Over the last few years, several studies have appeared that employ Artificial Intelligence (AI) techniques to improve sustainable development in the agricultural sector. Specifically, these intelligent techniques provide mechanisms and procedures to facilitate decision-making in the agri-food industry. One of the application areas has been the automatic detection of plant diseases. These techniques, mainly based on deep learning models, allow for analysing and classifying plants to determine possible diseases facilitating early detection and thus preventing the propagation of the disease. In this way, this paper proposes an Edge-AI device that incorporates the necessary hardware and software components for automatically detecting plant diseases from a set of images of a plant leaf. In this way, the main goal of this work is to design an autonomous device that allows the detection of possible diseases that can detect potential diseases in plants. This will be achieved by capturing multiple images of the leaves and implementing data fusion techniques to enhance the classification process and improve its robustness. Several tests have been carried out to determine that the use of this device significantly increases the robustness of the classification responses to possible plant diseases.

The use of head kinematic measurement devices has recently proliferated owing to technology advances that make such measurement more feasible. In parallel, demand to understand the biomechanics of head impacts and injury in sports and the military has increased as the burden of such loading on the brain has received focused attention. As a result, the field has matured to the point of needing methodological guidelines to improve the rigor and consistency of research and reduce the risk of scientific bias. To this end, a diverse group of scientists undertook a comprehensive effort to define current best practices in head kinematic measurement, culminating in a series of manuscripts outlining consensus methodologies and companion summary statements. Summary statements were discussed, revised, and voted upon at the Consensus Head Acceleration Measurement Practices (CHAMP) Conference in March 2022. This manuscript summarizes the motivation and methods of the consensus process and introduces recommended reporting checklists to be used to increase transparency and rigor of future experimental design and publication of work in this field. The checklists provide an accessible means for researchers to apply the best practices summarized in the companion manuscripts when reporting studies utilizing head kinematic measurement in sport and military settings.

In this work, we have focused on the profiling of 5647 street samples covering marijuana, common and new recreational illicit drugs. All samples were analyzed using gas chromatography-mass spectrometry (GC-MS) technique. In total we have identified 53 illicit drugs with Δ-9-tetrahydrocannabinol (THC), amphetamine, N-ethylhexedrone, 3,4-methylenedioxy methamphetamine (MDMA), 4-chloromethcathinone (4-CMC), α-pyrrolidinoisohexaphenone (α-PHiP), cocaine, and 4-chloroethcathinone (4-CEC) being most commonly found and making 38.5, 17.8, 15.5, 8.0, 3.5, 2.7, 2.1, and 2.0% of the total studied pool, respectively. Except for methadone, all analyzed street samples were spiked with at least one cutting agent. Caffeine was the most frequently found adulterating addition present in around 33% (excluding marijuana) of the analyzed samples. Other identified cutting agents make an impressive group of more than 160 compounds. Finally, we have tabulated, illustrated, and discussed presented data in a view of smart and portable sensors development.

To solve the real-time complex mission-planning problem for Multiple heterogeneous Unmanned Aerial Vehicles (UAVs) in the dynamic environments, this paper addresses a new approach by effectively adapting the Consensus-Based Bundle Algorithms (CBBA) under the constraints of task timing, limited UAV resources, diverse types of tasks, dynamic addition of tasks, and real-time requirements. We introduce the dynamic task generation mechanism, which satisfied the task timing constraints. The tasks that require the cooperation of multiple UAVs are simplified into multiple sub-tasks to perform by a single UAV independently. We also introduce the asynchronous task allocation mechanism. This mechanism reduces the computational complexity of the algorithm and the communication time between UAVs. The partial task redistribution mechanism has been adopted for achieving the dynamic task allocation. The real-time performance of the algorithm is assured on the premise of optimal results. The feasibility and real-time performance of the algorithm are validated by conducting dynamic simulation experiments.