Search Results (508)

Accurate fault location is crucial for enabling maintenance personnel to quickly reach the fault site for inspection and repair, thereby minimizing power outage duration. To address the low fault location accuracy caused by phase unsynchronization of double-ended recording data and the dependence of traditional algorithms on accurate line parameters, this paper introduces a novel fault location algorithm for hybrid transmission lines. The method integrates a data synchronization approach with a physics-informed neural network (PINN) implemented using a backpropagation (BP) neural network architecture. First, the proposed synchronization algorithm corrects the phase misalignment between double-ended recordings. Second, a distributed-parameter fault location model is developed to derive a location function, which is then used to construct physics-informed input features. This approach reduces the need for large fault datasets, addressing the challenge of the low occurrence of faults in practice. Finally, a BP neural network employing these physics-informed features is utilized to learn the nonlinear mapping to the fault location, allowing for accurate fault location, enabling accurate positioning without requiring precise line parameters. Validation using actual line data confirms the high precision of the synchronization algorithm. Furthermore, simulations show that the proposed fault location algorithm achieves high accuracy and remains robust against variations in fault position, type, transition resistance, inception angle, and load current, making it highly practical for real engineering applications. Full article

(Bi_0.5Na_0.5)TiO₃-based lead-free ferroelectric ceramics are among the most extensively researched energy storage materials today. In this paper, (1 − x)Bi_0.46Sr_0.06Na_0.5TiO_3−xCaTiO₃ ceramics were synthesized through a solid-phase sintering method by synergistically adjusting CaTiO₃ components after introducing Sr²⁺ at the A-site. The XRD patterns revealed that all samples formed a single perovskite solid solution, with the 111 and 200 peaks shifting to higher levels as the CaTiO₃ increased, indicating a gradual decrease in cell volume. The SEM images exhibited dense crystals without any apparent porosity, which were formed by the different components of the ceramics. Through energy storage, dielectric, and charge–discharge performance tests, it was found that with a 10%mol CaTiO₃ addition, the samples obtained a maximum breakdown field strength of 260 kV/cm and corresponding saturation polarization strength of 32.80 μC/cm² and thereby exhibited a reversible energy storage density valued 3.52 J/cm³. In addition, the dielectric constant varied by less than 10% within the temperature range of 63.7 °C to 132.7 °C and presented good frequency (10–250 Hz) stability at 180 kV/cm. Moreover, the ceramics demonstrated a maximum current density reaching 349.58 A/cm² and a maximum power density of 18.90 MW/cm³ for their charge–discharge performance, all of which makes them suitable for pulse system applications. Full article

This study examines binary blends of three types of polyhydroxyalkanoates (PHAs)—poly(3-hydroxybutyrate) (PHB), poly(3-hydroxybutyrate-co-3-hydroxyhexanoate) (PHBH), and poly(3-hydroxybutyrate-co-4-hydroxybutyrate) (P3HB4HB)—with a focus on their rheological, thermal, and mechanical behavior. The blends exhibit partial miscibility in both the melt and solid states. Glass transition analysis revealed that semicrystalline/amorphous PHA combinations are fully miscible (single Tg) at amorphous PHA contents below 30 wt%. Above this threshold, a two-phase morphology develops, consisting of crystalline spherulites embedded in an amorphous matrix. When the amorphous PHA content reached ≥30 wt%, the blends could be oriented by stretching, yielding materials that display thermoplastic elastomer (TPE)-like behavior without chemical modification of the base polymers. Thermal and mechanical characterization, supported by X-ray diffraction of samples before and after orientation, confirmed that the elastomeric properties originate from the multiphase architecture formed by crystalline and amorphous domains interconnected through a miscible amorphous fraction. Full article

Purpose: To evaluate anatomical and functional outcomes after switching from aflibercept to faricimab in patients with neovascular age-related macular degeneration (nAMD) with suboptimal response. Methods: This retrospective study included 72 eyes of 66 patients with nAMD previously treated with intravitreal aflibercept using a treat-and-extend regimen. Indications for switching included persistent retinal fluid, pigment epithelial detachment (PED), lack of best-corrected visual acuity (BCVA) improvement, or inability to extend treatment intervals beyond four weeks. Patients received three monthly loading doses of faricimab followed by individualized 8- to 16-week dosing. Follow-up comprised six visits over a mean of 8.5 ± 1.4 months. Outcomes included BCVA (logMAR), retinal morphology (subretinal fluid—SRF; intraretinal fluid—IRF; pigment epithelial detachment—PED), central subfoveal thickness (CST), and treatment interval changes. Results: Switching to faricimab led to significant short-term anatomical improvement, primarily reduction in subretinal fluid (p < 0.0001), with maximal effect during the loading phase. Resolution of SRF was significant at the end of the follow up; however, IRF changes were transient and not sustained beyond three months. PED reduction reached borderline significance (p = 0.0455). CST decreased during the loading phase (p < 0.0001) but returned to baseline thereafter. BCVA improved only after loading (p = 0.0287) but not at final follow-up. Treatment intervals were extended by a mean of ~2 weeks (p < 0.0001), increasing in 80% of eyes. Eyes with fewer prior injections and better baseline BCVA achieved superior final visual outcomes. Conclusions: Switching to faricimab provides short-term anatomical benefits and treatment-interval extension without sustained visual gain. Functional improvements tended to be greater in patients with fewer injections and shorter treatment duration prior to switch. Full article

The rapid expansion of AI applications in various domains demands models that balance predictive power with human interpretability, a requirement that has catalyzed the development of hybrid algorithms combining high accuracy with human-readable outputs. This study introduces a novel neuro-symbolic framework, OIKAN (Optimized Interpretable Kolmogorov–Arnold Network), designed to integrate the representational power of feedforward neural networks with the transparency of symbolic regression. The framework employs Gaussian noise-based data augmentation and a two-phase sparse symbolic regression pipeline using ElasticNet, producing analytical expressions suitable for both classification and regression problems. Evaluated on 60 classification and 58 regression datasets from the Penn Machine Learning Benchmarks (PMLB), OIKAN Classifier achieved a median accuracy of 0.886, with perfect performance on linearly separable datasets, while OIKAN Regressor reached a median R² score of 0.705, peaking at 0.992. In comparative experiments with ElasticNet, DecisionTree, and XGBoost baselines, OIKAN showed competitive accuracy while maintaining substantially higher interpretability, highlighting its distinct contribution to the field of explainable AI. OIKAN demonstrated computational efficiency, with fast training and low inference time and memory usage, highlighting its suitability for real-time and embedded applications. However, the results revealed that performance declined more noticeably on high-dimensional or noisy datasets, particularly those lacking compact symbolic structures, emphasizing the need for adaptive regularization, expanded function libraries, and refined augmentation strategies to enhance robustness and scalability. These results underscore OIKAN’s ability to deliver transparent, mathematically tractable models without sacrificing performance, paving the way for explainable AI in scientific discovery and industrial engineering. Full article

Gamma-aminobutyric acid (GABA) is the main inhibitory neurotransmitter in the vertebrate central nervous system, known for its role in promoting sleep, reducing anxiety, regulating blood pressure, and modulating stress, cognition, and behavior. Microbial fermentation offers an effective method for GABA production, with certain lactic acid bacteria (LAB) strains recognized as efficient producers. This study assessed the GABA-producing potential of 31 LAB strains, including isolates from traditional Bulgarian foods and plants. The strains were cultivated in an MRS medium supplemented with 1% monosodium glutamate (MSG), and GABA production was quantified using HPLC after derivatization with dansyl chloride. Most strains produced between 200 and 300 mg/L of GABA. However, Levilactobacillus brevis NCTC 13768 showed much higher productivity, reaching 3830.7 mg/L. To further evaluate its capacity, L. brevis NCTC 13768 was cultivated for 168 h in MRS medium with and without MSG. Without MSG, GABA production peaked at 371.0 mg/L during the late exponential phase. In contrast with MSG, GABA levels steadily increased, reaching 3333.6 mg/L after 168 h. RT-qPCR analyses of the glutamic acid decarboxylase (GAD) system showed that the genes of glutamate decarboxylase (gadB), glutamate-GABA antiporter (gadC), and transcriptional regulator (gadR) are significantly overexpressed when the culture reaches the late stationary phase of growth (96 h after the beginning of cultivation). These results identify L. brevis NCTC 13768 as a high-yield GABA producer, with potential applications in the production of fermented functional foods and beverages. Full article

Early and accurate detection of distributed bearing faults is essential to prevent equipment failures and reduce downtime in industrial environments. This study explores the optimal selection of input signal sources for high-accuracy distributed fault classification, employing wavelet transform and machine learning algorithms. The primary contribution of this work is to demonstrate that robust distributed bearing fault diagnosis can be achieved through optimal sensor fusion and wavelet-based feature engineering, without the need for deep learning or high-dimensional inputs. This approach provides interpretable, computationally efficient, and generalizable fault classification, setting it apart from most existing studies that rely on larger models or more extensive data. All experiments were conducted in a controlled laboratory environment across multiple loads and speeds. A comprehensive dataset, including three-axis vibration, stray magnetic flux, and two-phase current signals, was used to diagnose six distinct bearing fault conditions. The wavelet transform is applied to extract frequency-domain features, capturing intricate fault signatures. To identify the most effective input signal combinations, we systematically evaluated Random Forest, XGBoost, and Support Vector Machine (SVM) models. The analysis reveals that specific signal pairs significantly enhance classification accuracy. Notably, combining vibration signals with stray magnetic flux consistently achieved the highest performance across models, with Random Forest reaching perfect test accuracy (100%) and SVM showing robust results. These findings underscore the importance of optimal source selection and wavelet-transformed features for improving machine learning model performance in bearing fault classification tasks. While the results are promising, validation in real-world industrial settings is needed to fully assess the method’s practical reliability and impact on predictive maintenance systems. Full article

Metallic glass alloys exhibit excellent properties, yet suffer from poor room-temperature plasticity, a limitation that restricts their engineering applications. Bulk metallic glass matrix composites (BMGMCs) have proven effective in enhancing the plasticity of metallic glasses, and the addition of alloying elements serves as a key strategy to regulate their microstructure and optimize the properties of these composites. This study aims to investigate the effects of a vanadium (V) addition on the mechanical properties and microstructure of Ti-based BMGMCs, while exploring the underlying mechanism of V’s influence. Using (Ti₅₁Zr₂₅Cu₆Be₁₈)_100−xV_x (x = 0, 4, 8, 12, 16, 20) BMGMCs as test specimens, microstructural characterization was performed via X-ray diffraction (XRD) and scanning electron microscopy (SEM), and compressive mechanical properties were tested. The results indicate that a V addition refines dendrites without altering the phase composition, which remains composed of β-Ti crystals and an amorphous matrix. With the increase in V content, the compressive plastic strain shows a trend of first increasing and then decreasing; when x = 12, the specimen exhibits the maximum compressive plastic strain, reaching 7.9%. Additionally, the volume fraction of the crystalline phase gradually increases with increasing V content. This study clarifies the mechanism by which V regulates the microstructure and properties of Ti-based BMGMCs, thereby providing theoretical and experimental insights for optimizing alloy compositions to enhance the mechanical performance. Full article

Background: Legg–Calvé–Perthes disease (LCPD) is a rare avascular osteonecrosis of the proximal femoral epiphysis and typically occurs during the childhood growth phase. LCPD is a complex illness of unknown origin, which is considered the main difficulty in the study of this disease. Various theories on LCPD etiology have been proposed; however, no consensus has been reached about its origin. Our research objective was to evaluate the polymorphisms FVL rs6025, FVIII rs5987061, FIX Malmö rs6048, PAI-1 rs1799889, eNOS rs17899983/rs2070744, IL-23R rs1569922/rs154655686/7539625, and TNF-α rs180062, and their relationship with LCPD. Methods: A blood sample was taken from each study participant. Complete blood count, coagulation times and factors, antithrombotic proteins, and homocysteine (Hcy) were determined using a coagulometric method. DNA was obtained and genotyped using real-time PCR with TaqMan probes. Genotypic and allelic distributions were analyzed using comparative analysis, the Hardy–Weinberg equilibrium, and OR. Results: This study included 46 children: 23 with LCPD (cases) and 23 without (controls). Statistically significant differences were found in Prothrombin Time, Factor V, and Factor IX activity, as well as Hcy concentration; these values suggest the presence of hypercoagulable states in patients, which can cause thrombotic events. On the other hand, significant differences were also found in the neutrophil–lymphocyte ratio and systemic immune-inflammation index, showing major inflammation states in the patient group. Moreover, statistically significant differences were found in the IL-23R rs1569922 polymorphism; it was found that carriers of the T/T and C/T genotypes have an increased risk of developing LCPD. Conclusions: Our results show greater hemostatic activity and inflammation in the group of patients included in this study, supporting various theories previously proposed. Therefore, we believe that LCPD is a multifactorial condition in which hemostatic, inflammatory, and genetic factors play a central and triggering role in the disease. Full article

Background: Animal-assisted activities (AAA) are participative interventions, designed to lower hospitalization-related stress and anxiety, enhance communicative readiness, improve quality of life and encourage human–animal interaction. The aim of the present study was to evaluate AAA effects in the context of intensive rehabilitation of patients with spinal cord injury (SCI), traumatic brain injury (TBI), stroke. Methods: AAA in this study were structured by a local specialized association, for small groups of patients (5/7 a time), biweekly; each session lasted 60 min. Each patient participated in 10 sessions of AAA. Evaluation rating scales were administered at T0 (before the first session) and T1 (after the last session, five weeks later) as follows: Neurobehavioral Rating Scale (NRS) in case of patient with stroke/TBI without disorder of consciousness; Hospital Anxiety and Depression Scale (HADS) for SCI patients. Results: A total of 50 patients concluded the study. NRS score for joined TBI and stroke populations varied from a T0 mean value of 32.34 [C.I. 26.83–37.35] to 17.21 [C.I. 12.66–21.76] (46.7%); this difference proved to be statistically significant (p = 0.000). Stroke patients had a 57.6% NRS lowering by mean 28.10 [C.I. 20.55–35.65] points to 12 [C.I. 6.6–17.36], which was significant (p = 0.000); similarly, TBI patients showed a mean decrease of 35.8% points from the initial 41.6 points [C.I. 37.29–45.93] to 26.76 [C.I. 21.94–31.59] (p = 0.002). As for HADS scores a smaller improvement was found in the cohort of SCI patients: anxiety registered a 1 mean point decrease at T1 (21.5%), from the initial 6.5 points [C.I. 3.80–9.34] to 5.1 ones [C.I. 3.17–7.11]. This variation was near the threshold of significance (p = 0.05). Depression domain, instead, improved by 2.35 mean points (37%), from the 6.35 initial points [C.I. 3.45–9.26] to the final 4 [C.I. 2.15–5.98] with reaching of a significant p value (p = 0.03). ANCOVA did not confirm this last value and showed no influence of age and gender on outcome variations. Discussion: AAA showed preliminary evidence to decrease neurobehavioral disorders in patients with high-complexity neurological diseases, particularly stroke and TBI. The role of AAA in SCI patients remains unclear. Future studies should address confounders’ role for these populations, particularly severity of disease. Furthermore, AAA interventions will have to be studied on larger samples, deepening the exact phase to introduce AAA for neurological patients. Lastly, qualitative studies are needed to explore patients’ lived experiences. Full article

Food waste (FW) sourced from treatment facilities is predominantly in solid form, with low water content and high variations in organic content. High organic content in FW is ideal in anaerobic digestion for bioenergy applications, but proper monitoring during start-up operations should be employed to avoid imbalance in the acidogenic/methanogenic population due to volatile fatty acid (VFA) accumulation in the system. The seed inoculum (5 L) in each horizontal anaerobic reactor (HAR) was fed with food waste without effluent flow (filling-up phase) until it reached the final working volume of 10 L (continuous phase). The pH, alkalinity, chemical oxygen demand (COD), VFA, biogas production, methane concentration, and microbial community dynamics were set as stability indicators during reactor operation. The results revealed that introducing fluctuations in FW concentrations does not negatively affect the biogas production (1.7 ± 0.2 L/L_R/d) and methane concentration (59.0 ± 2.5%). Acclimatization of the methanogenic and bacterial population was also observed. This study aimed to evaluate the influence of fluctuating FW concentrations on the process performance of horizontal anaerobic reactors, focusing on process stability, microbial dynamics, and biogas output during filling-up and continuous phases. Full article

Accurate delineation of fields is difficult in fragmented landscapes where single-date images provide no seasonal cues and supervised models require labels. We propose a method that explicitly represents phenology to improve zero-shot delineation. Using 22 cloud-free PlanetScope scenes over a 5 × 5 km area, a single harmonic model is fitted to the NDVI per pixel to obtain the phase, amplitude and mean. These values are then mapped into cylindrical colour spaces (Hue–Saturation–Value, Hue–Whiteness–Blackness, Luminance-Chroma-Hue). The resulting recoloured composites are segmented using the Segment Anything Model (SAM), without fine-tuning. The results are evaluated object-wise, object-wise grouped by area size, and pixel-wise. Pixel-wise evaluation achieved up to F1 = 0.898, and a mean Intersection-over-Union (mIoU) of 0.815, while object-wise performance reached F1 = 0.610. HSV achieved the strongest area match, while HWB produced the fewest fragments. The ordinal time-of-day basis provided better parcel separability than the annual radian adjustment. The main errors were over-segmentation and fragmentation. As the parcel size increased, the IoU increased, but the precision decreased. It is concluded that recolouring using harmonic NDVI time series is a simple, scalable, and interpretable basis for field delineation that can be easily improved. Full article

This study presents an electro-thermal analysis of high-power lithium-ion battery modules for urban air mobility (UAM) applications, focusing on assessing the operational impact of installing a thermal barrier pad (TBP)—designed for thermal runaway delay—to ensure that the module maintains acceptable performance during normal operations. An integrated electro-thermal simulation model was developed and validated through single-cell experiments under step-load conditions, showing good agreement with measured voltage and temperature. In the baseline module without a TBP, higher discharge rates resulted in increased heat generation and cell temperatures, with approximately 42.5% of the electrical output dissipated as heat under the 5C condition. When the TBP was applied, the cooling performance of the heat sink decreased, leading to higher module temperatures and increased temperature differences between the cell and the heat sink, particularly as the TBP thickness increased. A simplified UAM flight scenario was simulated to evaluate temperature behavior throughout various operating phases. For the 1.5 mm TBP model, the maximum temperature (75.7 °C) remained within the design limit (80 °C). However, increasing the maximum take-off discharge rate to 6C or higher caused the module to reach its thermal limit or cut-off voltage before mission completion. These results indicate that TBP installation can be applied without unacceptable performance degradation under normal operation, provided that its thickness is optimized by considering cooling performance, thermal safety, and weight/volume constraints in UAM applications. Full article

Agronomic biofortification strategies have been used to increase selenium (Se) concentrations in edible parts, with broccoli cultivation showing high potential. Recent studies have demonstrated that prior application of selected elements during the seedling phase (priming) can enhance agronomic biofortification when this element is applied during the adult phase; however, no such effect has yet been reported for Se. Additionally, Se concentration in broccoli florets may be affected by post-harvest processing, thus determining losses is essential in the agronomic biofortification process. This study aimed to determine whether seedling production with priming using selenium (Se) could enhance different agronomic biofortification strategies for Se, and to evaluate the effect of post-processing on the Se concentration in broccoli. Seedlings were produced with and without priming (75 mg L⁻¹ of Se), and different application methods (soil and foliar), sources, and doses of Se were tested on Se concentration in broccoli florets. Foliar application strategies for Se were more effective than soil application for producing Se-biofortified broccoli. Seedlings produced and subjected to Se application to promote the priming effect enhanced Se absorption and increased Se concentration in broccoli florets. However, the highest Se absorption with a dry mass concentration exceeding 18 mg kg⁻¹ reduced broccoli production, except for Se applied via multi-nutrient fertilizer. Foliar fertilization strategies using 50 g of Se ha⁻¹ via multi-nutrient fertilizer, Se + organic compounds, and sodium selenate, along with the use of seedlings produced with priming and the application of 50 g of Se ha⁻¹ via multi-nutrient fertilizer using seedlings produced without priming, can provide Se amounts reaching the human dietary requirement of 60–70 µg day⁻¹, based on the adequate daily consumption of broccoli (40 g of broccoli). Different processing stages do not cause significant losses of Se in biofortified florets. Therefore, it is concluded that seedlings produced with priming combined with foliar Se applications are effective strategies for promoting agronomic biofortification of Se in broccoli florets for the human diet. Full article

This study reports a dual green strategy for obtaining and stabilizing phytocomplexes from Sango radish and kale microgreens. Phytochemicals were isolated through supramolecular extraction, which generated an upper amphiphilic phase and a lower aqueous phase, enabling the recovery of both hydrophilic and lipophilic molecules without toxic solvents. The resulting phytocomplexes were encapsulated in nutriosomes, phospholipid vesicles enriched with the soluble dextrin Nutriose^® FM06, and compared with conventional liposomes. The vesicles displayed mean diameters ≤ 110 nm, polydispersity indices < 0.11, and zeta potentials around −40 mV. Retention of antioxidant activity reached up to 99%. Freeze-dried formulations maintained acceptable physicochemical properties and microbiological safety, while storage studies confirmed stability over six months. In vitro release tests showed a gradual release of phenolics and carotenoids, and simulated digestion experiments indicated that nutriosomes preserved up to 20% more antioxidant capacity than liposomes in the intestinal phase. These results demonstrate an environmentally responsible strategy to prepare phytocomplex-rich vesicles with improved stability and bioaccessibility. Further biological and in vivo studies are needed to substantiate potential nutritional or health-related benefits. Full article