RESUMO

The present study aimed to analyze large volumes of tympanic temperature (TT) data to identify its use as a physiological indicator of climatic conditions and its relationship with milk production in grazing cows under tropical lowland conditions. Three dairy farms and 21 multiparous early lactation cows were included in the study. Seven animals were equipped with tympanic temperature wireless sensors within each farm, and permanent information was collected hourly for 22 days on average. Ambient temperature (AT), relative humidity (RH), wind speed (WS), precipitation (PP), and THI information were obtained from meteorological stations located close to each farm. Statistical analyses included Spearman correlations and random coefficient regression models (P < 0.05). TT presented moderate and significant correlations with AT (0.35 to 0.49), SR (0.25 to 0.32), THI (0.35 to 0.49), and RH (-0.35 to -0.49). Climatic variables like AT, PP, SR, and WS were the most contributing factors to TT prediction (R2 =0.42 to 0.86). Grazing dairy cows in tropical scenarios accumulate heat during the day and dissipate it at nighttime, although higher producing animals deal with more problems to reach thermal homeostasis. Correlations between TT and daily milk production varied according to animal yield; however, higher TT values were related to the most productive cows. The effect of TT on milk production prediction was not conclusive among farms, possibly by animal management or others characteristics of the systems. TT determination through remote sensors allows a reliable diagnosis of the physiological temperature response to climatic conditions.

Assuntos

RESUMO

The present study aimed to analyze large volumes of tympanic temperature (TT) data to identify its use as a physiological indicator of climatic conditions and its relationship with milk production in grazing cows under tropical lowland conditions. Three dairy farms and 21 multiparous early lactation cows were included in the study. Seven animals were equipped with tympanic temperature wireless sensors within each farm, and permanent information was collected hourly for 22 days on average. Ambient temperature (AT), relative humidity (RH), wind speed (WS), precipitation (PP), and THI information were obtained from meteorological stations located close to each farm. Statistical analyses included Spearman correlations and random coefficient regression models (P < 0.05). TT presented moderate and significant correlations with AT (0.35 to 0.49), SR (0.25 to 0.32), THI (0.35 to 0.49), and RH (-0.35 to -0.49). Climatic variables like AT, PP, SR, and WS were the most contributing factors to TT prediction (R2 =0.42 to 0.86). Grazing dairy cows in tropical scenarios accumulate heat during the day and dissipate it at nighttime, although higher producing animals deal with more problems to reach thermal homeostasis. Correlations between TT and daily milk production varied according to animal yield; however, higher TT values were related to the most productive cows. The effect of TT on milk production prediction was not conclusive among farms, possibly by animal management or others characteristics of the systems. TT determination through remote sensors allows a reliable diagnosis of the physiological temperature response to climatic conditions.(AU)

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RESUMO

This paper investigates the use of clustering technique to characterize the providers of maintenance services in a health-care institution according to their performance. A characterization of the inventory of equipment from seven pilot areas was carried out first (including 264 medical devices). The characterization study concluded that the inventory on a whole is old [exploitation time (ET)/useful life (UL) average is 0.78] and has high maintenance service costs relative to the original cost of acquisition (service cost /acquisition cost average 8.61%). A monitoring of the performance of maintenance service providers was then conducted. The variables monitored were response time (RT), service time (ST), availability, and turnaround time (TAT). Finally, the study grouped maintenance service providers into clusters according to performance. The study grouped maintenance service providers into the following clusters. Cluster 0: Identified with the best performance, the lowest values of TAT, RT, and ST, with an average TAT value of 1.46 days; Clusters 1 and 2: Identified with the poorest performance, highest values of TAT, RT, and ST, and an average TAT value of 9.79 days; and Cluster 3: Identified by medium-quality performance, intermediate values of TAT, RT, and ST, and an average TAT value of 2.56 days.

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RESUMO

Objetivo Utilizar técnicas de clustering para caracterizar a los proveedores de servicios de mantenimiento de una institución de salud. Métodos El estudio analiza el inventario del equipamiento perteneciente a 7 áreas pilotos (264 equipos médicos). Se aplican técnicas de clustering usando 26 variables. Entre las más significativas: el tiempo de respuesta (RT), la duración de las intervenciones (DR), la disponibilidad y el tiempo de cambio de estado (TAT). Resultados La obsolescencia del equipo biomédico en promedio es de 0,78. Se identifican 4 grupos de proveedores de servicios. Grupos (1 y 3): Mejor desempeño, menores valores de TAT, RT y DR; cuyos proveedores son: O, L, C, B, I, S, H, F, G; representan el 56 por ciento del total; con valores de TAT entre: 1,4 días

Objective Using clustering techniques for characterising companies providing health institutions with maintenance services. Methods The study analysed seven pilot areas' equipment inventory (264 medical devices). Clustering techniques were applied using 26 variables. Response time (RT), operation duration (OD), availability and turnaround time (TAT) were amongst the most significant ones. Results Average biomedical equipment obsolescence value was 0.78. Four service provider clusters were identified: clusters 1 and 3 had better performance, lower TAT, RT and DR values (56 percent of the providers coded O, L, C, B, I, S, H, F and G, had 1 to 4 day TAT values:

Assuntos

RESUMO

OBJECTIVE: Using clustering techniques for characterising companies providing health institutions with maintenance services. METHODS: The study analysed seven pilot areas' equipment inventory (264 medical devices). Clustering techniques were applied using 26 variables. Response time (RT), operation duration (OD), availability and turnaround time (TAT) were amongst the most significant ones. RESULTS: Average biomedical equipment obsolescence value was 0.78. Four service provider clusters were identified: clusters 1 and 3 had better performance, lower TAT, RT and DR values (56 % of the providers coded O, L, C, B, I, S, H, F and G, had 1 to 4 day TAT values:

Assuntos