Recent advances in animal tracking technology have ushered in a new era in biologging. However, the considerable size of many sophisticated biologging devices restricts their application to larger animals, whereas older techniques often still represent the state-of-the-art for studying small vertebrates. In industrial applications, low-power wireless sensor networks (WSNs) fulfill requirements similar to those needed to monitor animal behavior at high resolution and at low tag mass. We developed a wireless biologging network (WBN), which enables simultaneous direct proximity sensing, high-resolution tracking, and long-range remote data download at tag masses of 1 to 2 g. Deployments to study wild bats created social networks and flight trajectories of unprecedented quality. Our developments highlight the vast capabilities of WBNs and their potential to close an important gap in biologging: fully automated tracking and proximity sensing of small animals, even in closed habitats, at high spatial and temporal resolution.
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http://dx.doi.org/10.1371/journal.pbio.3000655 | DOI Listing |
Chem Rev
December 2024
Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California 90089, United States.
Conventional artificial intelligence (AI) systems are facing bottlenecks due to the fundamental mismatches between AI models, which rely on parallel, in-memory, and dynamic computation, and traditional transistors, which have been designed and optimized for sequential logic operations. This calls for the development of novel computing units beyond transistors. Inspired by the high efficiency and adaptability of biological neural networks, computing systems mimicking the capabilities of biological structures are gaining more attention.
View Article and Find Full Text PDFNanomaterials (Basel)
December 2024
Faculty of Mechatronics, Informatics, and Interdisciplinary Studies, Technical University of Liberec, 46001 Liberec, Czech Republic.
There are three components to every environmental protection system: monitoring, estimation, and control. One of the main toxic gases with considerable effects on human health is NO, which is released into the atmosphere by industrial activities and the transportation network. In the present research, a NO sensor is designed based on FeO piperidine-4-sulfonic acid grafted onto a reduced graphene oxide FeO@rGO-N-(piperidine-4-SOH) nanocomposite, due to the highly efficient detection of pollution in the air.
View Article and Find Full Text PDFBiosensors (Basel)
November 2024
Nanobioengineering Group, Institute for Bioengineering of Catalonia (IBEC), Barcelona Institute of Science and Technology (BIST), 12 Baldiri Reixac 15-21, 08028 Barcelona, Spain.
There are many examples in nature in which the ability to detect is combined with decision-making, such as the basic survival instinct of plants and animals to search for food. We can technically translate this innate function via the use of robotics with integrated sensors and artificial intelligence. However, the integration of sensing capabilities into robotics has traditionally been neglected due to the significant associated technical challenges.
View Article and Find Full Text PDFGels
December 2024
Research Institute of Polymer Materials, School of Materials Science and Engineering, Shandong University, Jinan 250061, China.
Hydrogels, known for their outstanding water absorption, flexibility, and biocompatibility, have been widely utilized in various fields. Nevertheless, their application is still limited by their relatively low mechanical performance. This study has successfully developed a dual-network hydrogel with exceptional mechanical properties by embedding amino-functionalized polysiloxane (APSi) networks into a polyvinyl alcohol (PVA) matrix.
View Article and Find Full Text PDFAppl Spectrosc
December 2024
Nuclear Mission Branch, Air Force Research Laboratory, Kirtland AFB, New Mexico, USA.
This work implements a mid-level data fusion methodology on spectral data from handheld X-ray fluorescence and laser-induced breakdown spectroscopy analyzers to quantify plutonium surrogate (CeO) contamination in soil samples for the first time. Spectral data from each analyzer were used independently to train supervised machine learning regressions to predict Ce concentration. Fused features from both data sets were then used to train the same models, comparing prediction performance by evaluating model precision and sensitivity.
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