Search Results (9,446)

Iron oxide nanoparticles (IONPs) have emerged as key materials in magnetic hyperthermia (MH), a minimally invasive cancer therapy capable of selectively inducing apoptosis, ferroptosis, and other cell death pathways while sparing surrounding healthy tissue. This review synthesizes advances in the design, functionalization, and biomedical application of magnetic nanoparticles (MNPs) for MH, highlighting strategies to optimize heating efficiency, biocompatibility, and tumor targeting. Key developments include tailoring particle size, shape, and composition; doping with metallic ions; engineering multicore nanostructures; and employing diverse surface coatings to improve colloidal stability, immune evasion, and multifunctionality. We discuss preclinical and clinical evidence for MH, its integration with chemotherapy, radiotherapy, and immunotherapy, and emerging theranostic applications enabling simultaneous imaging and therapy. Special attention is given to the role of MNPs in immunogenic cell death induction and metastasis prevention, as well as novel concepts for circulating tumor cell capture. Despite promising results in vitro and in vivo, clinical translation remains limited by insufficient tumor accumulation after systemic delivery, safety concerns, and a lack of standardized treatment protocols. Future progress will require interdisciplinary innovations in nanomaterial engineering, active targeting technologies, and real-time treatment monitoring to fully integrate MH into multimodal cancer therapy and improve patient outcomes. Full article

Effective protein immobilization is a critical step in biosensor development, as it ensures the stability, functionality, and orientation of biomolecules on the sensor surface. Here, we present a novel affinity-based terpolymer coating designed to enhance protein immobilization for biosensor applications. The novelty lies in the incorporation of nitrilotriacetic acid (NTA) ligands directly into the polymeric chains, facilitating histidine-tagged protein oriented binding through a robust metal-chelating interaction. To validate the system, magnetic microbeads coated with the polymer were tested for their ability to bind native and His-tagged proteins. The results demonstrated the superior binding capacity, enhanced stability, and reversibility of the interactions compared to traditional coatings, which immobilize proteins through nucleophile reactions with amine residues. Moreover, enzyme immobilization tests confirmed that the polymer preserves enzymatic activity, highlighting its potential for biosensor applications requiring functional biomolecules. This innovative polymeric coating offers a fast, versatile, and scalable solution for next-generation biosensor platforms, paving the way for improved sensitivity, reliability, and accessibility in diagnostic and analytical technologies. Full article

As global industry enters the digital era, automation is becoming increasingly pervasive. Due to their superior efficiency and reliability, Permanent Magnet Synchronous Motors (PMSMs) are playing an increasingly prominent role in industrial applications. Sliding Mode Control (SMC) has emerged as a modern control strategy that is widely employed not only in PMSM drive systems, but also across broader power and industrial control domains. This technique effectively mitigates key challenges associated with PMSMs, such as nonlinear behavior and susceptibility to external disturbances, thereby enhancing the precision of speed and torque regulation. This paper provides a thorough review and evaluation of recent advancements in SMC as applied to PMSM control. It outlines the fundamentals of SMC, explores various SMC-based strategies, and introduces integrated approaches that combine SMC with optimization algorithms. Furthermore, it compares these methods, identifying their respective strengths and limitations. This paper concludes by discussing current trends and potential future developments in the application of SMC for PMSM systems. Full article

Background: This narrative review summarizes recent progress in artificial intelligence (AI), especially radiomics and deep learning, for non-invasive diagnosis and molecular profiling of gliomas. Methodology: A thorough literature search was conducted on PubMed, Scopus, and Embase for studies published from January 2020 to July 2025, focusing on clinical and technical research. In key areas, these studies examine AI models’ predictive capabilities with multi-parametric Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET). Results: The domains identified in the literature include the advancement of radiomic models for tumor grading and biomarker prediction, such as Isocitrate Dehydrogenase (IDH) mutation, O6-methylguanine-dna methyltransferase (MGMT) promoter methylation, and 1p/19q codeletion. The growing use of convolutional neural networks (CNNs) and generative adversarial networks (GANs) in tumor segmentation, classification, and prognosis was also a significant topic discussed in the literature. Deep learning (DL) methods are evaluated against traditional radiomics regarding feature extraction, scalability, and robustness to imaging protocol differences across institutions. Conclusions: This review analyzes emerging efforts to combine clinical, imaging, and histology data within hybrid or transformer-based AI systems to enhance diagnostic accuracy. Significant findings include the application of DL to predict cyclin-dependent kinase inhibitor 2A/B (CDKN2A/B) deletion and chemokine CCL2 expression. These highlight the expanding capabilities of imaging-based genomic inference and the importance of clinical data in multimodal fusion. Challenges such as data harmonization, model interpretability, and external validation still need to be addressed. Full article

Rare-earth transition metal-based double perovskite ceramics E₂TMO₆ (where E = rare-earth metals, T = transition metals, and M = metal) have received impressive attention lately for cryogenic applications as a result of their intrinsic physical features such as multiferroicity, dielectric features, and adjustable magnetic transition temperature. However, determination and enhancement of magnetic transition temperature of E₂TMO₆ ceramic are subject to experimental procedures and processes with a significant degree of difficulties and cumbersomeness. This work proposes an extreme learning machine (ELM)-based intelligent method of determining magnetic transition temperature of E₂TMO₆ ceramics with activation function sigmoid (SM) and sine (SE) at varying magnetic field. The outcomes of the SE-ELM and SM-ELM models were compared with genetically optimized support vector regression (GEN-SVR) predictive models using RMSE, CC, and MAE metrics. Using the testing samples of E₂TMO₆ ceramics, SE-ELM predictive model outperforms GEN-SVR with a superiority of 6.3% (using RMSE metric) and 15.7% (using MAE metric). The SE-ELM predictive model further outperforms the SM-ELM model, with an improvement of 5.3%, using CC computed with training ceramic samples. The simplicity of the employed descriptors, coupled with the outstanding performance of the developed predictive models, would potentially strengthen E₂TMO₆ ceramics exploration for low-temperature cryogenic applications and circumvent energy challenges in different sectors. Full article

Interior Permanent Magnet Synchronous Motors (IPMSMs) are widely used in the electrification sector; however, reliance on rare-earth magnets imposes constraints stemming from supply instability and mining-related environmental impacts, raising sustainability concerns. To address these issues, this study investigates an IPMSM employing a consequent pole (CP) structure, in which one permanent magnet pole is replaced by iron. Because flux asymmetry in CP IPMSMs can cause torque ripple and associated vibration and noise, we propose an Intersect Consequent Pole (ICP) rotor geometry and evaluate it against a conventional IPMSM under identical stator conditions. The proposed ICP topology reduces permanent magnet usage and provides a rare-earth-reduced design alternative that addresses the vibration/noise trade-off, with a particular focus on electric power steering (EPS) applications. Electromagnetic characteristics and performance were analyzed using finite element analysis (FEA) and verified via FEA-based comparisons. Full article

Yield stress and thixotropy are critical rheological properties for enabling successful 3D printing of magnetic colloidal systems. However, conventional magnetic colloids, typically composed of a single dispersed phase, exhibit insufficient rheological tunability for reliable 3D printing. In this study, we developed a novel magnetic colloidal system comprising a carrier liquid, magnetic nanoparticles, and organic modified bentonite. A direct-ink-writing 3D-printing platform was specifically designed and optimized for thixotropic materials, incorporating three distinct extruder head configurations. Through an in-depth rheological investigation and printing trials, quantitative analysis revealed that the printability of magnetic colloids is significantly affected by multiple factors, including magnetic field strength, pre-shear conditions, and printing speed. Furthermore, we successfully fabricated 3D architectures through the precise coordination of deposition paths and magnetic field modulation. This work offers initial support for the material’s future applications in soft robotics, in vivo therapeutic systems, and targeted drug delivery platforms. Full article

The integration of miniaturized magnetometers with Unmanned Aerial Vehicles (UAVs) has revolutionized magnetic surveying, offering flexible, high-resolution, and cost-effective solutions for geophysical applications also in remote areas. This study presents a comparative analysis of two configurations using UAV-borne scalar magnetometers through several surveys conducted in the Altopiano di Verteglia (Southern Italy), chosen as a test-site since buried pipes are present. The two systems differ significantly in sensor–platform arrangement, noise sensitivity, and flight configuration. Specifically, the first employs the MagNimbus magnetometer with two sensors rigidly attached on two masts at fixed distances, respectively, above and below the UAV, enabling the vertical gradient estimation while increasing noise due to proximity to the platform. The second involves the use of the MagArrow magnetometer suspended at 3 m below the UAV through non-rigid ropes, which benefits from minimal electromagnetic interference but suffers from oscillation-related instability. The retrieved magnetic anomaly maps effectively indicate the location and orientation of buried pipes within the studied area. Our comparative analysis emphasizes the trade-offs between the two systems: the MagNimbus-based configuration offers greater stability and operational efficiency, whereas the MagArrow-based one provides cleaner signals, which deteriorate with the vertical gradient computation. This work underscores the need to optimize UAV-magnetometer configurations based on environmental, operational, and survey-specific constraints to maximize data quality in drone-borne magnetic investigations. Full article

Background/Objectives: This study presents a case of chronic internal carotid artery [ICA] occlusion initially misinterpreted as ICA agenesis on magnetic resonance angiography (MRA). The report underscores the importance of retrospective review of prior imaging, particularly computed tomography angiography [CTA], in establishing the correct diagnosis. Case Report: A 70-year-old man presented with persistent headache, pulsatile tinnitus, and intermittent dizziness. Neurological examination and laboratory results were unremarkable. Initial cranial MRA demonstrated absence of flow in the left ICA, raising suspicion of congenital agenesis. However, retrospective evaluation of a CTA performed nine years earlier revealed a well-formed left carotid canal without ICA opacification, confirming the diagnosis of chronic ICA occlusion. Results: Current imaging again showed lack of enhancement in the left ICA, with adequate cerebral perfusion supplied via the contralateral ICA and vertebrobasilar system. Recognition of the preserved carotid canal on earlier CTA clarified the diagnosis as chronic occlusion rather than agenesis. Although surgical or endovascular revascularization was recommended, the patient opted for conservative management. At three months of follow-up, symptoms had improved and clinical monitoring continues. Conclusions: This case underscores the importance of distinguishing ICA agenesis from chronic occlusion, particularly by evaluating the carotid canal on CT. The presence of a carotid canal strongly indicates prior patency of the ICA and supports a diagnosis of occlusion. Careful differentiation is critical to avoid misinterpretation and to guide appropriate clinical management. In addition, reviewing prior imaging can be valuable when current findings are inconclusive or potentially misleading. Since this is a single case report, these observations should be regarded as hypothesis-generating rather than definitive, and further studies are needed to validate their broader applicability. Full article

To promote energy efficiency and support sustainable electric transportation, this study addresses the challenge of real-time and energy-optimal control of permanent magnet synchronous motors (PMSMs) in electric vehicles operating under variable load conditions, proposing a novel Laguerre-based model predictive control (MPC) strategy integrated with maximum torque per ampere (MTPA) operation. Traditional MPC methods often suffer from limited prediction horizons and high computational burden when handling strong coupling and time-varying loads, compromising real-time performance. To overcome these limitations, a Laguerre function approximation is employed to model the dynamic evolution of control increments using a set of orthogonal basis functions, effectively reducing the control dimensionality while accelerating convergence. Furthermore, to enhance energy efficiency, the MTPA strategy is embedded by reformulating the current allocation process using d- and q-axis current variables and deriving equivalent reference currents to simplify the optimization structure. A cost function is designed to simultaneously ensure current accuracy and achieve maximum torque per unit current. Simulation results under typical electric vehicle conditions demonstrate that the proposed Laguerre-MTPA MPC controller significantly improves steady-state performance, reduces energy consumption, and ensures faster response to load disturbances compared to traditional MTPA-based control schemes. This work provides a practical and scalable control framework for energy-saving applications in sustainable electric transportation systems. Full article

This paper presents the design and experimental validation of a 3D-printed BLDC motor featuring a hollow-shaft rotor and nickel-reinforced stator. The rotor employs neodymium magnets to reduce inertia while maintaining torque density, and the stator integrates thin nickel laminations to improve flux density. A custom controller with Hall sensors, BiSS-C encoder, and CAN interface enables closed-loop position control. Experiments demonstrate stable tracking with short settling time and negligible steady-state error, confirming feasibility for robotic and precision applications. Full article

Bi₂Sr₂CaCu₂O_8+x (Bi2212) high-temperature superconductor exhibits broad application prospects in strong magnetic fields, superconducting magnets, and power transmission due to its exceptional electrical properties. However, during practical applications, Bi2212 superconducting round wires are prone to mechanical loading effects, leading to crack propagation and degradation of superconducting performance, which severely compromises their reliability and service life. To elucidate the damage mechanisms under mechanical loading and their impact on critical current, this study establishes a two-dimensional model with existing cracks based on phase field fracture theory, simulating crack propagation behaviors under varying conditions. The results demonstrate that crack nucleation and propagation paths are predominantly governed by stress concentration zones. The transition zone width of cracks is controlled by the phase field length scale parameter. By incorporating electric fields into the phase field model, coupled mechanical-electrical simulations reveal that post-crack penetration causes significant current shunting, resulting in a marked decline in current density. The research quantitatively explains the mechanism of critical current degradation in Bi2212 round wires under tensile strain from a mechanical perspective. Full article

This study investigates the structural and magnetic properties of CoFe₂O₄ nanoparticles prepared by five different synthesis methods: coprecipitation, ultrasound-assisted coprecipitation, coprecipitation coupled with mechanochemical treatment, microemulsion and microwave-assisted hydrothermal synthesis. The produced powders were additionally functionalized with starch to improve biocompatibility and colloidal stability. The starch-coating procedure itself by sonication in starch solution, as well as its result, affects the structural and magnetic properties of functionalized nanoparticles. The resulting changes of properties in the process of ligand addition depend significantly on the starting nanoparticles, or rather, on the method of their synthesis. The structural, magnetic and spectroscopic properties of the resulting materials were systematically investigated using X-ray diffraction (XRD), Raman spectroscopy, Mössbauer spectroscopy and magnetic measurements. Taken together, XRD, Raman and Mössbauer spectroscopy show that starch deposition reduces structural disorder and internal stress, resulting in nanoparticles with a more uniform size distribution. These changes, in turn, affect all magnetic properties—magnetization, coercivity and magnetic anisotropy. Magnetic responses are preserved what is desirable for future biomedical applications. This work emphasizes the importance of surface modification for tailoring the properties of magnetic nanoparticles while maintaining their desired functionality. Full article

Glycosaminoglycans (GAGs) are part of the extracellular matrix (ECM) and play a major role in maintaining their physiological function. During pathological processes, the ECM is remodeled and its GAG composition changes. Hyaluronic acid (HA) is one of the GAGs that plays an important role in pathological processes such as inflammation and cancer and is therefore an interesting target for imaging. To provide iron oxide nanoparticles (IONP) that bind to hyaluronic acid (HA) as specific probes for molecular imaging, a peptide with high affinity for HA was covalently bound to the surface of commercial IONP (synomag^®-D, NH2) leading to hyaluronic acid-specific iron oxide nanoparticles (HAIONPs). Affinity measurements using a quartz crystal microbalance (QCM) showed a very high affinity of HAIONP to HA, but not to the control chondroitin sulfate (CS). HAIONPs exhibit a very high magnetic particle spectroscopy (MPS) signal amplitude, which predestines them as HA-selective tracers for magnetic particle imaging (MPI). The high relaxivity coefficient r₂ also makes HAIONP suitable for magnetic resonance imaging (MRI) applications. HAIONP therefore offers excellent prerequisites for further development as a probe for the specific quantitative imaging of the HA content of the ECM in pathological areas. Full article

Background: Timely intervention and effective control of Alzheimer’s disease (AD) have been shown to limit memory loss and preserve cognitive function and the ability to perform simple activities in older adults. In addition, magnetic resonance imaging (MRI) scans are one of the most common and effective methods for early detection of AD. With the rapid development of deep learning (DL) algorithms, AD detection based on deep learning has wide applications. Methods: In this research, we have developed an AD detection method based on three-dimensional (3D) convolutional neural networks (CNNs) for 3D MRI images, which can achieve strong accuracy when compared with traditional 3D CNN models. The proposed model has four main blocks, and the multi-layer fusion functionality of each block was used to improve the efficiency of the proposed model. The performance of the proposed model was compared with three different pre-trained 3D CNN architectures (i.e., 3D ResNet-18, 3D InceptionResNet-v2, and 3D Efficientnet-b2) in both tasks of multi-/binary-class classification of AD. Results: Our model achieved impressive classification results of 91.4% for binary-class as well as 80.6% for multi-class classification on the Open Access Series of Imaging Studies (OASIS) database. Conclusions: Such results serve to demonstrate that multi-stage feature fusion of 3D CNN is an effective solution to improve the accuracy of diagnosis of AD with 3D MRI, thus enabling earlier and more accurate diagnosis. Full article