Potentially Inappropriate Medication Prescribing Detected by Computer Algorithm among Older Patients: Results from the MAPT Study

(1) Background: Some medications may be dangerous for older patients. Potentially inappropriate medication prescribing (PIP) among older patients represents a significant cause of morbidity. The aim of this study was to create an algorithm to detect PIP in a geriatric database (Multidomain Alzheimer Preventive Trial (MAPT) study), and then to assess the algorithm construct validity by comparing the prevalence of PIP and associated factors with literature data. (2) Methods: An algorithm was constructed to detect PIP and was based on different explicit criteria among which the European list of potentially inappropriate medications (EU(7)-PIM), the STOPP and START version 2 tools. For construct validity assessment, logistic mixed-effects model repeated measures analyses were used to identify factors associated with PIP. (3) Results: Prevalence of PIP was 59.0% with the EU(7)-PIM list criteria, 43.2% with the STOPP criteria and 51.3% with the START criteria. Age, polypharmacy, and higher Charlson comorbidity index were associated with PIP. (4) Conclusions: Prevalence of PIP and associated factors are consistent with literature data, supporting the construct validity of our algorithm. This algorithm opens up interesting perspectives both in terms of analysis of very large databases and integration into e-prescribing or pharmaceutical validation software.

Light positioning: A high-accuracy visible light indoor positioning system based on attitude identification and propagation model

With the booming development of green lighting technology, visible light-based indoor localization has attracted a lot of attention. Visible light-based indoor positioning technology leverages a light propagation model to pinpoint target location. Compared with the radio localization technology, visible light-based indoor positioning not only can achieve higher location accuracy, but also no electromagnetic interference. In this article, we propose LIPOS, a three-dimensional indoor positioning system based on attitude identification and visible light propagation model. The LIPOS system takes advantage of the existing lighting infrastructures to localize mobile devices that have light-sensing capabilities (e.g. a smartphone) using light emitting diode lamps as anchors. The system can accurately identify the attitude of a smartphone using its integrated sensors, distinguish different light emitting diode beacons using the fast Fourier transform algorithm, construct a position cost-function based on a visible light radiative decay model, and apply a nonlinear optimizing method to acquire the optimal estimation of final location. We have implemented the LIPOS system and evaluated it with a small-scale hardware testbed, as well as moderate-sized simulations. Extensive experiments are performed in three representative indoor environments—open-plan office, cubicle, and corridor, which not only demonstrate that the LIPOS can effectively avoid the negative effects of dynamic change of a smartphone’s attitude angle, but also show better locating accuracy and robustness, and obtain sub-meter level positioning accuracy.

An IPTV Recommendation Method Based on Latent Context

In the light of the data differences between network television and the Internet, this paper solve the problem of grading IPTV by the introduction of time context information and computing the latent scores based on the traditional and item-based collaborative filtering recommendation algorithm. Construct the user - item, the item - time model and optimize item similarity calculation so as to ease the difficulty of searching the similar item due to the data scarcity. The experimental results show that the improved method can obviously increase the recommendation precision and has a certain effect on reducing the impact of data scarcity compared with the traditional item-based collaborative filtering.

Improved Heuristic Search Algorithm for Multi-Vehicle and Multi-Cargo Loading Problem

This paper studies multi-vehicle and multi-cargo loading problem under the limited loading capacity. According to the characteristics of model, improved Heuristic Search Algorithm is used to get the optimization solution. Firstly, on the basis of cubage-weight balance algorithm, construct initial solution to improve the feasibility. Secondly, adopt the operations of 1-exchange and 2-opt* to fasten the speed of convergence and boost the search efficiency. Finally, the good performance of this algorithm can be proved by experiment calculation and concrete examples.

Study on Hybrid Heuristic Algorithm for Multi-Vehicle and Multi-Cargo Loading Problem

This paper studies multi-vehicle and multi-cargo loading problem under the limited loading capacity. Hybrid heuristic algorithm is used to get the optimization solution. Firstly, adopt hybrid coding so as to make the problem more succinctly. On the basis of cubage-weight balance algorithm, construct initial solution to improve the feasibility. Adopt the improved non-uniform mutation so as to enhance local search ability of chromosomes. Secondly, stock elite by tabu searching algorithm to improve the searching efficiency of algorithm. Finally, the example can be shown that the algorithm is effective and can provide for large-scale ideas to solve practical problems.

Study on Hybrid Genetic Simulated Annealing Algorithm for Multi-Vehicle and Multi-Cargo Loading Problem

This paper studies multi-vehicle and multi-cargo loading problem under the limited loading capacity. Hybrid genetic simulated annealing algorithm is used to get the optimization solution. Firstly, adopt hybrid coding so as to make the problem more succinctly. On the basis of cubage-weight balance algorithm, construct initial solution to improve the feasibility. Adopt the improved non-uniform mutation so as to enhance local search ability of chromosomes. Secondly, through utilizing Boltzmann mechanism of simulated annealing algorithm, control crossover and mutation operation of genetic algorithm, search efficiency so as to improve the solution quality of algorithm. Finally, the example can be shown that the above model and algorithm is effective and can provide for large-scale ideas to solve practical problems.

New Policy of Maximal Frequent Itemsets in Data Stream Mining

According to the features of data streams and combined sliding window, a new algorithm A-MFI which is based on self-adjusting and orderly-compound policy for mining maximal frequent itemsets in data stream is proposed. This algorithm which is based on basic window updates information from data stream flow fragments and scans the stream only once to gain and store it in frequent itemsets list when the data stream flows. The core idea of this algorithm: construct self-adjusting and orderly-compound FP-tree, use mixed subset pruning techniques to reduce the search space, merge nodes which has equal minsup in the same branch and compress to generate the orderly-compound FP-tree to avoid superset checking when mining maximal frequent itemsets. The experimental results show that the algorithm has higher efficiency in time and space, and also has good scalability.

Maximal Frequent Itemsets in Data Stream Mining Based on Orderly-Compound Policy

Mining maximal frequent itemsets get the advantage of a relatively small number of itemsets. Compared to mining frequent itemsets and mining frequent closed itemsets, such algorithm has higher time and space efficiency. According to the features of data streams and combined sliding window, a new algorithm E-FPMFI which is based on orderly-compound policy for mining maximal frequent itemsets in data stream is proposed. The algorithm based on basic window updates information from data stream flow fragment and scans the stream only once to gain and store it in frequent itemsets list. The algorithm construct FP-tree, then compress orderly FP-tree by merging nodes which has equal minsup in same branch, also uses subset mix pruning technique, avoid superset checking. The experimental results show the algorithm has higher time, space efficiency and good scalability.