Optimization of Analytic Window Functions

Analytic functions represent the state-of-the-art way of performing complex data analysis within a single SQL statement. In particular, an important class of analytic functions that has been frequently used in commercial systems to support OLAP and decision support applications is the class of window functions. A window function returns for each input tuple a value derived from applying a function over a window of neighboring tuples. However, existing window function evaluation approaches are based on a naive sorting scheme. In this paper, we study the problem of optimizing the evaluation of window functions. We propose several efficient techniques, and identify optimization opportunities that allow us to optimize the evaluation of a set of window functions. We have integrated our scheme into PostgreSQL. Our comprehensive experimental study on the TPC-DS datasets as well as synthetic datasets and queries demonstrate significant speedup over existing approaches.Comment: VLDB201

Medication use by early-stage breast cancer survivors: a 1-year longitudinal study.

PurposeThe aim of this study is to characterize the patterns of medication use by early-stage breast cancer (ESBC) survivors from diagnosis to 1 year post-chemotherapy.MethodsA single-center longitudinal study was conducted with ESBC patients diagnosed between December 2011 and June 2014. Data on the medication use of individual patients were retrieved from prescription databases, supplemented by records from the National Electronic Health Records. The data covered the period from ESBC diagnosis to 1 year post-chemotherapy. Medication types were classified according to the World Health Organization's Anatomical Therapeutic Chemical classification system, and medication for chronic diseases was created by adapting a list of 20 chronic diseases provided by the U.S. Department of Human and Health Services.ResultsOf the 107 patients involved in the study (mean age 51.1 ± 8.4 years; 78.5 % Chinese), 46.7 % manifested non-cancer comorbidities, of which hypertension (24.3 %) was the most prevalent, followed by hyperlipidemia (13.1 %) and diabetes (5.6 %). Calcium channel blockers (12.1 %) and lipid-modifying agents (11.2 %) were the most common chronic medication types used before chemotherapy, and their use persisted during chemotherapy (10.3 and 11.2 %, respectively) and after chemotherapy (11.2 and 13.1 %, respectively). Hormonal therapy was the predominant post-chemotherapy medication (77.6 %). A statistically significant increase (p < 0.0001) was observed in the mean number of chronic disease medication classes prescribed to patients between the pre-chemotherapy (0.53 ± 1.04) and chemotherapy (0.62 ± 1.08) periods and between the chemotherapy and post-chemotherapy (1.63 ± 1.35) periods.ConclusionsThere is an increase in trend of chronic medication usage in breast cancer survivors after cancer treatment. This study provides important insights into the design of medication management programs tailored to this population. Future studies should incorporate a control population to improve the interpretation of study results

Enhancement of Jaibot: Developing Safety and Monitoring Features for Jaibot Using IoT Technologies

The Hilti Jaibot, a state-of-the-art construction site drilling robot, has demonstrated remarkable productivity gains while also underscoring the need for improved safety and monitoring capabilities. This study aims to address this need by harnessing Internet of Things (IoT) technologies and predictive maintenance methodologies. The proposed enhancements encompass a comprehensive sensor and camera integration to monitor the robot's environment, coupled with the development of a Long Short-Term Memory (LSTM) predictive maintenance algorithm to preemptively identify operational issues. These improvements enable the Jaibot to autonomously detect and mitigate risks, such as obstacles and human activity, while providing real-time safety alerts to operators. Incorporating quantitative results from our predictive model, which successfully predicts three output variables (X, Y, and Z) using three input variables, we observed varying RMSE and MAPE values. Specifically, X exhibited an RMSE of 77.80% and a MAPE of 242.20%, while Y showed an RMSE of 31.10% and a MAPE of 69.70%, and Z had an RMSE of 34.53% and a MAPE of 82.74%. Notably, Y and Z data displayed high MAPE values, potentially attributed to data inconsistency. To enhance accuracy in our predictive model, we propose the utilization of more complex models and increased data volumes, which may mitigate the observed inconsistencies and lead to improved overall model performance. These findings from our quantitative analysis provide valuable insights for the integration of predictive maintenance algorithms into the Hilti Jaibot and lay the foundation for future advancements in robotic construction, emphasizing the pivotal role of IoT technology and predictive maintenance in shaping the industry's trajectory

A hybrid noise suppression filter for accuracy enhancement of commercial speech recognizers in varying noisy conditions

Commercial speech recognizers have made possible many speech control applications such as wheelchair, tone-phone, multifunctional robotic arms and remote controls, for the disabled and paraplegic. However, they have a limitation in common in that recognition errors are likely to be produced when background noise surrounds the spoken command, thereby creating potential dangers for the disabled if recognition errors exist in the control systems. In this paper, a hybrid noise suppression filter is proposed to inter-face with the commercial speech recognizers in order to enhance the recognition accuracy under variant noisy conditions. It intends to decrease the recognition errors when the commercial speech recognizers are working under a noisy environment. It is based on a sigmoid function which can effectively enhance noisy speech using simple computational operations, while a robust estimator based on an adaptive-network-based fuzzy inference system is used to determine the appropriate operational parameters for the sigmoid function in order to produce effective speech enhancement under variant noisy conditions.The proposed hybrid noise suppression filter has the following advantages for commercial speech recognizers: (i) it is not possible to tune the inbuilt parameters on the commercial speech recognizers in order to obtain better accuracy; (ii) existing noise suppression filters are too complicated to be implemented for real-time speech recognition; and (iii) existing sigmoid function based filters can operate only in a single-noisy condition, but not under varying noisy conditions. The performance of the hybrid noise suppression filter was evaluated by interfacing it with a commercial speech recognizer, commonly used in electronic products. Experimental results show that improvement in terms of recognition accuracy and computational time can be achieved by the hybrid noise suppression filter when the commercial recognizer is working under various noisy environments in factories

Tree-Encoded Bitmaps

We propose a novel method to represent compressed bitmaps. Similarly to existing bitmap compression schemes, we exploit the compression potential of bitmaps populated with consecutive identical bits, i.e., 0-runs and 1-runs. But in contrast to prior work, our approach employs a binary tree structure to represent runs of various lengths. Leaf nodes in the upper tree levels thereby represent longer runs, and vice versa. The tree-based representation results in high compression ratios and enables efficient random access, which in turn allows for the fast intersection of bitmaps. Our experimental analysis with randomly generated bitmaps shows that our approach significantly improves over state-of-the-art compression techniques when bitmaps are dense and/or only barely clustered. Further, we evaluate our approach with real-world data sets, showing that our tree-encoded bitmaps can save up to one third of the space over existing techniques

Application of Static Modeling in the Prediction of In Vivo Drug-Drug Interactions between Rivaroxaban and Antiarrhythmic Agents Based on In Vitro Inhibition Studies

ABSTRACT Rivaroxaban, a direct Factor Xa inhibitor, is indicated for stroke prevention in nonvalvular atrial fibrillation (AF). Studies have revealed that the clearance of rivaroxaban is largely attributed to CYP3A4, CYP2J2 metabolism, and P-glycoprotein (P-gp) efflux pathways. Amiodarone and dronedarone are antiarrhythmic agents employed in AF management. Amiodarone, dronedarone, and their major metabolites, N-desethylamiodarone (NDEA) and N-desbutyldronedarone (NDBD), demonstrate inhibitory effects on CYP3A4 and CYP2J2 with U.S. Food and Drug Administration-recommended probe substrates. In addition, both amiodarone and dronedarone are known P-gp inhibitors. Hence, the concomitant administration of these antiarrhythmic agents has the potential to augment the systemic exposure of rivaroxaban through simultaneous impairment of its clearance pathways. Currently, however, clinical data on the extent of these postulated drug-drug interactions are lacking. In this study, in vitro inhibition assays using rivaroxaban as the probe substrate demonstrated that both dronedarone and NDBD produced reversible inhibition as well as irreversible mechanism-based inactivation of CYP3A4-and CYP2J2-mediated metabolism of rivaroxaban. However, amiodarone and NDEA were observed to cause reversible inhibition as well as mechanism-based inactivation of CYP3A4 but not CYP2J2. In addition, amiodarone, NDEA, and dronedarone, but not NDBD, were determined to inhibit P-gpmediated rivaroxaban transport. The in vitro inhibition parameters were fitted into a mechanistic static model, which predicted a 37% and 31% increase in rivaroxaban exposure due to the inhibition of hepatic and gut metabolism by amiodarone and dronedarone, respectively. A separate model quantifying the inhibition of P-gpmediated efflux by amiodarone or dronedarone projected a 9% increase in rivaroxaban exposure

Adjunctive mood stabilizer treatment for hospitalized schizophrenia patients: Asia psychotropic prescripton study (2001-2008)

Recent studies indicate relatively high international rates of adjunctive psychotropic medication, including mood stabilizers, for patients with schizophrenia. Since such treatments are little studied in Asia, we examined the frequency of mood-stabilizer use and its clinical correlates among hospitalized Asian patients diagnosed with schizophrenia in 2001-2008. We evaluated usage rates of mood stabilizers with antipsychotic drugs, and associated factors, for in-patients diagnosed with DSM-IV schizophrenia in 2001, 2004 and 2008 in nine Asian regions: China, Hong Kong, India, Korea, Japan, Malaysia, Taiwan, Thailand, and Singapore. Overall, mood stabilizers were given to 20.4% (n=1377/6761) of hospitalized schizophrenia patients, with increased usage over time. Mood-stabilizer use was significantly and independently associated in multivariate logistic modeling with: aggressive behaviour, disorganized speech, year sampled (2008 vs. earlier), multiple hospitalizations, less negative symptoms, younger age, with regional variation (Japan, Hong Kong, Singapore>Taiwan or China). Co-prescription of adjunctive mood stabilizers with antipsychotics for hospitalized Asian schizophrenia patients increased over the past decade, and was associated with specific clinical characteristics. This practice parallels findings in other countries and illustrates ongoing tension between evidence-based practice vs. individualized, empirical treatment of psychotic disorders.published_or_final_versio