Counting Statistics Research Articles

KaMRaT: a C++ toolkit for k-mer count matrix dimension reduction.

KaMRaT is designed for processing large k-mer count tables derived from multi-sample, RNA-seq data. Its primary objective is to identify condition-specific or differentially expressed sequences, regardless of gene or transcript annotation. KaMRaT is implemented in C++. Major functions include scoring k-mers based on count statistics, merging overlapping k-mers into contigs and selecting k-mers based on their occurrence across specific samples. Source code and documentation are available via https://github.com/Transipedia/KaMRaT.

Spin transport study in a disordered-metal/ferromagnetic-insulator heterostructure based on full counting statistics within the coherent potential approximation

Spin transport study in a disordered-metal/ferromagnetic-insulator heterostructure based on full counting statistics within the coherent potential approximation

The Forms of Political Participation in Coastal Communities during the 2020 Mayoral Election in Makassar

This research aims to analyze the forms of political participation of coastal communities in the 2020 Local Election in Makassar City. The research method employed is qualitative and descriptive. The study is conducted in the Sangkarrang Islands Regency, with data collection techniques including observation, in-depth interviews, and document analysis. The research took place from February to March 2023. The selection of informants was done through purposive sampling, involving 6 (six) individuals, including representatives from district and sub-district governments, election organizers such as the Election Commission (KPU) and Election Supervisory Board (Panwaslu), youth leaders, community and religious leaders, as well as women leaders familiar with the political forms and participation of coastal communities. The research findings indicate that the forms of community participation in the Makassar mayoral election are reflected in political socialization, campaigns, voting, and the counting process. During political socialization, participation is notably low due to occupational factors. In political campaigns, some members of the community abstain from campaigning, considering it unproductive and a waste of time, while others view it as a communal endeavor. The voter turnout during the election is high, reaching a percentage of 66.84%. Simultaneously, the vote count also demonstrates significant participation, driven by the desire to assess whether the elected local leaders align with community expectations

Improved senescent cell segmentation on bright‐field microscopy images exploiting representation level contrastive learning

AbstractMesenchymal stem cells (MSCs) are stromal cells which have multi‐lineage differentiation and self‐renewal potentials. Accurate estimation of total number of senescent cells in MSCs is crucial for clinical applications. Traditional manual cell counting using an optical bright‐field microscope is time‐consuming and needs an expert operator. In this study, the senescence cells were segmented and counted automatically by deep learning algorithms. However, well‐performing deep learning algorithms require large numbers of labeled datasets. The manual labeling is time consuming and needs an expert. This makes deep learning‐based automated counting process impractically expensive. To address this challenge, self‐supervised learning based approach was implemented. The approach incorporates representation level contrastive learning component into the instance segmentation algorithm for efficient senescent cell segmentation with limited labeled data. Test results showed that the proposed model improves mean average precision and mean average recall of downstream segmentation task by 8.3% and 3.4% compared to original segmentation model.

Double Moving Average Control Chart for Time Series Data with Poisson INARCH(1)

The objectives of this research are to find the explicit formulas of the average run length (ARL) of a double moving average (DMA) control chart for first-order integer-valued autoregressive conditional heteroscedasticity (INARCH1))) of Poisson count data. In addition, the numerical results obtained from the proposed explicit formulas are compared with those obtained from Monte Carlo simulations (MC) for the Poisson INARCH(1) counting process. An out-of-control ARL (ARL1) is the criteria for measuring the performance of control charts. The numerical results found that the values of both ARL0 and ARL1 obtained from explicit formulas agree with the numerical results obtained from the Monte Carlo simulation (MC), but the latter is very timeconsuming.

Spatial–Temporal Evolution Analysis of the Development of Jingdang and Famen Townships in the Vicinity of the Capital City Site of the Western Zhou Dynasty in China

This study aimed to explore the evolution of Jingdang and Famen towns near the Zhouyuan site, the capital city site of the Western Zhou Dynasty in China, to elucidate the relationship between township development and the essential ancient relics conservation areas. Based on the remote sensing satellite images from 1982 to 2022, combining with historical demographic data, this study used four methods, including land use classification, land use transfer matrix, landscape pattern center of gravity changes, and population count statistics, to study the spatial–temporal evolution of land, population, and ecology in the region over the past 40 years. The results showed that under the strict relic conservation regimen, these two towns are in a declining stage. To improve their decaying status, some potential strategies valuable for township development are proposed to balance the relationship between relic conservation and township development for their mutual benefit and coexistence.

On Survival of Coherent Systems Subject to Random Shocks

We consider coherent systems subject to random shocks that can damage a random number of components of a system. Based on the distribution of the number of failed components, we discuss three models, namely, (i) a shock can damage any number of components (including zero) with the same probability, (ii) each shock damages, at least, one component, and (iii) a shock can damage, at most, one component. Shocks arrival times are modeled using three important counting processes, namely, the Poisson generalized gamma process, the Poisson phase-type process and the renewal process with matrix Mittag-Leffler distributed inter-arrival times. For the defined shock models, we discuss relevant reliability properties of coherent systems. An optimal replacement policy for repairable systems is considered as an application of the proposed modeling.

Investigation of operational parameters that affect the use of drones in goods’ stock count process: Evidence from experimental results

Purpose: Recently, the complexity of managing warehouses has been amplified significantly due to factors that include increased requests for more frequent and smaller order fulfilment, reduction of operational cost, and improvement of customer experience. Product stock count is a critical process in order to address the aforementioned challenges. This articles presents experimental results from the adoption of drones coupled with RFID tags used for real time goods’ stock count.Design/methodology/approach: The research methodology adopted combines three different methods, namely Systematic Literature Review (SLR) for identifying parameters that affect the performance of drones in stock count process, survey via questionnaire and interviews to logistics managers to map needs and requirements in warehouse operations, as well as laboratory testing via Design of Experiment (24 full factorial design & ANOVA) methodology to investigate how certain parameters corelate and affect the reading accuracy of RFID tags as well as the time needed by a drone for stock count completion. Findings: The results of the experiments are encouraging, showing that the use of drones coupled with RFID tags may support faster, cost-effective, and safer stock count in warehouses. In both ambient and chilled storage environment an 100% RFID tag reading accuracy was achieved. Less stock-count completion time when compared to manual stock-count was achieved in both cases.Research implications: Understanding the effect of technical and operational parameters of RFID technology in conjunction with unmanned aerial vehicles (UAVs)-drones may have the potential to radically transform the stock count process by considerably increase the efficiency and accuracy of the process.Practical implications: Real-time stock count via drones has significant cost-saving implications for organizations. The elimination of manual stock counting saves operational expenses and increases staff safety. Furthermore, real-time data collection of existing product stock allows managers to efficiently allocate resources, enhancing overall efficiency and performance.Originality/value: This research is among the first studies that aim to present evidence from experimental results that assess the use of drones coupled with RFID technology for real-time stock count. The results from laboratory experiments demonstrate the effect of certain operational parameters, such as drone speed, number of rack levels, and RFID tag location on products, during the execution of the stock count process in terms of RFID reading accuracy and stock-count completion time.

The Circumstance-Driven Bivariate Integer-Valued Autoregressive Model.

The novel circumstance-driven bivariate integer-valued autoregressive (CuBINAR) model for non-stationary count time series is proposed. The non-stationarity of the bivariate count process is defined by a joint categorical sequence, which expresses the current state of the process. Additional cross-dependence can be generated via cross-dependent innovations. The model can also be equipped with a marginal bivariate Poisson distribution to make it suitable for low-count time series. Important stochastic properties of the new model are derived. The Yule-Walker and conditional maximum likelihood method are adopted to estimate the unknown parameters. The consistency of these estimators is established, and their finite-sample performance is investigated by a simulation study. The scope and application of the model are illustrated by a real-world data example on sales counts, where a soap product in different stores with a common circumstance factor is investigated.

Evolution of temporal fluctuation scaling exponent in nonstationary time series using supersymmetric theory of stochastic dynamics.

Temporal fluctuation scaling (TFS) is an emergent property of complex systems that relates the variance (Ξ_{2}) and the mean (M_{1}) from an empirical data set in the form Ξ_{2}∼M_{1}^{α_{TFS}}, where the dispersion (fluctuation) of the data has been described in terms of Ξ_{2}. At present, it has been shown that this law of complex systems has different multidisciplinary applications such as characterizing the market rate based on its exponent, explaining the spatial spread of a pandemic or measuring dispersion in a counting process, among others, if it is known how the average value M_{1} of a representative quantity in a system changes. Then, using the path integral formalism and Parisi-Sourlas method, we propose an extension of path integral formalism to understand the origin of the temporal fluctuation scaling and the evolution of its exponent over time in nonstationary time series. To this end, we first show how the probability of transition between two states of a stochastic variable x(t) can be expressed once it is known its cumulant generating function. Also, we introduce a nonlinear term in a cumulant generating function of the form H^{(n)}(p,t;γ)∼p^{n} to obtain a model where the nth moment of the probability distribution evolves arbitrarily. Subsequently, in order to reproduce the temporal fluctuation scaling, a linear combination of H^{(n)}(p,t;γ) with n∈{1,2} is used. Therefore this allows describing how the mean M_{1}(t) and the variance Ξ_{2}(t) of empirical time series evolve. Thence, an analytical expression is deduced for the evolution of the temporal evolution of the temporal fluctuation scaling exponent α_{TFS}(t). Likewise, the validity of the expression found for α_{TFS}(t) is verified with a toy model based on white noise. Finally, this approach is verified in two stock indices (Dow Jones and Sao Paulo stock index) and two currencies (GBP-USD and EUR-USD) with daily data. It is found that this approach accurately captures the evolution of the mean and variance of these four financial derivatives after contrasting the results with a coefficient of determination that depends on H^{(n)}(p,t;γ). Also, it is shown that the temporal fluctuation scaling exponent is a measure of uncertainty or volatility in financial time series.

Development of an Automated Wooden Handle Packaging System with Integrated Counting Technology

Manual counting and packaging processes often involve repetitive, error-prone tasks. Specifically, packaging wooden handles, utilized in gardening tools and cutlery, typically relies on labor-intensive methods with dimensions varying in diameter, length and mass. These variations complicate packaging, requiring precise counting and diverse handling solutions. This article introduces an automated counting structure tailored for a wide array of wooden handles manufactured by a company in northern Portugal. Employing standardized mechanical design methodologies, we delineate crucial stages encompassing the design, development, implementation and testing of this specialized counting equipment. The machine has been partially integrated into the management system of the company, taking into account future global integration according to the Industry 4.0 concept.

Effect of Microstructure on the Precipitation of β-Mg2Si during Cooling after Homogenisation of Al-Mg-Si Alloys

For Al-Mg-Si alloys, cooling after homogenisation is a crucial step because the precipitation of the equilibrium β-Mg2Si phase determines the processing capabilities in subsequent steps, as well as the subsequent precipitation age hardening potential, and thus, the final properties. It is therefore important to understand how microstructural variations affect the transformation of β-Mg2Si during cooling after homogenisation. In the present work, alloys with similar effective solute contents of Mg and Si, but with different microstructures and a different amount of primary Al-Fe-Si phases, were produced. Characterisation of the precipitation reaction was performed using interrupted quench experiments with cooling rates of 1–6 K/min, monitored by light optical microscopy (LOM), scanning electron microscopy (SEM) and conductivity measurements. Precipitation kinetics for β-Mg2Si was found to increase in microstructures with shorter secondary dendrite arm spacing (DAS). However, despite measuring both a higher density and volume fraction of the primary phases, no effect on the phase transformation from an increased iron content was found in terms of precipitation kinetics or particle count statistics. Furthermore, comparisons with iron-free high-purity-based alloys revealed that the precipitation reaction for β-Mg2Si was identical in the two different microstructures both in terms of onset temperature and overall kinetics. The present results show that nucleation of β-Mg2Si is not dependent on the larger constituent phases and indicates that overall transformation kinetics is governed by bulk diffusion rates.

Time variation of high-risk groups for liver function deteriorations within fluctuating long-term liver function after hepatic radiotherapy in patients with hepatocellular carcinoma

PurposeThe purpose of this study is to find essential risk factors associated with liver function (LF) deteriorations within fluctuating long-term LF and their time-varying effects in patients with hepatocellular carcinoma (HCC) receiving hepatic radiotherapy and to identify high-risk groups for adverse LF deteriorations and their changes over time in facilitating the prevention of hepatic decompensation and the improvement of survival.Materials and methodsA total of 133 HCC patients treated by hepatic radiotherapy were enrolled. A study design was conducted to convert posttreatment long-term LF with fluctuating levels over time to recurrent LF events using defined upgrades in a grading scale. The hazard ratios (HR) of pretreatment biochemical, demographic, clinical, and dosimetric factors in developing posttreatment LF events were estimated using the Cox model. Methodologies of the counting process approach, robust variance estimation, goodness-of-fit testing based on the Schoenfeld residuals, and time-dependent covariates in survival analysis were employed to handle the correlation within subjects and evaluate the time-varying effects during long-term follow-up.ResultsBaseline LF score before radiotherapy and gender were significant factors. Initial HR in developing LF events was 1.17 (95% CI 1.11–1.23; P < 0.001) for each increase of baseline LF score and kept almost constant over time (HR, 1.00; 95% CI 1.00–1.01; P = 0.065). However, no difference was observed regarding initial hazards for gender (HR, 1.00; 95% CI 0.64–1.56; P = 0.994), but the hazard for women got higher monthly over time compared with men (HR, 1.04; 95% CI 1.01–1.07; P = 0.006).ConclusionsHigh-risk groups for adverse LF deteriorations after hepatic radiotherapy may change over time. Patients with poor baseline LF are vulnerable from the beginning. Women require prevention strategies and careful monitoring for deteriorations at a later stage.

Dynamics at the edge for independent diffusing particles.

We study the dynamics of the outliers for a large number of independent Brownian particles in one dimension. We derive the multitime joint distribution of the position of the rightmost particle, by two different methods. We obtain the two-time joint distribution of the maximum and second maximum positions, and we study the counting statistics at the edge. Finally, we derive the multitime joint distribution of the running maximum, as well as the joint distribution of the arrival time of the first particle at several space points.

Interplay of Andreev Reflection and Coulomb Blockade in Hybrid Superconducting Single-Electron Transistors.

We study the interplay between Coulomb blockade and superconductivity in a tunable superconductor-superconductor-normal-metal single-electron transistor. The device is realized by connecting the superconducting island via an oxide barrier to the normal-metal lead and with a break junction to the superconducting lead. The latter enables Cooper pair transport and (multiple) Andreev reflection. We show that these processes are relevant also far above the superconducting gap and that signatures of Coulomb blockade may reoccur at high bias while they are absent for small bias in the strong-coupling regime. Our experimental findings agree with simulations using a rate equation approach in combination with the full counting statistics of multiple Andreev reflection.

Digital Microscopy Augmented by Artificial Intelligence to Interpret Bone Marrow Samples for Hematological Diseases.

Analysis of bone marrow aspirates (BMAs) is an essential step in the diagnosis of hematological disorders. This analysis is usually performed based on a visual examination of samples under a conventional optical microscope, which involves a labor-intensive process, limited by clinical experience and subject to high observer variability. In this work, we present a comprehensive digital microscopy system that enables BMA analysis for cell type counting and differentiation in an efficient and objective manner. This system not only provides an accessible and simple method to digitize, store, and analyze BMA samples remotely but is also supported by an Artificial Intelligence (AI) pipeline that accelerates the differential cell counting process and reduces interobserver variability. It has been designed to integrate AI algorithms with the daily clinical routine and can be used in any regular hospital workflow.

Challenges of Cell Counting in Cell Therapy Products.

Cell counting is a common and fundamental cell measurement technique that plays a crucial role in the development and quality control of cell therapy products. However, accurate and reliable cell counting can be challenging owing to the complexity of cell preparations, diverse counting purposes, and various counting methods. This review summarizes the challenges encountered in cell counting for cell therapy products and provides strategies to improve the cell counting accuracy, thereby guiding the counting process and ensuring the quality of cell therapy products.

IR Sensor Optimization on Microcontroller-Based Coin Sorting and Counting Machine

The advancement of technology has greatly influenced every human task, where tasks that were once very time-consuming or laborious can now be easily and quickly accomplished thanks to technology. Take, for example, the activity of counting and sorting coins or tokens. If this activity were done in a traditional manner, which involves counting and separating each denomination, it would be very cumbersome and time-consuming. However, if the process of counting and sorting coins or tokens is done using technology, it can be easily and quickly completed. This research is conducted to analyze the performance optimization results on the IR sensor part of a coin sorting and counting machine that has been made based on a microcontroller. After several tests, it can be concluded that the machine can sort and count coins with denominations of 100, 200, 500, and 1000 rupiah. From 50 coins with four denominations, it can be inferred that the machine experienced an increase in accuracy in sorting coins from 98.6% to 99.6%. This increase in accuracy was achieved by optimizing the IR sensor, thus making the IR sensor readings more optimal.

ANALYSIS OF STUDENTS' ERROR IN WORKING ON MATHEMATICS PROBLEMS BASED ON THE CIRCULAR MATERIAL BASED ON KASTOLAN THEORY

The purpose of this study is to describe the types of student errors in solving HOTS-based circle questions based on kastolan theory, then it will also be explored regarding the factors that cause errors, and the percentage of student errors when solving problems. The method that will be used is descriptive qualitative. The instruments in this study consisted of written tests, interviews, and documentation. Data analysis techniques include data reduction, presenting data, and drawing conclusions. The subjects in this study were 13 students from class VIII A of SMP Negeri 4 Randublatung in semester 2 of the 2022/2023 academic year. Then interviews will be conducted by taking 3 samples of student answers grouped based on types of conceptual, procedural, and technical errors. The results showed that the most dominant errors occurred in technical errors where errors were made in the counting process with a percentage of 69.23%. Student procedural errors were made due to the incompatibility of the steps in solving the questions which resulted in wrong answers, with a percentage of 53.84%. Students' conceptual errors were made because students misinterpreted concepts and incorrectly determined the problem solving formula, with a percentage of 46.15%. Factors that cause students to make mistakes in solving HOTS-based questions are: students are not careful in the process of calculating ranks, use formulas that do not meet the prerequisites of the formula, and incompatibility of steps in solving problems.Keywords: Error Analysis, Cricles, HOTS Type Questions, Kastolan Theory

A flexible framework for spatial capture-recapture with unknown identities.

Camera traps or acoustic recorders are often used to sample wildlife populations. When animals can be individually identified, these data can be used with spatial capture-recapture (SCR) methods to assess populations. However, obtaining animal identities is often labor-intensive and not always possible for all detected animals. To address this problem, we formulate SCR, including acoustic SCR, as a marked Poisson process, comprising a single counting process for the detections of all animals and a mark distribution for what is observed (eg, animal identity, detector location). The counting process applies equally when it is animals appearing in front of camera traps and when vocalizations are captured by microphones, although the definition of a mark changes. When animals cannot be uniquely identified, the observed marks arise from a mixture of mark distributions defined by the animal activity centers and additional characteristics. Our method generalizes existing latent identity SCR models and provides an integrated framework that includes acoustic SCR. We apply our method to estimate density from a camera trap study of fisher (Pekania pennanti) and an acoustic survey of Cape Peninsula moss frog (Arthroleptella lightfooti). We also test it through simulation. We find latent identity SCR with additional marks such as sex or time of arrival to be a reliable method for estimating animal density.