Counting System Research Articles

Tritium determination in natural water samples in Fukushima from 2022 to 2023 using an ultra-low-level tritium counting system.

The Fukushima Daiichi Nuclear Power Plant (FDNPP) accident created large stockpiles of tritium containing cooling water, which is to be gradually released into the Pacific Ocean, gaining attention from surrounding countries, environmental groups, and local residents concerned with the possibility of increasing tritium concentrations in the water and food cycle. Establishing baseline concentration levels and monitoring tritium immission values are important for assuring public safety, providing data for scientific research and risk communication. Tritium concentrations in the environment are very low; therefore, tritium measurements require enrichment in order to estimate the radiation exposure from drinking water intake and provide information on the water cycle. Natural water samples were collected at Tomioka Town located south of the FDNPP. Samples were distilled, enriched by electrolysis, and re-distilled using an improved SPE method to preconcentrate tritium to measureable levels. Tritium concentrations were determined by a low-background liquid scintillation counter. The observed tritium concentrations were relatively low, rainwater had a mean and SD value of 0.40±0.13Bq/L, and freshwater samples showed similar concentrations, while brackish coastal water samples were below 0.13±0.02Bq/L. The observed tritium concentrations in this study are considered safe as effective doses based on annual drinking water intake; however, continous monitoring is necessary to assure public safety.

Real-Time Tracking System for City Bus Location And Passenger Count

This survey paper reviews the advancements in real-time tracking and passenger counting systems for city buses, emphasizing the growing need for efficient, data-driven public transit solutions. As urban populations rise, city transport systems face challenges in managing demand, reducing congestion, and improving service reliability. Real-time tracking systems using GPS, GIS, and IoT-enabled sensors enable passengers and transit authorities to monitor bus locations and optimize travel times. Additionally, passenger counting systems—utilizing infrared, AI-based vision models, and pressure sensors—provide data on bus occupancy levels, allowing commuters to make informed travel decisions and helping transit agencies manage resources efficiently. This paper examines existing technologies, compares various tracking and counting methods based on accuracy, cost, and scalability, and explores recent trends in artificial intelligence, big data analytics, and smart city integration. Furthermore, it identifies critical challenges, including data privacy, accuracy in urban environments, and energy consumption, proposing future research directions for enhancing the reliability, sustainability, and user-friendliness of these systems. Our findings underscore the transformative potential of real-time bus tracking and passenger counting in advancing public transportation infrastructure and enhancing urban mobility

Conditional over-expression of heme-oxygenase 1 in myelomonocytic cells reduces AngII-induced hypertension and vascular inflammation in mice

Abstract Background Systemic arterial Hypertension is one of the most important risk factor responsible for morbidity and mortality world-wide. Angiotensin II (Ang II), a potent vasoconstrictor and key player in the renin-angiotensin-aldosterone system (RAAS) is responsible for vascular dysfunction, inflammation, and tissue damage. Heme oxygenase-1 (HO-1) overexpression may confer antioxidant and anti-inflammatory properties in Ang II-induced hypertension through its action on heme catabolism, generating carbon monoxide (CO), ferritin and biliverdin/bilirubin by the action of biliverdin reductase A (BLVRA). Methods and results Male 9–12-weeks-old HO-1indxLySMCre/wt and LySMCre/wt mice were infused with AngII (1mgAngII/Day/Kg) for 7 days to induce hypertension and compared to sham treatment. Blood pressure monitoring by tail-cuff method, and endothelium-dependent vasodilator studies via organ chamber system using acetylcholine (ACh) in isolated aortic segments (~4 mm), revealed that overexpression of HO-1 in myeloid cells resulted in decreased systolic blood pressure and improved endothelial function in AngII-infused HO-1indxLySMCre/wt mice compared to controls. Concurrently, increased BLVRA expression in liver tissue and elevated plasma bilirubin levels were detected by transcriptome analysis and high-performance liquid chromatography, respectively. These results were supported by a reduction in the expression of inducible cytokines and cell adhesion molecules (CAMs) in the aorta, assessed using qPCR. Bone marrow transplant experiments demonstrated that bone marrow from LysMcre mice in HO-1indxLySMCre/wt mice abrogated the protective effect of HO-1, resulting in endothelial damage and increased blood pressure, potentially due to decreased liver BLVRA expression and the accumulation of bone marrow-derived cells in the vessel wall in response to AngII. Additionally, adeno-associated viral vector (AAV) transfection targeting specifically BLVRA activity in liver lead to an increase in blood pressure values and worse endothelium relaxation, in parallel with higher oxidative stress and the blood neutrophils concentration, as assessed in whole blood by using oxidative burst method and blood count system respectively. Conclusion The overexpression of HO-1 in myeloid cells confers anti-inflammatory protection in AngII-induced arterial hypertension in mice. Myeloid cells bone marrow-derived overexpressing HO-1 regulate the differentiation and infiltration of pro-inflammatory immune cells in the vasculature. BLVRA expression and bilirubin levels downstream of HO-1 play a critical role in protecting against vascular damage by reducing leukocyte infiltration.

Multiple moving object classification and tracking using DenCNN classifier

A Multiple moving object detection, tracking, and counting algorithm is mainly designed exclusively suitable for congested areas. The counting system can alleviate the betrayal performance in the crowded areas. Most of the existing methods developed for tracking and counting face serious challenges in detection due to high densities of the target. This condition urged the researchers to update the existing systems. The present methodology was designed to address such issues. In the present methodology, the contrast was initially enhanced between the objects and their backgrounds using a Double Plateau Histogram Equalization (DPHE). Then, the motion was estimated for the contrast-enhanced image to identify the moment of the object using the modified Adaptive Distance Covariance Rood Pattern Search (ADCRPS) algorithm. After that, the morphological operation was deployed to sharpen the images by removing all the unwanted things. Then, the features were extracted and important features were selected using the modified Chaotic Tent Shuffled Shepherd Optimization (CTSSO) Algorithm. With the selected features object, detection was done using the proposed Scaled Non-Monotonic Cauchy Dense Convolutional Neural Network (SNMC-DenCNN). The detected object was then tracked with the aid of Channel and Spatial Reliability Tracker (CSRT). Finally, the objects were counted by intersection over union (IOU) by explicitly computing the association between detected and tracked objects. Also, the experimental results showed the effectiveness and efficiency of the proposed system with enhanced accuracy.

A Crowded Object Counting System with Self-Attention Mechanism

Traditional object counting systems use object detection methods to count objects. However, when objects are small, crowded, and dense, object detection may fail, leading to inaccuracies in counting. To address this issue, we propose a crowded object counting system based on density map estimation. While most density map estimation models employ encoder–decoder or multi-branch approaches to generate feature maps at different scales for obtaining an accurate density map, improving the accuracy of crowded object counting remains a challenge. In this paper, we propose a novel model that can generate more accurate density maps, utilizing the context-aware network as the primary structure and integrating the self-attention mechanism. There are three main contributions in this paper. Firstly, the self-attention mechanism is employed to improve the accuracy of density map estimation. Secondly, the missing vehicle labels in the TRANCOS database are relabeled, ensuring that the ground truth data are more complete than the original TRANCOS database, thus enabling the proposed novel model to have higher crowded object counting accuracy. Thirdly, the parameters of the self-attention mechanism are analyzed to obtain the optimum parameter combination. The experimental results demonstrate that the accuracy of crowded object counting can reach 85.9%, 90.0%, 83.4%, and 92.6% for the TRANCOS, relabeled TRANCOS, ShanghaiTech Part A, and Part B datasets, respectively. Furthermore, the ablation study for the context-aware network with self-attention mechanism analyzes the optimum parameter combination.

Evaluation of the Microscanner C3 for Automated Cell Counting in Cerebrospinal Fluid Analysis

Background: Cerebrospinal fluid (CSF) analysis is essential for diagnosing various disorders affecting the central nervous system (CNS). Traditionally, CSF cell count analysis is performed manually using a Neubauer chamber hemocytometer, which is labor-intensive and prone to subjective interpretation. Methods: In this study, we evaluated the analytical and clinical performance of the Microscanner C3, an automated cell counting system, for CSF analysis using artificially prepared samples and 150 clinical CSF samples. Results: The lowest detectable white blood cell (WBC) count was 3.33 cells/µL, and the lowest detectable red blood cell (RBC) count was 3.67 cells/µL. The coefficients of variation (CV%) for the Microscanner C3 were lower than those for the Neubauer chamber at all cell concentrations. The correlation coefficients (R) between the Microscanner C3 and conventional methods were high: 0.9377 for WBCs and 0.9952 for RBCs when compared with the Neubauer chamber, and 0.8782 for WBCs and 0.9759 for RBCs when compared with the flow cytometer. Additionally, the Microscanner C3 showed good agreement with both the Neubauer chamber and flow cytometer in the Passing–Bablok regression analysis and Bland–Altman analysis for WBC count at all concentrations and RBC count at concentrations of 0–1000 cells/µL. Conclusions: The Microscanner C3 proved to be more sensitive, precise, and consistent compared to the conventional hemocytometer. The new system is also compact, convenient, and cost-effective, making it a valuable option for clinical laboratories.

An easier life to come for mosquito researchers: field-testing across Italy supports VECTRACK system for automatic counting, identification and absolute density estimation of Aedes albopictus and Culex pipiens adults

BackgroundDisease-vector mosquito monitoring is an essential prerequisite to optimize control interventions and evidence-based risk predictions. However, conventional entomological monitoring methods are labor- and time-consuming and do not allow high temporal/spatial resolution. In 2022, a novel system coupling an optical sensor with machine learning technologies (VECTRACK) proved effective in counting and identifying Aedes albopictus and Culex pipiens adult females and males. Here, we carried out the first extensive field evaluation of the VECTRACK system to assess: (i) whether the catching capacity of a commercial BG-Mosquitaire trap (BGM) for adult mosquito equipped with VECTRACK (BGM + VECT) was affected by the sensor; (ii) the accuracy of the VECTRACK algorithm in correctly classifying the target mosquito species genus and sex; (iii) Ae. albopictus capture rate of BGM with or without VECTRACK.MethodsThe same experimental design was implemented in four areas in northern (Bergamo and Padua districts), central (Rome) and southern (Procida Island, Naples) Italy. In each area, three types of traps—one BGM, one BGM + VECT and the combination of four sticky traps (STs)—were rotated each 48 h in three different sites. Each sampling scheme was replicated three times/area. Collected mosquitoes were counted and identified by both the VECTRACK algorithm and operator-mediated morphological examination. The performance of the VECTRACK system was assessed by generalized linear mixed and linear regression models. Aedes albopictus capture rates of BGMs were calculated based on the known capture rate of ST.ResultsA total of 3829 mosquitoes (90.2% Ae. albopictus) were captured in 18 collection-days/trap/site. BGM and BGM + VECT showed a similar performance in collecting target mosquitoes. Results show high correlation between visual and automatic identification methods (Spearman Ae. albopictus: females = 0.97; males = 0.89; P < 0.0001) and low count errors. Moreover, the results allowed quantifying the heterogeneous effectiveness associated with different trap types in collecting Ae. albopictus and predicting estimates of its absolute density.ConclusionsObtained results strongly support the VECTRACK system as a powerful tool for mosquito monitoring and research, and its applicability over a range of ecological conditions, accounting for its high potential for continuous monitoring with minimal human effort.Graphical

Monte Carlo modeling and simulation of a new 3D printed phantom for WBC calibration with ballistic gel as a tissue substitute

Whole-body counter (WBC) systems are used for in vivo monitoring in occupational internal dosimetry, typically calibrated using physical anthropomorphic phantoms. Our research group previously 3D-printed the Reference Female Phantom for Internal Dosimetry (RFPID) without internal organs specifically designed for WBC calibration. The RFPID and it is intended to fill it homogenously with ballistic gel, which is commonly used as a tissue equivalent in ballistic studies. However, comprehensive characterization of its physicochemical properties and radiological behavior as a tissue surrogate for dosimetry is limited. This study aims to evaluate the suitability of ballistic gel as a tissue substitute for physical phantoms in WBC system calibration and to analyze the RFPID as a model for WBC calibration. Ballistic gel tests determined its density and attenuation coefficients, comparing it to muscle, water, and PMMA. The RFPID was modeled and simulated using MCNP6.2 code and placed in an in vivo monitoring system using an 8”x4” NaI(Tl) scintillator detector previously validated. The simulations were repeated with the RCP_AF of ICRP-110. Results indicate that ballistic gel has a density approximately 6% different from muscle and shows similar linear attenuation coefficients to muscle at intermediate and high energy levels (186 to 2200 keV). Simulations revealed a disparity of less than 9% in counting efficiency between RFPID and RCP_AF for energies from 100 to 3000 keV, confirming the phantom's suitability for WBC calibration and ballistic gel's viability as a tissue surrogate in internal dosimetry.

Development of a TDCR counting system with anti-coincidence detectors

When employing the TDCR method for standardization of low-activity liquid scintillation samples, fluctuations in system background can significantly impact both measurement uncertainties and minimum detectable activity (MDA). To mitigate this impact, a TDCR counting system with anti-coincidence detectors was developed. By analyzing the time difference distribution spectrum between the anti-coincidence channel and β channel, optimal parameters for the anti-coincidence module are determined. The objective of the study is to enhance the effectiveness of the anti-coincidence technique while minimizing the removal of real events. Finally, two sets of 3H and 14C samples with known activities of around 1 Bq are prepared via dilution to validate the performance of the system. Compared to without anti-coincidence, the background has been reduced by nearly 84%, leading to decreased fluctuations in the activity results.

Automated fish counting system based on instance segmentation in aquaculture

Non-invasive methods of fish counting are crucial for the effective management of aquaculture and aquatic resources. However, accurate and reliable estimation of fish populations in real-time is a significant challenge due to severe occlusion, body deformation, and rapid movement of fish. Recent advancements in computer vision and deep learning algorithms offer substantial potential to revolutionize fish counting methodologies, thereby enhancing conservation efforts and promoting the sustainable management of aquatic resources. This paper presents a comprehensive dataset of 256,580 fish instances annotated with mask information at the pixel level. To address the challenges associated with geometric variations in fish size, pose, perspective, and body shape, we propose a lightweight instance segmentation model based on YOLOv8, which integrates the CSP bottleneck with dual convolutions and the deformable convolutional networks (DConv). In addition, an efficient feature fusion network incorporating multi-scale information was introduced to improve segmentation accuracy by enhancing the representation of feature information, and a decoupled head with attention mechanisms was designed to detect and segment fish bodies in real time. Our proposed approach achieved an outstanding mAP50 of 98.1 % at 60.6 FPS, with model parameters and floating point operations per second (FLOPS) amounting to 25.4 M and 96.9 G, respectively. The instance segmentation-based counting system exhibited impressive performance metrics, with Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R-Square (R2) values of 1.19, 1.65, and 0.992, respectively. The proposed method demonstrates strong potential for practical application as an intelligent solution for biomass estimation in recirculating aquaculture systems.

The hemostatic activity and Mechanistic roles of glucosyloxybenzyl 2-isobutylmalate extract (BSCE) from Bletilla striata (Thunb.) Rchb.f. in Inhibiting pulmonary hemorrhage

BackgroundHemorrhagic events cause numerous deaths annually worldwide, highlighting the urgent need for effective hemostatic drugs. The glucosyloxybenzyl 2-isobutylmalates Control Extract (BSCE) from the orchid plant Bletilla striata (Thunb.) Rchb.f. has demonstrated significant hemostatic activity in both in vitro and in vivo studies. However, the effect and mechanism of BSCE on non-traumatic bleeding remain unclear. MethodsPulmonary hemorrhage was induced in 40 Sprague-Dawley rats by administering Zingiber officinale Roscoe. for 14 days. These rats were then randomly divided into five groups: model (Mod), positive control (YNBY), and BSCE low, medium, and high-dose groups. An additional 8 rats served as the control group (Con). The BSCE groups received different doses of BSCE for 10 days, while the YNBY group received Yunnan Baiyao suspension. The effects on body weight, food and water intake, red blood cell count (RBC), hemoglobin concentration (HGB), lung tissue pathology, platelet count, coagulation parameters, and fibrinolytic system markers were evaluated. Network pharmacology and molecular docking analyses were also conducted to identify potential targets and pathways involved in BSCE's effects. ResultsBSCE treatment significantly improved body weight, food intake, and water consumption in rats with pulmonary hemorrhage. RBC and HGB levels increased significantly in the BSCE medium and high-dose groups compared to the Mod group (P < 0.05). Pathological examination revealed that BSCE reduced lung tissue hemorrhage and inflammation, with improvements in alveolar structure. BSCE also positively affected platelet count, thrombin time (TT), activated partial thromboplastin time (APTT), fibrinogen (FIB) levels, and fibrinolytic markers (D-dimer, PAI-1, and t-PA). Network pharmacology and molecular docking identified key targets such as MMPs, CASPs, and pathways including IL-17 and TNF signaling, suggesting BSCE's involvement in hemostasis and anti-inflammatory processes. ConclusionsBSCE exhibits significant hemostatic and protective effects on Z.officinale-induced pulmonary hemorrhage in rats by improving hematological parameters, reducing lung tissue damage, and modulating the coagulation and fibrinolytic systems. The study provides evidence supporting the potential of BSCE as a therapeutic agent for hemorrhagic diseases, with its efficacy linked to multi-target and multi-pathway interactions.

Microcontroller-based track axle counter system design

Abstract Axle counting technology is an extremely important part of many aspects in ensuring the safety of railroad operation. Axle counting equipment is set up on the railroad tracks, and through the operation of sensors, it detects the trains that drive into the train operating area where the axle counting equipment is arranged. A common axle counting system uses a detection device that utilizes a wheel sensor that senses the movement of the wheel cutting through magnetic lines of inductance causing a change in the magnetic field within the zone. This paper introduces a track axle counter system, the system will be a microcontroller as the core, the use of ultrasonic detection in the train interval of the train wheel distance changes, instead of the existing axle counting system using wheel sensors to sense the electromagnetic signals generated by the wheels, to achieve the axle counting and counting function, so as to determine the track occupancy.

Nishad: History of the origin of primitive man

The history of Nishads is an important chapter in the development of ancient human civilization and culture. It is a symbol of the formation of human settlements on earth, the establishment of residences and the advancement of early civilizations. Based on the skeletons and evidence obtained from the excavations of the Indus Valley Civilization, it is clear that the Nishad community was part of a strong and prosperous culture till the Vedic period. Nishad culture played an important role in the creation of Vedic literature, epics, and Puranas. Nishads have been mentioned in epics like Ramayana and Mahabharata as a symbol of ideals, bravery and cooperation. Nishad kings are especially mentioned in the Mahabharata period, in which important personalities like Nishad King Nala and Eklavya are described. The roots of Nishad culture were spread in wide areas of the Indian subcontinent, in which Nishads expanded from the Himalayan ranges to Rajasthan and Punjab. Nishad mountains and Nishad land are also mentioned in the Mahabharata, which shows their geography and places of residence. The social and cultural identity of the Nishads evolved over the centuries, and they became important components of Indian culture. The etymology and usage of the word Nishad has been used in various contexts of music, yajna, and society in different periods. The contribution of the Nishads is not only significant in the cultural heritage of ancient India, but their counting system and language have also had a significant impact globally.

Improved echo signal detection for pseudo-random single-photon counting laser ranging

In this paper, a new echo signal detection method, to the best of our knowledge, for pseudo-random single-photon counting ranging (PSPCR) LiDAR systems is proposed, which is applied for long distances, low repetition rates, and system cost reduction. First, in order to achieve a comparable temporal resolution as that in time-correlated single-photon counting (TCSPC) systems, we extend the pseudo-random code to discriminate the minimal time slot in time correlation. Second, we use the full width at half maxima (FWHM) in the duration of each pseudo-random code for correlation to reduce the impact of pulse width variation and timing jitters on ranging accuracy. Third, we study the bias and errors caused by using synchronous signals as the “START” signal, and propose to use the time of flight (ToF) at half energy to reduce the walk error. Simulation results show that, compared with existing PSPCR methods, the proposed method improves ranging accuracy with a lower repetition rate and lower peak and average power—centimeter-level ranging accuracy over tens of kilometers can be achieved using a laser with a repetition rate of 400 kHz, peak power of up to 1 kW, and average power of up to 1 W.

P-102 Incorporation of an automated sperm counting method: a matter of time

Abstract Study question Is a fully-automated system a faster and reliable sperm counting method? Summary answer A fully-automated sperm counting system is a reliable tool that significantly reduces time spent for semen evaluation. What is known already The emergence of automated tools in our everyday life is a reality. Fully-automated systems for the evaluation of seminal samples are available for the andrology laboratories. The expected advantages of such analysers would be a higher reliability and reproducibility of results, with lower inter and intra-individual variability. Although there are few published validation studies, available data have evidenced that results obtained with these methods are, in general, consistent with those obtained with manual/conventional analysis. Moreover, automatic sperm assessment could be useful in reducing the time spent for analysis. However, no data have been published in this regard. Study design, size, duration Observational retrospective study of 220 semen samples analyzed in 2022. Every sample was analyzed using three different methodologies: manual analysis, semi-automated and fully-automated. Manual assessment was performed by senior embryologists undergoing periodical quality control evaluations and included counting with improved Neubauer chamber, motility assessment with slide with phase-contrast optics, and morphology analysis by Diff-Quick stain. The semi-automated analysis was performed by SCATM plus manual morphology assessment. The fully-automated analysis was exclusively performed by SQA-VisionTM. Participants/materials, setting, methods Male patients consulting for infertility and undergoing a spermiogram in a single centre. The sperm parameters evaluated for correlation study among methodologies were: total sperm, motile sperm and progressive sperm concentrations (106 per ml), and morphologically normal forms (%). Duration of analysis (sperm count + motility + morphology) using each method was recorded and compared. Intraclass correlation coefficient (ICC[95%CI]) was used to compare agreement between methodologies and Wilcoxon’s test was used for time (median (p25-p75)). Main results and the role of chance The observed correlation between the three methodologies was moderate (ICC=0.5-0.79) to high (ICC &amp;gt;0.8) for all parameters analysed. Concerning total sperm concentration, both fully and semi-automated systems showed a high correlation with manual assessment (ICC=0.93 (0.90-0.95) fully-automated vs manual; ICC=0.83 (0.73-0.89) semi-automated vs manual, respectively). Concerning motile sperm concentration and progressive motile sperm concentration, both fully and semi-automated systems showed a high or moderate correlation with manual assessment (ICC =0.86 (0.77-0.91) and ICC=0.87 (0.76-0.92) fully-automated vs manual; ICC=0.54 (0.27-0.70) and ICC=0.62 (0.39-0.76) semi-automated vs manual, respectively). In terms of normal forms, fully-automated showed a moderate correlation with manual assessment (ICC=0.69 (0.57-0.78)). Regarding time spent for analysis, both fully and semi-automated analysers showed to be faster than manual method (p &amp;lt; 0.001) (1343 (1199-1472) seconds faster fully-automated vs manual and 608 (429-769) seconds faster semi-automated vs manual). Comparing time spent for analysis between the fully and the semi-automated systems, fully-automated was 707 (610-784) seconds faster (p &amp;lt; 0.001) than the semi-automated. Limitations, reasons for caution Retrospective study. SCATM is a semi-automated operator-dependent system based on image analysis and its accuracy relies on the number of sperm analysed and the operators’ ability in reviewing images. SQA-VisionTM is fully-automated, based on electro-optical signal analysing millions of sperm. Therefore, conclusions may not apply to other software operators. Wider implications of the findings Fully-automated systems for sperm assessment can be reliably introduced in andrology laboratories offering a dramatic reduction in the time spent for analysis. However, such analysers may only be cost-efficient in centres with a very high activity. Trial registration number not applicable

Extracellular vesicle isolation and counting system (EVics) based on simultaneous tandem tangential flow filtration and large field-of-view light scattering.

Although the isolation and counting of small extracellular vesicles (sEVs) are essential steps in sEV research, an integrated method with scalability and efficiency has not been developed. Here, we present a scalable and ready-to-use extracellular vesicle (EV) isolation and counting system (EVics) that simultaneously allows isolation and counting in one system. This novel system consists of (i) EVi, a simultaneous tandem tangential flow filtration (TFF)-based EV isolation component by applying two different pore-size TFF filters, and (ii) EVc, an EV counting component using light scattering that captures a large field-of-view (FOV). EVi efficiently isolated 50-200nm-size sEVs from 15µL to 2L samples, outperforming the current state-of-the-art devices in purity and speed. EVc with a large FOV efficiently counted isolated sEVs. EVics enabled early observations of sEV secretion in various cell lines and reduced the cost of evaluating the inhibitory effect of sEV inhibitors by 20-fold. Using EVics, sEVs concentrations and sEV PD-L1 were monitored in a 23-day cancer mouse model, and 160 clinical samples were prepared and successfully applied to diagnosis. These results demonstrate that EVics could become an innovative system for novel findings in basic and applied studies in sEV research.

How Would Preferential Voting Have Changed Taiwan’s 2024 Presidential Election?

Would the results of Taiwan’s 2024 presidential election have differed under preferential voting rules? Observers assume that the split in the opposition virtually guaranteed Lai Ching-te’s victory, but no analysis has aimed to measure ranked preferences across the three presidential candidates. This analysis combines original pre-election survey data that includes preference ordering of candidates with simulation analysis of the actual election results under the alternative vote and Borda count systems. The original survey finds Ko Wen-je would have won under both preferential systems, while simulating preference ordering using the 2024 election results suggests Lai would have won under alternative vote but lost to Hou You-ih under Borda count.

Counting nematodes made easy: leveraging AI-powered automation for enhanced efficiency and precision.

Counting nematodes is a labor-intensive and time-consuming task, yet it is a pivotal step in various quantitative nematological studies; preparation of initial population densities and final population densities in pot, micro-plot and field trials for different objectives related to management including sampling and location of nematode infestation foci. Nematologists have long battled with the complexities of nematode counting, leading to several research initiatives aimed at automating this process. However, these research endeavors have primarily focused on identifying single-class objects within individual images. To enhance the practicality of this technology, there's a pressing need for an algorithm that cannot only detect but also classify multiple classes of objects concurrently. This study endeavors to tackle this challenge by developing a user-friendly Graphical User Interface (GUI) that comprises multiple deep learning algorithms, allowing simultaneous recognition and categorization of nematode eggs and second stage juveniles of Meloidogyne spp. In total of 650 images for eggs and 1339 images for juveniles were generated using two distinct imaging systems, resulting in 8655 eggs and 4742 Meloidogyne juveniles annotated using bounding box and segmentation, respectively. The deep-learning models were developed by leveraging the Convolutional Neural Networks (CNNs) machine learning architecture known as YOLOv8x. Our results showed that the models correctly identified eggs as eggs and Meloidogyne juveniles as Meloidogyne juveniles in 94% and 93% of instances, respectively. The model demonstrated higher than 0.70 coefficient correlation between model predictions and observations on unseen images. Our study has showcased the potential utility of these models in practical applications for the future. The GUI is made freely available to the public through the author's GitHub repository (https://github.com/bresilla/nematode_counting). While this study currently focuses on one genus, there are plans to expand the GUI's capabilities to include other economically significant genera of plant parasitic nematodes. Achieving these objectives, including enhancing the models' accuracy on different imaging systems, may necessitate collaboration among multiple nematology teams and laboratories, rather than being the work of a single entity. With the increasing interest among nematologists in harnessing machine learning, the authors are confident in the potential development of a universal automated nematode counting system accessible to all. This paper aims to serve as a framework and catalyst for initiating global collaboration toward this important goal.

Integration of beta counting system with ORION-DSP coupled to NaI(Tl) detector-a comparative study with an HPGe detector.

The integration of the ORION digital signal processing-based MCA system coupled with a 3″ × 3″ NaI(Tl) detector assembly with a GM detector for counting beta (β) has eliminated the need for a standalone β-γ method in which U3O8 is determined by gross β and gross γ counting. Uraniferous and mixed U-Th samples were taken up for study and compared with the results obtained from Canberra p-type coaxial high resolution gamma ray spectrometry detector. In uraniferous samples, U3O8 values obtained are within ±10%, whereas in the case of mixed U-Th samples, U3O8 values are within ±15%. Regression graphs drawn between the outcomes from the two analytical systems indicate R2>0.95 for Ra(eU3O8) and ThO2. In uraniferous samples, the R2 value for U3O8 was found to be > 0.99, but in mixed U-Th samples, it is 0.92. The closeness of agreement between the results obtained from two methods at various concentrations over the analytical range shows that the integrated system is suitable for the quantitative determination of eU3O8, U3O8, Ra(eU3O8), ThO2 and K in geological rock samples.

Human Detection and Counting using YOLOV4

Object detection is a fundamental problem in computer vision that involves detecting and localizing objects within an image. In recent years, convolutional neural networks (CNNs) have become the state-of-the-art approach for object detection tasks. YOLO (You Only Look Once) is a popular real-time object detection system that uses a single CNN to predict the bounding boxes and class probabilities for objects in an image. In this paper, we present a human detection and counting system using YOLO. Our system is trained on a large dataset of annotated images and achieves high accuracy and real-time performance. We evaluate our system on a public dataset and demonstrate its effectiveness in various scenarios. This paper focuses on developing a system for human detection and counting using YOLOv4 and Flask API. The system aims to address the increasing importance of human detection and counting in various fields such as social distancing, crowd management, and surveillance.