Minimum Level Of Support Research Articles

EMG Based Clinical Evaluation of IndoKnee

IndoKnee is an unpowered knee assisting device designed and patented by Newndra Innovations. In this paper, we report a clinical study to characterize the reduction in load on the major muscles of the knee from the use of this device. Thirty healthy subjects without any history of knee problems underwent this trial. The protocol consists of three sets of events which are a) without IndoKnee (WOD), b) with IndoKnee at a minimum level of support (WDmin) and c) with IndoKnee at a maximum level of support (WDmax). Each set consists of five sitting and five standing activities. Surface electromyography was recorded from six major knee flexion/extension muscles, which are Bicep Femoris (BF), Semitendinosus (ST), Semimembranosus (SM), Rectus Femoris (RF), Vastus Medialis (VM) and Vastus Lateralis (VL). Paired t-tests were performed on our comparison between WOD vs WDmin and WOD vs WDmax to establish a significant difference in the EMG peak amplitudes during sitting and standing activities. The average reduction in all the six muscles for sitting activity is about 15% for both level of support and the standing activity, it is about 15% at a minimum level of support and 20% at a maximum level of support. In our investigation, IndoKnee did effectively reduce the required muscular effort for the knee flexion/extension. Thus, IndoKnee may prove to be an effective device in reducing knee pain, supporting osteoarthritis patients and faster rehabilitation from knee injuries.

Vertical Fragmentation for Database Using FPClose Algorithm

Vertical fragmentation technique is used to enhance the performance of database system and reduce the number of access to irrelevant instances by splitting a table or relation into different fragments vertically. The partitioning design can be derived using FPClose algorithm, which is a data mining algorithm used to extract the frequent closed itemsets in a dataset. A new design approach is implemented to perform fragmentation. A benchmark with different minimum support levels is tested. The obtained results from FPClose algorithm are compared with the Apriori algorithm.

Atlas Nods: The Libertarian Case for a Basic Income

Proposals for a universal basic income are generating interest across the globe, with pilot experiments underway or in the works in California, Canada, Finland, Italy, Kenya, and Uganda. Surprisingly, many of the most outspoken supporters of a universal basic income have been self-described libertarians — even though libertarians are generally considered to be antagonistic toward redistribution and a universal basic income is, at its core, a program of income redistribution. What explains such strong libertarian support for a policy that seems so contrary to libertarian ideals? This Article seeks to answer that question. We first show that a basic safety net is not only consistent with, but likely required by, several strands of libertarian thought. We then explain why libertarians committed to limited redistribution and limited government might support a system of unconditional cash transfers paid periodically. Delivering benefits in cash, rather than in-kind, furthers autonomy by recognizing that all citizens — even poor ones — are the best judges of their needs. Decoupling such transfers from a work requirement acknowledges that the state lacks the ability to distinguish between work-capable and work-incapable individuals. Providing payments periodically, rather than through a once-in-a-lifetime lump sum grant, ensures that all individuals can receive a minimum level of support over lifespans of variable lengths, while also allowing individuals to adjust payment flows through financial market transactions. Although our main objective is to assess the fit between libertarian theory and a universal basic income, we also address various design choices inherent in any basic income scheme: who should receive it?; how large should it be?; which programs might it replace?; and should it phase out as market income rises? Lastly, we consider the relationship between a basic income and the political economy of redistribution. We find that the case for a basic income as a libertarian “second-best” is surprisingly shaky: libertarians who oppose all redistribution but grudgingly accept a basic income as the least-worst form of redistribution should reconsider both aspects of their position. We conclude by drawing out lessons from our analysis for non-libertarians, regardless of whether they are supportive or skeptical of basic income arguments.

Comparative analysis of data mining methods for bankruptcy prediction

A great deal of research has been devoted to prediction of bankruptcy, to include application of data mining. Neural networks, support vector machines, and other algorithms often fit data well, but because of lack of comprehensibility, they are considered black box technologies. Conversely, decision trees are more comprehensible by human users. However, sometimes far too many rules result in another form of incomprehensibility. The number of rules obtained from decision tree algorithms can be controlled to some degree through setting different minimum support levels. This study applies a variety of data mining tools to bankruptcy data, with the purpose of comparing accuracy and number of rules. For this data, decision trees were found to be relatively more accurate compared to neural networks and support vector machines, but there were more rule nodes than desired. Adjustment of minimum support yielded more tractable rule sets.

Parallel mining of maximal sequential patterns using multiple samples

In this paper, we propose a new parallel algorithm, named PMSPX, which mines maximal frequent sequences by using multiple samples to exclude infrequent candidates effectively. A frequent sequence is maximal if none of its supersequences is frequent. Unlike the traditional single-sample methods developed for mining frequent itemsets, PMSPX uses multiple samples. Thus, it can avoid or alleviate some problems inherent in the single-sample methods. We theoretically analyzed how to increase the minimum support level to prevent misestimating infrequent candidates as frequent in the mining of samples. PMSPX is a parallel version of our sequential MSPX algorithm, and it is developed on a cluster of workstations. In PMSPX, each processing node uses MSPX to find a candidate set of local maximal frequent sequences first, independently from other processing nodes. Then, a top-down search is performed, starting with all the candidates, in a synchronous manner to identify real maximal frequent sequences. This asynchronous local mining followed by synchronous global mining approach minimizes the synchronization and communication among the processing nodes. Three database partitioning methods are proposed to distribute the database across the processing nodes, so that their workloads are balanced and the data skewness of the whole database is preserved in the data partition of each node. A comprehensive analysis was performed on PMSPX and existing parallel sequence mining algorithms, and extensive experiments were conducted on PMSPX. PMSPX demonstrates very good speedup and scaleup properties. It also requires less communication and synchronization than other parallel algorithms.

Vertical Fragmentation in Databases Using Data-Mining Technique

A new approach to vertical fragmentation in relational databases is proposed using association rules, a data-mining technique. Vertical fragmentation can enhance the performance of database systems by reducing the number of disk accesses needed by transactions. By adapting Apriori algorithm, a design methodology for vertical partitioning is proposed. The heuristic methodology is tested using two real-life databases for various minimum support levels and minimum confidence levels. In the smaller database, the partitioning solution obtained matched the optimal solution using exhaustive enumeration. The application of our method on the larger database resulted in the partitioning solution that has an improvement of 41.05% over unpartitioned solution and took less than a second to produce the solution. We provide future research directions on extending the procedure to distributed and object-oriented database designs.

Sharing the Land: Attracting Native American Students to the Geosciences

Native American reservation communities nationwide exercise sovereign control over natural resources and land-use within reservation boundaries. With the recent rapid economic growth of many of these communities, development pressures and infrastructure issues have become a foremost concern. Despite the clear need for geoscience professionals on reservations and the deep cultural connection many American Indian cultures have with the Earth, Native American students remain poorly represented in the earth sciences. The Indigenous Earth Sciences Project is an effort based at Purdue University designed to address this problem systemically by providing local, culturally-responsive avenues for success for college-bound American Indian students in the geosciences in partnership with regional universities. The Sharing the Land program is a partnership between Purdue and colleges and universities in the San Diego region, providing on-reservation education to environmental managers who often lack geoscientific expertise, and a portion of the Young Native Scholars residential summer college bridge program for area high school students. The program also reaches younger children through the Explorers Club outdoor education program. We have also constructed an internship program in tribal environmental offices for Native high school and college students. This far-reaching project provides the minimum level of support to create a truly integrated pathway for Native American students to gain geoscience degrees. Native-focused programs must have full community participation at all levels to succeed, and must provide authentic work experience to make the earth sciences relevant for students. Involvement of Native educators and elders is also critical to ensure cultural connection and continuity. This article outlines the entire STL program, strategies for replication elsewhere, and highlight research opportunities in cross-cultural science education.

Parallel mining of association rules from text databases

In this paper, we propose a new algorithm named Parallel Multipass with Inverted Hashing and Pruning (PMIHP) for mining association rules between words in text databases. The characteristics of text databases are quite different from those of retail transaction databases, and existing mining algorithms cannot handle text databases efficiently because of the large number of itemsets (i.e., sets of words) that need to be counted. The new PMIHP algorithm is a parallel version of our Multipass with Inverted Hashing and Pruning (MIHP) algorithm (Holt, Chung in: Proc of the 14th IEEE int'l conf on tools with artificial intelligence, 2002, pp 49---56), which was shown to be quite efficient than other existing algorithms in the context of mining text databases. The PMIHP algorithm reduces the overhead of communication between miners running on different processors because they are mining local databases asynchronously and prune the global candidates by using the Inverted Hashing and Pruning technique. Compared with the well-known Count Distribution algorithm (Agrawal, Shafer in: (1996) IEEE Trans Knowl Data Eng 8(6):962---969), PMIHP demonstrates superior performance characteristics for mining association rules in large text databases, and when the minimum support level is low, its speedup is superlinear as the number of processors increases. These experiments were performed on a cluster of Linux workstations using a collection of Wall Street Journal articles.

A scalable algorithm for mining maximal frequent sequences using a sample

In this paper, we propose an efficient scalable algorithm for mining Maximal Sequential Patterns using Sampling (MSPS). The MSPS algorithm reduces much more search space than other algorithms because both the subsequence infrequency-based pruning and the supersequence frequency-based pruning are applied. In MSPS, a sampling technique is used to identify long frequent sequences earlier, instead of enumerating all their subsequences. We propose how to adjust the user-specified minimum support level for mining a sample of the database to achieve better overall performance. This method makes sampling more efficient when the minimum support is small. A signature-based method and a hash-based method are developed for the subsequence infrequency-based pruning when the seed set of frequent sequences for the candidate generation is too big to be loaded into memory. A prefix tree structure is developed to count the candidate sequences of different sizes during the database scanning, and it also facilitates the customer sequence trimming. Our experiments showed MSPS has very good performance and better scalability than other algorithms.

Additional work of breathing and breathing patterns in spontaneously breathing patients during pressure support ventilation, automatic tube compensation and amplified spontaneous pattern breathing

Additional work of breathing and breathing patterns in spontaneously breathing patients during pressure support ventilation, automatic tube compensation and amplified spontaneous pattern breathing

Dairy processing.

The United States dairy processing sector is dynamic and adaptive to new changes in the market place. Changes in consumer preferences and manufacturing technologies are resulting in new challenges to the processing sector. Consumers want a wider array of quality dairy products. Fluid processors are adapting to changing consumer demands for beverage products by introducing new flavors, providing ultrapasteurization, and using creative packaging. In addition, United States food manufacturers are requesting dairy processors to provide new dairy fractions such as MPC for new nutrition products. United States dairy policy is attempting to adapt to these changes. Federal order reform has resulted in new market-oriented signals for dairy farmers to produce what the market wants; namely, quality milk components. US dairy farmers, however, also wants to maintain programs such as the DPSP that have had the unfortunate consequence of spurring demand for protein imports (i.e., MPCs, casein, and caseinates) and also resulted in a disincentive to produce these new innovative protein products here in the United States. Surplus skim milk solids are now moving into US Government warehouses rather than into commercial markets. The future of the United States dairy industry will clearly be toward producing innovative products that the market wants. There is a strong market for dairy products not only here in the United States but also overseas, which will mean learning to compete on a global scale. The challenge is to modernize our United States milk pricing programs to provide dairy farmers and processors proper price signals while providing a minimum level of support to dairy farmers. The benefit of a greater orientation toward the market place will be stronger rates of growth for United States-produced dairy products.

Mining mutually dependent patterns for system management

In some domains, such as isolating problems in computer networks and discovering stock market irregularities, there is more interest in patterns consisting of infrequent, but highly correlated items rather than patterns that occur frequently (as defined by minsup, the minimum support level). We describe m-pattern, a new pattern that is defined in terms of minp, the minimum probability of mutual dependence of items in the pattern. We show that all infrequent m-pattern can be discovered by an efficient algorithm that makes use of: (1) a linear algorithm to qualify an m-pattern; (2) an effective technique for candidate pruning based on a necessary condition for the presence of an m-pattern; and (3) a level-wise search for m-pattern discovery (which is possible because m-patterns are downward closed). Further, we consider frequent m-patterns, which are defined in terms of both minp and minsup. Using synthetic data, we study the scalability of our algorithm. Then, we apply our algorithm to data from a production computer network both to show the m-patterns present and to contrast with frequent patterns. We show that when minp=0, our algorithm is equivalent to finding frequent patterns. However, with a larger minp, our algorithm yields a modest number of highly correlated items, which makes it possible to mine for infrequent but highly correlated itemsets. To date, many actionable m-patterns have been discovered in production systems.

The Odyssey of the Juvenile Court: Policy Failure and Institutional Persistence in the Therapeutic State

The human services have come to occupy a vital position in the modern welfare state, penetrating nearly all its activities and institutions. Beyond providing a minimum level of support, these services seek to restructure the behavior of their clients so they can better cope with the demands and challenges of modern life. To realize this goal social personnel employ an approach that can most accurately be defined as therapeutic: the situation of the client is viewed as a condition that must be diagnosed; the caseworker establishes a helping relationship; a suitable treatment plan is designed and implemented. Although the middle class voluntarily avails itself of therapeutic services, public intervention by social personnel has a distinctly lower-class bias and an authoritarian character. The middle class purchases therapeutic services from private providers. The state focuses on marginal populations and its subjects usually have no choice about assuming a client status.