Abstract

A gas electron multiplier (GEM)-based detector was utilized for the first time on a spherical tokamak, MAST-U, during the 2023 campaign to investigate soft x-ray (SXR) radiation (1-20 keV) emitted from the plasma. GEM detectors, chosen for their resilience to harsh fusion environments and their ability to provide energy-resolved ($ E_{ r e s } \sim 25 \mathrm{\% } $ at 8 keV) SXR emission images (with a spatial resolution of few centimeters) with sub-millisecond time resolution, are a relatively new diagnostic compared to standard semiconductor diodes. In this study, the GEM detector features a pinhole geometry outside the vacuum chamber and observes the plasma through a beryllium window. Filled with an ArCO2 mixture, the detector consists of an Aluminized Mylar cathode, three Aluminum-coated GEM foils, and an anode made of a 16 × 16 matrix of 6 mm² pads for 2D readout. It employs custom GEMINI ASICs (Application Specific Integrated Circuits) for signal readout, enabling single photon-counting techniques with Time over Threshold analysis on each detector channel, for a maximum rate of 1 MHz per channel. Preliminary results from the 2023 campaign highlight the GEM detector’s ability to complement existing SXR camera systems by adding energy-resolved information to the spatial and temporal data. Case studies demonstrate the detector’s capability to capture Magnetohydrodynamic instabilities, such as Snake instabilities, while utilizing its energy-resolved measurements to analyze plasma events, including Internal Reconnection Events. Additionally, the GEM detector enables the estimation of Electron Temperature in Maxwellian plasmas from SXR measurements. These findings underscore the potential of the GEM-based diagnostic system to enhance the understanding of tokamak plasmas by providing simultaneous spatial, temporal, and energy-resolved insights.

• Book : 36(1)
• Pub. Date : 2025
• Page : pp.016019
• Keyword :

Abstract

Semiconductor devices contain defects and localized mechanical stress even in their pristine states, persisting after post-fabrication annealing. We hypothesize that these pre-existing conditions, with their lower threshold energy for defect proliferation and/or ionization, may serve as nuclei for radiation damage. To test this hypothesis, we adopted a two-pronged approach: (a) performing electron wind force (EWF) annealing preemptively on pristine Zener diodes to eliminate pre-existing defects before radiation exposure, and (b) applying EWF annealing restoratively on devices already damaged by radiation. The EWF process is non-thermal and can eliminate defects below 30 °C that persist through conventional thermal annealing. Both pristine and EWF-annealed pristine devices were exposed to 15 MeV protons with a fluence of 10¹⁴ cm⁻². Radiation damage increased the ideality factor from 1 to 2.33 in the pristine devices, while the preemptively EWF-annealed devices showed remarkable resilience, with an ideality factor of 1.5. Similar performance improvements were observed with restorative EWF annealing on radiation-damaged devices. This resilience and recovery in performance are further supported by Raman spectroscopy indicating enhanced crystallinity compared to the pristine condition. These findings demonstrate the potential of EWF annealing as both a protective and restorative treatment for semiconductor devices in high-radiation environments.

• Book : 100(1)
• Pub. Date : 2025
• Page : pp.015904
• Keyword :

Abstract

BACKGROUND

Rhamnolipids (RLS) are surfactants that improve the growth performance of poultry by improving the absorption of nutrients. This study aims to investigate the effects of RLS replacement of chlortetracycline (CTC) on growth performance, slaughtering traits, meat quality, antioxidant function and nuclear‐factor‐E2‐related factor 2 (Nrf2) signaling pathway in broilers. A total of 600 one‐day‐old Arbor Acres chicks were randomly assigned to five groups with eight replicates in each group, raised for 42 days. Broilers were respectively fed a basal diet with no CTC or RLS, 75 mg kg⁻¹ CTC, and 250, 500, 1000 mg kg⁻¹ RLS.

RESULTS

Dietary supplementation with RLS linearly increased the average daily gain, average daily feed intake, carcass yield, eviscerated yield, ether extract, enhanced total superoxide and glutathione peroxidase (GPx) activities, overexpressed the relative expressions of Nrf2, heme oxygenase 1, Copper/zinc superoxide dismutase, Manganese superoxide dismutase, GPx and catalase and decreased the lightness value at 24 h, drip loss and malondialdehyde contents of broilers (P < 0.05). Compared with the control group, broilers fed 1000 mg kg⁻¹ RLS reduced the drip loss and broilers fed 500 mg kg⁻¹ RLS increased muscle crude fat content (P < 0.05). Compared with the CTC group, dietary supplementation with 1000 mg kg⁻¹ RLS increased eviscerated yield (P < 0.05).

CONCLUSION

RLS could improve growth performance, crude fat content, meat quality and antioxidant capacity and activate relative expression of genes in the Nrf2 signaling pathway in broilers. It could be used as an antibiotic substitute in diets and the recommended supplemental dose of RLS in feed of broilers is 1000 mg kg⁻¹. © 2024 Society of Chemical Industry.

• Book : 105(2)
• Pub. Date : 2025
• Page : pp.858-865
• Keyword :

Abstract

Background

Though several nomograms exist, machine learning (ML) approaches might improve prediction of pathologic stage in patients with prostate cancer. To develop ML models to predict pathologic stage that outperform existing nomograms that use readily available clinicopathologic variables.

Methods

Patients with prostate adenocarcinoma who underwent surgery were identified in the National Cancer Database. Seven ML models were trained to predict organ‐confined (OC) disease, extracapsular extension, seminal vesicle invasion (SVI), and lymph node involvement (LNI). Model performance was measured using area under the curve (AUC) on a holdout testing data set. Clinical utility was evaluated using decision curve analysis (DCA). Performance metrics were confirmed on an external validation data set.

Results

The ML‐based extreme gradient boosted trees model achieved the best performance with an AUC of 0.744, 0.749, 0.816, 0.811 for the OC, ECE, SVI, and LNI models, respectively. The MSK nomograms achieved an AUC of 0.708, 0.742, 0.806, 0.802 for the OC, ECE, SVI, and LNI models, respectively. These models also performed the best on DCA. Findings were consistent on both a holdout internal validation data set as well as an external validation data set.

Conclusions

Our ML models better predicted pathologic stage relative to existing nomograms at predicting pathologic stage. Accurate prediction of pathologic stage can help oncologists and patients determine optimal definitive treatment options for patients with prostate cancer.

• Book : 85(1)
• Pub. Date : 2025
• Page : pp.3-12
• Keyword :

• Book : 178()
• Pub. Date : 2025
• Page : pp.105491
• Keyword :

• Book : 178()
• Pub. Date : 2025
• Page : pp.105521
• Keyword :

• Book : 229()
• Pub. Date : 2025
• Page : pp.112409
• Keyword :

• Book : 431()
• Pub. Date : 2025
• Page : pp.113733
• Keyword :

• Book : 431()
• Pub. Date : 2025
• Page : pp.113710
• Keyword :

• Book : 431()
• Pub. Date : 2025
• Page : pp.113670
• Keyword :