Application of Machine Learning Techniques to Parameter Selection for Flight Risk Identification

In recent years, the use of data mining and machine learning techniques for safety analysis, incident and accident investigation, and fault detection has gained traction among the aviation community. Flight data collected from recording devices contains a large number of heterogeneous parameters, sometimes reaching up to thousands on modern commercial aircraft. More data is being collected continuously which adds to the ever-increasing pool of data available for safety analysis. However, among the data collected, not all parameters are important from a risk and safety analysis perspective. Similarly, in order to be useful for modern analysis techniques such as machine learning, using thousands of parameters collected at a high frequency might not be computationally tractable. As such, an intelligent and repeatable methodology to select a reduced set of significant parameters is required to allow safety analysts to focus on the right parameters for risk identification. In this paper, a step-by-step methodology is proposed to down-select a reduced set of parameters that can be used for safety analysis. First, correlation analysis is conducted to remove highly correlated, duplicate, or redundant parameters from the data set. Second, a pre-processing step removes metadata and empty parameters. This step also considers requirements imposed by regulatory bodies such as the Federal Aviation Administration and subject matter experts to further trim the list of parameters. Third, a clustering algorithm is used to group similar flights and identify abnormal operations and anomalies. A retrospective analysis is conducted on the clusters to identify their characteristics and impact on flight safety. Finally, analysis of variance techniques are used to identify which parameters were significant in the formation of the clusters. Visualization dashboards were created to analyze the cluster characteristics and parameter significance. This methodology is employed on data from the approach phase of a representative single-aisle aircraft to demonstrate its application and robustness across heterogeneous data sets. It is envisioned that this methodology can be further extended to other phases of flight and aircraft

Seasonal pattern of peptic ulcer hospitalizations: analysis of the hospital discharge data of the Emilia-Romagna region of Italy

BACKGROUND: Previous studies have reported seasonal variation in peptic ulcer disease (PUD), but few large-scale, population-based studies have been conducted. METHODS: To verify whether a seasonal variation in cases of PUD (either complicated or not complicated) requiring acute hospitalization exists, we assessed the database of hospital admissions of the region Emilia Romagna (RER), Italy, obtained from the Center for Health Statistics, between January 1998 and December 2005. Admissions were categorized by sex, age ( or = 75 yrs), site of PUD lesion (stomach or duodenum), main complication (hemorrhage or perforation), and final outcome (intended as fatal outcome: in-hospital death; nonfatal outcome: patient discharged alive). Temporal patterns in PUD admissions were assessed in two ways, considering a) total counts per single month and season, and b) prevalence proportion, such as the monthly prevalence of PUD admissions divided by the monthly prevalence of total hospital admissions, to assess if the temporal patterns in the raw data might be the consequence of seasonal and annual variations in hospital admissions per se in the region. For statistical analysis, the chi2 test for goodness of fit and inferential chronobiologic method (Cosinor and partial Fourier series) were used. RESULTS: Of the total sample of PUD patients (26,848 [16,795 males, age 65 +/- 16 yrs; 10,053 females, age 72 +/- 15 yrs, p or = 75 yrs of age. There were more cases of duodenal (DU). (89.8%) than gastric ulcer (GU) (3.6%), and there were 1,290 (4.8%) fatal events. Data by season showed a statistically difference with the lowest proportion of PUD hospital admissions in summer (23.3%) (p < 0.001), for total cases and rather all subgroups. Chronobiological analysis identified three major peaks of PUD hospitalizations (September-October, January-February, and April-May) for the whole sample (p = 0.035), and several subgroups, with nadir in July. Finally, analysis of the monthly prevalence proportions yielded a significant (p = 0.025) biphasic pattern with a main peak in August-September-October, and a secondary one in January-February. CONCLUSIONS: A seasonal variation in PUD hospitalization, characterized by three peaks of higher incidence (Autumn, Winter, and Spring) is observed. When data corrected by monthly admission proportions are analyzed, late summer-autumn and winter are confirmed as higher risk periods. The underlying pathophysiologic mechanisms are unknown, and need further studies. In subjects at higher risk, certain periods of the year could deserve an appropriate pharmacological protection to reduce the risk of PUD hospitalization

An overview of the recent developments on fructooligosaccharide production and applications

Over the past years, many researchers have suggested that deficiencies in the diet can lead to disease states and that some diseases can be avoided through an adequate intake of relevant dietary components. Recently, a great interest in dietary modulation of the human gut has been registered. Prebiotics, such as fructooligosaccharides (FOS), play a key role in the improvement of gut microbiota balance and in individual health. FOS are generally used as components of functional foods, are generally regarded as safe (generally recognized as safe status—from the Food and Drug Administration, USA), and worth about 150€ per kilogram. Due to their nutrition- and health-relevant properties, such as moderate sweetness, low carcinogenicity, low calorimetric value, and low glycemic index, FOS have been increasingly used by the food industry. Conventionally, FOS are produced through a two-stage process that requires an enzyme production and purification step in order to proceed with the chemical reaction itself. Several studies have been conducted on the production of FOS, aiming its optimization toward the development of more efficient production processes and their potential as food ingredients. The improvement of FOS yield and productivity can be achieved by the use of different fermentative methods and different microbial sources of FOS producing enzymes and the optimization of nutritional and culture parameter; therefore, this review focuses on the latest progresses in FOS research such as its production, functional properties, and market data.Agencia de Inovacao (AdI)-Project BIOLIFE reference PRIME 03/347. Ana Dominguez acknowledges Fundacao para a Ciencia e a Tecnologia, Portugal, for her PhD grant reference SFRH/BD/23083/2005

Der Einfluss einer Wasser-Resistenz-Therapie auf Stimmlippenschwingungen bei Patienten mit organischer Dysphonie

Hintergrund: In der gegenwärtigen Literatur wächst das Verständnis um Stimmübungen in halb verschlossene Röhrensysteme (semi-occluded vocal tract exercises) und wie diese die stimmliche Effizienz beeinflussen. Für stimmgesunde Probanden hat gezeigt werden können, dass die Effekte nur kurz anhalten, wenn die Therapie einmalig durchgeführt wird. Jedoch ist derzeitig nicht verstanden, wie sich solche Therapien bei Patienten mit organischer Dysphonie auswirken.Material und Methoden: Acht Patienten mit organischer Dysphonie wurden gebeten, auf dem Vokal /i/ mit Grundfrequenz 125Hz für die Männer und 250Hz für die Frauen und angenehmer Lautstärke zu phonieren. Danach wurden sie aufgefordert, eine Wasser-Resistenz-Therapie (WRT) für 10 Minuten zu praktizieren (Schlauch: Länge 30cm, Durchmesser 9mm, 5cm unter der Wasseroberfläche). Danach wurden sie gebeten erneut nach 0, 5, 10 und 30 Minuten die Phonation zu wiederholen. Während der Phonation erfolgte eine silmultane Aufzeichnung von Hochgeschwindigkeitslaryngoskopie (transnasal mit 20.000fps), Elektroglottographie und Audiosignalen. Aus diesen wurden u.a. die Glottal Area Waveform (GAW) segmentiert und mathemathische Messdaten berechnet.Ergebnisse: Insgesamt wiesen die Verläufe große interindividuelle Differenzen auf. Im Mittel zeigen die Daten jedoch einen deutlichen Anstieg des GAW-Offenquotienten während der WRT. Direkt nach der Übung kam es initial zu einem geringen Abfall, jedoch 30 Minuten nach der Übung erneut zu einem Anstieg des Offenquotienten. Der GAW Offenquotient war nicht mit dem elektroglottographischen Offenquotient nach Howard Kriterium korreliert.Fazit: Insgesamt zeigen die Daten, dass bei Patienten mit organischer Dysphonie trotz Effekten während der WRT die Änderungen nach der Übung eher gering sind. Auch kam es zu einer Erhöhung des Offenquotienten, welches als Zeichen der Stimmermüdung gedeutet werden kann

A Triple Iron triathlon leads to a decrease in total body mass but not to dehydration

A loss in total body mass during an ultraendurance performance is usually attributed to dehydration. We identified the changes in total body mass, fat mass, skeletal muscle mass, and selected markers of hydration status in 31 male nonprofessional ultratriathletes participating in a Triple Iron triathlon involving 11.4 km swimming, 540 km cycling and 126.6 km running. Measurements were taken prior to starting the race and after arrival at the finish line. Total body mass decreased by 1.66 kg (SD = 1.92; -5.3 kg to +1.2 kg; p < .001), skeletal muscle mass by 1.00 kg (SD = 0.90; -2.54 kg to +2.07 kg; p < .001), and fat mass by 0.58 kg (SD = 0.78; -1.74 kg to +0.87 kg; p < .001). The decrease in total body mass was associated with the decrease in skeletal muscle mass (r = .44; p < .05) and fat mass (r = .51; p < .05). Total body water and urinary specific gravity did not significantly change. Plasma urea increased significantly (p < .001); the decrease in skeletal muscle mass and the increase in plasma urea were associated (r = .39; p < .05). We conclude that completing a Triple Iron triathlon leads to decreased total body mass due to reduced fat mass and skeletal muscle mass but not to dehydration. The association of decrease in skeletal muscle mass and increased plasma urea suggests a loss in skeletal muscle mass