This qualitative and descriptive study examined the feasibility of a bed-height alert system as a fall-prevention strategy. The alpha prototype was developed to measure and record bed height, and to remind staff to keep patient beds in the lowest position. This pilot project was conducted in a 52-bed adult acute surgical inpatient care unit of a Michigan community hospital. Qualitative and quantitative information was gathered during semi-structured interviews of nursing staff (18 RNs and 13 PCAs; January–April 2011). Descriptive content analysis and descriptive analyses were performed. The overall response rate was 44.9%. The mean values of the feasibility questions are all favorable. Staff’s comments also support the view that the alert system would promote patient safety and prevent falls. In short, this system was found to be somewhat useful, feasible, appropriate, and accurate. It has the potential to promote patient safety and prevent bed-associated injurious falls in inpatient care settings.
Dynamic information-flow tracking (DIFT) is useful for enforcing security policies, but rarely used in practice, as it can slow down a program by an order of magnitude. Static program analyses can be used to prove safe execution states and elide unnecessary DIFT monitors, but the performance improvement from these analyses is limited by their need to maintain soundness.In this paper, we present a novel optimistic hybrid analysis (OHA) to significantly reduce DIFT overhead while still guaranteeing sound results. It consists of a predicated whole-program static taint analysis, which assumes likely invariants gathered from profiles to dramatically improve precision. The optimized DIFT is sound for executions in which those invariants hold true, and recovers to a conservative DIFT for executions in which those invariants are false. We show how to overcome the main problem with using OHA to optimize live executions, which is the possibility of unbounded rollbacks. We eliminate the need for any rollback during recovery by tailoring our predicated static analysis to eliminate only safe elisions of noop monitors. Our tool, Iodine, reduces the overhead of DIFT for enforcing security policies to 9%, which is 4.4× lower than that with traditional hybrid analysis, while still being able to be run on live systems.
Detecting data races in multithreaded programs is a crucial part of debugging such programs, but traditional data race detectors are too slow to use routinely. This paper shows how to speed up race detection by spreading the work across multiple cores. Our strategy relies on uniparallelism, which executes time intervals of a program (called epochs) in parallel to provide scalability, but executes all threads from a single epoch on a single core to eliminate locking overhead. We use several techniques to make parallelization effective: dividing race detection into three phases, predicting a subset of the analysis state, eliminating sequential work via transitive reduction, and reducing the work needed to maintain multiple versions of analysis via factorization. We demonstrate our strategy by parallelizing a happens-before detector and a lockset-based detector. We find that uniparallelism can significantly speed up data race detection. With 4× the number of cores as the original application, our strategy speeds up the median execution time by 4.4× for a happens-before detector and 3.3× for a lockset race detector. Even on the same number of cores as the conventional detectors, the ability for uniparallelism to elide analysis locks allows it to reduce the median overhead by 13% for a happens-before detector and 8% for a lockset detector.
Garbage collection (GC) support for unmanaged languages can reduce programming burden in reasoning about liveness of dynamic objects. It also avoids temporal memory safety violations and memory leaks. Sound GC for weakly-typed languages such as C/C++, however, remains an unsolved problem. Current value-based GC solutions examine values of memory locations to discover the pointers, and the objects they point to. The approach is inherently unsound in the presence of arbitrary type casts and pointer manipulations, which are legal in C/C++. Such language features are regularly used, especially in low-level systems code. In this paper, we propose Dynamic Pointer Provenance Tracking to realize sound GC. We observe that pointers cannot be created out-of-thin-air, and they must have provenance to at least one valid allocation. Therefore, by tracking pointer provenance from the source (e.g., malloc) through both explicit data-flow and implicit control-flow, our GC has sound and precise information to compute the set of all reachable objects at any program state. We discuss several static analysis optimizations, that can be employed during compilation aided with profiling, to significantly reduce the overhead of dynamic provenance tracking from nearly 8× to 16% for well-behaved programs that adhere to the C standards. Pointer provenance based sound GC invocation is also 13% faster and reclaims 6% more memory on average, compared to an unsound value-based GC.
This descriptive study was intended to measure the percentage of the time that patient beds were kept in high position in an adult acute inpatient surgical unit with medical overflow in a community hospital in Michigan, United States. The percentage of the time was calculated for morning, evening, and night shifts. The results showed that overall, occupied beds were in a high position 5.6% of the time: 5.40% in the day shift, 6.88% in the evening shift, and 4.38% in the night shift. It is recognized that this study was unable to differentiate whether those times patient beds being kept in a high position were appropriate for an elevated bed height (e.g., staff were working with the patient). Further research is warranted. Falls committees may conduct high-bed prevalence surveys in a regular basis as a proxy to monitor staff members’ behaviors in keeping beds in a high position.
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