Enhancing flood mapping through ensemble machine learning in the Gamasyab watershed, Western Iran.

Floods are among the natural hazards that have seen a rapid increase in frequency in recent decades. The damage caused by floods, including human and financial losses, poses a serious threat to human life. This study evaluates two machine learning (ML) techniques for flood susceptibility mapping (FSM) in the Gamasyab watershed in Iran. We utilized random forest (RF), support vector machine (SVM), ensemble models, and a geographic information system (GIS) to predict FSM. The application of these models involved 10 effective factors in flooding, as well as 82 flood locations integrated into the GIS. The SVM and RF models were trained and tested, followed by the implementation of resampling techniques (RT) using bootstrap and subsampling methods in three repetitions. The results highlighted the importance of elevation, slope, and precipitation as primary factors influencing flood occurrence. Additionally, the ensemble model outperformed both the RF and SVM models, achieving an area under the curve (AUC) of 0.9, a correlation coefficient (COR) of 0.79, a true skill statistic (TSS) of 0.83, and a standard deviation (SD) of 0.71 in the test phase. The tested models were adapted to available input data to map the FSM across the study watershed. These findings underscore the potential of integrating an ensemble model with GIS as an effective tool for flood susceptibility mapping.

Slice-specific B1 + shimming improves the repeatability of multishot DWI at 7 T.

PURPOSE: Compared with lower field strengths, DWI at 7 T faces the combined challenges of increased distortion and blurring due to B0 inhomogeneity, and increased signal dropouts due to B1 + inhomogeneity. This study addresses the B1 + limitations using slice-specific static parallel transmission (pTx) in a multi-shot, readout-segmented EPI diffusion imaging sequence. METHODS: DWI was performed in 7 healthy subjects using MRI at 7 T and readout-segmented EPI. Data were acquired with non-pTx circular-polarized (CP) pulses (CP-DWI) and static pTx pulses (pTx-DWI) using slice-specific B1 + shim coefficients. Each volunteer underwent two scan sessions on the same day, with two runs of each sequence in the first session and one run in the second. The sequences were evaluated by assessing image quality, flip-angle homogeneity, and intrasession and intersession repeatability in ADC estimates. RESULTS: pTx-DWI significantly reduced signal voids compared with CP-DWI, particularly in inferior brain regions. The use of pTx also improved RF uniformity and symmetry across the brain. These effects translated into improved intrasession and intersession repeatability for pTx-DWI. Additionally, re-optimizing the pTx pulse between repeat scans did not have a negative effect on ADC repeatability. CONCLUSION: The study demonstrates that pTx provides a reproducible image-quality increase in multishot DWI at 7 T. The benefits of pTx also extend to quantitative ADC estimation with regard to the improvement in intrasession and intersession repeatability. Overall, the combination of multishot imaging and pTx can support the development of reliable, high-resolution DWI for clinical studies at 7 T.

Comparison between Three Radiomics Models and Clinical Nomograms for Prediction of Lymph Node Involvement in PCa Patients Combining Clinical and Radiomic Features.

PURPOSE: We aim to compare the performance of three different radiomics models (logistic regression (LR), random forest (RF), and support vector machine (SVM)) and clinical nomograms (Briganti, MSKCC, Yale, and Roach) for predicting lymph node involvement (LNI) in prostate cancer (PCa) patients. MATERIALS AND METHODS: The retrospective study includes 95 patients who underwent mp-MRI and radical prostatectomy for PCa with pelvic lymphadenectomy. Imaging data (intensity in T2, DWI, ADC, and PIRADS), clinical data (age and pre-MRI PSA), histological data (Gleason score, TNM staging, histological type, capsule invasion, seminal vesicle invasion, and neurovascular bundle involvement), and clinical nomograms (Yale, Roach, MSKCC, and Briganti) were collected for each patient. Manual segmentation of the index lesions was performed for each patient using an open-source program (3D SLICER). Radiomic features were extracted for each segmentation using the Pyradiomics library for each sequence (T2, DWI, and ADC). The features were then selected and used to train and test three different radiomics models (LR, RF, and SVM) independently using ChatGPT software (v 4o). The coefficient value of each feature was calculated (significant value for coefficient ≥ ±0.5). The predictive performance of the radiomics models and clinical nomograms was assessed using accuracy and area under the curve (AUC) (significant value for p ≤ 0.05). Thus, the diagnostic accuracy between the radiomics and clinical models were compared. RESULTS: This study identified 343 features per patient (330 radiomics features and 13 clinical features). The most significant features were T2_nodulofirstordervariance and T2_nodulofirstorderkurtosis. The highest predictive performance was achieved by the RF model with DWI (accuracy 86%, AUC 0.89) and ADC (accuracy 89%, AUC 0.67). Clinical nomograms demonstrated satisfactory but lower predictive performance compared to the RF model in the DWI sequences. CONCLUSIONS: Among the prediction models developed using integrated data (radiomics and semantics), RF shows slightly higher diagnostic accuracy in terms of AUC compared to clinical nomograms in PCa lymph node involvement prediction.

Development and Evaluation of Ferrite Core Inductively Coupled Plasma Radio Frequency Ion Source for High-Current Ion Implanters in Semiconductor Applications.

This study presents the development of a ferrite core inductively coupled plasma (ICP) radio frequency (RF) ion source designed to improve the lifetime of ion sources in commercial ion implanters. Unlike existing DC methods, this novel approach aims to enhance the performance and lifetime of the ion source. We constructed a high-vacuum evaluation chamber to thoroughly examine RF ion source characteristics using a Langmuir probe. Comparative experiments assessed the extraction current of two upgraded ferrite core RF ion sources in a commercial ion implanter setting. Additionally, we tested the plasma lifetime of the ICP source and took temperature measurements of various components to verify the operational stability and efficiency of the innovative design. This study confirmed that the ICP RF ion source operated effectively under a high vacuum of 10-5 torr and in a high-voltage environment of 30 kV. We observed that the extraction current increased linearly with RF power. We also confirmed that BF3 gas, which presents challenging conditions, was stably ionized in the ICP RF ion sources.

Role of impedance drop and lesion size index (LSI) to guide catheter ablation for atrial fibrillation.

BACKGROUND: When using lesion size index (LSI) to guide catheter ablation, it is unclear what combination of power, contact force and time would be preferable to use and what LSI target value to aim for. This study aimed at identifying desirable ablation settings and LSI targets by using tissue impedance drop as indicator of lesion formation. METHODS: Consecutive patients, undergoing their first left atrial (LA) catheter ablation for atrial fibrillation, with radiofrequency energy (RF) powers of 20, 30 and 40 W were enrolled. Tissue impedance, contact force (CF), Force Time Integral (FTI) and LSI values were continuously recorded during ablation and sampled at 100 Hz. Mean CF and Contact Force Variability (CFV) were calculated for every lesion. The effect of RF power, ablation time, CF and CFV on impedance drop and LSI were assessed. RESULTS: A total of 3258 lesions were included in the analysis. For any target LSI value, use of higher RF powers translated into progressively higher impedance drops. The impact of lower CF and higher CFV on impedance drop was more relevant when using lower powers. Target LSI values corresponding to maximum impedance drop were identified depending on RF power, mean CF and CFV used. CONCLUSIONS: Even in the context of an LSI-guided ablation strategy, use of lower or higher powers might lead to different lesion sizes. Different LSI targets might be needed depending on the combination of RF power, CF and CFV used for ablation. Incorporating indicators of catheter stability, like CFV, in the LSI formula could improve the predictive value of LSI for lesion size. Studies with clinical outcomes are required to confirm the clinical relevance of these findings.

A Study of MgZnO Thin Film for Hydrogen Sensing Application.

This research introduces a hydrogen sensor made from a thin film of magnesium zinc oxide (MgZnO) deposited using a technique called radiofrequency co-sputtering (RF co-sputtering). Separate magnesium oxide (MgO) and zinc oxide (ZnO) targets were used to deposit the MgZnO film, experimenting with different deposition times and power levels. The sensor performed best (reaching a sensing response of 2.46) when exposed to hydrogen at a concentration of 1000 parts per million (ppm). This peak performance occurred with a MgZnO film thickness of 432 nanometers (nm) at a temperature of 300 °C. Initially, the sensor's responsiveness increased as the film thickness grew. This is because thicker films tend to have more oxygen vacancies, which are imperfections that play a role in the sensor's function. However, further increases in film thickness beyond the optimal point harmed performance. This is attributed to the growth of grains within the film, which hindered its effectiveness. X-ray diffraction (XRD) and field-emission scanning electron microscopy (FE-SEM) were employed to thoroughly characterize the quality of the MgZnO thin film. These techniques provided valuable insights into the film's crystal structure and morphology, crucial factors influencing its performance as a hydrogen sensor.

The Impact of Autoantibodies on Outcomes in Patients with Idiopathic Pulmonary Fibrosis: Post-Hoc Analyses of the Phase III ASCEND Trial.

INTRODUCTION: Clinical practice guidelines recommend autoimmune serological testing in patients newly diagnosed with interstitial lung disease of apparently unknown cause who may have idiopathic pulmonary fibrosis (IPF), in order to exclude connective tissue disease (CTD). Autoantibody positivity has been associated with unique patient profiles and prognosis in patients with IPF who otherwise lack a CTD diagnosis. METHODS: This post-hoc analysis of patients with IPF from the Phase III ASCEND trial (NCT01366209) evaluated the association of antinuclear antibodies (ANA), rheumatoid factor (RF) and anti-cyclic citrullinated peptide (anti-CCP) status with baseline disease characteristics, disease progression [percent predicted forced vital capacity (%FVC), forced vital capacity (FVC) volume and progression-free survival (PFS)], and treatment outcomes with pirfenidone and placebo (%FVC, FVC and PFS). RESULTS: Of 555 participants, 244/514 (47.5%) were ANA positive (ANA+), 83/514 (16.1%) had high ANA+ (ANA titre ≥ 1:160 or positive nucleolar- or centromere-staining patterns), 60/555 (10.8%) were RF positive (RF+) and/or anti-CCP positive (anti-CCP+) and 270/514 (52.5%) were autoantibody negative (AAb-). Baseline demographics and characteristics were generally comparable between autoantibody subgroups. Although not statistically significant, more placebo-treated participants with ANA+ or high ANA+ had a decline from baseline to Week 52 of ≥ 10% in %FVC or death (48.7% and 55.9%, respectively) or in FVC volume or death (48.7% and 47.1%, respectively) compared with the AAb- group (%FVC or death: 42.0%; FVC volume or death: 42.0%). The RF+ and/or anti-CCP+ group was similar to AAb-. No differences were observed in PFS. A treatment benefit for pirfenidone versus placebo was observed regardless of autoantibody status [PFS: ANA+ HR (95% CI): 0.56 (0.37 to 0.86), P = 0.007; AAb- HR (95% CI): 0.50 (0.32 to 0.78), P = 0.002]. CONCLUSION: IPF disease course did not differ by autoantibody status in ASCEND. Pirfenidone had a treatment benefit regardless of the presence of ANA. TRIAL REGISTRATION: ClinicalTrials.gov identifier, NCT01366209.

People with idiopathic pulmonary fibrosis sometimes have abnormal antibodies, called autoantibodies, in their blood. Uncommonly, autoantibodies may mistakenly target the person's own tissues, including the lungs. It is unknown whether these autoantibodies cause idiopathic pulmonary fibrosis or make it worse. This analysis looked at data from the ASCEND clinical trial in people with idiopathic pulmonary fibrosis, who were split randomly into two groups to receive tablets of either a medicine called pirfenidone or a placebo for 52 weeks. One goal was to see whether people with certain autoantibodies called antinuclear antibodies ('ANA' for short), rheumatoid factor ('RF') and anti-cyclic citrullinated peptide ('anti-CCP') had different traits from people without autoantibodies, such as age, race or smoking history. Other goals were to see if autoantibodies affected (1) how well people's lungs worked during the trial, (2) how quickly people's idiopathic pulmonary fibrosis got worse or they died and (3) how well pirfenidone worked. The analysis showed that most traits were similar in people with and without autoantibodies. In people who received placebo, the change in lung function during the trial was not different for people with ANA, RF or anti-CCP compared with people with no autoantibodies. People who received pirfenidone were less likely to have worsening lung function, or die, than people who received placebo, regardless of whether or not they had autoantibodies. Doctors evaluating patients with idiopathic pulmonary fibrosis should consider the impact of autoantibodies and feel confident that pirfenidone is effective regardless of whether or not autoantibodies are present.

Improved gradient echo magnitude- and phase-based mapping of T 2 $$ {\mathrm{T}}_2 $$ using multiple RF spoiling increments at 3T and 7T.

PURPOSE: The transverse relaxation time T 2 $$ {}_2 $$ holds significant relevance in clinical applications and research studies. Conventional T 2 $$ {}_2 $$ mapping approaches rely on spin-echo sequences, which require lengthy acquisition times and involve high radiofrequency (RF) power deposition. An alternative gradient echo (GRE) phase-based T 2 $$ {}_2 $$ mapping method, utilizing steady-state acquisitions at one small RF spoil phase increment, was recently demonstrated. Here, a modified magnitude- and phase-based T 2 $$ {}_2 $$ mapping approach is proposed, which improves T 2 $$ {\mathrm{T}}_2 $$ estimations by simultaneous fitting of T 1 $$ {\mathrm{T}}_1 $$ and signal amplitude ( A ∝ P D $$ A\propto PD $$ ) at three or more RF spoiling phase increments, instead of assuming a fixed T 1 $$ {\mathrm{T}}_1 $$ value. METHODS: The feasibility of the magnitude-phase-based method was assessed by simulations, in phantom and in vivo experiments using skipped-CAIPI three-dimensional-echo-planar imaging (3D-EPI) for rapid GRE imaging. T 2 $$ {\mathrm{T}}_2 $$ , T 1 $$ {\mathrm{T}}_1 $$ and PD estimations obtained by our method were compared to T 2 $$ {\mathrm{T}}_2 $$ of the phase-based method and T 1 $$ {\mathrm{T}}_1 $$ and PD of spoiled GRE-based multi-parameter mapping using a multi-echo version of the same sequence. RESULTS: The agreement of the proposed T 2 $$ {\mathrm{T}}_2 $$ with ground truth and reference T 2 $$ {\mathrm{T}}_2 $$ values was higher than that of phase-based T 2 $$ {\mathrm{T}}_2 $$ in simulations and in phantom data. While phase-based T 2 $$ {\mathrm{T}}_2 $$ overestimation increases with actual T 2 $$ {\mathrm{T}}_2 $$ and T 1 $$ {\mathrm{T}}_1 $$ , the proposed method is accurate over a large range of physiologically meaningful T 2 $$ {\mathrm{T}}_2 $$ and T 1 $$ {\mathrm{T}}_1 $$ values. At the same time, precision is improved. In vivo results were in line with these observations. CONCLUSION: Accurate magnitude-phase-based T 2 $$ {}_2 $$ mapping is feasible in less than 5 min scan time for 1 mm nominal isotropic whole-head coverage at 3T and 7T.

Variable-density velocity-selective magnetization preparation for non-contrast-enhanced peripheral MR angiography.

Velocity-selective (VS) magnetization preparation has shown great promise for non-contrast-enhanced (NCE) magnetic resonance angiography (MRA) with the ability to generate positive angiographic contrast directly using a single 3D acquisition. However, existing VS-MRA methods have an issue of aliased saturation around a certain velocity, known as velocity field-of-view (vFOV), which can cause undesired signal loss in arteries. This study aimed to develop a new version of the VS preparation pulse sequence that overcomes the aliased saturation problem in conventional VS preparation. Utilizing the fact that an excitation profile is the Fourier transform of excitation k-space sampling, we sampled the k-space in a non-uniform fashion by scaling gradient pulses accordingly to have aliased excitation diffused over velocity. The variable density sampling function was numerically optimized to maximize the average of the velocity passband signal while minimizing its variance. The optimized variable density VS magnetization was validated through Bloch simulations and applied to peripheral NCE MRA in healthy subjects. The in-vivo experiments showed that the proposed variable density VS-MRA significantly lowered arterial signal loss observed in conventional VS-MRA, as evidenced by a higher arterial signal-to-noise ratio (58.50 ± 14.29 vs. 55.54 ± 12.32; p < 0.05) and improved artery-to-background contrast-to-noise ratio (22.75 ± 7.57 vs. 20.60 ± 6.51; p < 0.05).

Improvement of MRS at ultra-high field using a wireless RF array.

We aim to assess a straightforward technique to enhance spectral quality in the brain, particularly in the cerebellum, during 7 T MRI scans. This is achieved through a wireless RF array insert designed to mitigate signal dropouts caused by the limited transmit field efficiency in the inferior part of the brain. We recently developed a wireless RF array to improve MRI and 1H-MRS at 7 T by augmenting signal via inductive coupling between the wireless RF array and the MRI coil. In vivo experiments on a Siemens 7 T whole-body human scanner with a Nova 1Tx/32Rx head coil quantified the impact of the dorsal cervical array in improving signal in the posterior fossa, including the cerebellum, where the transmit efficiency of the coil is inherently low. The 1H-MRS experimental protocol consisted of paired acquisition of data sets, both with and without the RF array, using the semi-LASER and SASSI sequences. The overall results indicate that the localized 1H-MRS is improved significantly in the presence of the array. Comparison of in vivo 1H-MRS plots in the presence versus absence of the array demonstrated an average SNR enhancement of a factor of 2.2. LCModel analysis reported reduced Cramér-Rao lower bounds, indicating more confident fits. This wireless RF array can significantly increase detection sensitivity. It may reduce the RF transmission power and data acquisition time for 1H-MRS and MRI applications, specifically at 7 T, where 1H-MRS requires a high-power RF pulse. The array could provide a cost-effective and efficient solution to improve detection sensitivity for human 1H-MRS and MRI in the regions with lower transmit efficiency.

Extended Spectrum beta-Lactamase Bacteria and Multidrug Resistance in Jordan are Predicted Using a New Machine-Learning system.

Background: The incidence of microorganisms with extended-spectrum beta-lactamase (ESBL) is on the rise, posing a significant public health concern. The current application of machine learning (ML) focuses on predicting bacterial resistance to optimize antibiotic therapy. This study employs ML to forecast the occurrence of bacteria that generate ESBL and demonstrate resistance to multiple antibiotics (MDR). Methods: Six popular ML algorithms were initially trained on antibiotic resistance test patient reports (n = 489) collected from Al-Hussein/Salt Hospital in Jordan. Trained outcome models predict ESBL and multidrug resistance profiles based on microbiological and patients' clinical data. The results were utilized to select the optimal ML method to predict ESBL's most associated features. Results: Escherichia coli (E. coli, 82%) was the most commonly identified microbe generating ESBL, displaying multidrug resistance. Urinary tract infections (UTIs) constituted the most frequently observed clinical diagnosis (68.7%). Classification and Regression Trees (CART) and Random Forest (RF) classifiers emerged as the most effective algorithms. The relevant features associated with the emergence of ESBL include age and different classes of antibiotics, including cefuroxime, ceftazidime, cefepime, trimethoprim/ sulfamethoxazole, ciprofloxacin, and gentamicin. Fosfomycin nitrofurantoin, piperacillin/tazobactam, along with amikacin, meropenem, and imipenem, had a pronounced inverse relationship with the ESBL class. Conclusion: CART and RF-based ML algorithms can be employed to predict the most important features of ESBL. The significance of monitoring trends in ESBL infections is emphasized to facilitate the administration of appropriate antibiotic therapy.

Helmet Radio Frequency Phased Array Applicators Enhance Thermal Magnetic Resonance of Brain Tumors.

Thermal Magnetic Resonance (ThermalMR) integrates Magnetic Resonance Imaging (MRI) diagnostics and targeted radio-frequency (RF) heating in a single theranostic device. The requirements for MRI (magnetic field) and targeted RF heating (electric field) govern the design of ThermalMR applicators. We hypothesize that helmet RF applicators (HPA) improve the efficacy of ThermalMR of brain tumors versus an annular phased RF array (APA). An HPA was designed using eight broadband self-grounded bow-tie (SGBT) antennae plus two SGBTs placed on top of the head. An APA of 10 equally spaced SGBTs was used as a reference. Electromagnetic field (EMF) simulations were performed for a test object (phantom) and a human head model. For a clinical scenario, the head model was modified with a tumor volume obtained from a patient with glioblastoma multiforme. To assess performance, we introduced multi-target evaluation (MTE) to ensure whole-brain slice accessibility. We implemented time multiplexed vector field shaping to optimize RF excitation. Our EMF and temperature simulations demonstrate that the HPA improves performance criteria critical to MRI and enhances targeted RF and temperature focusing versus the APA. Our findings are a foundation for the experimental implementation and application of a HPA en route to ThermalMR of brain tumors.

Vertical Interconnection Technology for RF MicroSystem Packaging.

In this work, 1.5-level interconnections of RF microsystems with good RF performance, integration process compatibility, and high reliability are developed to meet the future demand for wireless communication microsystems in the millimeter wave band. Numerical models of 1.5-level interconnections based on solder balls with different diameters are established and analyzed using HFSS. The optimized structure parameters of 0.2 mm diameter Sn96.5Ag3Cu0.5 (SAC305) solder balls and 0.3 mm diameter Sn63Pb37 solder balls are selected for the interconnection between glass micro substrates and silicon micro substrates and between silicon micro substrates and HTCC substrates, respectively. Integration process parameters of the vertical interconnection are optimized. The micro substrate interconnection samples manufactured based on optimized integration methods and parameters show high reliability.

Analysis of Local Properties and Performance of Bilayer Epitaxial Graphene Field Effect Transistors on SiC.

Epitaxial bilayer graphene, grown by chemical vapor deposition on SiC substrates without silicon sublimation, is crucial material for graphene field effect transistors (GFETs). Rigorous characterization methods, such as atomic force microscopy and Raman spectroscopy, confirm the exceptional quality of this graphene. Post-nanofabrication, extensive evaluation of DC and high-frequency properties enable the extraction of critical parameters such as the current gain (fmax) and cut-off frequency (ft) of hundred transistors. The Raman spectra analysis provides insights into material property, which correlate with Hall mobilities, carrier densities, contact resistance and sheet resistance and highlights graphene's intrinsic properties. The GFETs' performance displays dispersion, as confirmed through the characterization of multiple transistors. Since the Raman analysis shows relatively homogeneous surface, the variation in Hall mobility, carrier densities and contact resistance cross the wafer suggest that the dispersion of GFET transistor's performance could be related to the process of fabrication. Such insights are especially critical in integrated circuits, where consistent transistor performance is vital due to the presence of circuit elements like inductance, capacitance and coplanar waveguides often distributed across the same wafer.

Discussion and Demonstration of RF-MEMS Attenuators Design Concepts and Modules for Advanced Beamforming in the Beyond-5G and 6G Scenario-Part 2.

In this paper, different concepts of reconfigurable RF-MEMS attenuators for beamforming applications are proposed and critically assessed. Capitalizing on the previous part of this work, the 1-bit attenuation modules featuring series and shunt resistors and low-voltage membranes (7-9 V) are employed to develop a 3-bit attenuator for fine-tuning attenuations (<-10 dB) in the 24.25-27.5 GHz range. More substantial attenuation levels are investigated using fabricated samples of coplanar waveguide (CPW) sections equipped with Pi-shaped resistors aiming at attenuations of -15, -30, and -45 dB. The remarkable electrical features of such configurations, showing flat attenuation curves and limited return losses, and the investigation of a switched-line attenuator design based on them led to the final proposed concept of a low-voltage 24-state attenuator. Such a simulated device combines the Pi-shaped resistors for substantial attenuations with the 3-bit design for fine-tuning operations, showing a maximum attenuation level of nearly -50 dB while maintaining steadily flat attenuation levels and limited return losses (<-11 dB) along the frequency band of interest.

Electrical storm after acute myocardial infarction treated with Radiofrequency ablation under the Escort of ICD.

The occurrence of ventricular tachycardia (VT) in patients with acute myocardial infarction (AMI) is associated with poor prognosis. Drug therapy and implantable cardioverter-defibrillators (ICDs) are effective methods to prevent sudden death. Radiofrequency (RF) catheter ablation can map the matrix and mechanism of VT, thereby effectively reducing the occurrence of ICD discharge. This paper reports on the case of a middle-aged man who underwent emergency percutaneous coronary intervention for AMI and developed VT and ventricular fibrillation on day 7 after reperfusion. An ICD was implanted. On day 19, he received catheter ablation because of refractory monomorphic ventricular tachycardia and frequent discharge of the ICD. After three months, the patient had not experienced any further ventricular tachycardia attacks. The conclusion is that RF catheter ablation can resolve the ES after myocardial infarction and significantly reduce the occurrence of ICD discharges.

Generalized inhomogeneity-resilient relaxation along a fictitious field (girRAFF) for improved robustness in rotating frame relaxometry at 3T.

PURPOSE: To optimize Relaxation along a Fictitious Field (RAFF) pulses for rotating frame relaxometry with improved robustness in the presence of B 0 $$ {\mathrm{B}}_0 $$ and B 1 + $$ {\mathrm{B}}_1^{+} $$ field inhomogeneities. METHODS: The resilience of RAFF pulses against B 0 $$ {\mathrm{B}}_0 $$ and B 1 + $$ {\mathrm{B}}_1^{+} $$ inhomogeneities was studied using Bloch simulations. A parameterized extension of the RAFF formulation was introduced and used to derive a generalized inhomogeneity-resilient RAFF (girRAFF) pulse. RAFF and girRAFF preparation efficiency, defined as the ratio of the longitudinal magnetization before and after the preparation ( M z ( T p ) / M 0 $$ {M}_z\left({T}_p\right)/{M}_0 $$ ), were simulated and validated in phantom experiments. T RAFF $$ {T}_{\mathrm{RAFF}} $$ and T girRAFF $$ {T}_{\mathrm{girRAFF}} $$ parametric maps were acquired at 3T in phantom, the calf muscle, and the knee cartilage of healthy subjects. The relaxation time maps were analyzed for resilience against artificially induced field inhomogeneities and assessed in terms of in vivo reproducibility. RESULTS: Optimized girRAFF preparations yielded improved preparation efficiency (0.95/0.91 simulations/phantom) with respect to RAFF (0.36/0.67 simulations/phantom). T girRAFF $$ {T}_{\mathrm{girRAFF}} $$ preparations showed in phantom/calf 6.0/4.8 times higher resilience to B 0 $$ {\mathrm{B}}_0 $$ inhomogeneities than RAFF, and a 4.7/5.3 improved resilience to B 1 + $$ {\mathrm{B}}_1^{+} $$ inhomogeneities. In the knee cartilage, T girRAFF $$ {T}_{\mathrm{girRAFF}} $$ (53 ± $$ \pm $$ 14 ms) was higher than T RAFF $$ {T}_{\mathrm{RAFF}} $$ (42 ± $$ \pm $$ 11 ms). Moreover, girRAFF preparations yielded 7.6/4.9 times improved reproducibility across B 0 $$ {\mathrm{B}}_0 $$ / B 1 + $$ {\mathrm{B}}_1^{+} $$ inhomogeneity conditions, 1.9 times better reproducibility across subjects and 1.2 times across slices compared with RAFF. Dixon-based fat suppression led to a further 15-fold improvement in the robustness of girRAFF to inhomogeneities. CONCLUSIONS: RAFF pulses display residual sensitivity to off-resonance and pronounced sensitivity to B 1 + $$ {\mathrm{B}}_1^{+} $$ inhomogeneities. Optimized girRAFF pulses provide increased robustness and may be an appealing alternative for applications where resilience against field inhomogeneities is required.

Passive Wireless Porous Biopolymer Sensors for At-Home Monitoring of Oil and Fatty Acid Nutrition.

Dietary oils─rich in omega-3, -6, and -9 fatty acids─exhibit critical impacts on health parameters such as cardiovascular function, bodily inflammation, and neurological development. There has emerged a need for low-cost, accessible method to assess dietary oil consumption and its health implications. Existing methods typically require specialized, complex equipment and extensive sample preparation steps, rendering them unsuitable for home use. Addressing this gap, herein, we study passive wireless, biocompatible biosensors that can be used to monitor dietary oils directly from foods either prepared or cooked in oil. This design uses broad-coupled split ring resonators interceded with porous silk fibroin biopolymer (requiring only food-safe materials, such as aluminum foil and biopolymer). These porous biopolymer films absorb oils at rates proportional to their viscosity/fatty acid composition and whose response can be measured wirelessly without any microelectronic components touching food. The engineering and mechanism of such sensors are explored, alongside their ability to measure the oil presence and fatty acid content directly from foods. Its simplicity, portability, and inexpensiveness are ideal for emerging needs in precision nutrition─such sensors may empower individuals to make informed dietary decisions based on direct-from-food measurements.

Modulated Radio Frequency Stealth Waveforms for Ultra-Wideband Radio Fuzes.

The increasingly complex electromagnetic environment of modern warfare and the proliferation of intelligent jamming threaten to reduce the survival rate of radio fuzes on the battlefield. Radio frequency (RF) stealth technology can fundamentally improve the anti-interception and reconnaissance capabilities of radio fuzes, thereby lessening the probability of them being intercepted, recognized, and jammed by the enemy. In this paper, an RF stealth waveform based on chaotic pulse-position modulation is proposed for ultra-wideband (UWB) radio fuzes. Adding a perturbation signal based on the Tent map ensures that the chaotic sequences have sufficiently long periods despite hardware byte limitations. Measuring the approximate entropy and sequence period shows that the Tent map with the addition of perturbation signals can maintain good randomness under byte constraints, closely approximating the Tent map with ideal precision. Simulations verify that the proposed chaotic mapping used to modulate the pulse position of an ultra-wideband radio fuze signal results in superior detection, anti-interception, and anti-jamming performance.

Automated Lung Cancer Diagnosis Applying Butterworth Filtering, Bi-Level Feature Extraction, and Sparce Convolutional Neural Network to Luna 16 CT Images.

Accurate prognosis and diagnosis are crucial for selecting and planning lung cancer treatments. As a result of the rapid development of medical imaging technology, the use of computed tomography (CT) scans in pathology is becoming standard practice. An intricate interplay of requirements and obstacles characterizes computer-assisted diagnosis, which relies on the precise and effective analysis of pathology images. In recent years, pathology image analysis tasks such as tumor region identification, prognosis prediction, tumor microenvironment characterization, and metastasis detection have witnessed the considerable potential of artificial intelligence, especially deep learning techniques. In this context, an artificial intelligence (AI)-based methodology for lung cancer diagnosis is proposed in this research work. As a first processing step, filtering using the Butterworth smooth filter algorithm was applied to the input images from the LUNA 16 lung cancer dataset to remove noise without significantly degrading the image quality. Next, we performed the bi-level feature selection step using the Chaotic Crow Search Algorithm and Random Forest (CCSA-RF) approach to select features such as diameter, margin, spiculation, lobulation, subtlety, and malignancy. Next, the Feature Extraction step was performed using the Multi-space Image Reconstruction (MIR) method with Grey Level Co-occurrence Matrix (GLCM). Next, the Lung Tumor Severity Classification (LTSC) was implemented by using the Sparse Convolutional Neural Network (SCNN) approach with a Probabilistic Neural Network (PNN). The developed method can detect benign, normal, and malignant lung cancer images using the PNN algorithm, which reduces complexity and efficiently provides classification results. Performance parameters, namely accuracy, precision, F-score, sensitivity, and specificity, were determined to evaluate the effectiveness of the implemented hybrid method and compare it with other solutions already present in the literature.