Slice Sizes Research Articles

Quality Evaluation and Heat and Mass Transfer Mechanism of Microwave Vacuum Drying of Astragalus Roots.

In this research, the objective was to optimize the drying process of Astragalus by investigating the effects of microwave vacuum drying parameters, including temperature (30, 35, 40, 45, and 50 °C) and slice thickness (2, 3, 4, 5, and 6 mm). In addition, utilizing COMSOL 6.0 finite element analysis software, we delved into the distribution of heat and moisture during the drying process. The results revealed that drying temperature played a significantly greater role than slice thickness in determining the drying dynamics. The thermal and mass transfer mechanism indicated that the whole drying process conforms to the microwave radiation mechanism and the basic principle of electromagnetic heating. In the case of low temperatures and thinner slice sizes, the more polysaccharide content was retained; The total phenol content peaked when the slice thickness was 5 mm; The increase of slice thickness was not conducive to the retention of total flavonoids content. The potent antioxidant capacity was detected at a temperature of 40 °C, with slice thickness having a negligible effect on this capacity; Low temperatures were beneficial for the preservation of active ingredients. Compared with the scanning electron microscope, the structure appeared more uniform at a temperature of 50 °C. Based on the analysis of the kinetic characteristics of microwave vacuum drying of Astragalus and the quality achieved under various drying conditions, the results of the study can provide valuable guidance for controlling the quality of microwave vacuum drying of Astragalus under different drying requirements.

Reducing the Length of Dynamic and Relevant Slices by Pruning Boolean Expressions

Dynamic and relevant (backward) slicing helps programmers in the debugging process by reducing the number of statements in an execution trace. In this paper, we propose an approach called pruned slicing, which can further reduce the size of slices by reasoning over Boolean expressions. It adds only those parts of a Boolean expression that are responsible for the evaluation outcome of the Boolean expression to the set of relevant variables. We empirically evaluate our approach and compare it to dynamic and relevant slicing using three small benchmarks: the traffic collision avoidance system (TCAS), the Refactory dataset, and QuixBugs. Pruned slicing reduces the size of the TCAS slices on average by 10.2%, but it does not reduce the slice sizes of the Refactory and QuixBugs programs. The times required for computing pruned dynamic and relevant slices are comparable to the computation times of non-pruned dynamic and relevant slices. Thus, pruned slicing is an extension of dynamic and relevant slicing that can reduce the size of slices while having a negligible computational overhead.

A Machine-Learning-Based Approach for Railway Track Monitoring Using Acceleration Measured on an In-Service Train.

In this paper, a novel railway track monitoring approach is proposed that employs acceleration responses measured on an in-service train to detect the loss of stiffness in the track sub-layers. An Artificial Neural Network (ANN) algorithm is developed that works with the energies of the train acceleration responses. A numerical model of a half-car train coupled with a track profile is employed to simulate the train vertical acceleration. The energy of acceleration signals measured from 100 traversing trains is used to train the ANN for healthy track conditions. The energy is calculated every 15 m along the track, each of which is called a slice. In the monitoring phase, the trained ANN is used to predict the energies of a set of train crossings. The predicted energies are compared with the simulated ones and represented as the prediction error. The damage is modeled by reducing the soil stiffness at the sub-ballast layer that represents hanging sleepers. A damage indicator (DI) based on the prediction error is proposed to visualize the differences in the predicted energies for different damage cases. In addition, a sensitivity analysis is performed where the impact of signal noise, slice sizes, and the presence of multiple damaged locations on the performance of the DI is assessed.

Device Hopping

Existing OS techniques for homogeneous many-core systems make it simple for single and multithreaded applications to migrate between cores. Heterogeneous systems do not benefit so fully from this flexibility, and applications that cannot migrate in mid-execution may lose potential performance. The situation is particularly challenging when a switch of language runtime would be desirable in conjunction with a migration. We present a case study in making heterogeneous CPU + GPU systems more flexible in this respect. Our technique for fine-grained application migration, allows switches between OpenMP, OpenCL, and CUDA execution, in conjunction with migrations from GPU to CPU, and CPU to GPU. To achieve this, we subdivide iteration spaces into slices, and consider migration on a slice-by-slice basis. We show that slice sizes can be learned offline by machine learning models. To further improve performance, memory transfers are made migration-aware. The complexity of the migration capability is hidden from programmers behind a high-level programming model. We present a detailed evaluation of our mid-kernel migration mechanism with the First Come, First Served scheduling policy. We compare our technique in a focused evaluation scenario against idealized kernel-by-kernel scheduling, which is typical for current systems, and makes perfect kernel to device scheduling decisions, but cannot migrate kernels mid-execution. Models show that up to 1.33× speedup can be achieved over these systems by adding fine-grained migration. Our experimental results with all nine applicable SHOC and Rodinia benchmarks achieve speedups of up to 1.30× (1.08× on average) over an implementation of a perfect but kernel-migration incapable scheduler when migrated to a faster device. Our mechanism and slice size choices introduce an average slowdown of only 2.44% if kernels never migrate. Lastly, our programming model reduces the code size by at least 88% if compared to manual implementations of migratable kernels.

CPU: Cross-Rack-Aware Pipelining Update for Erasure-Coded Storage

Erasure coding is widely used in distributed storage systems (DSSs) to efficiently achieve fault tolerance. However, when the original data need to be updated, erasure coding must update every encoded block, resulting in long update time and high bandwidth consumption. Exiting solutions are mainly focused on coding schemes to minimize the size of transmitted update information, while ignoring more efficient utilization of bandwidth among update racks. In this article, we propose a parallel Cross-rack Pipelining Update scheme ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CPU</i> ), which divides the update information into small-size units and transmits these units in parallel along with an update pipeline path among multiple racks. The performance of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CPU</i> is mainly determined by slice size and update path. More slices bring finer-grained parallel transmissions over cross-rack links, but also introduces more overheads. An update path that traverses all racks with large-bandwidth links provide short update time. We formulate the proposed pipelining update scheme as an optimization problem, based on a new theoretical pipelining update model. We prove the optimization problem is NP-hard and develop a heuristic algorithm to solve it based on the features of practical DSSs and our implementations, including <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Big chunk</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Small overhead</i> . Specifically, we determine the best update path first by solving a max-min problem and then decide the slice size. We further simplify the slice size selection by offline learning a range of interesting ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RoI</i> ), in which all slice sizes provide similar performance. We implement <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CPU</i> and conduct experiments on Amazon EC2 under a variety of scenarios. The results show that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CPU</i> can reduce the average update time by 48.2 percent, compared with the state-of-the-art update schemes.

Flexible sliced Latin hypercube designs with slices of different sizes

Sliced Latin hypercube designs (SLHDs) are widely used in various computer experiments. Literatures concerning the construction of SLHDs are all about constructing SLHDs with slices of the same size. However, in some cases, e.g. when an experiment with multiple computer models having different complexities or a sequential experiment with varying costs in different periods is considered, SLHDs with slices of different sizes are preferable. In this paper, we propose a new class of SLHD, named the flexible SLHD, in which the whole design is a Latin hypercube design (LHD), and each slice is also an LHD when its levels being properly collapsed, the difference lies in that its slices may have different run sizes. Several methods for constructing such designs are developed. Theoretical results on the constructed designs are derived, and discussion on the slice sizes of the constructed flexible SLHDs is provided. Furthermore, an optimization algorithm is developed to improve the space-filling property of the constructed SLHDs. The newly proposed flexible SLHD is also a special nested LHD (Qian in Biometrika 96:957–970, 2009), each of its slice can be viewed as a small LHD nested in the whole design.

Adult Small Bowel Obstruction

Small bowel obstruction (SBO) is a clinical condition that is often initially diagnosed and managed in the emergency department (ED). The high rates of potential complications that are associated with an SBO make it essential for the emergency physician (EP) to make a timely and accurate diagnosis. The primary objective was to perform a systematic review and meta-analysis of the history, physical examination, and imaging modalities associated with the diagnosis of SBO. The secondary objectives were to identify the prevalence of SBO in prospective ED-based studies of adult abdominal pain and to apply Pauker and Kassirer's threshold approach to clinical decision-making to the diagnosis and management of SBO. MEDLINE, EMBASE, major emergency medicine (EM) textbooks, and the bibliographies of selected articles were scanned for studies that assessed one or more components of the history, physical examination, or diagnostic imaging modalities used for the diagnosis of SBO. The selected articles underwent a quality assessment by two of the authors using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool. Data used to compile sensitivities and specificities were obtained from these studies and a meta-analysis was performed on those that examined the same historical component, physical examination technique, or diagnostic test. Separate information on the prevalence and management of SBO was used in conjunction with the meta-analysis findings of computed tomography (CT) to determine the test and treatment threshold. The prevalence of SBO in the ED was determined to be approximately 2% of all patients who present with abdominal pain. Having a previous history of abdominal surgery, constipation, abnormal bowel sounds, and/or abdominal distention on examination were the best history and physical examination predictors of SBO. X-ray was determined to be the least useful imaging modality for the diagnosis of SBO, with a pooled positive likelihood ratio (+LR) of 1.64 (95% confidence interval [CI] = 1.07 to 2.52). On the other hand, CT and magnetic resonance imaging (MRI) were both quite accurate in diagnosing SBO with +LRs of 3.6 (5- to 10-mm slices, 95% CI = 2.3 to 5.4) and 6.77 (95% CI = 2.13 to 21.55), respectively. Although limited to only a select number of studies, the use of ultrasound (US) was determined to be superior to all other imaging modalities, with a +LR of 14.1 (95% CI = 3.57 to 55.66) and a negative likelihood ratio (-LR) of 0.13 (95% CI = 0.08 to 0.20) for formal scans and a +LR of 9.55 (95% CI = 2.16 to 42.21) and a -LR of 0.04 (95% CI = 0.01 to 0.13) for beside scans. Using the CT results of the meta-analysis for the 5- to 10-mm slice subgroup as well as information on intravenous (IV) contrast reactions and nasogastric (NG) intubation management, the pretest probability threshold for further testing was determined to be 1.5%, and the pretest probability threshold for beginning treatment was determined to be 20.7%. The potentially useful aspects of the history and physical examination were limited to a history of abdominal surgery, constipation, and the clinical examination findings of abnormal bowel sounds and abdominal distention. CT, MRI, and US are all adequate imaging modalities to make the diagnosis of SBO. Bedside US, which can be performed by EPs, had very good diagnostic accuracy and has the potential to play a larger role in the ED diagnosis of SBO. More ED-focused research into this area will be necessary to bring about this change.

Wireless H.264 Video Quality Enhancement Through Optimal Prioritized Packet Fragmentation

We introduce a cross-layer priority-aware packet fragmentation scheme at the MAC layer to enhance the quality of pre-encoded H.264/AVC compressed bitstreams over bit-rate limited error-prone links in wireless networks. The H.264 slices are classified in four priorities at the encoder based on their cumulative mean square error (CMSE) contribution towards the received video quality. The slices of a priority class in each frame are aggregated into video packets of corresponding priority. We derive the optimal fragment size for each priority class which achieves the maximum expected weighted goodput at different encoded video bit rates, slice sizes and bit error rates. Priority-aware packet fragmentation invokes slice discard in the buffer due to channel bit rate constraints on allocating fragment header bits. We propose a slice discard scheme using frame importance and slice CMSE contribution to control error propagation effects. Packet fragmentation is extended to slice fragmentation by modifying the conventional H.264 decoder to handle partial slice decoding. Priority-aware slice fragmentation combined with the proposed slice discard scheme provides considerable PSNR and VQM gains as compared to priority-agnostic fragmentation.

Effect of drying temperature and slice size on quality of dried okra (Abelmoschus esculentus (L.) Moench)

Okra (Abelmoschus esculentus (L) Moench) is an important vegetable crop of India. Dried okra pods have wide use in snacks and are in great demand for domestic as well as export market. Hence, effect of four slice sizes (1, 2, 3 and 4cm) and four drying temperatures (50, 60, 70 and 80°C) on quality of hot air dried okra were studied. Okra pods were dried in the form of slices cut across the length at different temperatures. Quality assessment of okra was done on the basis of protein, ascorbic acid and fibre content. Okra slice sizes and drying temperatures affected all the quality parameters significantly (p < 0.05). Maximum retention of protein, ascorbic acid and fibre content were found in 2cm long slices dried at 60°C temperature.

Better Debugging via Output Tracing and Callstack-Sensitive Slicing

Debugging often involves 1) finding the point of failure (the first statement that produces bad output) and 2) finding and fixing the actual bug. Print statements and debugger break points can help with step 1. Slicing the program back from values used at the point of failure can help with step 2. However, neither approach is ideal: Debuggers and print statements can be clumsy and time-consuming and backward slices can be almost as large as the original program. This paper addresses both problems. We present callstack-sensitive slicing, which reduces slice sizes by leveraging the series of calls active when a program fails. We also show how slice intersections may further reduce slice sizes. We then describe a set of tools that identifies points of failure for programs that produce bad output. Finally, we apply our point-of-failure tools to a suite of buggy programs and evaluate callstack-sensitive slicing and slice intersection as applied to debugging. Callstack-sensitive slicing is very effective: On average, a callstack-sensitive slice is about 0.31 time the size of the corresponding full slice, down to just 0.06 time in the best case. Slice intersection is less impressive, on average, but may sometimes prove useful in practice.

PREDICTIVE MODELS ATTRIBUTE EFFECTS ON FISH ASSEMBLAGES TO TOXICITY AND HABITAT ALTERATION

Biological assessments should both estimate the condition of a biological resource (magnitude of alteration) and provide environmental managers with a diagnosis of the potential causes of impairment. Although methods of quantifying condition are well developed, identifying and proportionately attributing impairment to probable causes remain problematic. Furthermore, analyses of both condition and cause have often been difficult to communicate. We developed an approach that (1) links fish, habitat, and chemistry data collected from hundreds of sites in Ohio (USA) streams, (2) assesses the biological condition at each site, (3) attributes impairment to multiple probable causes, and (4) provides the results of the analyses in simple-to-interpret pie charts. The data set was managed using a geographic information system. Biological condition was assessed using a RIVPACS (river invertebrate prediction and classification system)-like predictive model. The model provided probabilities of capture for 117 fish species based on the geographic location of sites and local habitat descriptors. Impaired biological condition was defined as the proportion of those native species predicted to occur at a site that were observed. The potential toxic effects of exposure to mixtures of contaminants were estimated using species sensitivity distributions and mixture toxicity principles. Generalized linear regression models described species abundance as a function of habitat characteristics. Statistically linking biological condition, habitat characteristics including mixture risks, and species abundance allowed us to evaluate the losses of species with environmental conditions. Results were mapped as simple effect and probable-cause pie charts (EPC pie diagrams), with pie sizes corresponding to magnitude of local impairment, and slice sizes to the relative probable contributions of different stressors. The types of models we used have been successfully applied in ecology and ecotoxicology, but they have not previously been used in concert to quantify impairment and its likely causes. Although data limitations constrained our ability to examine complex interactions between stressors and species, the direct relationships we detected likely represent conservative estimates of stressor contributions to local impairment. Future refinements of the general approach and specific methods described here should yield even more promising results.

On the use of frame-based slice size for the robust transmission of MPEG video over ATM networks

We study the performance requirements for the transport of MPEG video streams over ATM networks. A control scheme is introduced to limit the propagation of impairments through MPEG-based video sequences as a result of cell losses. The scheme assigns different slice sizes to the video frames based on their type and order of appearance within the group of pictures. The main aim of the proposed scheme is to improve the robustness of the video delivery process at a minimum cost. Our results demonstrate the effectiveness of this scheme in reducing the propagation of impairments due to the loss of information during the transmission of MPEG video streams over ATM networks.

Decreased computerized tomography numbers in patients with presenile dementia. Detection in patients with otherwise normal scans.

Patients in their 50s who show clinical signs of dementia (possible Alzheimer's disease) often have ocmputerized tomographic (CT) scans that appear normal. This study examined the mean CT number within a 169-pixel sample in white matter in the centrum semiovale slice. Slice sizes ranged from 6,800 to 9,000 pixels; all were without contrast enhancement. The CT numbers were higher for six nondementia cases (CT numbers of 41 and above)than for 14 dementia cases (CT numbers of 40 and below). The non-overlap in mean CT numbers between the two groups was observed independent of the presence or absence of prominent sulci. Similar low CT numbers were observed for seven patients with senile dementia. Differential diagnosis between dementia and depression may be better aided by studying the CT numbers rather than the presence of prominent sulci.