Transition Count Research Articles

Last year of life care transitions between long-term care insurance services in Japan: Analysis of long-term care insurance claims data.

We determined the number of care transitions in the year before death among older adults requiring long-term care (LTC) and those receiving public LTC insurance (LTCI) services in Japan, along with care transition pathways and factors associated with the number of care transitions. This study used data from the Japanese LTCI claims, which store national information on certification of needed LTC and LTCI claims data. Services received in the year before death were classified as in-home, facility, mixture of in-home and facility, and not using LTCI services. The transition count is presented, and Sankey diagrams are produced to visualize care transition pathways. We used a multivariable analysis to identify factors associated with the number of care transitions. Of the participants, 276 896 (65.2%) experienced at least one transition in LTCI care settings in the year before death. Further, 72.0% of those requiring mild LTC underwent one or more transitions. Participants who were 75-84 years old (vs. 65-74 years old), male, without medical care needs, with symptoms of dementia, and with changes in LTC needs in the year before death were more likely to require care transitions. Moreover, participants with higher baseline LTC needs were less likely to require transitions. Over half the participants requiring LTC underwent one or more care transitions in the year before death. Policy deliberations regarding enhancing care under the LTCI system at the end of life and optimizing care transitions are necessary. Geriatr Gerontol Int 2024; ••: ••-••.

The hidden sensitivity of non-smooth dynamics

Switches in dynamical systems are known to exhibit wildly different behaviours depending on how they are modelled, for instance whether they occur as smooth transitions or abrupt jumps, and whether they involve delays or discrete perturbations. These differences arise because switches are sensitive to perturbation, but there is limited knowledge about where this sensitivity comes from. Here we take a switch in a simple one-dimensional system, then discretise time and introduce a small delay. The resulting system is described by a piecewise-linear map with incredibly complex dynamics, and is sensitive to parameter changes even if the time-steps and delays are made infinitesimally small. We show that this sensitivity reveals itself in the more versatile numerical tool of the transition count, which captures the likelihood of switching occurring at any instant in a simulation. We use this to show that sensitivity to parameters persists in a system with two switches, where it then has large-scale dynamical effects. The use of transition counts in this way may prove a versatile tool for studying more complex switching processes in general.

An efficient algorithm for estimating gate-level power consumption in large-scale integrated circuits

Estimating power dissipation in Very Large Scale Integrated (VLSI) circuits, particularly large-scale sequential circuits, is a significant challenge in Electronic Design Automation (EDA). Benchmarked against PrimeTime PX, the proposed algorithm proficiently analyzes large-scale combinational and sequential circuits. This research begins with a power analysis algorithm for combinational circuits, focusing on signal probability (SP) and transition count (TC). It then extends to sequential circuits, introducing methodologies for loopback issues and a validity-checking mechanism for spatial dependency topology. Developed on the OpenTimer open-source framework using the SMIC 40-nm process library, the algorithm was validated on academic and industrial datasets. Experimental evaluations show that our algorithm outperforms in terms of speed and accuracy. For power analysis with vector annotations, it achieves an average error within 0.16% in power testing of large-scale timing circuits. It also performs gate-level vectorless power analysis on complex topology and large-scale integrated circuits, demonstrating computational robustness.

A Hybrid Burst Assembly Algorithm Based on Transition Count Number for OBS Network

Background: Optical transport has emerged as a candidate solution to cope with the rising data transmission challenges of enormously evolving data. In Optical Burst Switching (OBS) networks, determining an adaptive burst size is a difficult task that must be performed efficiently during burst assembling. Methods: This research proposes a hybrid burst assembly algorithm that determines the optimal burst size during the burst creation time. The proposed algorithm uses the Transition Count Number (TCN) based method to maintain the optimal burst size when the incoming traffic is unpredictable. The efficiency of the proposed approach is investigated in terms of queuing delay, burst utilization, burst size, and burst size consistency. Findings: Three types of traffic variations (H = 0.5, H = 0.6, and H = 0.7) are imposed to evaluate the performance of the proposed burst assembly approach. As compared to the E-hybrid (time/length) strategy, the research outcomes demonstrate a 13.15% reduction in average queuing latency and a 21.26% improvement in average burst utilization. Novelty: A new burst assembly approach (hybrid burst assembly) has been proposed for OBS networks. Keywords: Burst assembly, Optical Burst Switching (OBS), burstification, burst consistency

A Novel Test Pattern Optimization Method Using Recurrent Neural Network

Testing modern integrated circuit (IC) is challenging with introduction of System-on Chip (SOC). Enormous test pattern count is required to authenticate the IC which inflates power consumption. Henceforth, it is mandatory to incorporate power minimization techniques at circuit design phase. Optimizing test patterns is one such effective technique that curtails test power without altering circuit design. In this paper, a new test pattern reordering algorithm is proposed based on Recurrent Neural Network. Hopfield Neural Network (HNN) constitutes an optimized solution for solving traveling salesman problem (TSP). Since, test pattern reordering can be interpreted as TSP, optimized test pattern order is attained with HNN. Energy function of this algorithm falls into local minimum, so to eradicate this issue, the algorithm is modified. Test patterns are reordered with minimal hamming distance among consecutive test patterns. The proposed algorithm is implemented in ISCAS’89 Sequential Benchmark circuits. Experimental results prove that significant reduction in transition count is accomplished with proposed algorithm without compromising fault coverage.

The cost-effectiveness of dapagliflozin in heart failure with preserved or mildly reduced ejection fraction: A European health-economic analysis of the DELIVER trial.

To determine the cost-effectiveness of dapagliflozin, added to usual care, in patients with heart failure (HF) with mildly reduced or preserved ejection fraction for the UK, German and Spanish payers using detailed patient-level data from the Dapagliflozin Evaluation to Improve the LIVEs of Patients with Preserved Ejection Fraction Heart Failure (DELIVER) trial. A lifetime Markov state-transition cohort model was developed. Quartiles of the Kansas City Cardiomyopathy Questionnaire total symptom score (KCCQ-TSS) defined health states and monthly transition count data informed transition probabilities. Multivariable generalized estimating equations captured the incidence of HF hospitalizations and urgent HF visits, while cardiovascular deaths and all-cause mortality were estimated using adjusted parametric survival models. Health state costs were assigned to KCCQ-TSS quartiles (2021 British pound [GBP]/Euro) and patient-reported outcomes were sourced from DELIVER. Future values of costs and effects were discounted according to country-specific rates. In the UK, dapagliflozin treatment was predicted to increase quality-adjusted life years (QALYs) and life-years by 0.231 and 0.354, respectively, and extend the time spent in the best quartile of KCCQ-TSS by 4.2 months. Comparable outcomes were projected for Germany and Spain. The incremental cost-effectiveness ratios were £7761, €9540 and €5343/QALY in the UK, Germany and Spain, respectively. According to regional willingness-to-pay thresholds, 91%, 89% and 92% of simulations in the UK, Germany and Spain, respectively, were cost-effective following probabilistic sensitivity analyses. Dapagliflozin, added to usual care, is very likely cost-effective for HF with mildly reduced or preserved ejection fraction in several European countries.

An Adaptive Symmetrical Load Balancing Scheme for Next Generation Wireless Networks

In dense Wi-Fi networks, achieving load balancing is critical to optimize network utilization and provide equitable network consumption among the users. Traditional Wi-Fi networks have issues in attaining effective load balancing. Software-Defined Networking (SDN) has presented a viable solution by isolating the data plane and control plane, enabling more agile and cost-effective networks. In this paper we put forward an Adaptive Symmetrical Load Balancing (ASLB) scheme to ensure fairness of load symmetry in Software Defined Wi-Fi Networks (SD-Wi-Fi), while also optimizing the flows transition process using an Analytical Hierarchal Process (AHP). User activity is monitored by access points (APs), which operate under OpenFlow standards. Three essential features, packet volume, packet category and delay hindrance, are used for flow assignment to various controllers. The controllers are arranged in two tiers, universal and regional controllers. The universal controller (UC) handles the workload statistics of regional controllers (RC) in the form of clusters. Extensive simulations using OMNeT++ simulator are performed. The performance parameters taken into consideration are throughput, delay, packet loss rate, network transition count and workload distribution. Our findings demonstrate that the ASLB technique effectively optimizes the network utilization and ensures equitable network consumption among the end users. The proposed scheme outperforms the Mean Probe Delay scheme (MPD), Channel Measurement-based Access Selection scheme (CMAS), Received Signal Strength Indicator-based scheme (RSSI) and Distributed Antenna Selection scheme (DASA), being 40% higher in throughput and 25% lower in delay.

Log-Linear Model and Multistate Model to Assess the Rate of Fibrosis in Patients With NAFLD

In this paper, the deleterious effects of obesity, type II diabetes, and insulin resistance, systolic and diastolic hypertension on the rate of progression of fibrosis in patients with non-alcoholic fatty liver disease (NAFLD) are illustrated using a new approach utilizing the Poisson regression to model the transition rate matrix. The observed counts in the transition count matrix are used as the response variables and the covariates are the risk factors for fatty liver. Then, the estimated counts from running the Poisson regression are used to estimate the transition rates using the continuous-time Markov chains (CTMCs) followed by exponentiation of the estimated rate matrix to obtain the transition probability matrix at specific time points. A depicted, hypothetical, observational, prospective longitudinal study of 150 participants followed up every year for a total of 29 years recording their demographic characteristics and their timeline follow-up is demonstrated. The findings revealed that insulin resistance expressed by HOMA2-IR had the most deleterious effects among other factors on increasing the rate of fibrosis progression from state 1 to state 2, from state 2 to state 3, and from state 3 to state 4. The higher the level of HOMA2-IR is, the more rapid the rate of progression is. This analysis helps the health policymakers and medical insurance managers to allocate the financial and human resources for investigating and treating high-risk patients with NAFLD. In addition, this analysis can be used by pharmaceutical companies to conduct longitudinal studies to assess the effectiveness of the newly emerging anti-fibrotic drugs.

Improved coupled Markov chain method for simulating geological uncertainty

Geological uncertainty is common in nature due to complex depositional processes as one layer appears to be randomly embedded in neighbouring layers. However, the geo-structures could be significantly affected by the geological uncertainty, often simulated using the coupled Markov chain (CMC) model. The objective of this study is to develop an improved CMC method by enhancing the rationality of determining the horizontal transition probability matrix (HTPM) in the CMC model to simulate complex multi-layer strata based on limited borehole data. This difficulty of determining HTPM is solved by combining the vertical transition count matrix and intermediate horizontal matrix with Walther's law. The effectiveness of this method is evaluated by the artificial HTPM and virtual boreholes. Boreholes collected from Norway are adopted to illustrate the proposed method for HTPM estimation. Estimating the HTPM has been dramatically enhanced for the improved CMC model and is not sensitive to the borehole layout schemes. The error of HTPM between estimated and actual strata is less than 3% for different borehole schemes. A matching rate indicator is defined to evaluate the accuracy of the simulated results. The results show that the proposed method has good performance and robustness. An optimizing design approach for the borehole location and number is presented to minimize the uncertainty of the stratum simulation based on the information entropy.

Consumer-grade sleep trackers are still not up to par compared to polysomnography.

Although polysomnography (PSG) is the gold standard for monitoring sleep, it has many limitations. We aimed to prospectively determine the validity of wearable sleep-tracking devices and smartphone applications by comparing the data to that of PSGs. Patients who underwent one night of attended PSG at a single institution from January, 2015 to July 2019 were recruited. Either a sleep application or wearable device was used simultaneously while undergoing PSG. Nine smartphone applications and three wearable devices were assessed. We analyzed the results of 495 cases of smartphone applications and 170 cases of wearables by comparing each against PSG. None of the tested applications were able to show a statistically significant correlation between sleep efficiency, durations of wake time, light sleep or deep sleep with PSG. Snore time correlated well in both of the two applications which provided such information. Deep sleep duration and WASO measured by two of the three wearable devices correlated significantly with PSG. Even after controlling for transition count and moving count, the correlation indices of the wearables did not increase, suggesting that the algorithms used by the wearables were not largely affected by tossing and turning. Most of the applications tested in this study showed poor validity, while wearable devices mildly correlated with PSG. An effective use for these devices may be as a tool to identify the change seen in an individual's sleep patterns on a day-to-day basis, instead of as a method of detecting absolute measurements.

Markov decision processes with dynamic transition probabilities: An analysis of shooting strategies in basketball

In this paper we model basketball plays as episodes from team-specific non-stationary Markov decision processes (MDPs) with shot clock dependent transition probabilities. Bayesian hierarchical models are employed in the modeling and parametrization of the transition probabilities to borrow strength across players and through time. To enable computational feasibility, we combine lineup-specific MDPs into team-average MDPs using a novel transition weighting scheme. Specifically, we derive the dynamics of the team-average process such that the expected transition count for an arbitrary state-pair is equal to the weighted sum of the expected counts of the separate lineup-specific MDPs. We then utilize these non-stationary MDPs in the creation of a basketball play simulator with uncertainty propagated via posterior samples of the model components. After calibration, we simulate seasons both on-policy and under altered policies and explore the net changes in efficiency and production under the alternate policies. Additionally, we discuss the game-theoretic ramifications of testing alternative decision policies.

Bank transaction data modeling by optimized hybrid machine learning merged with ARIMA

The bank transactions are needed to be modeled to predict the future transactions of the banks based on the previous transactions. In order to achieve efficient modeling of bank data transactions, Deep Belief Network (DBN) and Neural network (NN) classifiers are used in this paper. Initially, the bank transaction data such as transaction count and amount are subjected to feature extraction to extract the statistical features. Now, the extracted data are modeled using the combination of DBN and NN models, where the average modeled output from both the network is considered as the final result. The above procedure is utilized for the two prediction models such as transaction count and transaction amount. Moreover, the transaction count from prediction model 1 is subjected to the Auto-Regressive Integrated Moving Average (ARIMA) model to compute the relationship between the transition count and transition amount. Here, as the main contribution, the number of hidden neurons in both DBN and NN are optimized or tuned accurately using the hybridized optimization models with Lion Algorithm (LA), and Artificial Bee Colony (ABC) named L-ABC model. The average of entire transactional amounts, i.e. the modeled outputs are matched with the actual data to validate the performance of the implemented model.

Age Group Estimation Based on the Transition Count of 3rd Order Neighborhood using V and Inverted V Patterns

Age Classification is used in so many applications like crime detection, face detection and so on. . The age leads to significant variation in human face. The variation depends on many factors like gender, exposure to sunlight, drinking, weight loss or weight gain. In our paper the performance of face aging is established based on v pattern and Inverted v pattern by using the transition count of third order neighborhood. In our proposed method the age of the person is divided into 5 categories 1.Childhood (0-12years) 2.Young Adults (13-25years) 3.Middle Age Adults (26-40years) 4.Senior Adults (40-60years) 5.Senior Citizens (more than 60 years).The quantative evaluation and analysis is performed in our proposed method when compared to other existing methods after applying on 4 different facial image databases.

Stability of the Manual Ability Classification System in young children with cerebral palsy.

To examine the stability over time of the Manual Ability Classification System (MACS) levels in children with cerebral palsy (CP) aged 18 to 60months. This was a prospective longitudinal population-based study of 252 Australian children (160 males [63%] 92 females [37%]; mean age [SD] 41.7mo [14], range 17.2mo-69.2mo) with CP. Children were classified at 18months (n=70), 24 months (n=131), 30months (n=173), 36months (n=209), 48months (n=226), and 60months (n=221) of age. Stability of the MACS was examined using the proportion of specific positive agreement and transition proportions, which are measures of agreement. There were 1030 unique observations, with each of the 252 participants seen between two and six occasions (median=4). Average specific positive agreement over the study period was 76% for MACS level I, 67% for level II, 50% for level III, 51% for level IV, and 83% for level V. MACS levels I and V have the highest degree of stability, while levels III and IV have the lowest. We show how this may be explained by the proportion of children in each MACS level. Using measures of agreement rather than measures of reliability provides accurate information when measuring stability over time of an ordinal classification system. The relative stability of MACS levels can be explained by the proportion of children in each level. Children classified in Manual Ability Classification System (MACS) levels III and IV change levels at next assessment about 50% of the time. Children should be assessed with the MACS regularly, particularly those in levels III and IV. Stability within ordinal classification level categories can be predicted using a measurement error model. Transition proportions or specific agreement is recommended for reporting stability of ordinal classification systems.

Application of Structural and Topological Features to Recognize Online Handwritten Bangla Characters

This article presents a set of novel features for robust online Bangla handwritten character recognition. Two feature extraction methods are presented here. The first describes the transition from background to foreground pixels and vice versa. The second uses a combination of topological features and centre-of-gravity- (CG) based circular features where global information, local information, and Circular Quadrant Mass Distribution information have been extracted. The impact of each along with their combination have also been analyzed. A total of 15,000 isolated online Bangla character samples have been collected and used for the evaluation. A Support Vector Machine classifier records the best recognition rate when the transition count feature, CG-based circular features, and topological features are combined.

Script invariant handwritten digit recognition using a simple feature descriptor

Handwritten digit recognition is still considered as a difficult task because of the large variability of the digits shapes written by individuals. A lot of work have been done towards digit identification with excellent performance but mostly these works have been made focusing on digits written in a particular script. Hence, in a multilingual country like India, where different scripts are prevalent, methods which recognise numerals written in a single script may not always serve the purpose. To address this issue, we propose a script invariant handwritten digit recognition scheme in this paper. A novel feature extraction technique named as quadrangular transition count has been introduced. Experimentations performed using five conventional classifiers advocate that multi layer perceptron (MLP) is best among them which yields recognition accuracies of 98.33%, 97.85%, 96.72% and 95.35% on four popularly used scripts of the world namely, Arabic, Bangla, Devanagari, and Roman respectively.

Script invariant handwritten digit recognition using a simple feature descriptor

Handwritten digit recognition is still considered as a difficult task because of the large variability of the digits shapes written by individuals. A lot of work have been done towards digit identification with excellent performance but mostly these works have been made focusing on digits written in a particular script. Hence, in a multilingual country like India, where different scripts are prevalent, methods which recognise numerals written in a single script may not always serve the purpose. To address this issue, we propose a script invariant handwritten digit recognition scheme in this paper. A novel feature extraction technique named as quadrangular transition count has been introduced. Experimentations performed using five conventional classifiers advocate that multi layer perceptron (MLP) is best among them which yields recognition accuracies of 98.33%, 97.85%, 96.72% and 95.35% on four popularly used scripts of the world namely, Arabic, Bangla, Devanagari, and Roman respectively.

Asymmetry dual-LFSR reseeding for low power BIST

Growing test data volume and excessive test power consumption are two of the major concerns for the industry when testing large integrated circuits. LFSR-decompressor-based compression methods have been widely adopted to reduce test data volume because they can provide high compression ratio. These techniques have been enhanced to address the need for low power as well as low test data volume. The key idea behind these methods is to generate seeds for LFSR reseeding such that the don’t-cares in test cubes are filled with proper values to reduce the transition count. This paper presents a LFSR reseeding approach adopting dual-LFSR to effectively reduce the amount of test data while keeping the scan-in power as low. Two LFSRs are used to jointly generate test patterns for a given test set to increase the correlation between neighboring bits and thus to reduce the transition count. Experimental results on the ISCAS’89 benchmarks demonstrate that our proposed approach can achieve a better compression ratio than the existing related dual-LFSR scheme while keeping a roughly equal shifting power reduction.

Temperature and data size trade-off in dictionary based test data compression

This paper proposes a new thermal-aware test data compression technique using dictionary-based coding. Large test data volume and rise in chip temperature during a test, are the two major challenges for the test engineers. Efficient filling of the don't care bits of the test patterns minimises the transition count in the scan chains, which in turn reduces the temperature of the chip to a large extent. On the other hand, high test data compression can be achieved by filling the don't-cares in a manner to get more similar sub-vectors within the test vectors. Although, both the problems rely on don't-care bit filling, most of the existing works have considered them separately. Moreover, it has been observed that, in general, thermal-aware don't-care filling leads to poor test compression, while a compression-aware don't-care filling produces high temperature. However, a high test compression with low-temperature is the most desirable expectation. In our work, we have combined the temperature reduction and the test data compression into a single problem and solved it. We have presented an integrated approach that can perform a trade-off between temperature and test compression. Experimental results on ISCAS'89, ITC'99 and IWLS'05 benchmarks show the flexibility of the proposed method to achieve a balance between temperature and test compression. The work has further been extended to reduce temperature without sacrificing compression.

Localization Attacks Using Matrix and Tensor Factorization

It is known that various types of location privacy attacks can be carried out using a personalized transition matrix that is learned for each target user, or a population transition matrix that is common to all target users. However, since many users disclose only a small amount of location information in their daily lives, the training data can be extremely sparse. The aim of this paper is to clarify the risk of location privacy attacks in this realistic situation. To achieve this aim, we propose a learning method that uses tensor factorization (or matrix factorization) to accurately estimate personalized transition matrices (or a population transition matrix) from a small amount of training data. To avoid the difficulty in directly factorizing the personalized transition matrices (or population transition matrix), our learning method first factorizes a transition count tensor (or matrix), whose elements are the number of transition counts that the user has made, and then normalizes counts to probabilities. We focus on a localization attack, which derives an actual location of a user at a given time instant from an obfuscated trace, and compare our learning method with the maximum likelihood (ML) estimation method in both the personalized matrix mode and the population matrix mode. The experimental results using four real data sets show that the ML estimation method performs only as well as a random guess in many cases, while our learning method significantly outperforms the ML estimation method in all of the four data sets.