Search Results (1,409)

Model-based sensorless control of interior permanent-magnet synchronous motors (IPMSMs) typically employs an estimation observer with embedded position information, followed by a position extraction process. Although a type-2 phase-locked loop (PLL) is widely adopted for position and speed extraction, it suffers from steady-state tracking errors under variable-speed operation, leading to torque bias in IPMSM torque control. To mitigate this issue, this paper first proposes an adaptive bandpass full-order observer in the stationary reference frame. Subsequently, a Kalman filter (KF)-based compensation strategy is introduced for the PLL to eliminate tracking errors while maintaining system stability. Experimental validation on a 300 kW platform confirms the effectiveness of the proposed sensorless torque control algorithm, demonstrating significant reductions in position error and torque fluctuations during acceleration and deceleration. Full article

The rise in multidrug-resistant bacterial strains and persistent infections such as Lyme disease caused by Borrelia burgdorferi highlights the need for novel antimicrobial agents. The present study explores the antioxidant, antibacterial, and cytotoxic properties of extracts from submerged mycelial biomass of Fomitopsis pinicola, cultivated in synthetic and lignocellulosic media. Four extracts were obtained using hot water and 80% ethanol. The provided analysis of extracts confirmed the presence of various bioactive compounds, including flavonoids, alkaloids, and polyphenols. All extracts showed dose-dependent antioxidant activity (IC₅₀: 1.9–6.7 mg/mL). Antibacterial tests revealed that Klebsiella pneumoniae was most sensitive, with the L2 extract producing the largest inhibition zone (15.33 ± 0.47 mm), while the strongest bactericidal effect was observed against Acinetobacter baumannii (MBC as low as 0.5 mg/mL for L1). Notably, all extracts significantly reduced the viability of stationary-phase B. burgdorferi cells, with L2 reducing viability to 42 ± 2% at 5 mg/mL, and decreased biofilm mass, especially with S2. Cytotoxicity assays showed minimal effects on NIH 3T3 cells, with slight toxicity in HEK 293 cells for S2 and L1. These results suggest that F. pinicola extracts, particularly ethanolic L2 and S2, may offer promising natural antimicrobial and antioxidant agents for managing resistant infections. Full article

Chiral liquid chromatography (cLC) using chiral stationary phases (CSPs) has become a crucial technique for separating enantiomers. Understanding enantiomeric discrimination is essential for improving chromatographic conditions and elucidating chiral molecular recognition; the computational methods are extremely helpful for this. To assess the relevance of the association of these two approaches and to analyze the current trends, in this review, a systematic analysis of the scientific literature was performed, covering recently published works (from 2015 to January 2025) on enantioseparation by cLC using CSPs and computational studies. CSPs based on polysaccharides and Pirkle-type were the most described (accounting for 52% and 14% of the studies, respectively). Regarding the computational methods, molecular docking and molecular dynamics (MD) were the most reported (accounting for 50% and 25% of the studies, respectively). In the articles surveyed, a significant growth in research concerning both cLC enantioseparation and computational studies is evident, emphasizing the benefit of the synergy between these two approaches. Full article

Magnetotelluric (MT) sounding is a crucial technique in mineral exploration. However, MT data are highly susceptible to various types of noise. Traditional data processing methods, which rely on the assumption of signal stationarity, often result in severe distortion when suppressing non-stationary noise. In this study, we propose a novel, adaptive, and less parameter-dependent signal decomposition method for MT signal denoising, based on time–frequency domain analysis and the application of modal decomposition. The method uses Variational Mode Decomposition (VMD) to adaptively decompose the MT signal into several intrinsic mode functions (IMFs), obtaining the instantaneous time–frequency energy distribution of the signal. Subsequently, robust statistical methods are introduced to extract the independent components of each IMF, thereby identifying signal and noise components within the decomposition results. Synthetic data experiments show that our method accurately separates high-amplitude non-stationary interference. Furthermore, it maintains stable decomposition results under various parameter settings, exhibiting strong robustness and low parameter dependency. When applied to field MT data, the method effectively filters out non-stationary noise, leading to significant improvements in both apparent resistivity and phase curves, indicating its practical value in mineral exploration. Full article

In this study, a current vector pattern is analyzed for inter-turn fault (ITF) diagnosis of induction machines (IMs), and an ITF diagnosis algorithm is proposed. When an ITF occurs in IMs, a negative-sequence current is generated due to fault resistance, even though a positive-sequence voltage is applied to IMs. Based on the mathematical model of IMs with an ITF, the current vector patterns in the stationary coordinate frame are analyzed. The superposition of positive- and negative-sequence components results in an elliptical current vector trajectory, and its orientation varies depending on the fault conditions. The co-simulation using finite element analysis and circuit simulation is implemented to analyze the current vector pattern of IMs with an ITF. The ITF diagnosis is proposed based on the current vector pattern. A 12 kW, four-pole, three-phase IM and terminal box, which was used to implement an ITF, is manufactured, and an experiment setup is established to verify the ITF algorithm. The effectiveness of the proposed ITF algorithm is validated through experimental verification of the manufactured IM and terminal box. Full article

The separation of three proton pump inhibitors (omeprazole, lansoprazole, and rabeprazole) as exemplified molecules containing chiral sulfoxide groups was investigated in polar organic liquid chromatographic mode on seven different polysaccharide stationary phases (Chiralcel OD and OJ; Chiralpak AD, AS, and IA; Lux Cellulose-2 and -4). Different alcohols, such as methanol, ethanol, 1-propanol, 2-propanol, and their combinations, were used as eluents. After method optimization, semi-preparative enantioseparation was successfully applied for the three proton pump inhibitors to collect the individual enantiomers. A detailed investigation was conducted into elution order reversal, thermodynamic parameters, the effect of eluent mixtures, and the hysteresis of retention time and selectivity. Using Chiralpak AS, containing the amylose tris[(S)-α-methylbenzylcarbamate] chiral selector, the separation of the investigated enantiomers was achieved in all four neat eluents, with methanol providing the best results. In many cases, a reversal of the enantiomer elution order was observed. In addition to chiral-selector-dependent reversal, eluent-dependent reversal was also observed. Notably, even replacing methanol with ethanol altered the enantiomer elution order. Both enthalpy- and entropy-controlled enantioseparation were also observed in several cases; however, temperature-dependent elution order reversal was not. The hysteresis of retention and selectivity was further investigated on amylose-type columns in methanol–2-propanol and methanol–ethanol eluent mixtures. The phenomenon was observed on all amylose columns regardless of the eluent mixtures employed. Hystereticity ratios were calculated and used to compare the hysteresis behaviors of different systems. Multivariate statistical analysis revealed that Chiralpak AS exhibited the most distinct enantioselective behavior among the tested columns, likely due to the absence of a direct connection between the carbamate moiety and the aromatic substituent. The present study aided in understanding the mechanisms leading to enantiomer recognition, which is crucial for developing new chiral stationary phases and chiral HPLC method development in general. Full article

Carrier-based aircraft recovery is a critical and challenging phase in maritime operations due to the turbulent airwake generated by aircraft carriers, which significantly increases the workload of flight control systems and pilots. This study investigates the airwake effects of an aircraft carrier under varying wind direction conditions. A high-fidelity mathematical model combining delayed detached-eddy simulation (DDES) with the overset grid method was developed to analyze key flow characteristics, including upwash, downwash, and lateral recirculation. The model ensures precise control of aircraft speed and trajectory during landing while maintaining numerical stability through rigorous mesh optimization. The results indicate that the minimum lift occurs in the downwash region aft of the deck, marking it as the most hazardous zone during landing. Aircraft above the deck are primarily influenced by ground effects, causing a sudden increase in lift that complicates arresting wire engagement. Additionally, the side force on the aircraft undergoes an abrupt reversal during the approach phase. The dual overset mesh technique effectively captures the coupled motion of the hull and aircraft, revealing higher turbulence intensity along the glideslope and a wider range of lift fluctuations compared to stationary hull conditions. These findings provide valuable insights for optimizing carrier-based aircraft recovery procedures, offering more realistic data for simulation training and enhancing pilot preparedness for airwake-induced disturbances. Full article

The presence of polycyclic aromatic hydrocarbons (PAHs) in edible oil has a serious effect on human health and may potentially induce cancer. This study combined thin-layer chromatography and surface-enhanced Raman spectroscopy (TLC-SERS) to rapidly and quantitatively detect PAHs in culinary oil. Machine learning using the principle component analysis-back propagation neural network (PCA-BP) was integrated with TLC-SERS for the detection of PAHs. Ag nanoparticles on diatomite (diatomite/Ag) TLC-SERS substrate were prepared via an in situ growth process and employed as a stationary phase in the TLC channel. The analyte sample was dropped onto the TLC channel for separation and detection. The diatomite/Ag TLC channel demonstrated excellent separation capability and superior SERS performance and successfully detected PAHs from edible oil at a sensitivity of 0.1 ppm. The PCA-BP quantitative analysis model demonstrated outstanding prediction performance. This work demonstrates that the combination of TLC-SERS technology with PCA-BP is an efficient and accurate method for quantitatively detecting PAHs in edible oil, which can effectively improve the quality of food. Full article

This paper presents the results of studies on intra-annual runoff changes in the Ile River basin based on data from gauging stations up to 2021. Changes in climatic characteristics that determine runoff formation in the mountainous and foothill areas of the river catchment have led to alterations in the water regime of the watercourses. The analysis of the temporal and spatial patterns of river flow formation in the basin, as well as its distribution by seasons and months, is essential for solving applied water management problems and assessing the risks of hazardous hydrological phenomena, such as high floods and low water levels. The statistical analysis of annual and monthly river runoff fluctuations enabled the identification of relatively homogeneous estimation periods during stationary observations under varying climatic conditions. The obtained characteristics of annual and intra-annual river runoff in the Ile River basin for the modern period provide insights into changes in average monthly water discharge and, more broadly, runoff volume during different phases of the water regime. In the future, these characteristics are expected to guide the design of hydraulic structures and the rational use of surface runoff in this intensively developing region of Kazakhstan. Full article

In recent years, microalgae have gained significant biotechnological importance as a sustainable source of various metabolites of industrial interest. Among these, paramylon, a polysaccharide produced by the microalga Euglena gracilis, stands out for its diverse applications in biomedicine and pharmaceuticals. E. gracilis is an adaptable secondary eukaryote capable of growing photoautotrophically, heterotrophically and mixotrophically. During photoautotrophic growth, varying light conditions impact biomass and paramylon production. To investigate the effects of varying illumination more thoroughly, we designed and built a modular photobioreactor that allowed us to simultaneously evaluate the photoautotrophic growth of E. gracilis under twelve different light conditions: seven single-spectrum lights (ultraviolet, royal blue, blue, green, red, far-red, and infrared) and five composite-spectrum lights (3000 K, 10,000 K, and 30,000 K white lights, amber light, and “Full-spectrum” light). The 24-day growing kinetics were recorded, and the growth parameters were calculated for each light regime. Both growth curves and pigment composition present differences attributable to the light regime used for cell culture. Additionally, photosynthetic and respiratory machinery functionality were proven by oximetry. Finally, our results strongly suggest that the far-red component enhances paramylon production during the stationary phase. Full article

Electric passenger vehicles are set to dominate the European car market, driven by EU climate policies and the 2035 ban on internal combustion engine production. This study assesses the sustainability of this transition, focusing on global warming potential and Critical Raw Material (CRM) extraction throughout its life cycle. The intensive use of CRMs raises environmental, economic, social, and geopolitical concerns. These materials are scarce and are concentrated in a few politically sensitive regions, leaving the EU highly dependent on external suppliers. The extraction, transport, and refining of CRMs and battery production are high-emission processes that contribute to climate change and pose risks to ecosystems and human health. A Life Cycle Assessment (LCA) was conducted, using OpenLCA software and the Ecoinvent 3.10 database, comparing a Peugeot 308 in its diesel and electric versions. This study adopts a cradle-to-grave approach, analyzing three phases: production, utilization, and end-of-life treatment. Key indicators included Global Warming Potential (GWP100) and Abiotic Resource Depletion Potential (ADP) to assess CO₂ emissions and mineral resource consumption. Technological advancements could mitigate mineral depletion concerns. Li-ion battery recycling is still underdeveloped, but has high recovery potential, with the sector expected to expand significantly. Moreover, repurposing used Li-ion batteries for stationary energy storage in renewable energy systems can extend their lifespan by over a decade, decreasing the demand for new batteries. Such innovations underscore the potential for a more sustainable electric vehicle industry. Full article

Microalgae-based bioremediation is increasingly recognized as a sustainable, efficient, and straightforward technology. Despite this growing interest, the potential of Parachlorella hussii for metal biosorption remains underexplored. This study is the first report evaluating the metal biosorption activity in Parachlorella hussii ACOI 1508 (N9), highlighting the impact of the culture age on the monosaccharide composition and its correlation to the metal binding capacity. The capsular strain (N9) was isolated from the hypersaline ecosystem—Lake Chott Aïn El-Beida—in southeastern Algeria. Cultivated in Bold’s Basal medium, the strain produced 0.807 ± 0.059 g L⁻¹ of RPSs and 1.975 ± 0.120 g L⁻¹ of CPSs. Biochemical analysis of the extracts revealed a high total sugar content (% w/w) that ranged from 62.98 ± 4.87% to 95.60 ± 87% and a low protein content (% w/w) that ranged from 0.49 ± 0.08% to 1.35 ± 0.69%, with RPS-D7 and RPS-D14 having high molecular weight (≥2 MDa). HPLC-based monosaccharide characterization demonstrated compositional differences between the exponential and stationary phases, with rhamnose dominating (~55%) in RPS-D14 and with the presence of uronic acids comprising 7–11.3%. Metal removal efficiency was evaluated using the whole biomass in two growth phases. Copper uptake exhibited the highest capacity, reaching 18.55 ± 0.61 mg Cu g⁻¹ DW at D14, followed by zinc removal with 6.52 ± 0.61 mg Zn g⁻¹ DW. Interestingly, removal efficiencies increased to about twofold during the stationary phase, reaching 51.15 ± 1.14% for Cu, 51.08 ± 3.35% for Zn, and 36.55 ± 3.09% for Ni. The positive results obtained for copper/zinc removal highlight the biosorption potential of P. hussii, and notably, we found that the metal removal capacity significantly improved with culture age—a parameter that has been poorly investigated in prior studies. Furthermore, we observed a growth phase-dependent modulation in monosaccharide composition, which correlated with enhanced functional properties of the excreted biomolecules involved in biosorption. This metabolic adjustment suggests an adaptive response that may contribute to the species’ effectiveness in heavy metal uptake, underscoring its novelty and biotechnological relevance. Full article

As a fundamental technology in environmental perception, sound source localization (SSL) plays a critical role in public safety, marine exploration, and smart home systems. However, traditional methods such as beamforming and time-delay estimation rely on manually designed physical models and idealized assumptions, which struggle to meet practical demands in dynamic and complex scenarios. Recent advancements in deep learning have revolutionized SSL by leveraging its end-to-end feature adaptability, cross-scenario generalization capabilities, and data-driven modeling, significantly enhancing localization robustness and accuracy in challenging environments. This review systematically examines the progress of deep learning-based SSL across three critical domains: marine environments, indoor reverberant spaces, and unmanned aerial vehicle (UAV) monitoring. In marine scenarios, complex-valued convolutional networks combined with adversarial transfer learning mitigate environmental mismatch and multipath interference through phase information fusion and domain adaptation strategies. For indoor high-reverberation conditions, attention mechanisms and multimodal fusion architectures achieve precise localization under low signal-to-noise ratios by adaptively weighting critical acoustic features. In UAV surveillance, lightweight models integrated with spatiotemporal Transformers address dynamic modeling of non-stationary noise spectra and edge computing efficiency constraints. Despite these advancements, current approaches face three core challenges: the insufficient integration of physical principles, prohibitive data annotation costs, and the trade-off between real-time performance and accuracy. Future research should prioritize physics-informed modeling to embed acoustic propagation mechanisms, unsupervised domain adaptation to reduce reliance on labeled data, and sensor-algorithm co-design to optimize hardware-software synergy. These directions aim to propel SSL toward intelligent systems characterized by high precision, strong robustness, and low power consumption. This work provides both theoretical foundations and technical references for algorithm selection and practical implementation in complex real-world scenarios. Full article

Objective: This study aims to investigate the effect of shot selection on the muscle coordination characteristics during basketball shooting. Methods: A three-dimensional motion capture system, force platform, and wireless surface electromyography (sEMG) were used to simultaneously collect shooting data from 14 elite basketball players. An inverse mapping model of sEMG signals and spinal α-motor neuron pool activity was developed based on the Debra muscle segment distribution theory. Non-negative matrix factorization (NMF) and K-means clustering were used to extract muscle coordination features. Results: (1) Significant differences in spinal segment activation timing and amplitude were observed between stationary and jump shots at different distances. In close-range stationary shots, the C5-S3 segments showed higher activation during the TP phase and lower activation during the RP phase. For mid-range shots, the C6-S3 segments exhibited greater activation during the TP phase. In long-range shots, the C7-S3 segments showed higher activation during the TP phase, whereas the L3-S3 segments showed lower activation during the RP phase (p < 0.01). (2) The spatiotemporal structure of muscle coordination modules differed significantly between stationary and jump shots. In terms of spatiotemporal structure, the second and third coordination groups showed stronger activation during the RP phase (p < 0.01). Significant differences in muscle activation levels were also observed between the coordination modules within each group in the spatial structure. Conclusion: Shot selection plays a significant role in shaping neuromuscular control strategies during basketball shooting. Targeted training should focus on addressing the athlete’s specific shooting weaknesses. For stationary shots, the emphasis should be on enhancing lower limb stability, while for jump shots, attention should be directed toward improving core stability and upper limb coordination. Full article

A FEM model of Shallow Wave Equations (SWE-FEM) is studied, taking into account the variable bathymetry of semi-enclosed sea basins. The model, with a spatially varying Coriolis term, is implemented for the description of combined refraction–diffraction effects, from which the eigenperiods and eigenmodes of extended geographical sea areas are calculated by means of a low-order FEM scheme. The model is applied to the western Mediterranean basin, illustrating its versatility to easily include the effects of geographical characteristics like islands and other coastal features. The calculated resonant frequencies and modes depend on the domain size and characteristics as well as the location of the open sea boundary, and it is shown to provide results compatible with tide measurements at several stations in the coastal region of France. The calculation of the natural oscillation modes in the western Mediterranean basin, bounded by open boundaries at the Strait of Gibraltar and the Strait of Sicily, reveals a natural period of around 6 h corresponding to the quarter-diurnal tidal components, which are stationary and of roughly constant amplitude on the northern coast of the basin and on the west coast of Corsica (France). On the east coast of Corsica, on the other hand, these components are of very low amplitude and in phase opposition. The semi-diurnal tidal components observed on the same tide gauges north of the basin and west of Corsica are also quasi-stationary although they are not resonant. Resonant oscillations are also observed at lower periods, especially at a period of around 3 h at the Sète station. This period corresponds to a higher-order natural mode of the western Mediterranean basin, but this resonance seems to be essentially linked to the presence of the Gulf of Lion, whose shallowness and the width of the shelf at this point induce a resonance. Other oscillations are also observed at lower periods (T = 1.5 h at station Fos-sur-Mer, T = 45 min in the Toulon harbour station), due to more local forcing. Full article