Search Results (3,429)

Atomic magnetometers (AMs), recognized for their ultra-high magnetic sensitivity, demand highly uniform pump light fields to maximize measurement accuracy. In this paper, a phase modulation-based method using convolutional neural networks (CNN) and the Gerchberg–Saxton (GS) algorithm is proposed to generate the pumping light field, and the model was trained using a supervised learning approach with a custom dataset. The specific training settings are as follows: the backpropagation algorithm was adopted as the training algorithm, and the Adam optimization method was used for network training, with a learning rate of 0.001 and a total of 100 training epochs, utilizing a liquid crystal spatial light modulator (LCSLM) to regulate the light field phase distribution dynamically. By transforming Gaussian beams into flat-top beams, the method significantly enhances polarization uniformity within vapor cells, leading to improved magnetometric sensitivity. The proposed hybrid algorithm reduces the mean square error from 35% to 19% and peak non-uniformity from 21% to 7.6%. A reflective LCSLM-based optical setup is implemented to produce circular and square flat-top beams with a measured non-uniformity of 5.1%, resulting in an enhancement of magnetic sensitivity from $14.04\mathrm{fT}/{\mathrm{Hz}}^{1/2}$ to $7.80\mathrm{fT}/{\mathrm{Hz}}^{1/2}$. Full article

Background: Canine cranial cruciate ligament (CCL) rupture is a common orthopedic condition leading to stifle joint dysfunction, discomfort, and reduced mobility. Diagnosis typically involves radiography, computed tomography (CT), and magnetic resonance imaging (MRI). In this study, we conducted a retrospective analysis to evaluate the use of infrared thermography in assessing local temperature and thermal patterns in dogs with acute-onset lameness due to CCL rupture compared to those with intact ligaments. Methods: The study involved 12 dogs with cranial cruciate ligament rupture and nine dogs with intact ligaments. The stifle area of all dogs was clipped and scanned using a FLIR E50 thermographic camera. Two regions of interest (ROI), designated El1 and Bx1, were analyzed with FLIR Tools software 5.X by comparing the average of the maximum and of the mean temperature values between the two groups. Results: Thermal imaging revealed differences between the two groups of dogs, which were further supported by significantly higher temperatures in the El1 (lateral aspect of the stifle joint) and Bx1 (cranial aspect of the stifle joint) areas in the study group compared to the control group using a comparative analysis—two-sample t-test. In the El1 area, the study group showed a temperature increase of 1.8 °C compared to the control group, while in the Bx1 area, the difference was 1.76 °C. Conclusions: Infrared thermography shows potential to differentiate dogs with acute-onset lameness due to CCL rupture from dogs with intact ligaments, but further studies are needed to assess its accuracy in distinguishing it from other stifle pathologies. Full article

Lignin used in this work was isolated from sapele (Entandrophragma cylindricum) wood through a hybrid pulping process using soda/ethanol as pulping liquor and denoted soda-oxyethylated lignin (SOL). SOL was mixed with a polyethylene glycol (PEG)–glycerol mixture (80/20 v/v) as liquefying solvent with 98% wt. sulfur acid as catalyst, and the mixture was taken to boil at 140 °C for 2, 2.5, and 3 h. Three bio-polyols LBP1, LBP2, and LBP3 were obtained, and each of them exhibited a high proportion of -OH groups. Lignin-based polyurethane foams (LBPUFs) were prepared using the bio-polyols obtained with a toluene diisocyanate (TDI) prepolymer by the one-shot method. Gel permeation chromatography (GPC), Fourier transform infrared spectroscopy (FTIR), and carbon-13 nuclear magnetic resonance spectroscopy (¹³C NMR) were used characterize lignin in order to determine viscosity, yield, and composition and to characterize their structure. The PEG-400–glycerol mixture was found to react with the lignin bio-polyols’ phenolic -OHs. The bio-polyols’ viscosity was found to increase as the liquefaction temperature increased, while simultaneously their molecular weights decreased. All the NCO groups were eliminated from the samples, which had high thermal stability as the liquefaction temperature increased, leading to a decrease in cell size, density, and crystallinity and an improvement in mechanical performance. Based on these properties, especially the presence of some aromatic rings in the bio-polyols, the foams produced can be useful in automotive applications and for floor carpets. Full article

Stroke is a significant global health concern characterized by the abrupt disruption of cerebral blood flow, leading to neurological impairment. Accurate and timely diagnosis—enabled by imaging modalities such as computed tomography (CT) and magnetic resonance imaging (MRI)—is essential for differentiating stroke types and initiating interventions like thrombolysis, thrombectomy, or surgical management. In parallel, recent advancements in wearable technology, particularly smart clothing, offer new opportunities for stroke prevention, real-time monitoring, and rehabilitation. These garments integrate various sensors, including electrocardiogram (ECG) electrodes, electroencephalography (EEG) caps, electromyography (EMG) sensors, and motion or pressure sensors, to continuously track physiological and functional parameters. For example, ECG shirts monitor cardiac rhythm to detect atrial fibrillation, smart socks assess gait asymmetry for early mobility decline, and EEG caps provide data on neurocognitive recovery during rehabilitation. These technologies support personalized care across the stroke continuum, from early risk detection and acute event monitoring to long-term recovery. Integration with AI-driven analytics further enhances diagnostic accuracy and therapy optimization. This narrative review explores the application of smart clothing in conjunction with traditional imaging to improve stroke management and patient outcomes through a more proactive, connected, and patient-centered approach. Full article

Breast cancer is the leading cause of cancer-related mortality among women world-wide, and early screening is critical for improving patient survival. Medical imaging plays a central role in breast cancer screening, diagnosis, and treatment monitoring. However, conventional imaging modalities—including mammography, ultrasound, and magnetic resonance imaging—face limitations such as low diagnostic specificity, relatively slow imaging speed, ionizing radiation exposure, and dependence on exogenous contrast agents. Photoacoustic imaging (PAI), a novel hybrid imaging technique that combines optical contrast with ultrasonic spatial resolution, has shown great promise in addressing these challenges. By revealing anatomical, functional, and molecular features of the breast tumor microenvironment, PAI offers high spatial resolution, rapid imaging, and minimal operator dependence. This review outlines the fundamental principles of PAI and systematically examines recent advances in its application to breast cancer screening, diagnosis, and therapeutic evaluation. Furthermore, we discuss the translational potential of PAI as an emerging breast imaging modality, complementing existing clinical techniques. Full article

Herein, the energy dispersion relationship and the density of states of triangular and rectangular moiré patterns are investigated using a tight binding model. Their characteristics of Hofstadter butterflies under different magnetic fields are also examined. The results indicate that, by analyzing different moiré superlattices, Hofstadter butterflies arising from different moiré pattern structures are obtained, exhibiting considerable fractal characteristics and self-similarities. Moreover, it is also observed that under an alternating magnetic field, the redistribution of electronic states leads to a significant change in the density of states curve, and the Van Hove peak changes with the increase in magnetic field intensity. This study enriches the understanding of the electronic behavior of moiré systems, but it also provides multiple potential application directions for future technological development. Full article

The growing need for high-efficiency and reliable propulsion systems in naval applications, particularly within the evolving landscape of submarine warfare, has led to an increased interest in multiphase Permanent Magnet AC motors. This study presents a modelling and simulation approach for a 3MW, seventeen-phase Permanent Magnet AC motor designed for submarine propulsion, integrating an AI-based drive control system. Despite the advantages of multiphase motors, such as higher power density and enhanced fault tolerance, significant challenges remain in achieving precise torque and variable speed, especially for externally mounted motors operating under deep-sea conditions. Existing control strategies often struggle with the inherent nonlinearities, unmodelled dynamics, and extreme environmental variations (e.g., pressure, temperature affecting oil viscosity and motor parameters) characteristic of such demanding deep-sea applications, leading to suboptimal performance and compromised reliability. Addressing this gap, this research investigates advanced control methodologies to enhance the performance of such motors. A MATLAB/Simulink framework was developed to model the motor, whose drive system leverages an AI-optimised dual fuzzy-PID controller refined using the Harmony Search Algorithm. Additionally, a combination of Indirect Field-Oriented Control (IFOC) and Space Vector PWM strategies are implemented to optimise inverter switching sequences for precise output modulation. Simulation results demonstrate significant improvements in torque response and control accuracy, validating the efficacy of the proposed system. The results highlight the role of AI-based propulsion systems in revolutionising submarine manoeuvrability and energy efficiency. In particular, during a test case involving a speed transition from 75 RPM to 900 RPM, the proposed AI-based controller achieves a near-zero overshoot compared to an initial control scheme that exhibits 75.89% overshoot. Full article

Electromagnetic pneumatic microvalves, widely used in knitting machines, typically operate based on a spring-return mechanism. When the coil is energized, the electromagnetic force overcomes the spring force to attract the armature, opening the valve. Upon de-energization, the armature returns to its original position under the restoring force of the spring, closing the valve. However, most existing electromagnetic microvalves adopt a radially asymmetric magnetic yoke design, which generates additional radial forces during operation, leading to armature misalignment or even sticking. Additionally, the inductance effect of the coil causes a significant delay in the armature release response, making it difficult to meet the knitting machine’s requirements for rapid response and high reliability. To address these issues, this paper proposes an improved electromagnetic microvalve design. First, the magnetic yoke structure is modified to be radially symmetric, eliminating unnecessary radial forces and preventing armature sticking during operation. Second, a permanent magnet assist mechanism is introduced at the armature release end to enhance release speed and reduce delays caused by the inductance effect. The effectiveness of the proposed design is validated through electromagnetic numerical simulations, and a multi-objective genetic algorithm is further employed to optimize the geometric dimensions of the electromagnet. The optimization results indicate that, while maintaining the fundamental power supply principle of conventional designs, the new microvalve structure achieves a pull-in time comparable to traditional designs during engagement but significantly reduces the release response time by approximately 80.2%, effectively preventing armature sticking due to radial forces. The findings of this study provide a feasible and efficient technical solution for the design of electromagnetic microvalves in textile machinery applications. Full article

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 high incidence of melanoma leading to a poor prognosis rate endorses the development of alternative and innovative approaches in the treatment of melanoma. Therefore, the present study aims to develop and characterize, in terms of physicochemical features and biological impact, an aqueous suspension of magnetite (Fe₃O₄) coated with β-cyclodextrin (Fe₃O₄@β-CD) as a potential innovative alternative nanosystem for melanoma therapy. The nanosystem exhibited physicochemical characteristics suitable for biological applications, revealing a successful complexation of Fe₃O₄ NPs with β-CD and an average size of 18.1 ± 2.1 nm. In addition, the in vitro evaluations revealed that the newly developed nanosystem presented high biocompatibility on a human keratinocyte (HaCaT) monolayer and selective antiproliferative activity on amelanotic human melanoma (A375) cells, inducing early apoptosis features when concentrations of 10, 15, and 20 μg/mL were employed for 48 h and 72 h. Collectively, the Fe₃O₄@β-CD nanosystem reveals promising features for an adjuvant approach in melanoma treatment, mainly due to its β-cyclodextrin coating, thus endorsing a potential co-loading of therapeutic drugs. Furthermore, the intrinsic magnetic core of Fe₃O₄ NPs supports the magnetically based cancer treatment strategies. Full article

Background: Pancreatic cystic lesions (PCLs), including intraductal papillary mucinous neoplasms (IPMNs) and mucinous cystic neoplasms (MCNs), pose a diagnostic challenge due to their variable malignant potential. Current guidelines, such as Fukuoka and American Gastroenterological Association (AGA), have moderate predictive accuracy and may lead to overtreatment or missed malignancies. Artificial intelligence (AI), incorporating machine learning (ML) and deep learning (DL), offers the potential to improve risk stratification, diagnosis, and management of PCLs by integrating clinical, radiological, and molecular data. This is the first systematic review to evaluate the application, performance, and clinical utility of AI models in the diagnosis, classification, prognosis, and management of pancreatic cysts. Methods: A systematic review was conducted in accordance with PRISMA guidelines and registered on PROSPERO (CRD420251008593). Databases searched included PubMed, EMBASE, Scopus, and Cochrane Library up to March 2025. The inclusion criteria encompassed original studies employing AI, ML, or DL in human subjects with pancreatic cysts, evaluating diagnostic, classification, or prognostic outcomes. Data were extracted on the study design, imaging modality, model type, sample size, performance metrics (accuracy, sensitivity, specificity, and area under the curve (AUC)), and validation methods. Study quality and bias were assessed using the PROBAST and adherence to TRIPOD reporting guidelines. Results: From 847 records, 31 studies met the inclusion criteria. Most were retrospective observational (n = 27, 87%) and focused on preoperative diagnostic applications (n = 30, 97%), with only one addressing prognosis. Imaging modalities included Computed Tomography (CT) (48%), endoscopic ultrasound (EUS) (26%), and Magnetic Resonance Imaging (MRI) (9.7%). Neural networks, particularly convolutional neural networks (CNNs), were the most common AI models (n = 16), followed by logistic regression (n = 4) and support vector machines (n = 3). The median reported AUC across studies was 0.912, with 55% of models achieving AUC ≥ 0.80. The models outperformed clinicians or existing guidelines in 11 studies. IPMN stratification and subtype classification were common focuses, with CNN-based EUS models achieving accuracies of up to 99.6%. Only 10 studies (32%) performed external validation. The risk of bias was high in 93.5% of studies, and TRIPOD adherence averaged 48%. Conclusions: AI demonstrates strong potential in improving the diagnosis and risk stratification of pancreatic cysts, with several models outperforming current clinical guidelines and human readers. However, widespread clinical adoption is hindered by high risk of bias, lack of external validation, and limited interpretability of complex models. Future work should prioritise multicentre prospective studies, standardised model reporting, and development of interpretable, externally validated tools to support clinical integration. Full article

Quantum sensing based on NV-centers in diamonds has been demonstrated many times in multiple publications. The majority of publications use lasers in free space or lasers with fiber optics, expensive optical components such as dichroic mirrors, or beam splitters with dichroic filters and expensive detectors, such as Avalanche photodiodes or single photon detectors, overall, leading to custom and expensive setups. In order to provide an inexpensive NV-based magnetometer setup for educational use in schools, to teach the three topics, fluorescence, optically detected magnetic resonance, and Zeeman splitting, inexpensive, miniaturized, off-the-shelf components with high reliability have to be used. The cheaper such a setup, the more setups a school can afford. Hence, in this work, we investigated LEDs as light sources, considered different diamonds for our setup, tested different color filters, proposed an inexpensive microwave resonator, and used a cheap photodiode with an appropriate transimpedance amplifier as the basis for our quantum magnetometer. As a result, we identified cheap and functional components and present a setup and show that it can demonstrate the three topics mentioned at a hardware cost <EUR 100. Full article

Measuring weak magnetic fields proposes significant challenges to the sensing capabilities of magnetic field sensors. The magnetic field detection capacity of tunnel magnetoresistance (TMR) sensors is often insufficient for such applications, necessitating targeted optimization strategies to improve their performance in weak-field measurements. Utilizing magnetic flux concentrators (MFCs) offers an effective approach to enhance TMR sensitivity. In this study, the finite element method was employed to analyze the effects of different MFC geometric structures on the uniformity of the magnetic field in the air gap and the magnetic circuit gain (MCG). It was determined that the MCG of the MFC is not directly related to the absolute values of its parameters but rather to their ratios. Simulation analyses evaluated the impact of these parameter ratios on both the MCG and its spatial distribution uniformity, leading to the formulation of MFC design optimization principles. Building on these simulation-derived principles, several MFCs were fabricated using the 1J85 material, and an experimental platform was established to validate the simulation findings. The fabricated MFCs achieved an MCG of 7.325 times. Based on the previously developed TMR devices, a detection sensitivity of 2.46 nT/$\sqrt{\mathrm{Hz}}$ @1Hz was obtained. By optimizing parameter configurations, this work provides theoretical guidance for further enhancing the performance of TMR sensors in magnetic field measurements. Full article

Background: Hypoxic–ischemic encephalopathy (HIE) is a leading cause of severe neurological impairments in childhood. Therapeutic hypothermia (TH) is both safe and effective in neonates born at ≥36 weeks gestation with moderate to severe HIE. We aimed to evaluate short-term outcomes—including brain injury detected on magnetic resonance imaging (MRI)—in infants born at 34–35 weeks of gestation drawing on our clinical experience with neonates under 36 weeks of gestational age (GA). Methods: In this retrospective cohort study, 20 preterm infants with a GA of 34 to 35 weeks and a matched cohort of 80 infants with a GA of ≥36 weeks who were diagnosed with moderate to severe HIE and underwent TH were included. Infants were matched in a 1:4 ratio based on the worst base deficit in blood gas and sex. Maternal and neonatal characteristics, brain MRI findings and short term outcomes were compared. Results: Infants with a GA of 34–35 weeks had a lower birth weight and a higher rate of caesarean delivery (both p < 0.001). Apgar scores, sex, intubation rate in delivery room, blood gas pH, base deficit and lactate were comparable between the groups. Compared to infants born at ≥36 weeks of GA, preterm neonates were more likely to receive inotropes, had a longer time to achieve full enteral feeding, and experienced a longer hospital stay. The mortality rate was 10% in the 34–35 weeks GA group. Neuroimaging revealed injury in 66.7% of infants born at 34–35 weeks of gestation and in 58.8% of those born at ≥36 weeks (p = 0.56). Injury was observed across multiple brain regions, with white matter being the most frequently affected in the 34–35 weeks GA group. Thalamic and cerebellar abnormal signal intensity or diffusion restriction, punctate white matter lesions, and diffusion restriction in the corpus callosum and optic radiations were more frequently detected in infants born at 34–35 weeks of gestation. Conclusions: Our study contributes to the growing body of literature suggesting that TH may be feasible and tolerated in late preterm infants. Larger randomized controlled trials focused on this vulnerable population are necessary to establish clear guidelines regarding the safety and efficacy of TH in late preterm infants. Full article

4,4’-Methylenebis(N,N-diglycidylaniline) (AG80), as a high-performance thermosetting material, holds significant application value due to the enhancement of its strength, toughness, and thermal stability. However, conventional toughening methods often lead to a decrease in material strength, limiting their application. Modification of AG80 epoxy resin was performed using hydroxy-terminated polyphenylpropylsiloxane (Z-6018) and a self-synthesized epoxy compatibilizer (P/E30) to regulate the phase structure of the modified resin, achieving a synergistic enhancement in both strength and toughness. The modified resin was characterized by Fourier transform infrared analysis (FTIR), proton nuclear magnetic resonance (¹H NMR) spectroscopy, silicon-29 nuclear magnetic resonance (²⁹Si NMR) spectroscopy, and epoxy value titration. It was found that the phase structure of the modified resin significantly affects mechanical properties. Thus, P/E30 was introduced to regulate the phase structure, achieving enhanced toughness and strength. At 20 wt.% P/E30 addition, the tensile strength, impact strength, and fracture toughness increased by 50.89%, 454.79%, and 152.43%, respectively, compared to AG80. Scanning electron microscopy (SEM) and atomic force microscopy (AFM) analyses indicate that P/E30 regulates the silicon-rich spherical phase and interfacial compatibility, establishing a bicontinuous structure within the spherical phase, which is crucial for excellent mechanical properties. Additionally, the introduction of Z-6018 enhances the thermal stability of the resin. Full article