Reliable Performance Research Articles

Need for Objective Task-Based Evaluation of Image Segmentation Algorithms for Quantitative PET: A Study with ACRIN 6668/RTOG 0235 Multicenter Clinical Trial Data.

Reliable performance of PET segmentation algorithms on clinically relevant tasks is required for their clinical translation. However, these algorithms are typically evaluated using figures of merit (FoMs) that are not explicitly designed to correlate with clinical task performance. Such FoMs include the Dice similarity coefficient (DSC), the Jaccard similarity coefficient (JSC), and the Hausdorff distance (HD). The objective of this study was to investigate whether evaluating PET segmentation algorithms using these task-agnostic FoMs yields interpretations consistent with evaluation on clinically relevant quantitative tasks. Methods: We conducted a retrospective study to assess the concordance in the evaluation of segmentation algorithms using the DSC, JSC, and HD and on the tasks of estimating the metabolic tumor volume (MTV) and total lesion glycolysis (TLG) of primary tumors from PET images of patients with non-small cell lung cancer. The PET images were collected from the American College of Radiology Imaging Network 6668/Radiation Therapy Oncology Group 0235 multicenter clinical trial data. The study was conducted in 2 contexts: (1) evaluating conventional segmentation algorithms, namely those based on thresholding (SUVmax40% and SUVmax50%), boundary detection (Snakes), and stochastic modeling (Markov random field-Gaussian mixture model); (2) evaluating the impact of network depth and loss function on the performance of a state-of-the-art U-net-based segmentation algorithm. Results: Evaluation of conventional segmentation algorithms based on the DSC, JSC, and HD showed that SUVmax40% significantly outperformed SUVmax50%. However, SUVmax40% yielded lower accuracy on the tasks of estimating MTV and TLG, with a 51% and 54% increase, respectively, in the ensemble normalized bias. Similarly, the Markov random field-Gaussian mixture model significantly outperformed Snakes on the basis of the task-agnostic FoMs but yielded a 24% increased bias in estimated MTV. For the U-net-based algorithm, our evaluation showed that although the network depth did not significantly alter the DSC, JSC, and HD values, a deeper network yielded substantially higher accuracy in the estimated MTV and TLG, with a decreased bias of 91% and 87%, respectively. Additionally, whereas there was no significant difference in the DSC, JSC, and HD values for different loss functions, up to a 73% and 58% difference in the bias of the estimated MTV and TLG, respectively, existed. Conclusion: Evaluation of PET segmentation algorithms using task-agnostic FoMs could yield findings discordant with evaluation on clinically relevant quantitative tasks. This study emphasizes the need for objective task-based evaluation of image segmentation algorithms for quantitative PET.

UETOPSIS: A Data-Driven Intelligence Approach to Security Decisions for Edge Computing in Smart Cities

Despite considerable technological advances for smart cities, they still face problems such as instability of cloud server connection, insecurity during data transmission, and slight deficiencies in TCP/IP network architecture. To address such issues, we propose a data-driven intelligence approach to security decisions under Named Data Networking (NDN) architecture for edge computing, taking into consideration factors that impact device entry in smart cities, such as device performance, load, Bluetooth signal strength, and scan frequency. Despite existing techniques for Order Preference by Similarity to Ideal Solution (TOPSIS)-based on entropy weights methods are improved and applied, there exist unstable decision results. Due to this, we propose a technique for Order Preference by Similarity to Ideal Solution (TOPSIS)-based on utility function and entropy weights, named UETOPSIS, where the corresponding utility function is applied according to the influence of each attribute on the decision, ensuring the stability of the ranking of decision results. We rely on an entropy-based weights mechanism to select a suitable master controller for the design of the multi-control protocol in the smart city system, and utilize a utility function to calculate the attribute values and then combine the normalized attribute values of utility numbers, starting by analyzing the main work of the controllers. Lastly, a prototype is developed for performance evaluation purposes. Experimental evaluation and analysis show that the proposed work has better authenticity and reliability than existing works and can reduce the workload of edge computing devices when forwarding data, with stability 24.7% higher than TOPSIS, significantly improving the performance and stability of system fault tolerance and reliability in smart cities, as the second-ranked controller can efficiently take over the work when a central controller fails or damaged.

PHOTOGRAMMETRIC SURVEY OF NARROW SPACES IN CULTURAL HERITAGE: COMPARISON OF TWO MULTI-CAMERA APPROACHES

Abstract. Multi-camera devices are increasingly popular in various metrological applications, including cultural heritage digitalisation, where these devices are adopted as low-cost alternatives to more traditional methods or mobile mapping systems. They can be of two types: panoramic and non-panoramic configurations, with the former usually more compact and ready-made off-the-shelves and the latter usually custom-developed for metrological applications. In the paper, we compare the accuracy and reliability performance of two types of multi-camera: the spherical camera INSTA 360 Pro2 and the custom multi-camera rig Ant3D. The case study is a challenging spiral staircase environment, typical in many cultural heritage survey projects. The processed image datasets were evaluated in the most common constrain scenario (GCPs at both ends of the staircase) and the worst-case scenario (open-ended path, GCPs at the start). The datasets were processed with precalibrated IO and various degrees of multi-camera constraints up to precalibrated relative orientations. The results highlight that the nominal scale 1:50 can be achieved, e.g. an accuracy of <2 cm plus complete and precise point clouds and mesh results.

Optimized IEEE 802.11ax for smart warehouses

Industry 4.0 aspires to digitize factories processes wholly. Wireless technologies represent one of the elements of this goal. However, the current standards and proprietary solutions need to meet the industry’s tight requirements for various use cases, such as smart warehouses, where robots manage and control entire tasks. One of the key research challenges toward replacing wired fieldbuses with wireless links is the design of wireless systems that address short packet transmissions and multi-user environments. Nevertheless, this challenge is partially obstructed by the limits of the current proposals and the industrial channel models. The authors detect as a potential solution the optimization of IEEE 802.11ax that, due to the use of OFDMA, could offer a high-performant system for smart warehouses. This article proposes an ad-hoc wireless system based on IEEE 802.11ax for smart warehouses that comprises optimization for MAC and PHY layers. Furthermore, two retransmission schemes based on frequency domain are described. This article proposes a methodology to evaluate the latency and reliability performance. Compared to previous proposals, the system shows improved performance.

Reliable electrical performance of β-Ga2O3 Schottky barrier diode at cryogenic temperatures

Electrical and trap characteristics of a large-size (2 × 2 mm2) β-Ga2O3 Schottky barrier diode (SBD) from 50 to 350 K have been reported. The ideality factor (n) decreases from 1.34 to nearly unity as temperature rises from 50 to 350 K, demonstrating near-ideal Schottky characteristics. The leakage current at cryogenic temperature (100 K) was significantly suppressed, indicating excellent off-state blocking performance at low temperatures. The weak temperature dependence of the carrier concentration (NS) and Schottky barrier height (ΦB) infers stable electrical characteristics of the β-Ga2O3 SBD. The stressed current density-voltage (J-V) and on-the-fly measurements reveal reliable dynamic performance under harsh low temperature conditions. Via deep-level transient spectroscopy, an electron trap, which is related to the dynamic performance instability and Lorentzian hump in low frequency noise spectra, is revealed for a β-Ga2O3 epilayer. The study reveals enormous potential of the utilization of a large-size β-Ga2O3 SBD for extreme temperature environments.

Transfer-Free Analog and Digital Flexible Memristors Based on Boron Nitride Films.

The traditional von Neumann architecture of computers, constrained by the inherent separation of processing and memory units, faces challenges, for instance, memory wall issue. Neuromorphic computing and in-memory computing offer promising paradigms to overcome the limitations of additional data movement and to enhance computational efficiency. In this work, transfer-free flexible memristors based on hexagonal boron nitride films were proposed for analog neuromorphic and digital memcomputing. Analog memristors were prepared; they exhibited synaptic behaviors, including paired-pulse facilitation and long-term potentiation/depression. The resistive switching mechanism of the analog memristors were investigated through transmission electron microscopy. Digital memristors were prepared by altering the electrode materials, and they exhibited reliable device performance, including a large on/off ratio (up to 106), reproducible switching endurance (>100 cycles), non-volatile characteristic (>60 min), and effective operating under bending conditions (>100 times).

Research on Dynamic Target Search for Multi-UAV Based on Cooperative Coevolution Motion-Encoded Particle Swarm Optimization

Effectively strategizing the trajectories of multiple Unmanned Aerial Vehicles (UAVs) within a dynamic environment to optimize the search for and tracking of mobile targets presents a formidable challenge. In this study, a cooperative coevolution motion-encoded particle swarm optimization algorithm called the CC-MPSO search algorithm is designed to tackle the moving target search issue effectively. Firstly, a Markov process-based target motion model considering the uncertainty of target motion is investigated. Secondly, Bayesian theory is used to formulate the moving target search as an optimization problem where the objective function is defined as maximizing the cumulative probability of detection of the target in finite time. Finally, the problem is solved based on the CC-MPSO algorithm to obtain the optimal search path nodes. The motion encoding mechanism converts the search path nodes into a set of motion paths, which enables more flexible handling of UAV trajectories and improves the efficiency of dynamic path planning. Meanwhile, the cooperative coevolution optimization framework enables collaboration between different UAVs to improve global search performance through multiple swarm information sharing, which helps avoid falling into local optimal solutions. The simulation results show that the CC-MPSO algorithm demonstrates efficacy, reliability, and superior overall performance when compared to the five commonly used swarm intelligence algorithms.

A novel diagnostic model for tuberculous meningitis using Bayesian latent class analysis

BackgroundDiagnosis of tuberculous meningitis (TBM) is hampered by the lack of a gold standard. Current microbiological tests lack sensitivity and clinical diagnostic approaches are subjective. We therefore built a diagnostic model that can be used before microbiological test results are known.MethodsWe included 659 individuals aged ≥16\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\ge 16$$\\end{document} years with suspected brain infections from a prospective observational study conducted in Vietnam. We fitted a logistic regression diagnostic model for TBM status, with unknown values estimated via a latent class model on three mycobacterial tests: Ziehl–Neelsen smear, Mycobacterial culture, and GeneXpert. We additionally re-evaluated mycobacterial test performance, estimated individual mycobacillary burden, and quantified the reduction in TBM risk after confirmatory tests were negative. We also fitted a simplified model and developed a scoring table for early screening. All models were compared and validated internally.ResultsParticipants with HIV, miliary TB, long symptom duration, and high cerebrospinal fluid (CSF) lymphocyte count were more likely to have TBM. HIV and higher CSF protein were associated with higher mycobacillary burden. In the simplified model, HIV infection, clinical symptoms with long duration, and clinical or radiological evidence of extra-neural TB were associated with TBM At the cutpoints based on Youden’s Index, the sensitivity and specificity in diagnosing TBM for our full and simplified models were 86.0% and 79.0%, and 88.0% and 75.0% respectively.ConclusionOur diagnostic model shows reliable performance and can be developed as a decision assistant for clinicians to detect patients at high risk of TBM.SummaryDiagnosis of tuberculous meningitis is hampered by the lack of gold standard. We developed a diagnostic model using latent class analysis, combining confirmatory test results and risk factors. Models were accurate, well-calibrated, and can support both clinical practice and research.

Long-term monthly 0.05° terrestrial evapotranspiration dataset (1982–2018) for the Tibetan Plateau

Abstract. Evapotranspiration (ET) plays a crucial role in the water balance of the Tibetan Plateau (TP), often referred to as the “Asian water tower” region. However, accurately monitoring and comprehending the spatial and temporal variations of ET components (including soil evaporation Es, canopy transpiration Ec, and intercepted water evaporation Ew) in this remote area remains a significant challenge due to the limited availability of observational data. This study generates a 37-year dataset (1982–2018) of monthly ET components for the TP using the MOD16-STM (MOD16 soil texture model). This model utilizes up-to-date soil properties, meteorological data, and remote sensing datasets. The estimated ET results strongly correlate with measurements from nine flux towers, demonstrating a low root mean square error of 13.48 mm per month, a mean bias of 2.85 mm per month, a coefficient of determination of 0.83, and an index of agreement of 0.92. The annual average ET for the entire TP, defined as elevations higher than 2500 m, is approximately 0.93(± 0.037) × 103 Gt yr−1. The predominant contributor to ET on the TP is Es, accounting for 84 % of the total ET. Our findings reveal a noteworthy upward trend in ET in most central and eastern parts of the TP, with a rate of approximately 1–4 mm yr−1 (p<0.05) and a significant downward trend with rates between −3 and 1 mm yr−1 in the northwestern part of TP during the period from 1982 to 2018. The average annual increase in ET for the entire TP over the past 37 years is approximately 0.96 mm yr−1. This upward trend can be attributed to the TP's warming and wetting climate conditions. The MOD16-STM ET dataset demonstrates a reliable performance across the TP compared to previous research outcomes. This dataset is valuable for research on water resource management, drought monitoring, and ecological studies. The entire dataset is freely accessible through the Science Data Bank (https://doi.org/10.11922/sciencedb.00020, Ma et al., 2021) and the National Tibetan Plateau Data Center (TPDC) (https://doi.org/10.11888/Terre.tpdc.271913, Yuan, 2021).

An efficient ELISA protocol for measurement of SARS-CoV-2 spike-specific IgG in human plasma and serum samples

Here, we describe a protocol for the detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike-specific immunoglobulin G (IgG) by enzyme-linked immunosorbent assay (ELISA). The protocol was developed with a keen focus on optimizing several key parameters, including antigen coating concentration, antibody and sample dilutions, and assay development time. The final protocol features the following characteristics:•The capability to detect SARS-CoV-2 spike-specific IgG in both plasma and serum samples.•A streamlined procedure that requires only 1 hour and 20 minutes of hands-on time.•Reliable assay performance, with a remarkable sensitivity of 98.1 % and specificity of 99.5 %.

Skin Disease Detection Using Deep Learning

Skin diseases are a major public health problem worldwide, requiring effective and timely diagnosis for effective treatment. In this paper, we present a new approach to automatically detect skin diseases using deep learning technology. The model we propose uses a Convolutional Neural Network (CNN) to analyze dermatological images with high accuracy, providing reliable and fast diagnosis. The system was trained on a variety of datasets to provide reliable performance across a variety of skin conditions. Experimental results show that the proposed model outperforms existing methods, demonstrating its potential for integration into clinical settings. Implementation of this deep learning-based skin disease detection system has the potential to revolutionize dermatological diagnostics and provide a cost-effective and scalable solution to improve patient care.

Throwing it out there: Grip on multidimensional performance characteristics of judoka – a systematic review

Judo is a dynamic and complex sport, and therefore talent programs may benefit from a multidimensional approach to identify clear and reliable performance characteristics in order to monitor and train athletes. However, from both a practical and theoretical point of view, understanding on this topic is limited. This systematic review therefore aimed to (1) identify which multidimensional performance characteristics can discriminate between different performance levels of judoka and (2) find the gaps in literature. To categorize the findings the Groningen Sport Talent Model (GSTM) was applied. A systematic search of MEDLINE, PsycINFO, PubMed, and Web of Science was performed following the PRISMA guidelines. In total 23 studies with good to excellent methodological quality were included. All performance characteristics of the GSTM (anthropometrical, physiological, technical, tactical, and psychological) were represented in the included studies. There is strong evidence to suggest that a broad set of physiological characteristics is needed to manage the demands of judo combats. Additionally, there is strong evidence that performance characteristics related to grip fighting discriminate between judoka of different performance levels and higher performance level judoka are characterized by the ability to throw their opponent and to variate in technique. The findings of this review can be used as a basis for talent development and identification in judo, and to optimize training programs. Future research should focus on increasing the understanding of psychological characteristics of judoka and the multidimensional talent trajectory by conducting longitudinal studies.

Epigenetic-based age prediction in blood samples: Model development.

Aging is a complex process influenced by genetic, epigenetic, and environmental factors that lead to tissue deterioration and frailty. Epigenetic mechanisms, such as DNA methylation, play a significant role in gene expression regulation and aging. This study presents a new age estimation model developed for the Turkish population using blood samples. Eight CpG sites in loci TOM1L1, ELOVL2, ASPA, FHL2, C1orf132, CCDC102B, cg07082267, and RASSF5 were selected based on their correlation with age. Methylation patterns of these sites were analyzed in blood samples from 100 volunteers, grouped into age categories (20-35, 36-55, and ≥56). Sensitivity analysis indicated a reliable performance with DNA inputs ≥1 ng. Statistical modeling, utilizing Multiple Linear Regression, underscores the reliability of the primary 6-CpG model, excluding cg07082267 and TOM1L1. This model demonstrates strong correlations with chronological age (r = 0.941) and explains 88% of the age variance with low error rates (MAE = 4.07, RMSE = 5.73 years). Validation procedures, including a training-test split and fivefold cross-validation, consistently confirm the model's accuracy and consistency. The study indicates minimal variation in error scores across age cohorts and no significant gender differences. The developed model showed strong predictive accuracy, with the ability to estimate age within certain prediction intervals. This study contributes to the age prediction by using DNA methylation patterns, which can have disparate applications, including forensic and clinical assessments.

A Reliable and high performance Radiation Hardened Schmitt Trigger 12T SRAM cell for space applications

In the vacuum of space, alpha particles and cosmic radiation can cause the node data to be altered, leading to data loss. When a radiation particle hits a vulnerable point of the typical 6T static random access memory (SRAM) cell, it results in the alteration of the stored data within the cell, leading to a single-event upset (SEU). Traditional 6T SRAM cannot withstand the extreme conditions present in space. Hence, it is imperative to develop an SRAM that can endure these challenging space conditions. To address the impact of SEUs, a Radiation Hardened Schmitt Trigger 12 transistor (RHST12T) SRAM cell is proposed in this paper. To gauge the comparative effectiveness of the proposed cell, it is compared with other radiation hardened SRAM cells, namely, QUCCE12T, WE-QUATRO, RHPD12T, and SRRD12T. Even if a radiation strike flips the node values, all of RHST12T sensitive nodes can recover their data. It shows up to 1.6×/45.32×/0.29 ×106× less read delay, write delay, and leakage power consumption, respectively as compared to other considered SRAM cells. It achieves up to 5.75×/3.09× better read and hold stability, respectively than the considered SRAM cells.

Performance Analysis of BGP Dynamic Routing Protocol using QOS for TCP and UDP Services

The Border Gateway Protocol (BGP) is commonly used for TCP and UDP services, but it poses challenges in terms of Quality of Service (QoS) analysis. Parameters like throughput, packet loss, delay, and jitter are crucial for assessing network service quality. This study aims to analyze the performance and influence of the BGP routing protocol on TCP and UDP services using QoS parameters. The research used a GNS3 network simulation to conduct multiple packet transmission tests for TCP and UDP protocols, lasting 15, 30, and 60 seconds; and monitored using Wireshark. For TCP services, the average QoS index value is 3.75, categorizing the quality as "Good". The tested network topology and routing configuration exhibit reliable performance, providing good throughput, low packet loss rates, minimal delays, and stable jitter. Similarly, UDP services demonstrate “Good” performance with an average QoS index of 3.75. The BGP routing protocol in the tested network topology ensures high-quality service with good delivery speed, low packet loss rate, minimal delay, and stable jitter. Overall, the study concludes that the BGP routing protocol effectively provides satisfactory QoS for TCP and UDP services. This research contributes to understanding network performance and optimizing routing protocols for improved telecommunications services. The findings highlight the significance of routing protocols in facilitating efficient data transmission on the Internet, reinforcing the importance of QoS analysis for enhancing service quality.

3D‐Printed Polyetheretherketone Smart Polymer Nanocomposite Scaffolds: Mechanical, Self‐Sensing, and Biological Attributes

This study demonstrates the mechanical, self‐sensing, and biological characteristics of carbon nanotubes (CNTs) and graphene nanoplatelets (GNPs)‐engineered 3D‐printed polyetheretherketone (PEEK) composite scaffolds, utilizing custom‐made feedstocks. Microstructural analysis and macroscale testing reveal that the PEEK/CNT scaffolds with 6 wt% CNT content and 46% relative density achieve a gauge factor of up to 75, a modulus of 0.64 GPa, and a compressive strength of 64 MPa. The PEEK/CNT2.5/GNP2.5 scaffolds evince still better performance, at a relative density of 73%, reporting a modulus of up to 1.1 GPa and a compressive strength of 122 MPa. Importantly, stability in mechanical and piezoresistive performance up to 500 cycles is noted, indicating a durable and reliable performance under cyclic loading. Murine preosteoblast cells (MC3T3‐E1) are used to biologically characterize sulfonated scaffolds over 14 days. Cytotoxicity, DNA, and alkaline phosphatase (ALP) levels are quantified through in vitro assays, evaluating cell viability, proliferation, and osteogenic properties. Notably, PEEK/CNT 6 wt% scaffolds exhibit nearly 80% cytocompatibility, while PEEK/CNT2.5/GNP2.5 scaffolds reach nearly 100%. Both types of scaffolds support cell differentiation, as evidenced by elevated ALP levels. These findings carry significant promise in bone tissue engineering, paving the way for the development of adaptive, intelligent structural implants boasting enhanced biocompatibility and self‐sensing capabilities.

Quality of services in software defined networking: challenges and controller placement problems

<span>Quality of service (QoS) is pivotal for ensuring effective and reliable network performance, yet achieving end-to-end QoS within current network architectures remains a persistent challenge. The emergence of software defined networking (SDN) addresses limitations in traditional networking by offering a centralized control plane. This allows dynamic resource management and efficient enforcement of QoS policies by network administrators. However, the controller placement problem (CPP) within SDN poses a significant challenge, as identifying the optimal placement of controllers is a non-deterministic polynomial-time hardness (NP-hard) problem. Researchers are actively working on solutions to address this challenge, especially in large-scale networks where deploying controllers becomes complex. Additionally, maintaining QoS in terms of controller management presents another hurdle. This paper explores these challenges, delving into the literature and providing a comprehensive analysis of controller performance metrics related to QoS parameters such as load balancing, reliability, consistency, and scalability. By addressing these challenges, the research aims to enhance QoS within the SDN framework.</span>

Dynamic changes in AI-based analysis of endometrial cellular composition: Analysis of PCOS and RIF endometrium

BackgroundThe human endometrium undergoes a monthly cycle of tissue growth and degeneration. During the mid-secretory phase, the endometrium establishes an optimal niche for embryo implantation by regulating cellular composition (e.g., epithelial and stromal cells) and differentiation. Impaired endometrial development observed in conditions such as polycystic ovary syndrome (PCOS) and recurrent implantation failure (RIF) contributes to infertility. Surprisingly, despite the importance of the endometrial lining properly developing prior to pregnancy, precise measures of endometrial cellular composition in these two infertility-associated conditions are entirely lacking. Additionally, current methods for measuring the epithelial and stromal area have limitations, including intra- and inter-observer variability and efficiency. MethodsWe utilized a deep-learning artificial intelligence (AI) model, created on a cloud-based platform and developed in our previous study. The AI model underwent training to segment both areas populated by epithelial and stromal endometrial cells. During the training step, a total of 28.36 mm2 areas were annotated, comprising 2.56 mm2 of epithelium and 24.87 mm2 of stroma. Two experienced pathologists validated the performance of the AI model. 73 endometrial samples from healthy control women were included in the sample set to establish cycle phase-dependent dynamics of the endometrial epithelial-to-stroma ratio from the proliferative (PE) to secretory (SE) phases. In addition, 91 samples from PCOS cases, accounting for the presence or absence of ovulation and representing all menstrual cycle phases, and 29 samples from RIF patients on day 5 after progesterone administration in the hormone replacement treatment cycle were also included and analyzed in terms of cellular composition. ResultsOur AI model exhibited reliable and reproducible performance in delineating epithelial and stromal compartments, achieving an accuracy of 92.40% and 99.23%, respectively. Moreover, the performance of the AI model was comparable to the pathologists’ assessment, with F1 scores exceeding 82% for the epithelium and >96% for the stroma. Next, we compared the endometrial epithelial-to-stromal ratio during the menstrual cycle in women with PCOS and in relation to endometrial receptivity status in RIF patients. The ovulatory PCOS endometrium exhibited epithelial cell proportions similar to those of control and healthy women’s samples in every cycle phase, from the PE to the late SE, correlating with progesterone levels (control SE, r2 = 0.64, FDR < 0.001; PCOS SE, r2 = 0.52, FDR < 0.001). The mid-SE endometrium showed the highest epithelial percentage compared to both the early and late SE endometrium in both healthy women and PCOS patients. Anovulatory PCOS cases showed epithelial cellular fractions comparable to those of PCOS cases in the PE (Anovulatory, 14.54%; PCOS PE, 15.56%, p = 1.00). We did not observe significant differences in the epithelial-to-stroma ratio in the hormone-induced endometrium in RIF patients with different receptivity statuses. ConclusionThe AI model rapidly and accurately identifies endometrial histology features by calculating areas occupied by epithelial and stromal cells. The AI model demonstrates changes in epithelial cellular proportions according to the menstrual cycle phase and reveals no changes in epithelial cellular proportions based on PCOS and RIF conditions. In conclusion, the AI model can potentially improve endometrial histology assessment by accelerating the analysis of the cellular composition of the tissue and by ensuring maximal objectivity for research and clinical purposes.

Dramatic impact of raw chemicals on the electrical properties of SrTiO3 ceramics

The dielectric loss (tan δ) value of stoichiometric SrTiO3 (ST), prepared using the solid-state reaction method, exhibits a random variation between 0.0006 and 0.01968 at room temperature and a frequency of 1 kHz in the literature. This variability makes it challenging to obtain reliable and reproducible performance for ST-based devices, especially when the underlying reasons are not yet completely understood. This work reports the significant effects of different raw materials, with various forms of TiO2, on the dielectric and electrical properties of ST ceramics. The results show that the purity of SrCO3 and TiO2 ultimately leads to distinct differences in dielectric properties, impedance properties, activation energy across the ST ceramics. Crystal comparison demonstrates that anatase TiO2 is more suitable for dielectric applications. These results provide a reasonable explanation for the large variations in reported ST properties in the literature, which emphasizes the critical importance of selecting suitable reagents in ST-based ceramics. This insight opens up a new avenue for design optimization and highlights the promising potential of ST-based ceramics.

Segmentation of Wheat Lodging Areas from UAV Imagery Using an Ultra-Lightweight Network

Crop lodging is an important cause of direct economic losses and secondary disease transmission in agricultural production. Most existing methods for segmenting wheat lodging areas use a large-volume network, which poses great difficulties for annotation and crop monitoring in real time. Therefore, an ultra-lightweight model, Lodging-U2NetP (L-U2NetP), based on a novel annotation strategy which crops the images before annotating them (Crop-annotation), was proposed and applied to RGB images of wheat captured with an unmanned aerial vehicle (UAV) at a height of 30 m during the maturity stage. In the L-U2NetP, the Dual Cross-Attention (DCA) module was firstly introduced into each small U-structure effectively to address semantic gaps. Then, Crisscross Attention (CCA) was used to replace several bulky modules for a stronger feature extraction ability. Finally, the model was compared with several classic networks. The results showed that the L-U2NetP yielded an accuracy, F1 score, and IoU (Intersection over Union) for segmenting of 95.45%, 93.11%, 89.15% and 89.72%, 79.95%, 70.24% on the simple and difficult sub-sets of the dataset (CA set) obtained using the Crop-annotation strategy, respectively. Additionally, the L-U2NetP also demonstrated strong robustness in the real-time detection simulations and the dataset (AC set) obtained using the mainstream annotation strategy, which annotates images before cropping (Annotation-crop). The results indicated that L-U2NetP could effectively extract wheat lodging and the Crop-annotation strategy provided a reliable performance which is comparable with that of the mainstream one.