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A instabilidade postural esta relacionada à ocorrência de quedas, incapacidade e fragilidade em idosos. Objetivo: avaliar o equilíbrio e o medo de cair em idosos comunitários. Métodos: Participaram do estudo descritivo 58 idosos comunitários da cidade de Londrina/PR, com idade média de 73,3 DP= 7,52 anos, sendo 43 (74,1%) mulheres e 15 (25,9%) homens. A amostra foi composta somente por idosos que não apresentaram déficit cognitivo. A coleta de dados foi realizada por meio de entrevistas domiciliares com aplicação dos seguintes instrumentos: Mini-Exame do Estado Mental para avaliação das condições cognitivas, Avaliação da Mobilidade Orientada pelo Desempenho para avaliação do equilíbrio estático e dinâmico, Escala de Eficácia de Quedas para verificar o medo de cair e questionário abordando aspectos sócio-demográficos e condições de saúde. Resultados: Os indivíduos obtiveram bom desempenho na avaliação do equilíbrio e da marcha com mediana de 52 pontos para a pontuação total, mediana de 35 para o domínio equilíbrio e 17 pontos para a marcha. A investigação sobre o medo em cair apontou que 54 (93,1%) dos entrevistados apresentaram preocupação em cair. Verificou-se associação significativa em relação aos diferentes graus de preocupação de cair e ocorrência de queda. Conclusão: O presente estudo identificou bom equilíbrio nos idosos estudados, porém apesar do bom desempenho na avaliação do equilíbrio estático e dinâmico a amostra apresentou elevado medo de cair e alto índice de quedas.

Yield forecasting is of immeasurable value in modern viticulture to optimize harvest scheduling and quality management. Traditionally, this task is carried out through manual and destructive sampling of production components and their accurate assessment is expensive, time-consuming, and error-prone, resulting in erroneous projections. The number of inflorescences and flowers per vine is one of the main components and serves as an early predictor. The adoption of new non-invasive technologies can automate this task and drive viticulture yield forecasting to higher levels of accuracy. In this study, different Single Stage Instance Segmentation models from the state-of-the-art You Only Look Once (YOLO) family, such as YOLOv5 and YOLOv8, were benchmarked on a dataset of RGB images for grapevine inflorescence detection and segmentation, with the aim of validating and subsequently implementing the solution for counting the number of inflorescences and flowers. All models obtained promising results, with the YOLOv8s and the YOLOv5s models standing out with an F1-Score of 95.1% and 97.7% for the detection and segmentation tasks, respectively. Moreover, the low inference times obtained demonstrate the models' ability to be deployed in real-time applications, allowing for non-destructive predictions in uncontrolled environments. [ABSTRACT FROM AUTHOR]

The world wine sector is a multi-billion dollar industry with a wide range of economic activities. Therefore, it becomes crucial to monitor the grapevine because it allows a more accurate estimation of the yield and ensures a high-quality end product. The most common way of monitoring the grapevine is through the leaves (preventive way) since the leaves first manifest biophysical lesions. However, this does not exclude the possibility of biophysical lesions manifesting in the grape berries. Thus, this work presents three pre-trained YOLO models (YOLOv5x6, YOLOv7-E6E, and YOLOR-CSP-X) to detect and classify grape bunches as healthy or damaged by the number of berries with biophysical lesions. Two datasets were created and made publicly available with original images and manual annotations to identify the complexity between detection (bunches) and classification (healthy or damaged) tasks. The datasets use the same 10,010 images with different classes. The Grapevine Bunch Detection Dataset uses the Bunch class, and The Grapevine Bunch Condition Detection Dataset uses the OptimalBunch and DamagedBunch classes. Regarding the three models trained for grape bunches detection, they obtained promising results, highlighting YOLOv7 with 77% of mAP and 94% of the F1-score. In the case of the task of detection and identification of the state of grape bunches, the three models obtained similar results, with YOLOv5 achieving the best ones with an mAP of 72% and an F1-score of 92%. [ABSTRACT FROM AUTHOR]

The article presents a study aimed to analyze the work process of an Extended Nucleus of Family Health and Primary Care (NASF-AB), questioning: who are those that support the matrix supporter? and how these professionals learn/understand their work? The research studied the work process with the NASF-AB supporters and, articulated with the perspectives of servitude and freedom in Baruch Spinoza, problematized how they feel and how they act, evoking thought-affection and thoughtaction. Methodological choices involved cartography and research-with. It was possible to identify team behavior that promotes permanent education in the healthcare field and the necessary collective enunciation that the NASFAB must represent. It can be understood that "support for supporters" is objectively expressed through the processes of self-analysis (institutional), measures of permanent health education (among peers) and efforts to allow fear and hope to be expressed so that affections appear genuine and encounters may thrive (freedom). The action among peers, within the NASF-AB to learn, get ownership and practice the matrix support led to the understanding that it is something that happens as "among us" (the supporters themselves), invested with the perseverance of freedom in the face of servitude. [ABSTRACT FROM AUTHOR]

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Cascade cropping systems in soilless horticulture (where drainage collected from the main crop is used in fertigation of secondary crops) are potentially interesting for Mediterranean countries as they enhance water and nutrient use efficiency. However, their agronomic and long-term environmental impact has been poorly addressed. In this case study, lettuce grown hydroponically or in soil (previously exposed to drainage for five years) was fertigated, throughout the cultivation period, with a nutrient solution composed of 0, 25, 50 or 100 % of drainage (0D, 25D, 50D and 100D) mixed with a fresh nutrient solution. Plant performance analysis included growth parameters and leaf mineral composition. Drainage was analyzed for nutrients and Plant Protection Products (PPP) residues, and bioassays were performed exposing aquatic organisms (Raphidocelis subcapitata , Aliivibrio fischeri and Daphnia magna) to drainage and soil elutriate. When analyzing plant performance in both cultivation systems, a significant effect was only found at 100D in hydroponics, resulting in 41 % less leaf area, 20 % smaller head diameter and 43 % lower yield. Drainage analysis showed high nutrient content, presence of PPP residues (up to 6 substances, reaching 3.29 µg·L−1 in total) and revealed toxicity to D. magna (EC 50 = 66.6 %). Moreover, soil elutriate presented toxicity to R. subcapitata (EC 50 = 20.6 %) and to A. fischeri (EC 50 = 14.9 %). This study demonstrates the potential of using relatively high drainage percentages (up to 50 %) from soilless cultivation systems if applied to hydroponically-grown secondary crops. However, attention should be paid to the use of cascade cropping systems when drainages are applied to fertigate soil-grown crops, as it may contribute to soil degradation and environmental pollution on a long run. [Display omitted] • Drainage (D) from soilless cultivation systems led to soil degradation. • Incorporating 50% D in fertigation did not impact soil or soilless lettuce growth. • Chemical analyses and bioassays showed that D use should target soilless systems. • Battery of bioassays revealed environmental hazard following cumulative D emission. • Guidelines and strict monitoring are needed when using D in Mediterranean countries. [ABSTRACT FROM AUTHOR]

The objective of this work was to assess the impact of processing parameters (inlet temperature, 170-200C; drying aid-to-acerola ratio, 2:1-5:1; and percent replacement of maltodextrin by cashew tree gum as drying aid, 0-100%) on degrees of retention of ascorbic acid (AA) and anthocyanins (AC) during spray drying of acerola pomace extract. The experiment was conducted according to response surface methodology. Degrees of AC and AA retention were impaired by increasing the inlet temperature, and favored by increasing drying aid-to-acerola ratio. Maximum degrees of retention (higher than 95%) were predicted to be achieved by using an inlet temperature of 170C, with a drying aid-to-acerola ratio of 5:1. Cashew tree gum may or may not be used to replace maltodextrin as the drying aid, without significantly changing the retentions of the compounds of interest. PRACTICAL APPLICATIONS Acerola is an important source of ascorbic acid (vitamin C) and anthocyanins (its main pigments, responsible for its red color), compounds of commercial interest, related to their widely reported antioxidant properties, which have been associated to prevention of degenerative diseases. This research involves the preparation of spray-dried acerola extract from an inexpensive residue from acerola juice processing. The product may be used as a food additive imparting a red color and/or antioxidant properties to a wide variety of foods. The study also indicates the potential of cashew tree gum, a virtually unknown exsudate from a widely available tree in Brazil, as a maltodextrin replacement as drying aid in spray drying of sugar-rich products such as fruit extracts. [ABSTRACT FROM AUTHOR]

Abstract: The objective of this study was to assess the impact of some processing parameters on moisture content, flowability, hygroscopicity and water solubility of spray dried acerola pomace extract using maltodextrin and cashew tree gum as drying aids. The experiment was conducted according to Response Surface Methodology, with the independent variables being: inlet temperature (170–200°C), drying aid/acerola ratio (2:1–5:1), and percent replacement of maltodextrin by cashew tree gum (0–100%). Higher inlet temperatures favored the desired physical properties of the powders, decreasing their moisture contents and hygroscopicity, and increasing flowability. The drying aids decreased the powder hygroscopicity, especially cashew tree gum (CTG), which also enhanced the powder flowability. The best processing conditions to obtain a free-flowing and least hygroscopic acerola pomace extract powder by spray drying were: inlet temperature above 194°C; drying aid/acerola solid ratio, 4:1; percent replacement of maltodextrin by CTG, at least 80%. [Copyright &y& Elsevier]

The harvesting operation is a recurring task in the production of any crop, thus making it an excellent candidate for automation. In protected horticulture, one of the crops with high added value is tomatoes. However, its robotic harvesting is still far from maturity. That said, the development of an accurate fruit detection system is a crucial step towards achieving fully automated robotic harvesting. Deep Learning (DL) and detection frameworks like Single Shot MultiBox Detector (SSD) or You Only Look Once (YOLO) are more robust and accurate alternatives with better response to highly complex scenarios. The use of DL can be easily used to detect tomatoes, but when their classification is intended, the task becomes harsh, demanding a huge amount of data. Therefore, this paper proposes the use of DL models (SSD MobileNet v2 and YOLOv4) to efficiently detect the tomatoes and compare those systems with a proposed histogram-based HSV colour space model to classify each tomato and determine its ripening stage, through two image datasets acquired. Regarding detection, both models obtained promising results, with the YOLOv4 model standing out with an F1-Score of 85.81%. For classification task the YOLOv4 was again the best model with an Macro F1-Score of 74.16%. The HSV colour space model outperformed the SSD MobileNet v2 model, obtaining results similar to the YOLOv4 model, with a Balanced Accuracy of 68.10%. [ABSTRACT FROM AUTHOR]

The development of robotic solutions for agriculture requires advanced perception capabilities that can work reliably in any crop stage. For example, to automatise the tomato harvesting process in greenhouses, the visual perception system needs to detect the tomato in any life cycle stage (flower to the ripe tomato). The state-of-the-art for visual tomato detection focuses mainly on ripe tomato, which has a distinctive colour from the background. This paper contributes with an annotated visual dataset of green and reddish tomatoes. This kind of dataset is uncommon and not available for research purposes. This will enable further developments in edge artificial intelligence for in situ and in real-time visual tomato detection required for the development of harvesting robots. Considering this dataset, five deep learning models were selected, trained and benchmarked to detect green and reddish tomatoes grown in greenhouses. Considering our robotic platform specifications, only the Single-Shot MultiBox Detector (SSD) and YOLO architectures were considered. The results proved that the system can detect green and reddish tomatoes, even those occluded by leaves. SSD MobileNet v2 had the best performance when compared against SSD Inception v2, SSD ResNet 50, SSD ResNet 101 and YOLOv4 Tiny, reaching an F1-score of 66.15 %, an mAP of 51.46 % and an inference time of 16.44 m s with the NVIDIA Turing Architecture platform, an NVIDIA Tesla T4, with 12 GB. YOLOv4 Tiny also had impressive results, mainly concerning inferring times of about 5 m s. [ABSTRACT FROM AUTHOR]