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flc  (Santa Cruz Biotechnology)


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    Santa Cruz Biotechnology flc
    Flc, supplied by Santa Cruz Biotechnology, used in various techniques. Bioz Stars score: 93/100, based on 108 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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    Average 93 stars, based on 108 article reviews
    flc - by Bioz Stars, 2026-04
    93/100 stars

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    The overall structure of the proposed QAOA-FLC control system.

    Journal: Scientific Reports

    Article Title: Adaptive intelligent controller for a lower limb rehabilitation robot using QAOA-based online membership optimization

    doi: 10.1038/s41598-026-41647-4

    Figure Lengend Snippet: The overall structure of the proposed QAOA-FLC control system.

    Article Snippet: The hardware-in-the-loop simulation was performed using the developed lower-limb rehabilitation robot model and the proposed QAOA-FLC implemented in MATLAB/Simulink ® with real-time interfacing to microcontroller hardware.

    Techniques: Control

    Comparison of QAOA-FLC and PID performance under baseline, disturbance, and noise conditions.

    Journal: Scientific Reports

    Article Title: Adaptive intelligent controller for a lower limb rehabilitation robot using QAOA-based online membership optimization

    doi: 10.1038/s41598-026-41647-4

    Figure Lengend Snippet: Comparison of QAOA-FLC and PID performance under baseline, disturbance, and noise conditions.

    Article Snippet: The hardware-in-the-loop simulation was performed using the developed lower-limb rehabilitation robot model and the proposed QAOA-FLC implemented in MATLAB/Simulink ® with real-time interfacing to microcontroller hardware.

    Techniques: Comparison

    Hip joint tracking with fixed and QAOA-FLC under moderate load disturbance RMSE: 0.5179 → 0.0372, Overshoot: 12.6% → 2.9%, transit time: 2.60 → 0.95 s.

    Journal: Scientific Reports

    Article Title: Adaptive intelligent controller for a lower limb rehabilitation robot using QAOA-based online membership optimization

    doi: 10.1038/s41598-026-41647-4

    Figure Lengend Snippet: Hip joint tracking with fixed and QAOA-FLC under moderate load disturbance RMSE: 0.5179 → 0.0372, Overshoot: 12.6% → 2.9%, transit time: 2.60 → 0.95 s.

    Article Snippet: The hardware-in-the-loop simulation was performed using the developed lower-limb rehabilitation robot model and the proposed QAOA-FLC implemented in MATLAB/Simulink ® with real-time interfacing to microcontroller hardware.

    Techniques:

    Knee joint tracking with fixed and QAOA-FLC moderate load disturbance, RMSE: 0.3137 → 0.0187, Overshoot: 8.2% → 1.4%, transit time: 2.10 → 0.80 s.

    Journal: Scientific Reports

    Article Title: Adaptive intelligent controller for a lower limb rehabilitation robot using QAOA-based online membership optimization

    doi: 10.1038/s41598-026-41647-4

    Figure Lengend Snippet: Knee joint tracking with fixed and QAOA-FLC moderate load disturbance, RMSE: 0.3137 → 0.0187, Overshoot: 8.2% → 1.4%, transit time: 2.10 → 0.80 s.

    Article Snippet: The hardware-in-the-loop simulation was performed using the developed lower-limb rehabilitation robot model and the proposed QAOA-FLC implemented in MATLAB/Simulink ® with real-time interfacing to microcontroller hardware.

    Techniques:

    Ankle joint tracking with fixed and QAOA-FLC under moderate load disturbance, RMSE: 0.3177 → 0.0223, Overshoot: 9.8% → 2.0%, transit time: 2.35 → 0.90 s.

    Journal: Scientific Reports

    Article Title: Adaptive intelligent controller for a lower limb rehabilitation robot using QAOA-based online membership optimization

    doi: 10.1038/s41598-026-41647-4

    Figure Lengend Snippet: Ankle joint tracking with fixed and QAOA-FLC under moderate load disturbance, RMSE: 0.3177 → 0.0223, Overshoot: 9.8% → 2.0%, transit time: 2.35 → 0.90 s.

    Article Snippet: The hardware-in-the-loop simulation was performed using the developed lower-limb rehabilitation robot model and the proposed QAOA-FLC implemented in MATLAB/Simulink ® with real-time interfacing to microcontroller hardware.

    Techniques:

    Hip joint tracking with fixed and QAOA- FLC under severe load disturbance, RMSE: 0.4820 → 0.0415, Overshoot: 11.8% → 3.1%, Transit time: 2.55 → 0.92 s.

    Journal: Scientific Reports

    Article Title: Adaptive intelligent controller for a lower limb rehabilitation robot using QAOA-based online membership optimization

    doi: 10.1038/s41598-026-41647-4

    Figure Lengend Snippet: Hip joint tracking with fixed and QAOA- FLC under severe load disturbance, RMSE: 0.4820 → 0.0415, Overshoot: 11.8% → 3.1%, Transit time: 2.55 → 0.92 s.

    Article Snippet: The hardware-in-the-loop simulation was performed using the developed lower-limb rehabilitation robot model and the proposed QAOA-FLC implemented in MATLAB/Simulink ® with real-time interfacing to microcontroller hardware.

    Techniques:

    Knee joint tracking with fixed and QAOA- FLC under severe load disturbance, RMSE: 0.4365 → 0.0398, Overshoot: 10.9% → 2.7%, Transit time: 2.45 → 0.90 s.

    Journal: Scientific Reports

    Article Title: Adaptive intelligent controller for a lower limb rehabilitation robot using QAOA-based online membership optimization

    doi: 10.1038/s41598-026-41647-4

    Figure Lengend Snippet: Knee joint tracking with fixed and QAOA- FLC under severe load disturbance, RMSE: 0.4365 → 0.0398, Overshoot: 10.9% → 2.7%, Transit time: 2.45 → 0.90 s.

    Article Snippet: The hardware-in-the-loop simulation was performed using the developed lower-limb rehabilitation robot model and the proposed QAOA-FLC implemented in MATLAB/Simulink ® with real-time interfacing to microcontroller hardware.

    Techniques:

    Ankle joint tracking with fixed and QAOA- FLC under severe load disturbance, RMSE: 0.3952 → 0.0441, Overshoot: 9.7% → 2.5%, Transit time: 2.40 → 0.88 s.

    Journal: Scientific Reports

    Article Title: Adaptive intelligent controller for a lower limb rehabilitation robot using QAOA-based online membership optimization

    doi: 10.1038/s41598-026-41647-4

    Figure Lengend Snippet: Ankle joint tracking with fixed and QAOA- FLC under severe load disturbance, RMSE: 0.3952 → 0.0441, Overshoot: 9.7% → 2.5%, Transit time: 2.40 → 0.88 s.

    Article Snippet: The hardware-in-the-loop simulation was performed using the developed lower-limb rehabilitation robot model and the proposed QAOA-FLC implemented in MATLAB/Simulink ® with real-time interfacing to microcontroller hardware.

    Techniques:

    Joint tracking performance of QAOA-FLC and standard FLC under 4% sensor noise. Hip RMSE: 0.1729→ 0.0225, Knee RMSE: 0.1144 → 0.0247, Ankle RMSE: 0.0979 → 0.0275.

    Journal: Scientific Reports

    Article Title: Adaptive intelligent controller for a lower limb rehabilitation robot using QAOA-based online membership optimization

    doi: 10.1038/s41598-026-41647-4

    Figure Lengend Snippet: Joint tracking performance of QAOA-FLC and standard FLC under 4% sensor noise. Hip RMSE: 0.1729→ 0.0225, Knee RMSE: 0.1144 → 0.0247, Ankle RMSE: 0.0979 → 0.0275.

    Article Snippet: The hardware-in-the-loop simulation was performed using the developed lower-limb rehabilitation robot model and the proposed QAOA-FLC implemented in MATLAB/Simulink ® with real-time interfacing to microcontroller hardware.

    Techniques:

    Joint tracking performance of QAOA-FLC and standard FLC under 40% sensor noise, Hip RMSE: 4.2875→ 0.1605, Knee RMSE: 2.2249 → 0.1857, Ankle RMSE: 1.6203 → 0.2313.

    Journal: Scientific Reports

    Article Title: Adaptive intelligent controller for a lower limb rehabilitation robot using QAOA-based online membership optimization

    doi: 10.1038/s41598-026-41647-4

    Figure Lengend Snippet: Joint tracking performance of QAOA-FLC and standard FLC under 40% sensor noise, Hip RMSE: 4.2875→ 0.1605, Knee RMSE: 2.2249 → 0.1857, Ankle RMSE: 1.6203 → 0.2313.

    Article Snippet: The hardware-in-the-loop simulation was performed using the developed lower-limb rehabilitation robot model and the proposed QAOA-FLC implemented in MATLAB/Simulink ® with real-time interfacing to microcontroller hardware.

    Techniques:

    Total system energy of the 3-DOF rehabilitation robot under FLC and QAOA-FLC.

    Journal: Scientific Reports

    Article Title: Adaptive intelligent controller for a lower limb rehabilitation robot using QAOA-based online membership optimization

    doi: 10.1038/s41598-026-41647-4

    Figure Lengend Snippet: Total system energy of the 3-DOF rehabilitation robot under FLC and QAOA-FLC.

    Article Snippet: The hardware-in-the-loop simulation was performed using the developed lower-limb rehabilitation robot model and the proposed QAOA-FLC implemented in MATLAB/Simulink ® with real-time interfacing to microcontroller hardware.

    Techniques:

    The overall more resistant clinical isolates to agricultural (field triazoles) than clinical azoles. F. fujikuroi SC demonstrated significantly less resistance to agricultural azoles, whereas F. solani SC exhibited higher resistance to field azoles. Violin plot of EC 50 distribution for 130 clinical Fusarium isolates in three major SCs against clinical ( A ) and agricultural ( B ) azoles. The data set includes 74 isolates of F. solani SC, 37 isolates of F. oxysporum SC, and 19 isolates of F. fujikuroi SC. The x-axis is presented on a natural logarithm (LN) scale, with corresponding EC 50 values (µg/mL) overlaid on each graph and connected by a dotted line. A natural logarithm (LN) value of 0 corresponds to an EC 50 of 1. The solid black line within each violin plot represents the median EC 50 for each group. Clinical azoles include VOZ, POZ, ITZ, FCZ, and clotrimazole (CLZ). Agricultural azoles compose TRI (triflumizole), FLU (flutriafol), DIN, OXI, and EPO. Medians between groups were statistically analyzed using one-way ANOVA, followed by the Kruskal-Wallis test. Significance levels are indicated as follows: *P < 0.05, **P < 0.01, ***P < 0.001, and ****P < 0.0001.

    Journal: Antimicrobial Agents and Chemotherapy

    Article Title: Prevalence and diversity of antifungal resistance in Fusarium isolates across clinical and agricultural settings in the United States

    doi: 10.1128/aac.01208-25

    Figure Lengend Snippet: The overall more resistant clinical isolates to agricultural (field triazoles) than clinical azoles. F. fujikuroi SC demonstrated significantly less resistance to agricultural azoles, whereas F. solani SC exhibited higher resistance to field azoles. Violin plot of EC 50 distribution for 130 clinical Fusarium isolates in three major SCs against clinical ( A ) and agricultural ( B ) azoles. The data set includes 74 isolates of F. solani SC, 37 isolates of F. oxysporum SC, and 19 isolates of F. fujikuroi SC. The x-axis is presented on a natural logarithm (LN) scale, with corresponding EC 50 values (µg/mL) overlaid on each graph and connected by a dotted line. A natural logarithm (LN) value of 0 corresponds to an EC 50 of 1. The solid black line within each violin plot represents the median EC 50 for each group. Clinical azoles include VOZ, POZ, ITZ, FCZ, and clotrimazole (CLZ). Agricultural azoles compose TRI (triflumizole), FLU (flutriafol), DIN, OXI, and EPO. Medians between groups were statistically analyzed using one-way ANOVA, followed by the Kruskal-Wallis test. Significance levels are indicated as follows: *P < 0.05, **P < 0.01, ***P < 0.001, and ****P < 0.0001.

    Article Snippet: FCZ was obtained from LKT Laboratory, Inc. (Cat. F4682); ITZ from Spectrum Chemical MFG.

    Techniques: