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“decrease in density”

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paper W7152163739 ✕

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

Overall energy consumption fell 18% against baseline control.

Outcome: overall energy consumptione0
Direction: decreasee0
Value: 18%e0
Comparator: baseline rule-based controle0

Not reportedTaxon

Verbatim

e0
“outperformed the baseline rule-based control with a 28% increase in fresh biomass, a 22% improvement in dry matter accumulation, a 25% reduction in actuator duty-cycle switching, and an 18% decrease in overall energy consumption.”
✓ located verbatim in the source · abstract, characters 1400–1629

Title

FPGA-Accelerated Machine Learning for Computational Environmental Information Processing in IoT-Integrated High-Density Nanosensor Networks

Alaa Kamal Yousif Dafhalla; Fawzia Awad Elhassan Ali; Asma Ibrahim Gamar Eldeen; et al. Information 2026. doi:10.3390/info17040354

Other claims from this paper (9)

Method / resource The control model achieved R2 of 0.985.
Method / resource The control model achieved temperature RMSE of 0.28 °C.
Method / resource The IoT gateway had mean communication latency of 140 ms.
Method / resource The IoT gateway had packet delivery reliability of 99.8%.
Method / resource The control system maintained temperature within ±0.3 °C of its setpoint.
Method / resource The control system maintained humidity within ±1.2% of its setpoint.
Intervention effect Pepper fresh biomass increased 28% over baseline rule-based control.
Intervention effect Pepper dry matter accumulation improved 22% over baseline rule-based control.
Method / resource Actuator duty-cycle switching fell 25% against baseline control.

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

Actuator duty-cycle switching fell 25% against baseline control.

Outcome: actuator duty-cycle switchinge0
Direction: reductione0
Value: 25%e0
Comparator: baseline rule-based controle0

Not reportedTaxon

Verbatim

e0
“outperformed the baseline rule-based control with a 28% increase in fresh biomass, a 22% improvement in dry matter accumulation, a 25% reduction in actuator duty-cycle switching, and an 18% decrease in overall energy consumption.”
✓ located verbatim in the source · abstract, characters 1400–1629

Title

FPGA-Accelerated Machine Learning for Computational Environmental Information Processing in IoT-Integrated High-Density Nanosensor Networks

Alaa Kamal Yousif Dafhalla; Fawzia Awad Elhassan Ali; Asma Ibrahim Gamar Eldeen; et al. Information 2026. doi:10.3390/info17040354

Other claims from this paper (9)

Method / resource The control model achieved R2 of 0.985.
Method / resource The control model achieved temperature RMSE of 0.28 °C.
Method / resource The IoT gateway had mean communication latency of 140 ms.
Method / resource The IoT gateway had packet delivery reliability of 99.8%.
Method / resource The control system maintained temperature within ±0.3 °C of its setpoint.
Method / resource The control system maintained humidity within ±1.2% of its setpoint.
Intervention effect Pepper fresh biomass increased 28% over baseline rule-based control.
Intervention effect Pepper dry matter accumulation improved 22% over baseline rule-based control.
Method / resource Overall energy consumption fell 18% against baseline control.

Intervention effectobservedoriginal resultpositivemeasured

Claim · model-written summary

Pepper fresh biomass increased 28% over baseline rule-based control.

Taxon: peppere1
Treatment: control systeme0
Outcome: fresh biomasse0
Direction: increasee0
Value: 28%e0
Comparator: baseline rule-based controle0

Verbatim

e0
“The control system maintained a temperature and humidity within ±0.3 °C and ±1.2% of the setpoints, respectively, and outperformed the baseline rule-based control with a 28% increase in fresh biomass, a 22% improvement in dry matter accumulation, a 25% reduction in actuator duty-cycle switching, and an 18% decrease in overall energy consumption.”
✓ located verbatim in the source · abstract, characters 1282–1629

e1
“subsequent experiments with pepper (Capsicum annuum)”
✓ located verbatim in the source · abstract, characters 1106–1158

Title

FPGA-Accelerated Machine Learning for Computational Environmental Information Processing in IoT-Integrated High-Density Nanosensor Networks

Alaa Kamal Yousif Dafhalla; Fawzia Awad Elhassan Ali; Asma Ibrahim Gamar Eldeen; et al. Information 2026. doi:10.3390/info17040354

Other claims from this paper (9)

Method / resource The control model achieved R2 of 0.985.
Method / resource The control model achieved temperature RMSE of 0.28 °C.
Method / resource The IoT gateway had mean communication latency of 140 ms.
Method / resource The IoT gateway had packet delivery reliability of 99.8%.
Method / resource The control system maintained temperature within ±0.3 °C of its setpoint.
Method / resource The control system maintained humidity within ±1.2% of its setpoint.
Intervention effect Pepper dry matter accumulation improved 22% over baseline rule-based control.
Method / resource Actuator duty-cycle switching fell 25% against baseline control.
Method / resource Overall energy consumption fell 18% against baseline control.

Intervention effectobservedoriginal resultpositivemeasured

Claim · model-written summary

Pepper dry matter accumulation improved 22% over baseline rule-based control.

Taxon: peppere1
Treatment: control systeme0
Outcome: dry matter accumulatione0
Direction: improvemente0
Value: 22%e0
Comparator: baseline rule-based controle0

Verbatim

e0
“The control system maintained a temperature and humidity within ±0.3 °C and ±1.2% of the setpoints, respectively, and outperformed the baseline rule-based control with a 28% increase in fresh biomass, a 22% improvement in dry matter accumulation, a 25% reduction in actuator duty-cycle switching, and an 18% decrease in overall energy consumption.”
✓ located verbatim in the source · abstract, characters 1282–1629

e1
“subsequent experiments with pepper (Capsicum annuum)”
✓ located verbatim in the source · abstract, characters 1106–1158

Title

FPGA-Accelerated Machine Learning for Computational Environmental Information Processing in IoT-Integrated High-Density Nanosensor Networks

Alaa Kamal Yousif Dafhalla; Fawzia Awad Elhassan Ali; Asma Ibrahim Gamar Eldeen; et al. Information 2026. doi:10.3390/info17040354

Other claims from this paper (9)

Method / resource The control model achieved R2 of 0.985.
Method / resource The control model achieved temperature RMSE of 0.28 °C.
Method / resource The IoT gateway had mean communication latency of 140 ms.
Method / resource The IoT gateway had packet delivery reliability of 99.8%.
Method / resource The control system maintained temperature within ±0.3 °C of its setpoint.
Method / resource The control system maintained humidity within ±1.2% of its setpoint.
Intervention effect Pepper fresh biomass increased 28% over baseline rule-based control.
Method / resource Actuator duty-cycle switching fell 25% against baseline control.
Method / resource Overall energy consumption fell 18% against baseline control.

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“decrease in density”

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