Evolution Characteristics of Void in the Caving Zone Using Fiber Optic Sensing.

Addressing the issue of low filling efficiency in gangue slurry backfilling due to unclear evolution characteristics of voids in the overlying collapsed rock mass during mining, this study utilizes fiber optic sensing technology to monitor real-time strain changes within the rock mass. It proposes a void zoning method based on fiber optic sensing for mining the overlying rock and, in combination with physical model experiments, systematically investigates the dimensions, distribution, and deformation characteristics of rock mass voids. By analyzing fiber optic sensing data, the correlation between the rate of void expansion and the stress state of the rock mass is revealed. The research results demonstrate that as mining progresses, the internal voids of the rock mass gradually expand, exhibiting complex spatial distribution patterns. During the mining process, the expansion of voids within the overlying collapsed rock mass is closely related to the stress state of the rock mass. The rate of void expansion is influenced by changes in stress, making stress regulation a key factor in preventing void expansion and rock mass instability. The application of fiber optic sensing technology allows for more accurate monitoring of changes in rock mass voids, enabling precise zoning of voids in the overlying collapsed rock mass during mining. This zoning method has been validated against traditional theoretical calculations and experimental results. This research expands our understanding of the evolution characteristics of voids in overlying collapsed rock mass and provides valuable reference for backfilling engineering practices and backfilling parameter optimization.

Data mining process for predicting diabetes mellitus based model about other chronic diseases: a case study of the northwestern part of Nigeria.

To predict diabetes mellitus model data mining (DM) based approaches on the dataset collected from the seven northwestern states of Nigeria. Data were collected from both primary and secondary sources through questionnaires and verbal interviews from patients with diabetic mellitus and other chronic diseases. Some hospital data were also used from the records of patients involved in this work. The dataset comprises 281 instances with 8 attributes. R programming software (version 5.3.1) was used in the experiments. The DM techniques used in this research were binomial logistic regression, classification, confusion matrix and correlation coefficient. The data were partitioned into training and testing sets. Training data were used in building the model while testing data were used to validate the model. The algorithm for the best-fitted model converges with null deviance: 281.951, residual deviance: 16.476 and AIC: 30.476. The significance variables are AGE, GLU, DBP and KDYP with 0.025, 0.01, 0.05 and 0.025 P values, respectively. The predicted model accounted for the accuracy of ∼97.1%. The correlation analysis results revealed that diabetic patients are more likely to be hypertensive than patients with other chronic diseases considered in the research.

Integración de técnicas de Minería de procesos para la detección de la variabilidad en procesos hospitalarios desde sistemas automatizados / Integrating mining processes techniques in hospital procedures for the detection of variability from their information systems

Los sistemas de información hospitalaria cuentan con un volumen importante de datos, sin embargo, carecen de mecanismos que permitan analizar la ejecución de los procesos e identificar variabilidad. La variabilidad puede observarse en prácticamente cada paso del proceso asistencial y a varios niveles de agrupación: poblacional e individual. Desde el punto de vista poblacional se comparan tasas de realización de un procedimiento clínico, como pueden ser intervenciones quirúrgicas o ingresos hospitalarios en un período de tiempo. Las técnicas de minería de procesos analizan los datos reales de sistemas informáticos y son útiles para la detección de variabilidad en la ejecución de los procesos de negocio. La presente investigación propone la aplicación de técnicas de minería de procesos, seleccionadas a partir de un riguroso estudio del estado del arte, para el análisis de los procesos hospitalarios desde sus sistemas de información y materializadas en un modelo computacional. El Modelo para la Detección de Variabilidad (MDV) se instrumentó exitosamente en el sistema XAVIA HIS desarrollado por la Universidad de las Ciencias Informáticas UCI, donde fueron adaptadas e integradas las técnicas de minería de procesos. El modelo MDV contribuye al proceso de informatización de la salud en Cuba. La solución propicia la utilización de una tecnología emergente en áreas como la industrial y empresarial en el entorno sanitario. Esta beneficia importantes funciones gerenciales como la gestión, control y planificación de recursos y servicios sanitarios(AU)

The hospital information systems collect an important volume of data, however, they lack mechanisms to analyze the execution of the processes and identify variability. In practically every step of the care process and at various levels of grouping: population and individual the variability is present. From a population point of view, performance rates of a clinical procedure such as surgical interventions or hospital admissions, are compared over time. Process mining techniques analyze the real data of computer systems and are useful for the detection of variability in the execution of business processes. Based on a rigorous study of the state of the art, this research proposes the application of process mining techniques for the analysis of hospital processes from their information systems, providing a computational model. Model for Variability Detection (MDV) implemented successfully in the XAVIA HIS system developed by the UCI University of Informatics Sciences, where techniques of process mining were adapted and integrated. The MDV model contributes to the process of computerization of health in Cuba. The solution encourages the use of an emerging technology in areas such as industrial and business in the healthcare environment. This benefits important management functions such as control and planning of resources and health services(AU)

Integración de técnicas de Minería de procesos para la detección de la variabilidad en procesos hospitalarios desde sistemas automatizados / Integrating mining processes techniques in hospital procedures for the detection of variability from their information systems

Los sistemas de información hospitalaria cuentan con un volumen importante de datos, sin embargo, carecen de mecanismos que permitan analizar la ejecución de los procesos e identificar variabilidad. La variabilidad puede observarse en prácticamente cada paso del proceso asistencial y a varios niveles de agrupación: poblacional e individual. Desde el punto de vista poblacional se comparan tasas de realización de un procedimiento clínico, como pueden ser intervenciones quirúrgicas o ingresos hospitalarios en un período de tiempo. Las técnicas de minería de procesos analizan los datos reales de sistemas informáticos y son útiles para la detección de variabilidad en la ejecución de los procesos de negocio. La presente investigación propone la aplicación de técnicas de minería de procesos, seleccionadas a partir de un riguroso estudio del estado del arte, para el análisis de los procesos hospitalarios desde sus sistemas de información y materializadas en un modelo computacional. El Modelo para la Detección de Variabilidad (MDV) se instrumentó exitosamente en el sistema XAVIA HIS desarrollado por la Universidad de las Ciencias Informáticas UCI, donde fueron adaptadas e integradas las técnicas de minería de procesos. El modelo MDV contribuye al proceso de informatización de la salud en Cuba. La solución propicia la utilización de una tecnología emergente en áreas como la industrial y empresarial en el entorno sanitario. Esta beneficia importantes funciones gerenciales como la gestión, control y planificación de recursos y servicios sanitarios(AU)

The hospital information systems collect an important volume of data, however, they lack mechanisms to analyze the execution of the processes and identify variability. In practically every step of the care process and at various levels of grouping: population and individual the variability is present. From a population point of view, performance rates of a clinical procedure such as surgical interventions or hospital admissions, are compared over time. Process mining techniques analyze the real data of computer systems and are useful for the detection of variability in the execution of business processes. Based on a rigorous study of the state of the art, this research proposes the application of process mining techniques for the analysis of hospital processes from their information systems, providing a computational model. Model for Variability Detection (MDV) implemented successfully in the XAVIA HIS system developed by the UCI University of Informatics Sciences, where techniques of process mining were adapted and integrated. The MDV model contributes to the process of computerization of health in Cuba. The solution encourages the use of an emerging technology in areas such as industrial and business in the healthcare environment. This benefits important management functions such as control and planning of resources and health services(AU)