Overview of Mobile Communications in Colombia and Introduction to 5G.

The deployment of 5G around the world continues to progress at a rapid pace, especially in North America and Asia. Its advantages and efficiency as a data transmission network have been widely demonstrated in different fields such as agriculture, education, health, and surveillance. However, this process does not have the same dynamics in Latin America, specifically in Colombia. The country is currently implementing actions aimed at facilitating the deployment of this technology in the short term, including pilot tests for the use of the radio spectrum, spectrum auctions, the planning of future auctions, and the review of spectrum caps. The results of this review allow us to conclude that despite the forecasts and the intentions of the Colombian government and mobile communication service operators, 5G in standalone mode will not be commercially available in Colombia before the end of 2023. The main failures in its deployment are related to the lack of available spectrum to support the ultrahigh-reliability and low-latency, enhanced mobile broadband, and massive machine-type communications scenarios, as well as the delay in the auction processes for its assignment.

Precision Agriculture and Sensor Systems Applications in Colombia through 5G Networks.

The growing global demand for food and the environmental impact caused by agriculture have made this activity increasingly dependent on electronics, information technology, and telecommunications technologies. In Colombia, agriculture is of great importance not only as a commercial activity, but also as a source of food and employment. However, the concept of smart agriculture has not been widely applied in this country, resulting in the high production of various types of crops due to the planting of large areas of land, rather than optimization of the processes involved in the activity. Due to its technical characteristics and the radio spectrum considered in its deployment, 5G can be seen as one of the technologies that could generate the greatest benefits for the Colombian agricultural sector, especially in the most remote rural areas, which currently lack mobile network coverage. This article provides an overview of the current 5G technology landscape in Colombia and presents examples of possible 5G/IoT applications that could be developed in Colombian fields. The results show that 5G could facilitate the implementation of the smart farm in Colombia, improving current production and efficiency. It is useful when designing 5G implementation plans and strategies, since it categorizes crops by regions and products. This is based on budget availability, population density, and regional development plans, among others.

Empirical Model of Radio Wave Propagation in the Presence of Vegetation inside Greenhouses Using Regularized Regressions.

Spain is Europe's leading exporter of tomatoes harvested in greenhouses. The production of tomatoes should be kept and increased, supported by precision agriculture to meet food and commercial demand. The wireless sensor network (WSN) has demonstrated to be a tool to provide farmers with useful information on the state of their plantations due to its practical deployment. However, in order to measure its deployment within a crop, it is necessary to know the communication coverage of the nodes that make up the network. The multipath propagation of radio waves between the transceivers of the WSN nodes inside a greenhouse is degraded and attenuated by the intricate complex of stems, branches, leaf twigs, and fruits, all randomly oriented, that block the line of sight, consequently generating a signal power loss as the distance increases. Although the COST235 (European Cooperation in Science and Technology - COST), ITU-R (International Telecommunications Union-Radiocommunication Sector), FITU-R (Fitted ITU-R), and Weisbberger models provide an explanation of the radio wave propagation in the presence of vegetation in the 2.4 GHz ICM band, some significant discrepancies were found when they are applied to field tests with tomato greenhouses. In this paper, a novel method is proposed for determining an empirical model of radio wave attenuation for vegetation in the 2.4 GHz band, which includes the vegetation height as a parameter in addition to the distance between transceivers of WNS nodes. The empirical attenuation model was obtained applying regularized regressions with a multiparametric equation using experimental signal RSSI measurements achieved by our own RSSI measurement system for our field tests in four plantations. The evaluation parameters gave 0.948 for R2, 0.946 for R2 Adj considering 5th grade polynomial (20 parameters), and 0.942 for R2, and 0.940 for R2 Adj when a reduction of parameters was applied using the cross validation (15 parameters). These results verify the rationality and reliability of the empirical model. Finally, the model was validated considering experimental data from other plantations, reaching similar results to our proposed model.

Potenciales biomarcadores predictores de mortalidad en pacientes COVID-19 en el Servicio de Urgencias / Potential biomarkers predictors of mortality in COVID-19 patients in the Emergency Department

OBJETIVO: Identificar qué biomarcadores realizados en la primera analítica de urgencias ayudan a estratificar según riesgo de mortalidad a pacientes COVID 19. MÉTODO: Estudio observacional descriptivo y transversal realizado con datos recogidos de los pacientes con sospecha de COVID-19 en el Servicio de Urgencias del 24 de febrero al 16 de marzo del 2020. Se realizó el estudio univariante y multivariante para encontrar los marcadores independientes de mortalidad y calcular el riesgo mediante la construcción de una escala de gravedad. RESULTADOS: Se incluyeron 163 pacientes de los que fallecieron 33 y 29 de ellos resultaron positivos para la prueba PCR COVID-19. Obtuvimos como posibles factores para conformar el score de riesgo de mortalidad edad>75 años ((OR ajustada=12,347, IC95%: 4,138-36,845 p = 0.001), leucocitos totales> 11.000 cel/mm3 (OR ajustada=2,649, IC95%: 0,879-7,981 p = 0,083), glucosa> 126 mg/dL (OR ajustada=3,716, IC95%: 1,247-11,074 p = 0,018) y creatinina>1,1 mg/dL (OR ajustada= 2,566, IC95%: 0,889-7,403, p = 0,081). Este score se denominó COVEB (COVID, Edad, perfil Básico analítico) con un AUC 0,874 (IC95%: 0,816-0,933, p < 0.001; punto de corte= 1 (sensibilidad= 89,66% (IC95%: 72,6%-97,8%), especificidad= 75,59% (IC95%: 67,2%-82,8%). Un score < 1 posee un valor predictivo negativo = 100% (IC95%: 93,51%-100%) y un valor predictivo positivo = 18,59% (IC95%: 12,82%-25,59%). CONCLUSIONES: Las escalas clínicas de gravedad, los biomarcadores de función renal, los parámetros del recuento leucocitario, el ratio neutrófilos totales/linfocitos y procalcitonina son factores de riesgo tempranos de mortalidad. Destacan las variables edad, glucosa, creatinina y leucocitos totales como mejores predictores de mortalidad. Un score COVEB< 1 indica con un 100% de probabilidad, que el paciente con sospecha de COVID-19 no va a fallecer en los próximos 30 días

OBJECTIVE: Identify which biomarkers performed in the first emergency analysis help to stratify COVID-19 patients according to mortality risk. METHOD: Observational, descriptive and cross-sectional study performed with data collected from patients with suspected COVID-19 in the Emergency Department from February 24 to March 16, 2020. The univariate and multivariate study was performed to find independent mortality markers and calculate risk by building a severity score. RESULTS: A total of 163 patients were included, of whom 33 died and 29 of them were positive for the COVID-19 PCR test. We obtained as possible factors to conform the Mortality Risk Score age> 75 years ((adjusted OR = 12,347, 95% CI: 4,138-36,845 p = 0.001), total leukocytes> 11,000 cells / mm3 (adjusted OR = 2,649, 95% CI: 0.879-7.981 p = 0.083), glucose> 126 mg / dL (adjusted OR = 3.716, 95% CI: 1.247-11.074 p = 0.018) and creatinine> 1.1 mg / dL (adjusted OR = 2.566, 95% CI: 0.889- 7.403, p = 0.081) This score was called COVEB (COVID, Age, Basic analytical profile) with an AUC 0.874 (95% CI: 0.816-0.933, p <0.001; Cut-off point = 1 (sensitivity = 89.66 % (95% CI: 72.6% -97.8%), specificity = 75.59% (95% CI: 67.2% -82.8%). A score <1 has a negative predictive value = 100% (95% CI: 93.51% -100%) and a positive predictive value = 18.59% (95% CI: 12.82% -25.59%). CONCLUSIONS: Clinical severity scales, kidney function biomarkers, white blood cell count parameters, the total neutrophils / total lymphocytes ratio and procalcitonin are early risk factors for mortality. The variables age, glucose, creatinine and total leukocytes stand out as the best predictors of mortality. A COVEB score <1 indicates with a 100% probability that the patient with suspected COVID-19 will not die in the next 30 days