable energy-based power systems. This paper outlines the benefits of smart and resilient energy systems and the contributions of the Internet of Things across several applications, devices and networks. Artificial Intelligence can be utilized for predictive maintenance, demand-side management, fault detection, forecasting and scheduling. This paper highlights crucial future research directions aimed at overcoming the challenges that are associated with the adoption of emerging technologies in the power system by focusing on market policy and regulation and the human-centric and ethical aspects of Artificial Intelligence and the Internet of Things. The outcomes of this study can be used by policymakers, researchers and development agencies to improve global access to electricity and accelerate the development of sustainable energy systems. Full article

Aim: This review aims to validate the Experience-Based Onboarding Model (EBOM) and refine it into an improved adaptive onboarding model, OnMod. Methods: In this review, autoethnography is combined with a Multivocal Literature Review (MLR) that combines white and gray literature sources. Evidence is mapped to entities and semantic relations and assessed using predefined decision rules. Main findings: The validation of the model confirms the core EBOM entities and semantic relations. It also introduces several new or renamed entities or semantic relations that close the feedback loop and yield the refined OnMod model. Implications: The theoretical contribution is reflected in the application of autoethnography in combination with the MLR, where it represents a good basis for the development of an onboarding model. In industrial practice, the presented OnMod model can be used by mentors and managers as a guide for improving operational and daily activities, as well as for the development of onboarding strategies in IT and software companies. Full article

Background: Hip fractures are common in the elderly and often require ICU admission post-surgery due to high ASA scores and comorbidities. Length of hospital stay after ICU is a crucial indicator affecting patient recovery, complication rates, and healthcare costs. This study aimed to develop and validate a machine learning-based model to predict the factors associated with extended hospital stay (>7 days from surgery to discharge) in hip fracture patients requiring postoperative ICU care. The findings could help clinicians optimize ICU bed utilization and improve patient management strategies. Methods: In this retrospective single-centre cohort study conducted in a tertiary ICU in Turkey (2017–2024), 366 ICU-admitted hip fracture patients were analysed. Conventional statistical analyses were performed using SPSS 29, including Mann–Whitney U and chi-squared tests. To identify independent predictors associated with extended hospital stay, Least Absolute Shrinkage and Selection Operator (LASSO) regression was applied for variable selection, followed by multivariate binary logistic regression analysis. In addition, machine learning models (binary logistic regression, random forest (RF), extreme gradient boosting (XGBoost) and decision tree (DT)) were trained to predict the likelihood of extended hospital stay, defined as the total number of days from the date of surgery until hospital discharge, including both ICU and subsequent ward stay. Model performance was evaluated using AUROC, F1 score, accuracy, precision, recall, and Brier score. SHAP (SHapley Additive exPlanations) values were used to interpret feature contributions in the XGBoost model. Results: The XGBoost model showed the best performance, except for precision. The XGBoost model gave an AUROC of 0.80, precision of 0.67, recall of 0.92, F1 score of 0.78, accuracy of 0.71 and Brier score of 0.18. According to SHAP analysis, time from fracture to surgery, hypoalbuminaemia and ASA score were the variables that most affected the length of stay of hospitalisation. Conclusions: The developed machine learning model successfully classified hip fracture patients into short and extended hospital stay groups following postoperative intensive care. This classification model has the potential to aid in patient flow management, resource allocation, and clinical decision support. External validation will further strengthen its applicability across different settings. Full article

Fractional differential equations have emerged as a prominent focus of modern scientific research due to their advantages in describing the complexity and nonlinear behavior of many physical phenomena. In particular, when considering problems with initial-boundary value conditions, the solution of nonlinear fractional differential equations becomes particularly important. This paper aims to explore the fractional soliton solutions for the three-component fractional nonlinear Schrödinger (TFNLS) equation under the zero background. According to the Lax pair and fractional recursion operator, we obtain fractional nonlinear equations with Riesz fractional derivatives, which ensure the integrability of these equations. In particular, by the completeness relation of squared eigenfunctions, we derive the explicit form of the TFNLS equation. Subsequently, in the reflectionless case, we construct the fractional N-soliton solutions via the Riemann–Hilbert (RH) method. The analysis results indicate that as the order of the Riesz fractional derivative increases, the widths of both one-soliton and two-soliton solutions gradually decrease. However, the absolute values of wave velocity, phase velocity, and group velocity of one component of the vector soliton exhibit an increasing trend, and show power-law relationships with the amplitude. Full article

In this study, the behavior of the spherical bearing component of the L-100 bridge part (AlfaTech LLC, Perm, Russia) is considered within the framework of a finite element model. The influence of the pattern of the coupling of the antifriction interlayer with the lower steel plate on the operation of the part is examined in terms of ideal contact, full adhesion, and frictional contact. The thickness of the antifriction interlayer varied from 4 to 12 mm. The dependencies of the contact parameters and the stress–strain state on the thickness were determined. Structurally modified polytetrafluoroethylene (PTFE) without AR-200 fillers was considered the material of the antifriction interlayer. The gradual refinement of the behavioral model of the antifriction material to account for structural and relaxation transitions was carried based on a wide range of experimental studies. The elastic–plastic and primary viscoelastic models of material behavior were constructed based on a series of homogeneous deformed-state experiments. The viscoelastic model of material behavior was refined using data from dynamic mechanical analysis over a wide temperature range [−40; +80] °C. In the first approximation, a model of the deformation theory of plasticity with linear elastic volumetric compressibility was identified. As a second approximation, a viscoelasticity model for the Maxwell body was constructed using Prony series. It was established that the viscoelastic model of the material allows for obtaining data on the behavior of the part with an error of no more than 15%. The numerical analog of the construction in an axisymmetric formulation can be used for the predictive analysis of the behavior of the bearing, including when changing the geometric configuration. Recommendations for the numerical modeling of the behavior of antifriction layer materials and the coupling pattern of the bearing elements are given in this work. A spherical bearing with an antifriction interlayer made of Arflon series material is considered for the first time. Full article

In this paper, we begin with the classical concept of tight frames in Hilbert spaces. First, we introduce the orthogonal projection P between H and $\theta \left(H\right)$ (the range of the frame transform $\theta$ associated with a traditional tight frame) and investigate the relationship between P and $\theta$. We then explore fusion frames and extend the index set to an infinite set through a concrete example. Second, we examine the role of orthogonal projections in fusion frames with particular emphasis on robustness and redundancy illustrated by examples. Finally, we study dual fusion frames and establish several important results, especially concerning the relationship between the frame operators of two types of dual fusion frames. Full article

This paper presents the design and evaluation of a Linear Time-Varying Linear Quadratic Gaussian (LTV-LQG) controller for an energy efficient electric vehicle, using a predetermined driving strategy as the reference trajectory. The proposed approach begins with the development of a structured nonlinear vehicle model based on relevant subsystems, enabling accurate energy consumption estimation with a deviation of less than 2% from experimental measurements. This model serves as the basis for computing a near-optimal driving trajectory. The nonlinear model is linearized along the predefined trajectory to support control design. A time-varying control structure is then developed, integrating a Kalman filter that estimates unmeasured external disturbances, such as wind, and enhances feedback performance. The proposed control strategy is evaluated through simulations and compared to a rule-based switching controller that replicates human-like driving behavior. The simulation results demonstrate that the LTV-LQG controller consistently satisfies the time constraints in both headwind- and tailwind-dominant scenarios, where the switching controller tends to exceed the time limit. Moreover, in tailwind-dominant cases, the LTV-LQG controller achieves lower energy consumption (up to 15.4%). The proposed framework represents a computationally efficient and practically feasible control solution for electric vehicles operating under realistic disturbance conditions. Full article