Multiple Performance Optimization for Microstrip Patch Antenna Improvement.

As the Internet of Things (IOT) becomes more widely used in our everyday lives, an increasing number of wireless communication devices are required, meaning that an increasing number of signals are transmitted and received through antennas. Thus, the performance of antennas plays an important role in IOT applications, and increasing the efficiency of antenna design has become a crucial topic. Antenna designers have often optimized antennas by using an EM simulation tool. Although this method is feasible, a great deal of time is often spent on designing the antenna. To improve the efficiency of antenna optimization, this paper proposes a design of experiments (DOE) method for antenna optimization. The antenna length and area in each direction were the experimental parameters, and the response variables were antenna gain and return loss. Response surface methodology was used to obtain optimal parameters for the layout of the antenna. Finally, we utilized antenna simulation software to verify the optimal parameters for antenna optimization, showing how the DOE method can increase the efficiency of antenna optimization. The antenna optimized by DOE was implemented, and its measured results show that the antenna gain and return loss were 2.65 dBi and 11.2 dB, respectively.

Mortality Evaluation and Life Expectancy Prediction of Patients with Hepatocellular Carcinoma with Data Mining.

BACKGROUND: The complexity of systemic variables and comorbidities makes it difficult to determine the best treatment for patients with hepatocellular carcinoma (HCC). It is impossible to perform a multidimensional evaluation of every patient, but the development of guidelines based on analyses of said complexities would be the next best option. Whereas conventional statistics are often inadequate for developing multivariate predictive models, data mining has proven more capable. Patients, methods and findings: Clinical profiles and treatment responses of 537 patients diagnosed with Barcelona Clinic Liver Cancer stages B and C from 2009 to 2019 were retrospectively analyzed using 4 decision tree algorithms. A combination of 19 treatments, 7 biomarkers, and 4 states of hepatitis was tested to determine which combinations would result in survival times greater than a year in duration. Just 2 of the algorithms produced complete models through single trees, which made them only the ones suitable for clinical judgement. A combination of alpha fetoprotein ≤210.5 mcg/L, glutamic oxaloacetic transaminase ≤1.13 µkat/L, and total bilirubin ≤ 0.0283 mmol/L was shown to be a good predictor of survival >1 year, and the most effective treatments for such patients were radio-frequency ablation (RFA) and transarterial chemoembolization (TACE) with radiation therapy (RT). In patients without this combination, the best treatments were RFA, TACE with RT and targeted drug therapy, and TACE with targeted drug therapy and immunotherapy. The main limitation of this study was its small sample. With a small sample size, we may have developed a less reliable model system, failing to produce any clinically important results or outcomes. CONCLUSION: Data mining can produce models to help clinicians predict survival time at the time of initial HCC diagnosis and then choose the most suitable treatment.

Smart Monitoring of Manufacturing Systems for Automated Decision-Making: A Multi-Method Framework.

Smart monitoring plays a principal role in the intelligent automation of manufacturing systems. Advanced data collection technologies, like sensors, have been widely used to facilitate real-time data collection. Computationally efficient analysis of the operating systems, however, remains relatively underdeveloped and requires more attention. Inspired by the capabilities of signal analysis and information visualization, this study proposes a multi-method framework for the smart monitoring of manufacturing systems and intelligent decision-making. The proposed framework uses the machine signals collected by noninvasive sensors for processing. For this purpose, the signals are filtered and classified to facilitate the realization of the operational status and performance measures to advise the appropriate course of managerial actions considering the detected anomalies. Numerical experiments based on real data are used to show the practicability of the developed monitoring framework. Results are supportive of the accuracy of the method. Applications of the developed approach are worthwhile research topics to research in other manufacturing environments.

Traditional versus Microsphere Embolization for Hepatocellular Carcinoma: An Effectiveness Evaluation Using Data Mining.

Background: For hepatocellular carcinoma ("HCC"), the current standard of treatment is hepatic artery embolization, generally through trans-catheter arterial chemoembolization ("TACE"). There are two types: traditional ("conventional" or "cTACE") and microsphere ("DC bead TACE"). Unfortunately, the literature comparing the relative effectiveness of cTACE versus DC bead TACE is inconclusive, partially due to the complexity of HCC and its response to treatment. Data mining is an excellent method to extract meaning from complex data sets. Purpose: Through the application of data mining techniques, to compare the relative effectiveness of cTACE and DC bead TACE using a large patient database and to use said comparison to establish usable guidelines for developing treatment plans for HCC patients. Materials and Methods: The data of 372 HCC patients who underwent TACE in Taichung Veterans General Hospital were analyzed. The chi-square test was used to compare the difference in the effectiveness of the two therapies was compared. Logistic regression was used to calculate the odds ratios. Furthermore, using the C4.5 decision tree, the two therapies were classified into applicable fields. Chi-square test, the t-test, and logistic regression were used to verify the classification results. Results: In Barcelona Clinic Stages A and B cancers, cTACE was found to be 22.7% more effective than DC bead TACE. By using the decision tree C4.5 as a classifier, the effectiveness of either treatment for small tumors was 8.475 times than that for large tumors. DC bead TACE was 3.39 times more successful in treating patients with a single tumor than with multiple tumors. For patients with a single tumor, the chi-square test showed that 100-300 µm microspheres were significantly more effective than 300-500 µm. While these findings provide a reference for the selection of an appropriate TACE approach, we noted that overall accuracy was somewhat low, possibly due to the limited population. Conclusions: We found that data mining could be applied to develop clear guidelines for physician and researcher use in the case of complex pathologies such as HCC. However, some of our results contradicted those elsewhere in the literature, possibly due to a relatively small sample size. Significantly larger data sets with appropriate levels of granularity could produce more accurate results.

Applying Data Mining to Investigate Cancer Risk in Patients with Pyogenic Liver Abscess.

Pyogenic liver abscess is usually a complication of biliary tract disease. Taiwan features among the countries with the highest incidence of colorectal cancer (CRC) and hepatocellular carcinoma (HCC). Few studies have investigated whether patients with pyogenic liver abscess (PLA) have higher incidence rates of CRC and HCC. However, these findings have been inconclusive. The risks of CRC and HCC in patients with PLA and the factors contributing to cancer development were assessed in these patients. The clinical tests significantly associated with cancers in these patients with PLA were determined to assist in the early diagnosis of these cancers. Odds ratios (ORs) and 95% confidence intervals (CIs) were determined using binary logistic regression Cancer classification models were constructed using the decision tree algorithm C5.0 to compare the accuracy among different models with those risk factors of cancers and then determine the optimal model. Thereafter, the rules were summarized using the decisi8on tree model to assist in the diagnosis. The results indicated that CRC and HCC (OR, 3.751; 95% CI, 1.149-12.253) and CRC (OR, 6.838; 95% CI, 2.679-17.455) risks were higher in patients with PLA than those without PLA. The decision tree analysis demonstrated that the model with the PLA variable had the highest accuracy, and that classification could be conducted using fewer factors, indicating that PLA is critical in HCC and CRC. Two rules were determined for assisting in the diagnosis of CRC and HCC using the decision tree model.

Prevalence and demographic characteristics of comorbid epilepsy in children and adolescents with cerebral palsy: a nationwide population-based study.

INTRODUCTION: The nationwide prevalence of cerebral palsy (CP) is unknown due to the lack of a population-based registration system for CP in Taiwan. This study was the largest nationwide, population-based, cross-sectional study to estimate the prevalence of CP, prevalence rates of comorbid epilepsy in patients with CP, and association with socioeconomic status (SES) in Taiwan. The crude prevalence rate and age- and gender-specific prevalence rates were estimated. METHODS: A total of 8419 patients with CP were enrolled, and the estimated prevalence of CP was 1.76‰ in the pediatric population and 1.51‰ and 1.98‰ in girls and boys, respectively. The prevalence rate of epilepsy in patients with CP was 29.8%. RESULTS: The result revealed a higher prevalence of CP and epileptic CP in members of families with lower insurance premiums than those with higher insurance premiums and those from East Taiwan compared with those from other areas of Taiwan. Moreover, a higher prevalence of CP is shown in rural area than urban area. DISCUSSION: SES and geographic variables were significantly associated with the risk of epilepsy in children with CP. Patients with epileptic CP had a higher odds ratio of several neuropsychiatric diseases, including mental retardation, ophthalmologic problems, hearing impairment, and hydrocephalus.

The Development of Skull Prosthesis Through Active Contour Model.

Skull defects result in brain infection and inadequate brain protection and pose a general danger to patient health. To avoid these situations and prevent re-injury, a prosthesis must be constructed and grafted onto the deficient region. With the development of rapid customization through additive manufacturing and 3D printing technology, skull prostheses can be fabricated accurately and efficiently prior to cranioplasty. However, an unfitted skull prosthesis made with a metal implant can cause repeated infection, potentially necessitating secondary surgery. This paper presents a method of creating suitably geometric graphics of skull defects to be applied in skull repair through active contour models. These models can be adjusted in each computed tomography slice according to the graphic features, and the curves representing the skull defect can be modeled. The generated graphics can adequately mimic the natural curvature of the complete skull. This method will enable clinical surgeons to rapidly implant customized prostheses, which is of particular importance in emergency surgery. The findings of this research can help surgeons provide patients with skull defects with treatment of the highest quality.

Intermittent Demand Forecasting in a Tertiary Pediatric Intensive Care Unit.

Forecasts of the demand for medical supplies both directly and indirectly affect the operating costs and the quality of the care provided by health care institutions. Specifically, overestimating demand induces an inventory surplus, whereas underestimating demand possibly compromises patient safety. Uncertainty in forecasting the consumption of medical supplies generates intermittent demand events. The intermittent demand patterns for medical supplies are generally classified as lumpy, erratic, smooth, and slow-moving demand. This study was conducted with the purpose of advancing a tertiary pediatric intensive care unit's efforts to achieve a high level of accuracy in its forecasting of the demand for medical supplies. On this point, several demand forecasting methods were compared in terms of the forecast accuracy of each. The results confirm that applying Croston's method combined with a single exponential smoothing method yields the most accurate results for forecasting lumpy, erratic, and slow-moving demand, whereas the Simple Moving Average (SMA) method is the most suitable for forecasting smooth demand. In addition, when the classification of demand consumption patterns were combined with the demand forecasting models, the forecasting errors were minimized, indicating that this classification framework can play a role in improving patient safety and reducing inventory management costs in health care institutions.

Prevalence and neuro-psychiatric comorbidities of pediatric epilepsy in Taiwan: a national population-based study.

OBJECTIVE: Children with epilepsy may have comorbidities that result in significant disability. Epidemiological information for pediatric patients with epilepsy in Taiwan is scant. This research estimates the prevalence and common neuro-psychiatric comorbidities of children with epilepsy in Taiwan. METHODS: Patients aged less than 20 years old who had received a diagnosis of epilepsy and suffered from epileptic seizures in 2005 were identified in the NHIRD based on ICD-9-CM and prescription records for the use of at least one AED. We used cases of epileptic seizure to survey outpatient service data, and identify common neuro-psychiatric comorbidities. The crude prevalence rate and the age- and sex-specific prevalence were estimated. We also examined the effects of urbanization. RESULTS: The estimated prevalence of epilepsy was 0.33% in the pediatric population, with 0.29% for girls and 0.36% for boys. The most common neuropsychiatric comorbidities were learning disability and developmental delay, cerebral palsy, and mental retardation. Epilepsy was more prevalent in boys than in girls, especially among infants, preschool children, and those living in rural areas. In addition, boys with epilepsy had a higher rate of neurological comorbidities. The prevalence of psychiatric comorbidities was lower than that reported in previous studies performed in other countries, especially among children with epilepsy living in rural areas. CONCLUSION: This research provides the largest nationwide, population-based study of childhood epilepsy to estimate the prevalence and the associated neuropsychiatric comorbidities of pediatric epilepsy in Taiwan. Potential rural-urban disparity basing on prevalence and associated neuropsychiatric comorbidities cannot be ignored in Taiwan.

Deployment of RFID in healthcare facilities-experimental design in MRI department.

Patient safety has become an important issue due to medical errors. Some health care systems use Radio Frequency Identification (RFID) to identify patients during medical procedures. However, the RFID data readability especially depends upon the environment, an investigation of data reliability and signal loss is essential to making an effective deployment plan. The operation of Magnetic Resonance Imaging (MRI) is the major source of electromagnetic interference in the hospital. Therefore, this research conducts an experimental design of reading performance considering various notable factors in the MRI department. In addition to the readability experiment, this paper also measures the efficiency and reliability of implementing RFID technology in the MRI department using a simulation approach and helps hospitals by providing the measured outcomes.