The potential negative impact of antibiotic pack on antibiotic stewardship in primary care in Switzerland: a modelling study.

BACKGROUND: In Switzerland, oral antibiotics are dispensed in packs rather than by exact pill-count. We investigated whether available packs support compliance with recommended primary care treatment regimens for common infections in children and adults. METHODS: Hospital-based guidelines for oral community -based treatment of acute otitis media, sinusitis, tonsillopharyngitis, community-acquired pneumonia and afebrile urinary tract infection were identified in 2017 in an iterative process by contacting hospital pharmacists and infectious diseases specialists. Furthermore, newly available national guidelines published in 2019 were reviewed. Available pack sizes for recommended solid, dispersible and liquid antibiotic formulations were retrieved from the Swiss pharmaceutical register and compared with recommended regimens to determine optimal (no leftovers) and adequate (optimal +/- one dose) matches. RESULTS: A large variety of recommended regimens were identified. For adults, optimal and adequate packs were available for 25/70 (36%) and 8/70 (11%) regimens, respectively. Pack-regimen matching was better for WHO Watch (optimal: 15/24, 63%) than Access antibiotics (optimal: 7/39, 18%). For the four paediatric weight-examples and 42 regimens involving child-appropriate formulations, optimal and adequate packs were available for only 14/168 (8%) and 27/168 (16%), respectively. Matching was better for older children with higher body and for longer treatment courses > 7 days. CONCLUSIONS: Fixed antibiotic packs often do not match recommended treatment regimens, especially for children, potentially resulting in longer than necessary treatments and leftover doses in the community. As part of national stewardship, a move to an exact pill-count system, including for child-appropriate solid formulations, should be considered

Evaluación de dos fechas de siembra de Hibiscus cannabinus L. 'kenaf' (Malvaceae) en Villa del Totoral, Córdoba, Argentina

31-42El objetivo de este trabajo fue evaluar el rendimiento de fibra del cultivo de 'kenaf'en dos fechas de siembra en Villa del Totoral, Córdoba, Argentina. Se analizaron: altura de planta durante el ciclo del cultivo; diámetro basal y medio del tallo y altura a cosecha; rendimiento de fibra y contenido e índice de corteza. Se observó interacción altamente significativa entre año y fecha de siembra. En el primer año, las plantas de la siembra temprana presentaron mayores valores de altura, diámetro basal y medio y rendimiento, que las de la siembra tardía. Por el contrario en el segundo año, sólo se encontraron diferencias significativas entre fechas de siembra para ambos diámetros. El rendimiento correlacionó con altura, diámetros e índice de corteza y no con el contenido de corteza. Se sostiene que para obtener alto rendimiento de fibra de 'kenaf' en las condiciones ambientales del ensayo, es conveniente la siembra al inicio de la temporada de lluvias sin embargo, dependiendo de las condiciones ambientales, se puede esperar hasta fines de noviembre sin detrimento en los rendimientos

Connaissance et suivi de la qualité des sols en France. Etat des lieux, enjeux, besoins en données, propositions pour une gestion raisonnée de la ressource en sol

* INRA Science du Sol, 34060 Montpellier Cedex Diffusion du document : INRA Science du Sol, 34060 Montpellier CedexNational audienc

Development of chebyshev neural network-based smart sensors for noisy harsh environment

Smart sensing of environmental parameters is an important task in robotics, process industries, sensor networks and autonomous systems. In this paper, we propose a novel Chebyshev neural network (ChNN) to develop smart sensors which can provide linearized and accurate readout, and can compensate for nonlinear environmental disturbances including additive noise. By taking two environmental models and using a capacitive pressure sensor as an example, through computer simulations, performance comparison was carried out between the proposed ChNN and Multilayer Perceptron (MLP)-based sensor models over a wide temperature range and additive noise. We have shown that the performances in terms of full scale error and sensor readout of both the NNs are similar. But a major advantage of the ChNN is that due to its single-layer architecture it provides substantial computational advantage over MLP

Nonlinear dynamic system identification using Legendre neural network

We propose a computationally efficient Legendre neural network (LeNN) for identification of nonlinear dynamic systems. Due to its single-layer architecture, the LeNN offers much less computational complexity than that of a multilayer perceptron (MLP). By taking several plant models of increasing complexity and with extensive simulations we have shown superior performance of the LeNN-based plant model in comparison to that of an MLP model in terms of estimated output, mean square error (MSE) and computational complexity, in presence of additive noise

La banque de donnees sur les sols regionaux du Languedoc-Roussillon. Presentation, conception et possibilites d'exploitation

National audienc

La banque de données sur les sols régionaux du Languedoc-Roussillon. Présentation, conception et possibilités d'exploitation

National audienc

A novel CRT-based watermarking technique for authentication of multimedia contents

Digital watermarking techniques have been proposed as a solution to the problem of copyright protection of multimedia data. In this paper, we propose a novel Chinese remainder theorem (CRT)-based technique for digital watermarking. The use of CRT for this purpose provides additional security along with resistance to some familiar attacks. We have shown that this technique is quite resilient to addition of the noise. We have compared performance of the proposed technique with recently reported two singular value decomposition (SVD)-based watermarking techniques and shown its superior performance in terms of tampering assessment function (TAF), computational efficiency and peak signal to noise ratio (PSNR). For example, the embedding time of the proposed CRT-based scheme is 6 and 3 times faster than the SVD-based Schemes 1 and 2, respectively. This technique can also be applied to document, audio and video contents

Hermite neural network-based intelligent sensors for harsh environments

We propose a novel computationally efficient artificial neural network (NN) for design and development of intelligent sensors to operate in harsh environments which can have wide variation of environmental conditions. The proposed Hermite NN (HeNN) models the inverse characteristics of a sensor and can provide linearized sensor response characteristics irrespective of change in environmental conditions, even when the environmental parameters influence the sensor characteristics nonlinearly. By taking an example of a capacitive pressure sensor, we have shown through extensive computer simulations that the HeNN-based model can linearize its response with maximum full scale (FS) error of +/-0.5% when it is operated in a harsh environment with temperature variation of -50 to 200 degrees C and influenced nonlinearly. We have compared performance of the proposed HeNN-based model with a MLP-based model and shown its superior performance in terms of FS error and computational complexity