Results 171 to 180 of about 609,366 (250)

Experiment-Based Optimization of LED Spectral Irradiance Ratios for Enhancing Biomass, Secondary Metabolite, and Essential Oil Yields and Compositions of <i>Ocimum</i> × <i>africanum</i> Lour. Under Controlled Environmental Conditions. [PDF]

Molecules
Chu HTT, Vu TN, Tong QC, Tien TQ, Nguyen TP, Dinh TTT, Pappert I, Wirth F, Jokic L, Schiesser A, Tran KQ.   +10 more
europepmc   +1 more source

Data‐Guided Photocatalysis: Supervised Machine Learning in Water Splitting and CO2 Conversion

Advanced Intelligent Discovery, EarlyView.
This review highlights recent advances in supervised machine learning (ML) for photocatalysis, emphasizing methods to optimize photocatalyst properties and design materials for solar‐driven water splitting and CO2 reduction. Key applications, challenges, and future directions are discussed, offering a practical framework for integrating ML into the ...
Paul Rossener Regonia, Christian Mark Pelicano   +1 more
wiley   +1 more source

Greenhouse gas emissions from surgical care: a retrospective cohort study of scope 1 and scope 2 emissions across specialties and operative approaches. [PDF]

BMJ Open
Akashi Y, Yamada K, Ogawa K, Owada Y, Enomoto T, Furuya K, Hashimoto S, Takahashi K, Shimomura O, Miyazaki Y, Oda T.   +10 more
europepmc   +1 more source

Functionalized TiO2 Catalysts: Revolutionary Candidate for Low‐Temperature MgH2 and Prospects for Intelligent Integration

Advanced Intelligent Discovery, EarlyView.
Recent advances in TiO2 modification strategies: structure regulation and composite engineering for improved activity and functionality. TiO2 has emerged as a pivotal catalyst for enhancing MgH2, a high‐capacity solid‐state hydrogen storage material, owing to its structural versatility.
Xiaopeng Chu, Jinghao Wang, Haojie Shi, Chuanxin Peng, Jiacheng Qi, Ge Gao, Guoquan Na, Haibo Yu, Kela Xiao, Gangqiang Wu, Zhendong Yao   +10 more
wiley   +1 more source

Establishment of the first speed breeding facility in Central Asia: development of an accessible bread wheat (<i>Triticum aestivum</i> L.) protocol for resource-limited research institutions. [PDF]

Front Plant Sci
Yermekbayev K, Abduakassov A, Shoken M, Sartbayeva Z, Baimyrzayev K, Bauyrzhan M, Abugalieva S, Turuspekov Y, Mahmood Z.   +8 more
europepmc   +1 more source

A Review on Recent Trends of Bioinspired Soft Robotics: Actuators, Control Methods, Materials Selection, Sensors, Challenges, and Future Prospects

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
This article reviews the current state of bioinspired soft robotics. The article discusses soft actuators, soft sensors, materials selection, and control methods used in bioinspired soft robotics. It also highlights the challenges and future prospects of this field.
Abhirup Sarker, Tamzid Ul Islam, Md. Robiul Islam   +2 more
wiley   +1 more source

GCP-YOLO: high-precision detection of tiny chili flowers in complex greenhouse scenes. [PDF]

Front Plant Sci
Kuang M, Wang Y, Li X, Zou X, Liu D, Xiang Y, Bader S, Xie F, Zhang Y, Li X.   +9 more
europepmc   +1 more source

Haptic In‐Sensor Computing Device Based on CNT/PDMS Nanocomposite Physical Reservoir

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
Using a porous carbon nanotube‐polydimethylsiloxane nanocomposite, a sensor array integrated with a physical reservoir computing paradigm capable of in‐sensor computing is demonstrated. The device is able to classify between nine objects with an accuracy above 80%, opening the possibility for low‐power sensing/computing for future robotics.
Kouki Kimizuka, Saman Azhari, Shoshi Tokuno, Ahmet Karacali, Yuki Usami, Shuhei Ikemoto, Hakaru Tamukoh, Hirofumi Tanaka   +7 more
wiley   +1 more source

YOLO-FLBM: a lightweight and high-performance model for tomato ripeness detection in complex greenhouse environments. [PDF]

Front Plant Sci
Su Y, Zhang P, Li H, Kang F, Zhao P, Wang L.   +5 more
europepmc   +1 more source

Ecoefficiency Analysis and Regression in Data Conversion for Spiking Neural Network Training

Advanced Intelligent Systems, EarlyView.
The environmental footprint of spiking neural networks is quantified during dataset encoding and training for autonomous driving regression across three benchmarks. Temporal depth emerges as the dominant driver of energy consumption and CO2 emissions, while the accuracy–energy trade‐off proves dataset‐dependent. On conventional hardware, spiking models
Fernando S. Martínez, Raúl Parada, Jordi Casas‐Roma, Laia Subirats   +3 more
wiley   +1 more source