Search Results (1,466)

Conventional classifiers are generally unable to identify samples from classes absent during the model’s training. However, such samples frequently emerge in real-world scenarios, necessitating the extension of classifier capabilities. Open-Set Recognition (OSR) models are designed to address this challenge. Previously, we developed a robust OSR method that employs generated—“fake”—features to model the space of unknown classes encountered during deployment. Like most OSR models, this method was initially designed for image datasets. However, it is essential to extend OSR techniques to other data types, given their widespread use in practice. In this work, we adapt our model to time-series data while preserving its core efficiency advantage. Thanks to the model’s modular design, only the feature extraction component required modification. We implemented three approaches: a one-dimensional convolutional network for accurate representation, a lightweight method based on predefined statistical features, and a frequency-domain neural network. Further, we evaluated combinations of these methods. Experiments on a biometric time-series dataset, used here as a case study, demonstrate that our model achieves excellent open-set detection and closed-set accuracy. Combining feature extraction strategies yields the best performance, while individual methods offer flexibility: CNNs deliver high accuracy, whereas handcrafted features enable resource-efficient deployment. This adaptability makes the proposed framework suitable for scenarios with varying computational constraints. Full article

Background: Pelvic injuries of the sacroiliac joint are unstable and require surgical intervention following high-energy trauma. In this study, we aimed to present the long-term clinical outcomes of patients with sacroiliac joint separation and sacroiliac fracture dislocation (crescent) injury. We compared the surgical interventions performed on the sacroiliac joint based on patient clinical data. Methods: By reviewing the records of 850 pelvic fractures treated in our clinic between 2000 and 2020, we identified 110 patients with sacroiliac joint injuries who were included in the study. The fractures were classified based on patient files and radiographs. The patients were categorized according to the surgical interventions performed on the sacroiliac joint into two groups: closed reduction with percutaneous iliosacral screws and open reduction with plates and screws. We further divided the patients who underwent open reduction and plate–screw fixation into anterior and posterior surgical approaches. Clinical outcomes were obtained by evaluating patients using a subjective pelvic scoring system. Additionally, complications observed after surgeries were investigated. Results: A total of 121 fractures from 110 patients were included in the study. Eleven of the patients had bilateral sacroiliac joint injuries, for which bilateral surgery was performed. The mean age of the patients at the time of injury was 35.15 years (range from 6 to 80 years). The mean follow-up period was 103.45 months (range from 16 to 253 months). According to the scoring system, the highest success rate was observed in plate–screw operations performed through the anterior approach to the sacroiliac joint, with excellent to good results in approximately 92% of patients. Both open reduction and internal fixation through the posterior approach and closed reduction and percutaneous iliosacral screw surgery yielded successful functional results, with no statistically significant difference between the methods (p = 0.880). Regarding complications, the most important problems were infections associated with plate–screw procedures using the posterior approach and neurologic injuries resulting from closed reduction screw surgery. Conclusions: Effective management of sacroiliac joint injuries requires surgical expertise and individualized treatment strategies. With appropriate technique and fixation, both open and closed surgical methods can achieve satisfactory anatomical reduction and functional outcomes. Although standardized treatment protocols may be developed, tailoring the approach to each patient is more important for optimal clinical success. Full article

An atrial septal defect (ASD) is the most common congenital heart defect (CHD) diagnosed in adulthood. It is characterized by significant anatomical heterogeneity and complications that evolve over time. While often asymptomatic in children, the signs of adverse effects of ASD increase with age, including a greater risk of heart failure, stroke, atrial fibrillation (AF), and reduced life expectancy. ASD is traditionally considered a right-heart lesion due to long-term complications such as arrhythmias, right-sided heart failure, thromboembolism, and, in a subset of patients, pulmonary arterial hypertension (PAH). The pathophysiology of atrial shunts also affects the left heart due to volume overload and adverse ventriculo-ventricular interaction. Early diagnosis of interatrial septal anomalies is essential to prevent hemodynamic consequences and/or thromboembolic events. Electrocardiographic (ECG) findings play a crucial role in this early diagnosis. This narrative review aims to update clinicians on the latest evidence regarding the pathophysiological link between ASD and cardiac rhythm disorders, the nuances of optimal diagnostics, treatment options (surgical, interventional, pharmacological), and the need for long-term follow-up for patients with ASD. The review will determine the risk of conduction disorders compared to a healthy population and to compare the prevalences of conduction disorders, mortality, and pacemaker use in patients with closed ASDs versus those with open ASDs. Full article

This paper presents a flutter analysis of the UHBR Open Fan Testcase ECL5 for an off-design point at part speed and focuses on the second eigenmode, which has a strong torsional character near the blade tip. Recent studies by Pagès et al., using a time-linearized solver, showed strong negative damping for an operating point at 80% speed close to the maximal pressure ratio. This was identified as a phenomenon of convective resonance; for a certain nodal diameter and frequency, the blade vibration is in resonance with convective disturbances that are linearly unstable. In this work, a nonlinear frequency domain method (harmonic balance) is applied to the problem of aerodynamic damping prediction for this off-design operating point. It is shown that, to obtain plausible results, it is necessary to treat the turbulence model as unsteady. The impact of spurious reflections due to numerical boundary conditions is estimated for this case. While strong negative damping is not predicted by the analysis presented here, we observe particularly high sensitivity of the aerodynamic response with respect to turbulence model formulation and the frequency for certain nodal diameters. The combination of nodal diameter and frequency of maximal sensitivities are interpreted as points near resonance. We recover from these near-resonance points convective speeds and compare them to studies of the onset of nonsynchronous vibrations of the ECL5 fan at part-speed conditions. Full article

Various industrial technologies require flexible material webs to undergo processes such as thermal treatment (e.g., drying), printing, or laminating. Such processes are usually performed within winding systems, where the web goes through a set of rolls, and the precision of the web movement determines the quality of the final product. Therefore, high accuracy in the control of both the longitudinal and lateral movement of the web is of paramount importance. Designing the proper control system requires insightful analysis of the technological setup and precise modeling of its dynamic properties. In this paper, the transfer function model of the roll-to-roll system with closed-loop web circulation has been developed based on the mathematical description of the open-loop system. It has been proven that the analyzed system can be efficiently represented by an integral block with negligible inertia. Having established this, several control algorithms have been analyzed, and, as a result, the dedicated adaptive–predictive control algorithm has been proposed. The developed solutions have been verified both by simulations and real experiments performed using the semi-industrial laboratory setup. The high control quality of the proposed algorithm (e.g., considerable reductions in overshoot and settling time compared to PI control), outperforming classical approaches, has been confirmed under various disturbances. Full article

The accurate estimation of parameters in dynamical systems stands for an open key research issue in modeling, control, and fault diagnosis. The presence of noise in input and output signals poses a serious challenge for accurate real-time dynamical system parameter estimation. This paper proposes a new robust algebraic parameter estimation methodology for integer-order dynamical systems that explicitly incorporates the signal filtering dynamics within the estimator structure and enhances noise attenuation through fractional differentiation in frequency domain. The introduced estimation methodology is valid for Liouville-type fractional derivatives and can be applied to estimate online the parameters of differentially flat, oscillating or vibrating systems of multiple degrees of freedom. The parametric estimation can be thus implemented for a wide class of oscillating or vibrating, nth-order dynamical systems under noise influence in measurement and control signals. Positive values are considered for the inertia, stiffness, and viscous damping parameters of vibrating systems. Parameter identification can be also used for development of actuators and control technology. In this sense, validation of the algebraic parameter estimation is performed to identify parameters of a differentially flat, permanent-magnet direct-current motor actuator. Parameter estimation for both open-loop and closed-loop control scenarios using experimental data is examined. Experimental results demonstrate that the new parameter estimation methodology combining signal filtering dynamics and fractional calculus outperforms other conventional methods under presence of significant noise in measurements. Full article

Diabetic peripheral neuropathy (DPN) is a common complication of type 2 diabetes that impairs gait and balance, increasing fall risk. This study investigated gait characteristics and postural control in individuals with DPN, compared to age- and gender-matched healthy controls. Fifteen DPN patients and fifteen controls underwent assessments of gait, static balance, and mobility. Gait parameters were measured during overground walking using motion capture and force platforms. Static balance was evaluated via tandem stance tests (eyes open/closed), while mobility was assessed with the Timed-Up-and-Go (TUG) test. Dynamic stability was assessed by computing the center-of-pressure Time-to-Contact (TTC) with the mediolateral (ML) stability boundary. We hypothesized that patients with DPN would exhibit an altered gait and reduced ML postural stability during walking. The study results show no significant differences in ML center-of-pressure (COP) excursion or its velocity during walking between groups. Patients with DPN walked relatively slowly, with shorter steps, and showed markedly poorer static balance (earlier failure during tandem stance test), as well as slower TUG performance. Clinically, these findings support routine fall risk screening in DPN using both static balance tests (e.g., tandem stance) and mobility measures (e.g., TUG or gait speed). These findings further suggest that while dynamic postural control during walking may be preserved, DPN patients exhibit gait adaptations and significant static balance deficits, highlighting the need for comprehensive balance assessment in this population. Full article

The synthesis of two series of poly(lactic acid) (PLA)-based polymer nanocomposites (PNCs) filled with small amounts (0.5 and 1%) of Ag and Cu nanoparticles (NPs) was performed. Moreover, two methods for the PNC synthesis were performed, namely, ‘conventional mixing techniques’ and ‘in situ ring opening polymerization (ROP)’. The latter method was employed for the first time; moreover, it was found to be more effective in achieving very good NP dispersion in the polymer matrix as well as the formation of interfacial polymer–NP interactions. The in situ ROP for PLA/Cu was not productive due to the oxidation of Cu NPs being faster than the initiation of ROP. The presence of NPs resulted in suppression of the glass transition temperature, T_g (23–60 °C), with the effects being by far stronger in the case of ROP-based PNCs, e.g., exhibiting T_g decrease by tens of K. Due to that surprising result, the ROP-based PLA/Ag PNCs exhibited elevated ionic conductivity phenomena (at room temperature). This can be exploited in specific applications, e.g., mimicking the facilitated small molecules permeation. The effects of NPs on crystallinity (2–39%) were found opposite between the two series. Crystallinity was facilitated/suppressed in the mixing/ROP -based PNCs, respectively. The local and segmental molecular mobility map was constructed for these systems for the first time. Combining the overall data, a concluding scenario was employed, that involved the densification of the polymer close to the NPs’ surface and the free volume increase away from them. Finally, an exceptional effect was observed in PLA + 0.5% Ag (ROP). The crystallization involvement resulted in a severe suppression of T_g (−25 °C). Full article

Pyometra is a frequent and life-threatening reproductive disorder in bitches, characterized by profound immune and inflammatory responses within the uterus. The endocannabinoid system (eCS) is a key modulator of immune regulation, tissue homeostasis, and inflammation; however, its role in canine uterine physiology and pathology remains unexplored. This study aimed to characterize the presence and regulation of eCS components in the uterus of healthy and pyometra-affected dogs. Twenty-eight bitches were categorized into four groups: closed-cervix pyometra (CP; n = 7), open-cervix pyometra (OP; n = 7), diestrus (DE; n = 7), and anestrus (AE; n = 7). Uterine tissues were obtained by ovariohysterectomy. Serum progesterone, anandamide (AEA), and 2-arachidonylglycerol (2-AG) concentrations were quantified, while the uterine expression of cannabinoid receptors (CB1 and CB2) was assessed using real-time PCR and localized by immunohistochemistry (IHC). Serum AEA levels were significantly reduced in CP compared with AE (p = 0.017), whereas 2-AG differences did not reach significance (p = 0.072). Both CB1 and CB2 were consistently expressed across all groups, with IHC revealing receptor-specific patterns within uterine compartments. Collectively, these findings demonstrate for the first time that the canine uterus possesses a functional eCS, and that its modulation is linked to reproductive physiology and pyometra-associated inflammatory processes. Full article

We propose a general and experimentally accessible framework to quantify transition timing in discrete quantum systems via the time-of-flow (TF) distribution. Defined from the rate of population change in a target state, the TF distribution can be reconstructed through repeated projective measurements at discrete times on independently prepared systems, thus avoiding Zeno inhibition. In monotonic regimes, it admits a clear interpretation as a time-of-arrival (TOA) or time-of-departure (TOD) distribution. We apply this approach to optimize time-dependent Hamiltonians, analyze shortcut-to-adiabaticity (STA) protocols, study non-adiabatic features in the dynamics of a three-level time-dependent detuning model, and derive a transition-based quantum speed limit (TF-QSL) for both closed and open quantum systems. We also establish a lower bound on temporal uncertainty and examine decoherence effects, demonstrating the versatility of the TF framework for quantum control and diagnostics. This method provides both a conceptual tool and an experimental protocol for probing and engineering quantum dynamics in discrete-state platforms. Full article

Timely detection of road surface defects such as cracks and potholes is critical for ensuring traffic safety and reducing infrastructure maintenance costs. While recent advances in image-based deep learning techniques have shown promise for automated road defect detection, existing models remain limited to closed-set detection settings, making it difficult to recognize newly emerging or fine-grained defect types. To address this limitation, we propose an attribute-aware open-vocabulary crack detection (AOVCD) framework, which leverages the alignment capability of pretrained vision–language models to generalize beyond fixed class labels. In this framework, crack types are represented as combinations of visual attributes, enabling semantic grounding between image regions and natural language descriptions. To support this, we extend the existing PPDD dataset with attribute-level annotations and incorporate a multi-label attribute recognition task as an auxiliary objective. Experimental results demonstrate that the proposed AOVCD model outperforms existing baselines. In particular, compared to CLIP-based zero-shot inference, the proposed model achieves approximately a 10-fold improvement in average precision (AP) for novel crack categories. Attribute classification performance—covering geometric, spatial, and textural features—also increases by 40% in balanced accuracy (BACC) and 23% in AP. These results indicate that integrating structured attribute information enhances generalization to previously unseen defect types, especially those involving subtle visual cues. Our study suggests that incorporating attribute-level alignment within a vision–language framework can lead to more adaptive and semantically grounded defect recognition systems. Full article

Responsiveness—the speed at which a text-to-speech (TTS) system produces audible output—is critical for real-time voice assistants yet has received far less attention than perceptual quality metrics. Existing evaluations often touch on latency but do not establish reproducible, open-source standards that capture responsiveness as a first-class dimension. This work introduces a baseline benchmark designed to fill that gap. Our framework unifies latency distribution, tail latency, and intelligibility within a transparent and dataset-diverse pipeline, enabling a fair and replicable comparison across 13 widely used open-source TTS models. By grounding evaluation in structured input sets ranging from single words to sentence-length utterances and adopting a methodology inspired by standardized inference benchmarks, we capture both typical and worst-case user experiences. Unlike prior studies that emphasize closed or proprietary systems, our focus is on establishing open, reproducible baselines rather than ranking against commercial references. The results reveal substantial variability across architectures, with some models delivering near-instant responses while others fail to meet interactive thresholds. By centering evaluation on responsiveness and reproducibility, this study provides an infrastructural foundation for benchmarking TTS systems and lays the groundwork for more comprehensive assessments that integrate both fidelity and speed. Full article

Hurley Stage II or III hidradenitis suppurativa (HS) may necessitate surgical excision of diseased skin and subcutaneous fat for symptom control and disease management. These excisions result in open wounds in topographically challenging regions and typically cannot be primarily closed. This study evaluates the use of a synthetic electrospun fiber matrix (SEFM) as a post-resection regeneration template to accelerate re-granulation and improve subsequent skin graft incorporation. This prospective pilot study enrolled Hurley Stage II or III patients undergoing surgical resection of HS lesions. SEFM was applied to the resulting wounds in conjunction with negative pressure wound therapy (NPWT). Patients were monitored post-operatively for granulation tissue formation and underwent skin grafting once granulation was sufficient. Skin graft incorporation was assessed at follow-up visits. Complications, including graft loss (partial or complete) and infection, were assessed at each encounter. A total of 21 wounds in eight patients met the inclusion criteria and were enrolled. The average time to skin grafting was 14 ± 3.2 days. After grafting, the average graft incorporation was 71 ± 28%. No complications occurred during the study. These initial results indicate that by supporting granulation tissue formation, combined use of SEFM and NPWT may aid in successful engraftment of topographically challenging areas post-HS excision. Full article

Perovskite solar cells (PSCs) have emerged as promising photovoltaic technologies owing to their high power conversion efficiency (PCE) and material versatility. Conventional optimization of PSC architectures largely depends on iterative experimental approaches, which are often labor-intensive and time-consuming. In this study, a data-driven modeling strategy is introduced to accelerate the design of efficient and mechanically robust PSCs. Seven supervised regression models were evaluated for predicting key photovoltaic parameters, including PCE, short-circuit current density (Jsc), open-circuit voltage (Voc), and fill factor (FF). Among these, a stacking ensemble framework exhibited superior predictive accuracy, achieving an R² of 0.8577 and a root mean square error of 2.084 for PCE prediction. Model interpretability was ensured through Shapley Additive exPlanations(SHAP) analysis, which identified precursor solvent composition, A-site cation ratio, and hole-transport-layer additives as the most influential parameters. Guided by these insights, ten device configurations were fabricated, achieving a maximum PCE of 24.9%, in close agreement with model forecasts. Furthermore, multiscale mechanical assessments, including bending, compression, impact resistance, peeling adhesion, and nanoindentation tests, were conducted to evaluate structural reliability. The optimized device demonstrated enhanced interfacial stability and fracture resistance, validating the proposed predictive–experimental framework. This work establishes a comprehensive approach for performance-oriented and reliability-driven PSC design, providing a foundation for scalable and durable photovoltaic technologies. Full article

Smart meters (SMs) are essential components of modern smart grids, enabling real-time and accurate monitoring of electricity consumption. However, their evaluation is often hindered by proprietary communication protocols and the high cost of commercial testing tools. This study presents a low-cost, open-source experimental platform for smart meter validation, using a microcontroller and light sensor to detect optical pulses emitted by standard SMs. This non-intrusive approach circumvents proprietary restrictions while enabling transparent and reproducible comparisons. A case study was conducted comparing the static meter GAMA 300 model, manufactured by Elgama-Elektronika Ltd. (Vilnius, Lithuania), which is a closed-source commercial meter, with theTexas Instruments EVM430-F67641 evaluation module, manufactured by Texas Instruments Inc. (Dallas, TX, USA), which serves as an open-source reference design. Statistical analyses—based on confidence intervals and ANOVA—revealed a mean deviation of less than 1.5% between the devices, confirming the platform’s reliability. The system supports indirect power monitoring without hardware modification or access to internal data, making it suitable for both educational and applied contexts. Compared to existing tools, it offers enhanced accessibility, modularity, and open-source compatibility. Its scalable design supports IoT and environmental sensor integration, aligning with Internet of Energy (IoE) principles. The platform facilitates transparent, reproducible, and cost-effective smart meter evaluations, supporting the advancement of intelligent energy systems. Full article