Search Results (1,017)

Hormone-dependent breast cancer, particularly in its treatment-resistant forms, remains a significant therapeutic challenge. In this study, we applied a fully computational strategy to design steroid-based compounds capable of simultaneously targeting three key receptors involved in disease progression: progesterone receptor (PR), estrogen receptor alpha (ER-α), and HER2. Using a robust 3D-QSAR model (R² = 0.86; Q²_LOO = 0.86) built from 52 steroidal structures, we identified molecular features associated with high anticancer potential, specifically increased polarizability and reduced electronegativity. From a virtual library of 271 DFT-optimized analogs, 31 compounds were selected based on predicted potency (pIC₅₀ > 7.0) and screened via molecular docking against PR (PDB 2W8Y), HER2 (PDB 7JXH), and ER-α (PDB 6VJD). Seven candidates showed strong binding affinities (ΔG ≤ −9 kcal/mol for at least two targets), with Estero-255 emerging as the most promising. This compound demonstrated excellent conformational stability, a robust hydrogen-bonding network, and consistent multitarget engagement. Molecular dynamics simulations over 100 nanoseconds confirmed the structural integrity of the top ligands, with low RMSD values, compact radii of gyration, and stable binding energy profiles. Key interactions included hydrophobic contacts, π–π stacking, halogen–π interactions, and classical hydrogen bonds with conserved residues across all three targets. These findings highlight Estero-255, alongside Estero-261 and Estero-264, as strong multitarget candidates for further development. By potentially disrupting the PI3K/AKT/mTOR signaling pathway, these compounds offer a promising strategy for overcoming resistance in hormone-driven breast cancer. Experimental validation, including cytotoxicity assays and ADME/Tox profiling, is recommended to confirm their therapeutic potential. Full article

The objective of this study was to evaluate the physical-hydric properties of a Planosol under an Integrated Crop–Livestock–Forest (ICLF) system in the Agreste region of Paraíba, Brazil, after eight years of implementation, and to compare them with areas under a conventional cropping system and secondary native vegetation. The experiment was conducted at the experimental station located in Alagoinha, in the Agreste mesoregion of the State of Paraíba, Brazil. The experimental design adopted was a randomized block design (RBD) with five treatments and four replications (5 × 4 + 2). The treatments consisted of: (1) Gliricidia (Gliricidia sepium (Jacq.) Steud) + Signal grass (Urochloa decumbens) (GL+SG); (2) Sabiá (Mimosa caesalpiniaefolia Benth) + Signal grass (SB+SG); (3) Purple Ipê (Handroanthus avellanedae (Lorentz ex Griseb.) Mattos) + SG (I+SG); (4) annual crop + SG (C+SG); and (5) Signal grass (SG). Two additional treatments were included for statistical comparison: a conventional cropping system (CC) and a secondary native vegetation area (NV), both located near the experimental site. The CC treatment showed the lowest bulk density (1.23 g cm⁻³) and the lowest degree of compaction (66.3%) among the evaluated treatments, as well as a total porosity (TP) higher than 75% (0.75 m³ m⁻³). In the soil under the integration system, the lowest bulk density (1.38 g cm⁻³) and the highest total porosity (0.48 m³ m⁻³) were observed in the SG treatment at the 0.0–0.10 m depth. High S-index values (>0.035) and a low relative field capacity (RFc < 0.50) and K_θ indicate high structural quality and low soil water storage capacity. It was concluded that the SG, I+SG, SB+SG, and CC treatments presented the highest values of soil bulk and degree of compaction in the layers below 0.10 m. The I+SG and C+SG treatments showed the lowest hydraulic conductivities and macroaggregation. The SG and C+SG treatments had the lowest available water content and available water capacity across the three analyzed soil layers. Full article

Thermal management is an area of study in electronics focused on managing temperature to improve reliability and efficiency. When temperatures are too high, cooling systems are activated to prevent overheating, which can lead to reliability issues. To monitor the temperatures, sensors are often placed on-chip near hotspot locations. These sensors should be very small to allow them to be placed among compact, high-activity circuits. Often, they are connected to a central control circuit located far away from the hot spot locations where more area is available. This paper proposes sensing units for a novel temperature sensing architecture in the TSMC 180 nm process. This architecture functions by approximating the current through the sensing unit at a reference voltage, which is used to approximate the temperature in the digital back end using fitting functions. Sensing units are selected based on how well its temperature–current relationship can be modeled, sensing unit area, and power consumption. Many sensing units will be experimented with at different reference voltages. These temperature–current curves will be modeled with various fitting functions. The sensing unit selected is a diode-connected p-type MOSFET (Metal Oxide Semiconductor Field Effect Transistor) with a size of W = 400 nm, L = 180 nm. This sensing unit is exceptionally small compared to existing work because it does not rely on multiple devices at the sensing unit location to generate a PTAT or IPTAT signal like most work in this area. The temperature–current relationship of this device can also be modeled using a 2nd order polynomial, requiring a minimal number of trim temperatures. Its temperature error is small, and the power consumption is low. The range of currents for this sensing unit could be reasonably made on an IDAC. Full article

The growing popularity of Personal Light Electric Vehicles (PLEVs), such as e-scooters, has revolutionized urban mobility by offering compact, cost-effective, and environmentally friendly transportation solutions. However, safety concerns, including inadequate infrastructure, poor protective measures, and high accident rates, remain critical challenges. This paper presents the design and development of an innovative self-balancing microvehicle under the H2020 LEONARDO project, which aims to address these challenges through advanced engineering and user-centric design. The vehicle combines features of monowheels and e-scooters, integrating cutting-edge technologies to enhance safety, stability, and usability. The design adheres to European regulations, including Germany’s eKFV standards, and incorporates user preferences identified through representative online surveys of 1500 PLEV users. These preferences include improved handling on uneven surfaces, enhanced signaling capabilities, and reduced instability during maneuvers. The prototype features a lightweight composite structure reinforced with carbon fibers, a high-torque motorized front wheel, and multiple speed modes tailored to different conditions, such as travel in pedestrian areas, use by novice riders, and advanced users. Braking tests demonstrate deceleration values of up to 3.5 m/s², comparable to PLEV market standards and exceeding regulatory minimums, while smooth acceleration ramps ensure rider stability and safety. Additional features, such as identification plates and weight-dependent motor control, enhance compliance with local traffic rules and prevent misuse. The vehicle’s design also addresses common safety concerns, such as curb navigation and signaling, by incorporating large-diameter wheels, increased ground clearance, and electrically operated direction indicators. Future upgrades include the addition of a second rear wheel for enhanced stability, skateboard-like rear axle modifications for improved maneuverability, and hybrid supercapacitors to minimize fire risks and extend battery life. With its focus on safety, regulatory compliance, and rider-friendly innovations, this microvehicle represents a significant advancement in promoting safe and sustainable urban mobility. Full article

A broadband, efficient monolithic microwave integrated circuit power amplifier (MMIC PA) in OMMIC’s 0.1 μm GaN-on-Si technology for 5G millimeter-wave communication is presented. This study concentrates on the output matching design, which has an important influence on the PA’s performance. A compact one-order synthesized transformer network (STN) is adopted to match the 50 Ω load to the extracted large-signal output model of the transistor. A dual-objective strategy is developed for parameter optimization, incorporating the impedance transformation trajectory inside the predefined optimal impedance domain (OID) that satisfies the required specifications, with approximation to selected optimal load impedances. By introducing a custom adjustment factor β into the error function, coupled with an automated iterative tuning process based on S-parameter simulations, desired broadband matching results can be rapidly achieved. The proposed two-stage PA occupies a small chip area of only 1.23 mm² and demonstrates good frequency consistency over the 24–31 GHz band. Continuous-wave characterization shows a flat small-signal gain of 19.7 ± 0.5 dB; both the output power (P_out) and the power-added efficiency (PAE) at the 4 dB compression point remain smooth, ranging from 32.3 to 32.7 dBm and 35.5% to 37.8%, respectively. The peak PAE reaches up to nearly 40% at the center frequency. Full article

The classification of Unexploded Ordnance (UXO) from magnetometer data is a critical but challenging task, frequently hindered by the data scarcity required for training robust machine learning models. To address this, we leverage a high-fidelity digital twin to generate a comprehensive dataset of magnetometer signals from both UXO and non-UXO objects, incorporating complex remanent magnetization effects. In this study, we design and evaluate a custom Convolutional Neural Network (CNN) for UXO classification and compare it against classical machine learning baseline, including Random Forest and kNN. Our CNN model achieves a balanced accuracy of 84.65%, significantly outperforming traditional models that exhibit performance collapse under slight distortions such as additive noise, drift, and time-wrapping. Additionally, we present a compact two-block CNN variant that retains competitive accuracy while reducing the number of learnable parameters by approximately 33%, making it suitable for real-time onboard classification in underwater vehicle missions. Through extensive ablation studies, we confirm that architectural components, such as residual skip connections and element-wise batch normalization, are crucial for achieving model stability and performance. The results also highlight the practical implications of underwater vehicles for survey design, emphasizing the need to mitigate signal drift and maintain constant survey speeds. This work not only provides a robust deep learning model for UXO classification, but also offers actionable suggestions for improving both model deployment and data acquisition protocols in the field. Full article

Compact high-frequency surface wave radars (HFSWRs) utilize miniaturized antennas, resulting in lower antenna gain and a reduced signal-to-noise ratio (SNR) for target echoes. Due to noise interference, ship echoes in the noise region often fall below the detection threshold, leading to missed detections. To address this issue, this paper proposes a cascaded detection method in the time–frequency (TF) domain to improve ship detection performance under such conditions. First, TF features are extracted from TF representations of ship and noise signals. Supervised machine learning algorithms are then employed to distinguish targets from noise, reducing false alarms. Next, a non-constant false alarm rate (CFAR) threshold is computed based on the noise mean, standard deviation, and an adjustment factor to improve detection robustness. Experiments show that the classification accuracy between the ship and noise signals exceeds 99%, and the proposed method significantly outperforms the conventional CFAR and TF-domain CFAR in terms of detection performance. Full article

Conventional detectors based on ionization chambers, semiconductors, or thermoluminescent materials generally cannot be used to verify the in vivo dose delivered during brachytherapy treatments with γ-ray sources. However, certain adaptations and alternative methods, such as the use of miniaturized detectors or other specialized techniques, have been explored to address this limitation. One approach to solving this problem involves the use of dosimetric materials based on efficient scintillation crystals, which can be placed in the patient’s body using a long optical fiber inserted intra-cavernously, either in front of or next to the tumor. Scintillation crystals with a density close to that of tissue can be used in any location, including the respiratory tract, as they do not interfere with dose distribution. However, in many cases of radiation therapy, the detector may need to be positioned behind the target. In such cases, the use of heavy, high-density, and high-Z_eff scintillators is strongly preferred. The delivered radiation dose was registered using the radioluminescence response of the crystal scintillator and recorded with a compact luminescence spectrometer connected to the scintillator via a long optical fiber (so-called fiber-optic dosimeter). This proposed measurement method is completely non-invasive, safe, and can be performed in real time. To complete the abovementioned task, scintillation detectors based on YAG:Ce (ρ = 4.5 g/cm³; Z_eff = 35), LuAG:Ce (ρ = 6.75 g/cm³; Z_eff = 63), and GAGG:Ce (ρ = 6.63 g/cm³; Z_eff = 54.4) garnet crystals, with different densities ρ and effective atomic numbers Z_eff, were used in this work. The results obtained are very promising. We observed a strong linear correlation between the dose and the scintillation signal recorded by the detector system based on these garnet crystals. The measurements were performed on a specially prepared phantom in the brachytherapy treatment room at the Oncology Center in Bydgoszcz, where in situ measurements of the applied dose in the 0.5–8 Gy range were performed, generated by the ¹⁹²Ir (394 keV) γ-ray source from the standard Fexitron Elektra treatment system. Finally, we found that GAGG:Ce crystal detectors demonstrated the best figure-of-merit performance among all the garnet scintillators studied. Full article

In modern wireless communication systems, achieving high isolation and consistent signal gain is essential for optimizing Multiple-Input Multiple-Output (MIMO) antenna performance. This study presents a metamaterial-integrated smart sensor platform featuring a hexagonal two-element MIMO antenna designed to improve isolation and directive gain. Constructed on an FR4 substrate (1.6 mm thick), the proposed antenna configurations include a base hexagonal patch, an orthogonally oriented two-element system (TEH_OC), and further enhanced variants employing metamaterial arrays as the superstrate and reflector (TEH_OC_MTS and TEH_OC_MTR). The metamaterial structures significantly suppress mutual coupling, yielding superior diversity parameters such as Envelope Correlation Coefficient (ECC), Mean Effective Gain (MEG), and Channel Capacity Loss (CCL). All configurations were fabricated and validated through comprehensive anechoic chamber measurements. The results demonstrate robust isolation and radiation performance across the 3 GHz and 5 GHz bands, making these antennas well-suited for deployment in compact, low-latency smart sensor networks operating in 5G and IoT environments. Full article

Integrated optical waveguide amplifiers, with their compact footprint, low power consumption, and scalability, are the basis for optical communications. The realization of high gain in such integrated devices is made more challenging by the tight optical constraints. In this work, we present efficient amplification in an erbium–ytterbium-based hybrid slot waveguide consisting of a silicon nitride waveguide and a thin-film active layer/electron-beam resist. The electron-beam resist as the upper cladding layer not only possesses the role of protecting the waveguide but also has tighter optical confinement in the vertical cross-section direction. On this basis, an accurate and comprehensive dynamic model of an erbium–ytterbium co-doped amplifier is realized by introducing quenched ions. A modal gain of above 20 dB is achieved at the signal wavelength of 1530 nm in a 1.4 cm long hybrid slot waveguide, with fractions of quenched ions f_q = 30%. In addition, the proposed hybrid waveguide amplifiers exhibit higher modal gain than conventional air-clad amplifiers under the same conditions. Endowing silicon nitride photonic integrated circuits with efficient amplification enriches the integration of various active functionalities on silicon. Full article

Automatic modulation classification (AMC) is a fundamental technique in wireless communication systems, as it enables the identification of modulation schemes at the receiver without prior knowledge, thereby promoting efficient spectrum utilization. Recent advancements in deep learning (DL) have significantly enhanced classification performance by enabling neural networks (NNs) to learn complex decision boundaries directly from raw signal data. However, many existing NN-based AMC methods employ deep or specialized network architectures, which, while effective, tend to involve substantial structural complexity. To address this issue, we present a simple NN architecture that utilizes features derived from Hankelized matrices to extract informative signal representations. In the proposed approach, received signals are first transformed into Hankelized matrices, from which informative features are extracted using singular value decomposition (SVD). These features are then fed into a compact, fully connected (FC) NN for modulation classification across a wide range of signal-to-noise ratio (SNR) levels. Despite its architectural simplicity, the proposed method achieves competitive performance, offering a practical and scalable solution for AMC tasks at the receiver in diverse wireless environments. Full article

The ability to perform instantaneous frequency measurement (IFM) of unknown microwave signals holds significant importance across various application domains. This paper presents a power-independent microwave photonic IFM system. The proposed system implements frequency measurement through the construction of an amplitude comparison function (ACF) curve, achieved by introducing a frequency-dependent time delay via an optical tunable delay line (OTDL) for the signal under test (SUT). System simulation demonstrates the measurement capability across a wide bandwidth of 0.1–40 GHz with high precision, exhibiting frequency errors ranging from −0.03 to 0.04 GHz. The scheme also maintains consistent performance under varying input power levels. Key implementation aspects, including single-sideband modulation selection and system extension methods, are analyzed in detail to optimize measurement accuracy. Notably, the proposed architecture features a simple and compact design with excellent integration potential. These characteristics, combined with its wide operational bandwidth and high measurement precision, make this approach particularly suitable for demanding applications in electronic reconnaissance and communication. Full article

Focusing on the common problems of phase-locked loop dependence, multiple current sensor requirements, a large number of controllers, and complex settings in traditional parallel active power filter (APF) control methods, this paper proposes a harmonic compensation control strategy based on an improved proportional resonant (PR) controller. The proposed method introduces an instantaneous power theory to construct a reference current model, which relies solely on grid voltage and current signals, does not require load-side current detection and phase-locked loop modules, and effectively simplifies the sensor configuration and system structure. At the same time, compared with the traditional solution that requires PR modules to be configured for each order of harmonics, this study only uses one set of PR controllers for fundamental current tracking, which has advantages in terms of compactness and computing resource occupation. To guide the controller parameter setting, this paper systematically discusses the influence of changes in K_p and K_r on pole distribution and dynamic performance based on discrete domain modeling and root locus analysis methods. The results were verified on the MATLAB/Simulink simulation platform and the 1 kVA experimental platform and compared with the traditional control method that requires the use of phase-locked loops (PLLs), load current sensors, and multiple PR controllers. The simulation and experimental results show that the proposed method has achieved a certain degree of optimization in terms of harmonic suppression effect, dynamic response performance, and system structure complexity. Full article

Precise timing synchronization remains a fundamental requirement for modern navigation and communication systems, where the miniaturization of Loran-C infrastructure presents both technical challenges and practical significance. Conventional miniaturized loop antennas cannot simultaneously meet the requirements of the Loran-C signal for both radiation intensity and bandwidth due to inherent quality factor (Q) limitations. A sub-cubic-meter impedance matching (IM) antenna is proposed, featuring a −20 dB bandwidth of 18 kHz and over 7-fold radiation enhancement. The proposed design leverages a planar-transformer-based impedance matching network to enable efficient 100 kHz operation in a compact form factor, while a resonant coil structure is adopted at the receiver side to enhance the system’s sensitivity. The miniaturized Loran-C timing system incorporating the IM antenna achieves an extended decoding range of >100 m with merely 100 W input power, exceeding conventional loop antennas limited to 30 m operation. This design successfully achieves overall miniaturization of the Loran-C timing system while breaking through the current transmission distance limitations of compact antennas, extending the effective transmission range to the hundred-meter scale. The design provides a case for developing compact yet high-performance Loran-C systems. Full article

This study proposes MSFF-Net, a lightweight deep learning framework for bearing fault diagnosis based on frequency-domain multi-sensor fusion. The vibration and acoustic signals are initially converted into the frequency domain using the fast Fourier transform (FFT), enabling the extraction of temporally invariant spectral features. These features are processed by a compact one-dimensional convolutional neural network, where modality-specific representations are fused at the feature level to capture complementary fault-related information. The proposed method demonstrates robust and superior performance under both full and scarce data conditions, as verified through experiments on a publicly available dataset. Experimental results on a publicly available dataset indicate that the proposed model attains an average accuracy of 99.73%, outperforming state-of-the-art (SOTA) methods in both accuracy and stability. With only about 70.3% of the parameters of the SOTA model, it offers faster inference and reduced computational cost. Ablation studies confirm that multi-sensor fusion improves all classification metrics over single-sensor setups. Under few-shot conditions with 20 samples per class, the model retains 94.69% accuracy, highlighting its strong generalization in data-limited scenarios. The results validate the effectiveness, computational efficiency, and practical applicability of the model for deployment in data-constrained industrial environments. Full article