Traditional Counting Research Articles

The Development of an IoT-Based Palm Solid Waste Counting System for Empty Fruit Bunches in the Palm Oil Industry

The recent application of palm empty fruit bunches as solid waste directly on soil or as organic fertilizer has enhanced palm farms. Moreover, the co-products of this palm oil mill constitute a dependable metric for assessing mill processing performance, specifically regarding quantity and weight. The quantity of palm empty fruit bunches is usually ascertained through manual counting using a hand counter during processing. The imprecision in calculating palm empty fruit bunches is a limitation of this method, as operators sometimes perform erroneous calculations, overlook quantities, and produce contradictory results for mill management. To address this issue, the sector requires enhanced execution of a monitoring and counting system methodology. The aim of this community service project is to create an empty fruit bunches count station using an IoTbased palm solid waste counting system for use in palm mills. The Ministry of Industry - Politeknik Teknologi Kimia Industri (PTKI) Medan will engage in and oversee the advancement of Industry 4.0 within the industrial sector. The initiative was executed at Tanah Gambus Estate in Batubara Regency, North Sumatra. Assistance was provided by a palm mill partner associated with PT. Socfin Indonesia. The implementation of the empty fruit bunches palm oil counting system was conducted efficiently, facilitating optimal operations, real–time online monitoring, uninterrupted functionality exceeding 24 hours daily, swift assessment of the empty fruit bunches volume, immediate identification of traditional counting inaccuracies by operators and management, and its station provides benefits to the palm industry.

A novel model for count data: zero-inflated Probit Bell model with applications

A novel model for count data is proposed in this work. This new model is briefly called the Zero-Inflated Probit Bell (ZIPBell) model. The ZIPBell model can be used to simulate and analyze traditional count data and can be used well in the simulation and analysis of count data with the phenomenon that the frequency for zero occupies a very high percentage. In the scope of this study, we first provide the general formula, functions or related equations for the ZIPBell model. The results of asymptotic inferences for the ZIPBell model are also given in this work. Maximum likelihood estimator (MLE) is used to check its performance of the proposed model. According to the numerical illustrations via several simulation researches and the analysis of a practical data set, it has been seen that the MLE method has offered correct and reliable inferences. Finally, the inferences and conclusions of this study are mentioned.

Counting sheep: human experience vs. Yolo algorithm with drone to determine population

This study assessed the efficiency of traditional human counting methods and the YOLOv7 algorithm in sheep population management at SAIS Pachacutec S.A.C., Peru. Human counters with varying experience levels (M1-M4) and the YOLOv7 algorithm (M5) were evaluated across six sheep flocks of different sizes. Traditional counting involved "linear pair counting" with human assistants, while the YOLOv7 algorithm utilized drone-captured images for automated counting. Using ANOVA and post-hoc tests, data analysis indicated that 24 months of human experience achieved 100% accuracy, highlighting the importance of expertise in accurate population management. The YOLOv7 algorithm achieved 85% accuracy, affected by factors such as the number of training images, hardware limitations, and training parameters. Despite its lower accuracy, YOLOv7 significantly reduced counting time compared to manual methods, making it a viable option for rapid object counting tasks. Further improvements in algorithm training and computational resources could enhance the algorithm's accuracy. These findings suggest that while human expertise remains critical for precise sheep counting, advancements in computer vision algorithms like YOLOv7 offer promising support, particularly for reducing counting time.

Driving user adoption of mobile apps through platform multihoming: The effects of multihoming delay and multihoming customization

Developers release their mobile apps through multiple app store ecosystems to extend their reach and reduce their dependency on a single distribution platform and its governance mechanisms. While this topic has piqued the interest of a growing number of studies, key multihoming decisions faced by developers including when to multihome mobile apps and whether and how much to customize them for the multihomed platform have not yet been jointly examined. In addition, the potential role that the user value of mobile apps (hedonic or utilitarian) plays in developers’ multihoming decisions has largely been overlooked. To address these gaps, the present study jointly investigates the effects of multihoming delay and multihoming customization on the user adoption of mobile apps on the multihomed platform. It further investigates the moderating effects of the user value of mobile apps. The study employed a large dataset of mobile apps that were initially released through Apple's App Store and subsequently multihomed into Google's Play Store (N = 24,906). By combining traditional count regression model with a machine learning approach, the study derives a range of theoretical and predictive insights that significantly contribute to the mobile app multihoming literature.

Enhanced gradient boosting for zero-inflated insurance claims and comparative analysis of CatBoost, XGBoost, and LightGBM

The property and casualty (P&C) insurance industry faces challenges in developing claim predictive models due to the highly right-skewed distribution of positive claims with excess zeros. To address this, actuarial science researchers have employed ‘zero-inflated’ models that combine a traditional count model and a binary model. This paper investigates the use of boosting algorithms to process insurance claim data, including zero-inflated telematics data, to construct claim frequency models. Three popular gradient boosting libraries - XGBoost, LightGBM, and CatBoost -- are evaluated and compared to determine the most suitable library for training insurance claim data and fitting actuarial frequency models. Through a comprehensive analysis of two distinct datasets, it is determined that CatBoost is the best for developing auto claim frequency models based on predictive performance. Furthermore, we propose a new zero-inflated Poisson boosted tree model, with variation in the assumption about the relationship between inflation probability p and distribution mean μ, and find that it outperforms others depending on the characteristics of the data. This model enables us to take advantage of particular CatBoost tools, which makes it easier and more convenient to investigate the effects and interactions of various risk features on the frequency model when using telematics data.

A Time-to-Digital Converter for Low-Power Consumption Single Slope Analog-to-Digital Converters in a High-Speed CMOS Image Sensor.

To reduce the power consumption of a TDC in high-speed applications, a TDC architecture applied to SS ADC is proposed to reduce redundant counting. This structure can remove the identical part between two rows of pixel signals in a CMOS image sensor by adjusting the start and stop signal of the TDC, which will reduce the number of flipping of D flip-flops in the TDC. This structure requires the simultaneous readout of two rows of pixels in the high-speed CMOS image sensor. In the 110 nm CMOS process, simulation results show that the designed 5-bit TDC achieves an effective number of bits (ENOB) at 4.72 bits and a figure-of-merit (FOM) at 104.7-162.3 fJ/step, with a power consumption ranging from 60 µW to 93 µW. Compared with traditional counting methods, the proposed TDC can reduce counting power consumption by 30%.

Estimation of alpha exposure on CR-39 detector using a UV-VIS spectrophotometer

This work is dedicated to study the possibility of using UV-VIS spectrophotometer, a non-invasive technique with a versatile applications that is being used to determine the optical properties of matter, to estimate the CR-39 exposure to alpha particles. CR-39 detectors were exposed to alpha particles from two different alpha sources: 241Am and radon gas. Tracks densities on CR-39 were determined using the traditional counting method by an optical microscope. The transmittances of CR-39 detectors were measured using UV-VIS spectroscopy in the range of 400–1000 nm, and results were correlated with measured tracks densities. The comparison showed that this method is effective in estimating exposure of CR-39 to alpha particles, and that its efficiency has increased by increasing the etching time of the detectors.

FROM LOOM TO CLASSROOM: EXPLORING THE EDUCATIONAL POTENTIAL OF JOLAHA ART OF WEAVING'S EMBEDDED MATHEMATICAL CONCEPTS

One of the current challenges in mathematics education is how to link school mathematics to student’s culture and everyday activities to improve the quality of the student’s mathematical learning. Drawing ideas from scholarship in ethno-mathematics, this study attempted to comprehend the mathematical concepts embedded in the weaving art of jolaha peoples. The study employed a qualitative research method to unravel the mathematical elements. Evidence of mathematical practices of the Jolaha community in the art of weaving indicated that irrespective of the mathematical experience, they all employed a certain level of informal mathematics in the discharge of their duties. The researcher explored the Jolaha weaving skills from a mathematical perspective and noted the mathematical ideas, concepts of traditional counting systems, traditional measurement units, concepts of translation, and rhombus that can be used to teach mathematics through ethno-mathematical strategies in the classroom, and concluded with discussion of mathematical elements that can be integrated with the mathematics curriculum to enhance mathematics teaching in the classroom.

ICNet: A Dual-Branch Instance Segmentation Network for High-Precision Pig Counting

A clear understanding of the number of pigs plays a crucial role in breeding management. Computer vision technology possesses several advantages, as it is harmless and labour-saving compared to traditional counting methods. Nevertheless, the existing methods still face some challenges, such as: (1) the lack of a substantial high-precision pig-counting dataset; (2) creating a dataset for instance segmentation can be time-consuming and labor-intensive; (3) interactive occlusion and overlapping always lead to incorrect recognition of pigs; (4) existing methods for counting such as object detection have limited accuracy. To address the issues of dataset scarcity and labor-intensive manual labeling, we make a semi-auto instance labeling tool (SAI) to help us to produce a high-precision pig counting dataset named Count1200 including 1220 images and 25,762 instances. The speed at which we make labels far exceeds the speed of manual annotation. A concise and efficient instance segmentation model built upon several novel modules, referred to as the Instances Counting Network (ICNet), is proposed in this paper for pig counting. ICNet is a dual-branch model ingeniously formed of a combination of several layers, which is named the Parallel Deformable Convolutions Layer (PDCL), which is trained from scratch and primarily composed of a couple of parallel deformable convolution blocks (PDCBs). We effectively leverage the characteristic of modeling long-range sequences to build our basic block and compute layer. Along with the benefits of a large effective receptive field, PDCL achieves a better performance for multi-scale objects. In the trade-off between computational resources and performance, ICNet demonstrates excellent performance and surpasses other models in Count1200, AP of 71.4% and AP50 of 95.7% are obtained in our experiments. This work provides inspiration for the rapid creation of high-precision datasets and proposes an accurate approach to pig counting.

Automatic Counting and Location Labeling of Rice Seedlings from Unmanned Aerial Vehicle Images

Traditional counting of rice seedlings in agriculture is often labor-intensive, time-consuming, and prone to errors. Therefore, agricultural automation has gradually become a prominent solution. In this paper, UVA detection, combining deep learning with unmanned aerial vehicle (UAV) sensors, contributes to precision agriculture. We propose a YOLOv4-based approach for the counting and location marking of rice seedlings from unmanned aerial vehicle (UAV) images. The detection of tiny objects is a crucial and challenging task in agricultural imagery. Therefore, we make modifications to the data augmentation and activation functions in the neural elements of the deep learning model to meet the requirements of rice seedling detection and counting. In the preprocessing stage, we segment the UAV images into different sizes for training. Mish activation is employed to enhance the accuracy of the YOLO one-stage detector. We utilize the dataset provided in the AIdea 2021 competition to evaluate the system, achieving an F1-score of 0.91. These results indicate the superiority of the proposed method over the baseline system. Furthermore, the outcomes affirm the potential for precise detection of rice seedlings in precision agriculture.

Count Data Regression Modelling: An Application to Monkeypox Confirmed Cases

Introduction: With the presence of COVID 19, some countries also faced an increase in number of cases due to Monkeypox virus. The main aim of this research was to investigate whether it is possible to fit count data regression models to predict the daily incidence of Monkeypox confirmed cases.
 Methods: In this study we have used two types of traditional count regression models like Poisson regression model and Negative binomial regression model using identity and logarithmic link function. Since our data was overdispersed, Negative binomial regression model with logarithmic link function fitted well as compared to other models. The parameters were estimated using SPSS, version 23.0.
 Results: The Negative Binomial Regression model with logarithm function fits well to the data related to Monkeypox cases. Therefore, the model shows that majority of the countries like Brazil, Canada, France, Germany, Peru, Spain, United Kingdom and United States of America shows significant decrease in number of cases with respect to time. The prediction line was plotted using this model where the line predicts well about the daily Monkeypox cases reported by different countries.
 Conclusion: From our study, we concluded that the count data regression model can be used widely to predict the incidence of any disease. The countries like Canada and Brazil have largest and smallest slope coefficient which shows maximum and minimum decrease in expected number of cases confirmed each day respectively.

Artificial Intelligence-based Flow Cytometer for Real-time Algae Monitoring

Flow cytometry is a laser-based technology that rapidly detects and analyzes the chemical and physical characteristics of single cells or particles and is already well established in environmental and toxicological studies for microalgae and bacterial quantification. This study introduces an imaging Flow Cytometer (FC) system, designed specifically for the enhanced analysis of microalgae biomass populations and aggregate groups through Artificial Intelligence (AI) integration. The FC incorporates a single flow line, critical hardware components, and a trifurcated software setup. The system employs a multi-step process for counting algae units and an Artificial Neural Network (ANN) for classifying them in groups of two or four. To demonstrate its capabilities, the system was tested on its ability to capture, count, and categorize algal units, specifically the Desmodesmus sp. morphotype with high accuracy. Furthermore, the FC's capabilities were contrasted with traditional counting methods, validating its enhanced precision and efficiency against a hematocytometer. With its capability to provide rapid, accurate, and high-throughput analyses, this innovative FC paves the way for a revolutionary approach to cellular research.

Object counting in remote sensing via selective spatial‐frequency pyramid network

AbstractThe integration of remote sensing object counting in the Mobile Edge Computing (MEC) environment is of crucial significance and practical value. However, the presence of significant background interference in remote sensing images poses a challenge to accurate object counting, as the results are easily affected by background noise. Additionally, scale variation within remote sensing images presents a further difficulty, as traditional counting methods face challenges in adapting to objects of different scales. To address these challenges, we propose a selective spatial‐frequency pyramid network (SSFPNet). Specifically, the SSFPNet consists of two core modules, namely the pyramid attention (PA) module and the hybrid feature pyramid (HFP) module. The PA module accurately extracts target regions and eliminates background interference by operating on four parallel branches. This enables more precise object counting. The HFP module is introduced to fuse spatial and frequency domain information, leveraging scale information from different domains for object counting, so as to improve the accuracy and robustness of counting. Experimental results on RSOC, CARPK, and PUCPR+ benchmark datasets demonstrate that the SSFPNet achieves state‐of‐the‐art performance in terms of accuracy and robustness.

Dissociating memory and executive function impairment through temporal features in a word list verbal learning task

The Rey Auditory Verbal Learning Test (RAVLT) is an established verbal learning test commonly used to quantify memory impairments due to Alzheimer's Disease (AD) both at a clinical dementia stage or prodromal stage of mild cognitive impairment (MCI). Focal memory impairment—as quantified e.g. by the RAVLT—at an MCI stage is referred to as amnestic MCI (aMCI) and is often regarded as the cognitive phenotype of prodromal AD. However, recent findings suggest that not only learning and memory but also other cognitive domains, especially executive functions (EF) and processing speed (PS), influence verbal learning performance. This research investigates whether additional temporal features extracted from audio recordings from a participant's RAVLT response can better dissociate memory and EF in such tasks and eventually help to better describe MCI subtypes.675 age-matched participants from the H70 Swedish birth cohort were included in this analysis; 68 participants were classified as MCI (33 aMCI and 35 due to executive impairment). RAVLT performances were recorded and temporal features extracted. Novel temporal features were correlated with established neuropsychological tests measuring EF and PS. Lastly, the downstream diagnostic potential of temporal features was estimated using group differences and a machine learning (ML) classification scenario.Temporal features correlated moderately with measures of EF and PS. Performance of an ML classifier could be improved by adding temporal features to traditional counts. We conclude that RAVLT temporal features are in general related to EF and that they might be capable of dissociating memory and EF in a word list learning task.

Compatibility predictive model for regeneration quantities of Larix gmelinii natural forest in Daxing'anling Mountains, China.

The natural regeneration grade is an important foundation for formulating forest management measures. Traditional studies have only considered the regeneration quantities predictive model of the total stand or dominant tree species, but the consistency among the prediction results of different tree species and the total regeneration quantities of stand is not solved. That is, the regeneration prediction results at the stand level are not equal to the sum of the predicted results of all tree species. To address this, on the basis of the traditional counting model, we attempted to construct a compatibility predictive model for regeneration quantities of different tree species within the stand, which would provide a theoretical basis for the rational management and decision-making of natural forest. Based on the survey data from 96 standard plots of Cuigang Forest Farm, Xinlin Forest Farm, and Zhuangzhi Forest Farm in Daxing'an Mountains, we selected 30 basic indices from five aspects of site factor, soil factor, stand factor, tree diversity and stand spatial structure, and used Poisson model and negative binomial model as the basic models to construct the regeneration prediction models of Larix gmelinii, Betula platyphylla and other tree species. By comparing the accuracy and fitting effect of the two traditional counting models, we selected the optimal model and used the seemingly unrelated regressions to further construct the compatibility predictive model for regeneration quantities of different tree species. Poisson model was the best one for the regeneration of L. gmelinii, B. platyphylla, and other tree species. The test index RMSE of the compatibility predictive model for regeneration quantities of L. gmelinii, B. platyphylla, other tree species and total stand regeneration quantities were 388, 413, 504, and 871 trees·hm-2, respectively. The adjusted R2 was 0.389, 0.421, 0.488, and 0.407, respectively. The most influential variables for regeneration quantities of L. gmelinii, B. platyphylla and other tree species were Pielou evenness index of DBH (25.2%), herbal coverage (34.6%) and organic matter in B layer (23.2%). In this study, the compatibility predictive model system for regeneration quantities satisfied the additive logic among L. gmelinii, B. platyphylla, other tree species, and total stands, and provided a basis for accurately estimating natural regeneration.

On Fuzzy and Crisp Solutions of a Novel Fractional Pandemic Model

Understanding disease dynamics is crucial for accurately predicting and effectively managing epidemic outbreaks. Mathematical modeling serves as an essential tool in such understanding. This study introduces an advanced susceptible, infected, recovered, and dead (SIRD) model that uniquely considers the evolution of the death parameter, alongside the susceptibility and infection states. This model accommodates the varying environmental factors influencing disease susceptibility. Moreover, our SIRD model introduces fractional changes in death cases, which adds a novel dimension to the traditional counts of susceptible and infected individuals. Given the model’s complexity, we employ the Laplace-Adomian decomposition method. The method allows us to explore various scenarios, including non-fuzzy non-fractional, non-fuzzy fractional, and fuzzy fractional cases. Our methodology enables us to determine the model’s equilibrium positions, compute the basic reproduction number, confirm stability, and provide computational simulations. Our study offers insightful understanding into the dynamics of pandemic diseases and underscores the critical role that mathematical modeling plays in devising effective public health strategies. The ultimate goal is to improve disease management through precise predictions of disease behavior and spread.

Development of Digital Culture Research — Bibliometric Analysis Using CiteSpace

The article contains a bibliometric analysis conducted on the basis of bibliographic data from the years 1996–2022, extracted from the Scopus database (May 2022). The method used involved steps beyond the traditional counting of publications and citations as well as drawing conclusions based on reading the content of academic papers. Complete biographic datasets were used for advanced analyses performed in the program CiteSpace. The state of research into digital culture may be described as follows: (1) it constitutes a new area of research that has seen particularly intensive development for the last 15 years or so; (2) systematic research is conducted by a small set of researchers; (3) they publish the findings of their research in a small selection of journals (around a dozen) and in books; (4) books contribute most to the development of such research (mainly around a dozen of the most popular ones); (5) the subject-matter of this research embraces mainly such issues as: copyright law, critical digital studies, digital art, network society, digital media, digital modernity, information technology, digitalisation, influencers, the internet era, memes, new media, social media, social networks, video games, and visual culture.

Reduced-variance orientational distribution functions from torque sampling

We introduce a method to sample the orientational distribution function in computer simulations. The method is based on the exact torque balance equation for classical many-body systems of interacting anisotropic particles in equilibrium. Instead of the traditional counting of events, we reconstruct the orientational distribution function via an orientational integral of the torque acting on the particles. We test the torque sampling method in two- and three-dimensions, using both Langevin dynamics and overdamped Brownian dynamics, and with two interparticle interaction potentials. In all cases the torque sampling method produces profiles of the orientational distribution function with better accuracy than those obtained with the traditional counting method. The accuracy of the torque sampling method is independent of the bin size, and hence it is possible to resolve the orientational distribution function with arbitrarily small angular resolutions.

MaskID: An effective deep-learning-based algorithm for dense rebar counting.

As a dense instance segmentation problem, rebar counting in a complex environment such as rebar yard and rebar transpotation has received significant attention in both academic and industrial contexts. Traditional counting approaches, such as manual counting and machine vision-based algorithms, are often inefficient or inaccurate since rebars with varied sizes and shapes are stacked overlapping, rebar image is not clear for complex light condition such as dawn, night and strong light, and other environmental noises exist in rebar image; thus, they no longer fulfil the requirements of modern automation. This paper proposes MaskID, an innovative counting method based on deep learning and heuristic strategies. First, an improved version of the Mask region-based convolutional neural network (Mask R-CNN) was designed to obtain the segmentation results through splitting and rescaling so as to capture more detail in a large-scale rebar image. Then, a series of intelligent denoising strategies corresponding to aspect ratio of recognized box, overlapping recognized objects, object distribution and environmental noise, were applied to improve the segmentation results. The performance of the proposed method was evaluated on open-competition and test-platform datasets. The F1-score was found to be over 0.99 on all datasets. The experimental results demonstrate that the proposed method is effective for dense rebar counting and significantly outperforms existing state-of-the-art methods.

Comparing and combining use of autonomous recording units and traditional counts to monitor Northern Bobwhite

Comparing and combining use of autonomous recording units and traditional counts to monitor Northern Bobwhite