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(1) Background: Marker-based 3D motion capture systems (MBS) are considered the gold standard in gait analysis. However, they have limitations for which markerless camera-based 3D motion capture systems (MCBS) could provide a solution. The aim of this systematic review and meta-analysis is to compare the accuracy, validity, and reliability of MCBS and MBS. (2) Methods: A total of 2047 papers were systematically searched according to PRISMA guidelines on 7 February 2024, in two different databases: Pubmed (1339) and WoS (708). The COSMIN-tool and EBRO guidelines were used to assess risk of bias and level of evidence. (3) Results: After full text screening, 22 papers were included. Spatiotemporal parameters showed overall good to excellent accuracy, validity, and reliability. For kinematic variables, hip and knee showed moderate to excellent agreement between the systems, while for the ankle joint, poor concurrent validity and reliability were measured. The accuracy and concurrent validity of walking speed were considered excellent in all cases, with only a small bias. The meta-analysis of the inter-rater reliability and concurrent validity of walking speed, step time, and step length resulted in a good-to-excellent intraclass correlation coefficient (ICC) (0.81; 0.98). (4) Discussion and conclusions: MCBS are comparable in terms of accuracy, concurrent validity, and reliability to MBS in spatiotemporal parameters. Additionally, kinematic parameters for hip and knee in the sagittal plane are considered most valid and reliable but lack valid and accurate measurement outcomes in transverse and frontal planes. Customization and standardization of methodological procedures are necessary for future research to adequately compare protocols in clinical settings, with more attention to patient populations. [ABSTRACT FROM AUTHOR]

This paper explores the benefits and pitfalls associated with integrating a common blockchain model within a distinct administrative environment, focusing particularly on its effects on decision-making processes. The study is grounded in the doctrine of procedural due process, a holistic concept encompassing intertwined administrative rules and principles, designed to promote fairness and justice in decision-making, which can be succinctly distilled into three core principles: accuracy, integrity, and transparency. On initial examination, these principles seem to correspond with the fundamental characteristics of blockchain technology, which are identified as authenticity, integrity, and transparency. In our analysis, we measure the extent to which the attributes of blockchain technology align with the principles of the due process doctrine. An essential component of this assessment includes a meticulous examination of the practical merging of each characteristic within the decision-making procedure. The approach and practical applications explored in this study highlight the potential of blockchain technology to enhance adherence to due process, especially in areas where traditional trust-based systems have faltered in maintaining basic procedural safeguards. However, given the intricate complexity tied to its implementation, this technology should be regarded as a fallback option, strictly reserved for situations where the benefits of its utilisation unmistakably outweigh the numerous inherent risks. In the ensuing sections of this paper, we will delve deeper into these risks and propose actionable mitigation strategies. [ABSTRACT FROM AUTHOR]

The research in this paper is related to the accuracy of the tilt compensation function of the GNSS/IMU receivers, which were examined in an open sky environment. The purpose of the paper is to point out to geodesists the conditions and limitations of using GNSS/IMU technology in precise measurements to not jeopardize the coordinate's accuracy. The environment in which the measurement is made affects the quality of the GNSS signal and can limit the visibility of the satellite, leading to larger errors in the measurement. In this experiment, the current performance of the GNSS/IMU receivers was checked. Seven GNSS/IMU receivers were used for the realization of the experiment. For six receivers the compensation angle was α = 30°, while for one receiver, the compensation angle was α = 45°. The standard uncertainty of GNSS coordinates of the antenna phase center has values less than 9 mm. The standard uncertainty of the IMU component has values less than 31 mm. The measurement uncertainty of the position of the used GNSS receivers is in the range of 18.1 mm to 31.7 mm. The limit values for the differences along the coordinate axes x and y were determined, and their values are from 26 mm to 44 mm. In the conducted experiment, it was confirmed that three GNSS/IMU receivers have a "Satisfactory" result. The results show that GNSS/IMU measurements with a slope greater than 30° significantly affect the accuracy and reliability of GNSS/IMU technology. A slope greater than 45° has a deviation along the coordinate axes of 121.3 mm. The conducted research is particularly important for geodetic works that require high positioning performance. The testing method of the GNSS/IMU receiver presented in this paper can help its users to make correct conclusions regarding the coordinate accuracy of the measured point of interest. [ABSTRACT FROM AUTHOR]

The limitations of Content Based Video Retrieval (CBVR), such as large pools of video data, selection of features, limited processing capacity, and content-related issues, can be overcome by Deep Belief Neural Networks (DBNs). The search engine does the processing, and the results are effectively returned to the users. The deep learning model also has some comparable challenges to be solved at various levels. Typically, the selection of frames is one of the most essential tasks for a face retrieval system which is efficiently accomplished by using deep learning models. Also, the performance of recognition models highly depends on the selection of frames that include both low-level and high-level features from the given video sequence. This paper proposes ALPHA-TO-OMEGA (ALMEGA-VIR) Visual Information Retrieval scheme that analyzes the contents of the video file for a particular face and retrieves the user's relevant videos. VIR aims to solve the challenge of locating relevant pictures and videos based on a query. VIR can be based on text-based search, content-based search, and Mixed based search. VIR here focuses on content-based search approach. The YouTube Celebrities (YTC) dataset, the Youtube Face (YTF) dataset, and Honda / UCSD dataset are used in this work. The used dataset addresses issues such as illumination changes, occlusion, rotation, and scale. We propose an ALMEGA-VIR framework for visual novelty face identification for specific investigation and evaluation. The classification accuracy of the proposed ALMEGA-VIR model shows that 4% is more effective than traditional classifier algorithms such as SVM, Naïve Bayesian classifier, MLP and Random Forest classifier. A comparative study is also implemented with other existing works to check the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

The semiconductor industry is the core industry of the information age. As a key link in the semiconductor industry, wafer fabrication plays a key role in its development. In the testing stage of the wafer, each die of the wafer is detected and marked, and a wafer map with a certain spatial pattern can be formed. The analysis and classification of these spatial patterns can identify the cause of wafer defects, thereby improving production yield. However, as wafer size increases, line widths become smaller, etc., the probability of a mixed defect mode wafer pattern increases. Moreover, the mixed defect mode wafer map is more difficult to identify and classify than the single defect mode wafer map. Therefore, this paper proposes an improved deep convolutional neural network (DCNN) structure model for the recognition and classification of mixed defect pattern wafer maps. From the perspective of increasing the width of the DCNN, the improved network structure can avoid problems such as over-fitting and limited extraction of features due to the continuous deepening of the DCNN. The network is called Multi-Path DCNN (MP-DCNN) structure. The experimental results show that the proposed Multi-Path DCNN structure has better performance and higher classification accuracy than existing methods. [ABSTRACT FROM AUTHOR]

Chemical mechanical planarization (CMP) is vital for ensuring chip fabrication uniformity at nanometer scales. The emergence of a series of phenomenological CMP process models (Stine et al., 1997; Gbondo-Tugbawa, 2002; Xie, 2007; Vasilev, 2011) suggests that the existing model upgrade approach is largely based on a change in phenomenological model assumptions, demanding deep insights into complex process mechanisms and protracted period for accuracy improvements. To tackle this issue, this paper proposes a decoupling regression strategy for model upgrades. This strategy employs a data-driven approach to enhance the coupling relationships within the model, facilitating continuous improvement of simulation accuracy based on the existing model. It is capable of achieving improvements in model accuracy even in scenarios where modelers lack insight into complex process mechanisms. We validate our method by upgrading the Density Step Height (DSH) model to the Extend-DSH model to address poor erosion predictions at the 28nm node. Comparing model predictions with silicon data reveals that the Extend-DSH model aligns better with the measured data, reducing the root mean square error from 159.31Å to 6.89Å and increasing the coefficient of determination from -0.83561 to 0.6058, showcasing the effectiveness of the proposed chip-level CMP model upgrade method grounded in the decoupling regression strategy. [ABSTRACT FROM AUTHOR]

Smart manufacturing optimizes processes and marketing transactions, leading to more efficient manufacturing processes. Computational modeling is used to execute the improvement, which requires trustworthy and meaningful information as input. This paper uses a vision sensor for capturing accurate data. Hybrid techniques for vision-based classification and feature extraction methods were used. The ORB (Oriented FAST and Rotated BRIEF) and BRISK (Binary Robust Invariant Scalable Keypoints) feature recognition and characterization processes were chosen based on evaluating several feature recognition and characterization processes when applied to acoustic information recorded by an Apple dataset. After the feature extraction, a Deep CNN (DCNN) method is used for the classification process to achieve efficient accuracy. The vision-based algorithms classify the apple as damaged or not damaged, allowing for automated examination, classification, and analysis. To test the classifiers, 600 apple photos were obtained. There are 300 non-faulty apples and 300 damaged apples in the photographs. The faulty group encompasses a wide range of defective kinds and severities. The effectiveness of twofold, threefold, fourfold, fivefold, and tenfold detectors is evaluated. The Softmax classifiers were used in the study, achieving 98.84% classification accuracy for a tenfold dataset. [ABSTRACT FROM AUTHOR]

Machine learning-based prediction of the output characteristics of a negative capacitance (NC) double gate (DG) Tunnel FET (TFET) (NC DG TFET) has been demonstrated in this paper. On current (ION), OFF current (IOFF) and sub-threshold slope (SS) have been predicted for different values of the body thickness (TSi), oxide thickness (TOX) and channel length (LCh) and the work function (WF). Random Forest Regression (RFR) model is used in the ML framework to explore the device characteristics. A total of 5444 simulated data has been generated by using the SILVACO ATLAS tool. After pre-processing the data, RFR-ML has been applied to obtain maximum accuracy. Root means square error (RMSE) (~0.34), R2 score (~0.965) and accuracy (~98.51%) parameter have been used as the figures of merit (FOM) to judge the efficiency of the proposed model with respect to the state-of-the-art literature. Besides, with higher accuracy, the model is capable of predicting the data within a few seconds compared to the few hundred hours using TCAD tools. This fast and accurate design strategy will surely be efficacious for the present VLSI industries. [ABSTRACT FROM AUTHOR]

In the paper a new, fractional order, discrete model of a two-dimensional temperature field is addressed. The proposed model uses Grünwald-Letnikov definition of the fractional operator. Such a model has not been proposed yet. Elementary properties of the model: practical stability, accuracy and convergence are analysed. Analytical conditions of stability and convergence are proposed and they allow us to estimate the orders of the model. Theoretical considerations are validated using experimental data obtained with the use of a thermal imaging camera. Results of analysis supported by experiments point that the proposed model assures good accuracy and convergence for low order and relatively short memory length. [ABSTRACT FROM AUTHOR]

Ekman and Friesen's (1969) seminal theoretical paper on the leakage hierarchy sparked decades of research on the relationship between nonverbal cues and deception. Yet skepticism over the strength and reliability of behavioral cues to deception has been building over the years (DePaulo et al., 2003; Patterson et al., 2023; Vrij et al., 2019). However, the last two decades have seen dramatic growth in research paradigms, interviewing techniques, integration of technology, automated coding methods, and facial research, suggesting a need for reexamination of the current state of the field. This special issue includes theoretical and empirical papers that advance our understanding of the link between nonverbal cues and deception. This collection of papers suggests there is cause for some optimism in the field of nonverbal deception detection and signals some fruitful avenues for future research. Specifically, deception research in ecologically valid, high-stakes lie-detection situations using a multi-modal approach has good promise for differentiating truth-tellers from liars. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to verify whether the management of taxable income through aggressive tax practices negatively impacts the accuracy of market analysts' forecasts. Originality/value: The central contribution of this study is to relate tax aggressiveness with more significant errors in analysts' forecasts. Thus, the relevance of this paper is in showing that this practice can lead to a greater perception of companies' risks and more significant informational asymmetry, in addition to influencing stock prices and resulting in lower analyst accuracy. Design/methodology/approach: This research paper was developed from regression models with panel data with random effects, also using estimators based on instrumental variables with the application of Jensen's test to verify the validity of the generalized method of moments (GMM) estimation. The data were treated with the winsorization technique at the 1% level. Findings: A sample of companies listed on the B3 between 2010 and 2017 (with a total of 2,805 valid observations) showed that companies with higher tax aggressiveness are subject to a higher analyst forecast error. Thus, it is inferred that, for more aggressive companies, the degree of analyst predictability decreases. With this drop in the quality of analysts' forecasts, the users of their forecasts and reports are more vulnerable to informational asymmetry. [ABSTRACT FROM AUTHOR]

Simple Summary: This paper delves into the latest developments in the emerging therapies for cholangiocarcinoma, with a focus on precision medicine, immunotherapy, and targeted therapies. We explore the potential of genomic profiling to tailor treatments to individual patients, as well as the promising results of immunotherapeutic agents in clinical trials. Furthermore, we examine the role of specific molecular targets and their inhibitors in slowing disease progression. The findings discussed in this paper offer hope for improved outcomes and prolonged survival in cholangiocarcinoma patients using more effective and personalized treatment strategies. Cholangiocarcinoma is a heterogeneous group of biliary tract cancers that has a poor prognosis and globally increasing incidence and mortality. While surgical resection remains the only curative option for the treatment of cholangiocarcinoma, the majority of cancers are unresectable at the time of diagnosis. Additionally, the prognosis of cholangiocarcinoma remains poor even with the current first-line systemic therapy regimens, highlighting the difficulty of treating locally advanced, metastatic, or unresectable cholangiocarcinoma. Through recent developments, targetable oncogenic driver mutations have been identified in the pathogenesis of cholangiocarcinoma, leading to the utilization of molecular targeted therapeutics. In this review, we comprehensively discuss the latest molecular therapeutics for the treatment of cholangiocarcinoma, including emerging immunotherapies, highlighting promising developments and strategies. [ABSTRACT FROM AUTHOR]

In this growing world of the internet, most of our daily routine tasks are somehow connected to the internet, from smartphones to internet of things (IoT) devices to cloud networks. Internet users are growing rapidly, and the internet is accessible to everyone from anywhere. Data phishing is a cyber security attack that uses deception to trick internet users to get their content and information. In this attack, malicious users try to steal personal data such as login credentials, credit card details, health care information, etc., of the users on the internet. They exploit users' sensitive information using vulnerabilities. Information stealers are known as phishers. Phishers use different techniques for phishing. One of the most common methods is to direct the users to a false website to enter their login credentials and their details on these phishing sites. Phishing websites look like the original websites. Phishers use these details to get access to the user's accounts and hijack them for monetary purposes. Many internet users fall for this trap of phishing sites and share their personal and sensitive details. In this paper, we will analyze and implement machine learning (ML) techniques to detect phishing attacks. There are different methods to identify phishing attacks, one of them is by checking the uniform resource locator (URL) address using ML. ML is used to teach a machine to differentiate between phishing and original site URLs. There are many different techniques to overcome this attack. This research paper aims to provide accurate and true phishing detection with less time complexity. [ABSTRACT FROM AUTHOR]

For users of Precise Point Positioning (PPP), Multi-GNSS Advanced Orbit and Clock Augmentation PPP signals provide corrective data. When using the PPP approach and/or PPP-Ambiguity Resolution (AR) method, the QZSS signal provides globally applicable error corrections on satellite orbit, clock offset, and code/phase biases. In addition, from FY2024, as a part of the MADOCA-PPP technology demonstration, wide-area ionospheric correction data will be provided for the Asia-Oceania region. A software estimator of precise satellite information developed by JAXA, Multi-GNSS Advanced Demonstration Tool for Orbit and Clock Analysis (MADOCA), allows u-blox CO99-ZED-F9P and MSJ 3008-GM4-QZS utilizing MADOCA-PPP to be used in GNSS applications that need sub-decimetre precision but don't have to be expensive. Errors caused by positioning satellites are computed by using observation data from domestic and overseas GNSS monitoring station networks such as IGS and MIRAI, and obtained correction data is transmitted from QZSS signal to provide highly precise positioning augmentation services that can be used in the Asia-Oceania Region. Users may utilize the PPP technique for high-precision locating by employing a GNSS receiver that supports the QZSS signals. This paper describes an experiment carried out with the static method to combine GPS, GLONASS, and QZSS signals in the project site (ISHI, USUD and MIZU stations in Japan). This paper examines the GPS/GLONASS/QZSS obtainable accuracy. These obtained results indicate that integrating GPS system with GLONASS and QZSS is favoured for surveying applications. It appears that integrating GPS/GLONASS/QZSS (MADOCA precise ephemeris file) static measurements in the study area between 0-4 millimetres accuracy can be guaranteed on all occasions. [ABSTRACT FROM AUTHOR]

Objective: Oral metronomic cyclophosphamide has been used as a single agent or in combination with other drugs for several solid tumors with interesting results in disease palliation and mild to moderate toxicity, notably in patients with recurrent epithelial ovarian cancer (EOC) progressing after systemic chemotherapy. In this paper, we report a review and a metanalysis of heterogeneous data published up to date. Data sources: The literature search was restricted to single-agent MOC. The analysis was conducted through March 2023 by consulting PubMed, Embase, Google Scholar, and The Cochrane Library databases. Research string and Medical Subject Headings included "ovarian tumor," "ovarian carcinoma," or "ovarian cancer," "fallopian tube cancer," "primary peritoneal cancer," "oral chemotherapy," and "metronomic cyclophosphamide." All articles were assessed for quality by at least two investigators independently, and a < 18 patients sample size cutoff was chosen as a lower limit with a Cohen's kappa statistical coefficient for accuracy and reliability. Metanalysis of selected papers was carried out according to a fixed model. Data summary: The percentage of agreement between investigators on literature study selection was very high, reaching 96.9% with a Cohen's k of 0.929. MOC pooled objective response rate (ORR) and disease control rate for recurrent or platinum-refractory ovarian cancer were 18.8% (range 4–44%) and 36.2% (range 16–58.8%), respectively. The mean progressive-free survival and overall survival were 3.16 months (range 1.9 to 5.0 months) and 8.7 months (range 8 to 13 months), respectively. The fixed model metanalysis of selected studies showed a 16% median ORR (12–20% CI, p < 0.001). Conclusions: Single-agent oral cyclophosphamide in EOC holds promise as a treatment option, even in the era of precision medicine. Genetic factors, such as DNA repair gene polymorphisms, may influence treatment response. Combining cyclophosphamide with biological agents such as PARP inhibitors or immunotherapy agents is an area of active investigation. [ABSTRACT FROM AUTHOR]

Financial statement analysis is a critical component of decision-making for businesses, investors, and financial professionals. To enhance the accuracy and effectiveness of such analysis, this paper introduces the application of an innovative approach known as the Intelligent Swarm Regression ARIMA Model. This advanced model combines the power of swarm intelligence with ARIMA (AutoRegressive Integrated Moving Average) time series forecasting, offering a robust methodology for predicting and analyzing key financial metrics. The study begins by providing an overview of the Intelligent Swarm Regression ARIMA model and its application to financial data. Through a comprehensive analysis of financial statements, including market capitalization, revenue, net income, and other crucial indicators, the model's efficacy in predicting future values is evaluated. Additionally, the paper examines the deviations between predicted and actual financial values, offering insights into the model's accuracy and areas for potential improvement. The findings of this research are invaluable for investors, financial analysts, and companies seeking to optimize their financial performance and strategic decision-making. By leveraging the Intelligent Swarm Regression ARIMA Model, stakeholders can make well-informed choices that lead to better financial outcomes and a competitive advantage in a dynamic economic landscape. This paper represents a significant step forward in the financial analysis, providing a practical methodology and a pathway to enhanced financial decision-making. As the importance of financial data continues to grow, this research offers a promising avenue for achieving financial success and stability. [ABSTRACT FROM AUTHOR]

Accurate and reliable mathematical modeling is essential for the optimal control and performance analysis of polymer electrolyte membrane fuel cell (PEMFC) systems, which are mainly implemented based on accurate parameter estimation. In this paper, a multi-strategy tuna swarm optimization (MS-TSO) is proposed to estimate the parameters of PEMFC voltage models and compare them with other optimizers such as differential evolution, the whale optimization approach, the salp swarm algorithm, particle swarm optimization, Harris hawk optimization and the slime mould algorithm. In the optimizing routine, the unidentified factors of the PEMFCs are used as the decision variables, which are optimized to minimize the sum of square errors between the estimated and measured data. The optimizers are examined based on three PEMFC datasets including BCS500W, NedStackPS6 and harizon500W as well as a set of experimental data which are measured using the Greenlight G20 platform with a 25 cm2 single cell at 353 K. It is confirmed that MS-TSO gives better performance in terms of convergence speed and accuracy than the competing algorithms. Furthermore, the results achieved by MS-TSO are compared with other reported approaches in the literature. The advantages of MS-TSO in ascertaining the optimum factors of various PEMFCs have been comprehensively demonstrated. [ABSTRACT FROM AUTHOR]

The rapid development of artificial intelligence applications based on machine learning is creating not only many opportunities but also risks. The recent regulatory and political debate, at the international level, emphasizes the urgent need to develop appropriate statistical methods that can measure the safety and the risks of AI applications. In line with this emerging need, the aim of this work is to launch a call for discussion papers in the Statistics journal on safe machine learning methods. To this aim, we define which metrics to develop and an example of a recent proposal, along with its advantages and disadvantages [ABSTRACT FROM AUTHOR]

Achieving a balance between accuracy and efficiency in target detection applications is an important research topic. To detect abnormal targets on power transmission lines at the power edge, this paper proposes an effective method for reducing the data bit width of the network for floating-point quantization. By performing exponent prealignment and mantissa shifting operations, this method avoids the frequent alignment operations of standard floating-point data, thereby further reducing the exponent and mantissa bit width input into the training process. This enables training low-data-bit width models with low hardware-resource consumption while maintaining accuracy. Experimental tests were conducted on a dataset of real-world images of abnormal targets on transmission lines. The results indicate that while maintaining accuracy at a basic level, the proposed method can significantly reduce the data bit width compared with single-precision data. This suggests that the proposed method has a marked ability to enhance the real-time detection of abnormal targets in transmission circuits. Furthermore, a qualitative analysis indicated that the proposed quantization method is particularly suitable for hardware architectures that integrate storage and computation and exhibit good transferability. [ABSTRACT FROM AUTHOR]

To address the issue of the lack of specialized data filtering algorithms for dataset production, we proposed an image filtering algorithm. Using feature fusion methods to improve discrete wavelet transform algorithm (DWT) and enhance the robustness of image feature extraction, a weighted hash algorithm was proposed to hash features to reduce the complexity and computational cost of feature comparison. To minimize the time cost of image filtering as much as possible, a fast distance calculation method was also proposed to calculate the similarity of images. The experimental results showed that compared with other advanced methods, the algorithm proposed in this paper had an average accuracy improvement of 3% and a speed improvement of at least 30%. Compared with traditional manual filtering methods, while ensuring accuracy, the filtering speed of a single image is increased from 9.9s to 0.01s, which has important application value for dataset production. [ABSTRACT FROM AUTHOR]

Utilizing broadband low Earth orbit satellite signals in an opportunistic manner for navigation is becoming increasingly popular. This paper deals with a particularly useful approach for navigation based on satellite signals of opportunity, which uses carrier Doppler-shift observables. We provide analytically derived and simplified formulas for the Jacobian involved in the numerical computation of the navigation solution and derive a global navigation satellite system-like dilution-of-precision metric that can be used to assess accuracy. A numerical study provides preliminary computational results. [ABSTRACT FROM AUTHOR]

Distribution-to-distribution point cloud registration algorithms are fast and interpretable and perform well in unstructured environments. Unfortunately, existing strategies for predicting the solution error for these methods are overly optimistic, particularly in regions containing large or extended physical objects. In this paper, we introduce the iterative closest ellipsoidal transform (ICET), a novel three-dimensional (3D) lidar scan-matching algorithm that re-envisions the normal distributions transform (NDT) in order to provide robust accuracy prediction from first principles. Like NDT, ICET subdivides a lidar scan into voxels in order to analyze complex scenes by considering many smaller local point distributions; however, ICET assesses the voxel distribution to distinguish random noise from deterministic structure. ICET then uses a weighted least-squares formulation to incorporate this noise/structure distinction while computing a localization solution and predicting the solution-error covariance. To demonstrate the reasonableness of our accuracy predictions, we verify 3D ICET in three lidar tests involving real-world automotive data, high-fidelity simulated trajectories, and simulated corner-case scenes. For each test, ICET consistently performs scan matching with sub-centimeter accuracy. With this level of accuracy, combined with the fact that the algorithm is fully interpretable, this algorithm is well suited for safety-critical transportation applications. Code is available at https://github.com/mcdermatt/ICET. [ABSTRACT FROM AUTHOR]

Network security faces challenges, including reduced true positives, increased false positives, and inadequate anomaly detection efficacy. This paper introduces the DeepRoughNetID (DRNID) approach to address these issues and confront the increasing cyberattack threat in modern communication systems. DRNID presents an innovative framework for network intrusion detection, incorporating kNNImputer, GreedyRoughSelector, DeepVAEEnsembler, and IntrusionNet components. kNNImputer enables adaptable data preprocessing by leveraging instance-based learning, facilitating efficient handling of evolving datasets without necessitating full retraining. GreedyRoughSelector enhances classifier performance through systematic attribute selection, focusing on relevant features while eliminating redundancies. DeepVAEEnsembler leverages Variational Autoencoders (VAEs) to learn underlying data distributions, enabling robust anomaly detection. IntrusionNet utilizes VAEs to classify intrusions, providing a comprehensive solution to network security challenges. By integrating these components, DRNID offers a refined approach that promises significant advancements in network intrusion detection. The experimental results demonstrate the effectiveness of DRNID, with an impressive accuracy of 98.8% achieved through 10-fold cross-validation. DRNID exhibits superior performance compared to existing methods across various datasets, showcasing its robust anomaly detection capabilities. This methodology empowers organizations to proactively prevent security violations and safeguard sensitive data from malicious entities. Beyond its theoretical contributions, our research carries tangible implications for strengthening cybersecurity defenses across diverse sectors, including telecommunications, finance, and healthcare. [ABSTRACT FROM AUTHOR]

These results highlight the transformative potential of neural network algorithms in providing consistency and transparency while reducing the inherent subjectivity in human evaluations, revolutionizing translation quality assessment in academia. The findings have significant implications for academia, as reliable translation quality evaluations are crucial for fostering cross-cultural knowledge exchange. However, challenges such as domain-specific adaptation require further investigation to improve and maximize the effectiveness of this novel approach, ultimately enhancing the accessibility of academic content and promoting global academic discourse. The proposed method involves using neural network algorithms for assessing college-level English translation quality, starting with data collection and preparation, developing a neural network model, and evaluating its performance using human assessment as a benchmark. The study employed both human evaluators and a neural network model to assess the quality of translated academic papers, revealing a strong correlation (0.84) between human and model assessments. These findings suggest the model’s potential to enhance translation quality in academic settings, though additional research is needed to address certain limitations. The results show that the Neural Network-Based Model achieved higher scores in accuracy, precision, F-measure, and recall compared to Traditional Manual Evaluation and Partial Automated Model, indicating its superior performance in evaluating translation quality. [ABSTRACT FROM AUTHOR]

Tuberculosis (TB) stands as the leading cause of death and a significant threat to humanity in the contemporary world. Early detection of TB is crucial for precise identification and treatment, and Chest X-Rays (CXR) serve as a valuable tool in this regard. Computer-Aided Diagnosis (CAD) systems play a vital role in easing the classification process of active and latent TB. This paper uses an approach called the Double Attention Res-U-Net-based Deep Neural Network (DARUNDNN) to enhance TB detection in the lungs. The detection process involves pre-processing, noise removal, image level balancing, the application of the DARUNDNN model and using the Whale Optimization Algorithm (WOA) for improved accuracy. Experimental validation using Montgomery Country (MC), Shenzhen China (SC), and NIH CXR Datasets compares the results with U-Net, AlexNet, GoogleNet, and convolutional neural network (CNN) models. The findings, particularly from the SC dataset, demonstrate the efficiency of the proposed DARUNDNN model with an accuracy of 98.6%, specificity of 96.24%, and sensitivity of 97.66%, outperforming benchmarked deep learning models. Additionally, validation with the MC dataset reveals an excellent accuracy of 98%, specificity of 97.56%, and sensitivity of 98.52%. [ABSTRACT FROM AUTHOR]

This paper investigates the manufacturing uncertainties at a 60 GHz millimeter-wave band for the monolithic hybrid microwave integrated circuits (MHMIC) fabrication process. It specifically deals with the implementation tolerances of thin-film gold microstrip transmission lines, titanium oxide thin-layer resistors, microstrip quarter-wavelength radial stubs, and active device implementation using the gold-bonding ribbons. The impacts of these manufacturing tolerances are assessed and experimentally quantified through prototyped MHMIC circuits. This allows us, on one hand, to identify the acceptable amount of dimensional variation enabling reasonable performances. On the other hand, it aims to establish a relationship between the manufacturing tolerances and the circuit parameters to provide more flexibility for the tolerance compensation and accuracy enhancement of the MHMIC fabrication processes. [ABSTRACT FROM AUTHOR]

Since the integration of electronic devices and intelligent electronic devices into the power grid, power quality (PQ) has consistently remained a significant concern for system operators and experts. Maintaining high standards of power quality is crucial to preventing malfunctions and faults in electric assets and connected loads. Recently, PQ studies have shifted their focus to a specific frequency range, previously not considered problematic—the supraharmonic 2 kHz to 150 kHz range. This range is not populated by easily recognizable harmonic components of the 50 Hz to 60 Hz mains fundamental, but by a combination of intentional emissions, switching non-linearities and byproducts, and various types of resonances. This paper aims to provide a detailed analysis of the impact of supraharmonics (SHs) on power network operation and assets, focusing on the most relevant documented negative effects, namely power loss and the heating of grid elements, aging of dielectric materials, failure of medium voltage (MV) cable terminations, and interference with equipment and power line communication (PLC) technology in particular. Under some shareable assumptions, limits are derived and compared to existing ones for harmonic phenomena, providing a clear identification of the primary issues associated with supraharmonics and suggestions for the standardization process. Strictly related is the problem of grid monitoring and assessment of SH distortion, discussing the suitability of normative requirements for instrument transformers (ITs) with a specific focus on their accuracy. [ABSTRACT FROM AUTHOR]

Software is playing a growing role in many safety-critical applications, and software systems dependability is a major concern. Predicting faulty modules of software before the testing phase is one method for enhancing software reliability. The ability to predict and identify the faulty modules of software can lower software testing costs. Machine learning algorithms can be used to solve software fault prediction problem. Identifying the faulty modules of software with the maximum accuracy, precision, and performance are the main objectives of this study. A hybrid method combining the autoencoder and the K-means algorithm is utilized in this paper to develop a software fault predictor. The autoencoder algorithm, as a preprocessor, is used to select the effective attributes of the training dataset and consequently to reduce its size. Using an autoencoder with the K-means clustering method results in lower clustering error and time. Tests conducted on the standard NASA PROMIS data sets demonstrate that by removing the inefficient elements from the training data set, the proposed fault predictor has increased accuracy (96%) and precision (93%). The recall criteria provided by the proposed method is about 87%. Also, reducing the time necessary to create the software fault predictor is the other merit of this study. [ABSTRACT FROM AUTHOR]

Smart farming is revolutionizing agriculture by integrating advanced technologies to enhance productivity, efficiency, and sustainability. This paper proposes a novel, 5G-enabled Pest and Disease Detection and Response System (PDDRS) that synergizes environmental sensor data with image analytics for comprehensive Plant Disease Detection (PDD). By leveraging the high bandwidth and ultra-low latency capabilities of 5G, our integrated system surpasses traditional communication technologies, facilitating real-time data analytics and immediate intervention strategies. We introduce two Machine Learning (ML) models: an image-based Mask R-CNN with FPN, which achieves a precision of 91.1% and an accuracy of 95.1%, and an environmental-based FFNN + LSTM model, evaluated for ACC, AUC, and F1-Score, showing promising results in disease forecasting. Our experiments demonstrate that the PDDRS significantly enhances throughput and latency performance under various connected devices, showcasing a scalable, cost-effective solution suitable for next-generation smart farming. These advancements collectively empower the PDDRS to deliver actionable insights, enabling targeted applications such as precise pesticide deployment, and stand as a testament to the potential of 5G in agricultural innovation. [ABSTRACT FROM AUTHOR]

In the rapidly upgrading world, blockchain is evolving where smart contract emerges as an important aspect that is automated to execute when the pre-conditions are satisfied. Properties like self-execution, non-reversible and non-terminating properties are mainly developed to eliminate the presence of third parties. However, such properties are misused by scammers to create malicious contracts, causing financial losses. Self-destruction property is also misused whereas the scammers remain anonymous after causing the destruction. So, it is necessary to identify the fraudulent nodes before they escape acquiring large sum of money. In this paper, the smart contract Ethereum Fraud Detection dataset was tested against various machine learning algorithms. Categorial Boosting (CatBoost) emerges as the best algorithm having a balanced tree structure contributing to its superior performance. Finally, comparative analysis shows that CatBoost outperforms obtaining 96.85% accuracy and obtains 97% when normalizing continuous features. Hence, this research addresses a way to detect scammers and contributes to interacting with safer smart contracts in blockchain networks. [ABSTRACT FROM AUTHOR]

Self-diagnosis in psychiatry is where individuals diagnose themselves rather than rely upon official diagnosticians to supply a psychiatric diagnosis. The accuracy of self-diagnosis is a contested topic. In this paper, I outline what arguments are needed to see self-diagnosis as accurate and how different approaches to self-diagnosis require different arguments. I show how different arguments are required to justify accuracy for an autistic individual judging they are autistic compared to non-autistic individuals judging they are not autistic. Different arguments are required if a self-diagnosing individual accepts or rejects official diagnostic criteria. Finally, different arguments are required depending upon whether diagnoses are seen as objective entities, the product of theoretical virtues or practically useful groupings. All these approaches require unique arguments to justify the accuracy of self-diagnosis. Identifying the required arguments for different approaches is a stepping stone toward establishing whether those arguments and which of those arguments can be successfully justified. [ABSTRACT FROM AUTHOR]

This paper introduces a novel procedure that can increase the signal-to-noise ratio in psychological experiments that use accuracy as a selection variable for another dependent variable. This procedure relies on the fact that some correct responses result from guesses and reclassifies them as incorrect responses using a trial-by-trial reclassification evidence such as response time. It selects the optimal reclassification evidence criterion beyond which correct responses should be reclassified as incorrect responses. We show that the more difficult the task and the fewer the response alternatives, the more to be gained from this reclassification procedure. We illustrate the procedure on behavioral and ERP data from two different datasets (Caplette et al. NeuroImage 218, 116994, 2020; Faghel-Soubeyrand et al. Journal of Experimental Psychology: General 148, 1834–1841, 2019) using response time as reclassification evidence. In both cases, the reclassification procedure increased signal-to-noise ratio by more than 13%. Matlab and Python implementations of the reclassification procedure are openly available (https://github.com/GroupeLaboGosselin/Reclassification). [ABSTRACT FROM AUTHOR]

The limitations of the existing Global Navigation Satellite Systems (GNSS) integrated with Inertial Measurement Units (IMU) have presented significant challenges in meeting the stringent demands of precision maneuvering. The identified constraints in terms of accuracy and availability have required the development of an alternative solution to enhance the performance of navigation systems in dynamic and diverse environments. This paper summarizes the research regarding the integration of ultra-wideband (UWB) technology as an augmentation of the conventional GNSS+IMU system; it proposes an approach that aims to overcome the limitations of conventional navigation systems. By making use of UWB technology, the proposed low-cost UWB-augmented GNSS+IMU system not only fulfils the required performance standards but also offers the unique capability to navigate seamlessly across indoor and outdoor environments. The developed system was validated through comprehensive testing and analysis in both the automotive and maritime sectors. The obtained results highlight the system's capacity as a dependable and resilient solution for precise navigation, and they promote its use within the domain of accurate maneuvering. [ABSTRACT FROM AUTHOR]

Based on fieldwork carried out at the Early Drug Development Service of a world‐leading cancer institution, our study sheds lights on decision‐making processes at the stage where decisions are made about which clinical trial to pursue and thus which experimental drugs will feed the growing pipeline of molecularly guided therapies and therapeutic strategies available to treating physicians. The paper shows how such collective decision‐making practices by a translational research unit employ formal tools and ad hoc valuation strategies that interweave technical‐scientific matters of concern with patient‐oriented clinical ones, as part of the institutional assetization of biomedical knowledge production. In the process, decision‐making practices in part define the conditions of possibility for the provision of care in what is increasingly becoming a 'clinic of variants.' They do so by reconfiguring on an evolving basis the socio‐material ecosystem through which precision oncology is enacted as a rapidly evolving assemblage of patients, physicians, research and support staff, protocols, molecular markers, drugs and administrative components. [ABSTRACT FROM AUTHOR]

Scoring rules measure the deviation between a forecast, which assigns degrees of confidence to various events, and reality. Strictly proper scoring rules have the property that for any forecast, the mathematical expectation of the score of a forecast p by the lights of p is strictly better than the mathematical expectation of any other forecast q by the lights of p. Forecasts need not satisfy the axioms of the probability calculus, but Predd et al. [9] have shown that given a finite sample space and any strictly proper additive and continuous scoring rule, the score for any forecast that does not satisfy the axioms of probability is strictly dominated by the score for some probabilistically consistent forecast. Recently, this result has been extended to non-additive continuous scoring rules. In this paper, a condition weaker than continuity is given that suffices for the result, and the condition is proved to be optimal. [ABSTRACT FROM AUTHOR]

Child and family social workers routinely make professional judgements involving significant legal and moral questions (e.g. whether a child has been abused) and more ‘everyday’ issues (e.g. will the child be re‐referred again if we close the case now?) Yet the world is capricious, and we rarely know with certainty what is going to happen in future or the likely impact of our different choices. Given the consequences of their judgements and decisions, it is imperative that social workers are provided with the best possible support. This paper reports a proof‐of‐concept study of a set of interventions to improve the judgemental accuracy of social workers: (i) a survey to identify respondents with above‐average existing abilities, (ii) training sessions on cognitive debiasing and (iii) structured group working and (iv) three methods for aggregating individual judgements. Findings indicate that it is possible to measure the accuracy of social work judgements in relation to case‐study materials and retrospective questions, while the feedback about the training was largely positive. Any future studies should aim to recruit a more diverse set of respondents, test judgemental accuracy in relation to prospective judgements and explore what types of questions would be most helpful for real‐world decision‐making. [ABSTRACT FROM AUTHOR]

This manuscript proposes a hybrid technique for fault detection and classification in a three-phase induction motor (IM). The proposed hybrid technique combines enhanced ladder spherical evaluation (ELSE) and recurrent neural networks (RNN); hence, it is called the ELSE–RNN method. The main goal of the manuscript is to detect and categorize the faults that occur in the IM. The key objective of the proposed method is to enhance the performance of supercapacitor (SC) storage technology and fuzzy-tuned proportional–integral (PI) supervision over conventional control. The main contribution of this paper is developing an effective fault diagnosis method for three-phase IMs. The presence of stator, rotor, winding, and bearing faults is employed using signal processing techniques, such as the Hilbert transform and SIFT. The proposed ELSE–RNN technique is utilized with the end goal of detecting and classifying faults. Here, the proposed ELSE–RNN technique recognizes motors’ healthy or unhealthy conditions in many situations to distinguish faults for protection. The proposed ELSE–RNN technique reduces the complexity of detecting and classifying faults with a validated system and increases the system's accuracy. The ELSE–RNN technique is implemented in MATLAB, and its performance is compared to existing techniques. [ABSTRACT FROM AUTHOR]

Background: The World Health Organization recommends the use of Schisto point-of-care circulating cathodic antigens (Schisto POC-CCA) for screening of Schistosoma mansoni as it offers better sensitivity than microscopy. However, there are limitation facing the use of this method including timely availability of the test cassettes. The aim of this study was to determine the reliability of dried urine spot (DUS) method for collection of urine and detection of S. mansoni using Schisto POC-CCA cassettes in a resource-limited settings. Methods: A cross-sectional study was conducted between October and November 2022 among 250 primary school children in Sengerema District, northwestern Tanzania. S. mansoni CCA was detected in filter paper-based DUS, liquid urine using DUS Schisto POC-CCA (index), and direct urine Schisto POC-CCA (comparator) methods respectively. S. mansoni eggs in stool were detected using duplicate Kato-Katz (KK) method. The measures of accuracy were computed and compared between the index and comparator methods. The strength of agreement between inter-raters precisions was tested using Cohen’s kappa (k). Results: This study revealed S. mansoni prevalence rates of 28.8%, 54.0% and 50.8% by duplicate KK, direct urine Schisto POC-CCA and DUS Schisto POC-CCA methods respectively. The mean intensity of infection among infected participants was 86.3 eggs per gram of stool (EPG) ranging from 12.0 EPG to 824.0 EPG. The sensitivity of DUS Schisto POC-CCA and direct urine Schisto POC-CCA was 94.44% (95% CI: 89.15–99.74%) and 97.22% (95% CI: 93.43–100.00%) respectively. The DUS Schisto POC-CCA method had slightly higher specificity (66.85%) than direct urine Schisto POC-CCA method (63.48%). The accuracy of the DUS Schisto POC-CCA was found to be slightly high (74.80%, 95% CI: 68.94–79.06%) compared to that of direct urine Schisto POC-CCA (73.20%, 95% CI: 67.25–78.59%). There was good agreement between two laboratory technologists who performed the DUS Schisto POC-CCA method on similar samples (k = 0.80, 95% CI: 0.59–0.95). Conclusions: The DUS Schisto POC-CCA method had comparable S. mansoni detection accuracy to direct urine Schisto POC-CCA. This suggests that the method could be a potential alternative to direct urine Schisto POC-CCA for screening S. mansoni in resource-limited situations. [ABSTRACT FROM AUTHOR]

Solving inverse kinematic (IK) of general robot manipulators remains significant challenge in current industrial manufacturing, particularly in human–robot collaborative scenarios. Most current approaches employ numerical, analytical, or machine learning methods to solve IK. However, accurately determining the end-effector (EE) position, solving complexity, and handling multiple solutions are unresolved challenges in these existing methods. In this paper, we propose a hybrid method that combines forward and backward reaching inverse kinematics (FABRIK) with a custom artificial neural network (ANN) to solve IK for a broad range of serial robot manipulators. The results demonstrate that the hybrid method yields a unique solution and achieves a lower position error (up to 0.003 in) compared to a standard ANN implementation. Furthermore, compared to the numerical method (FABRIK and Jacobian), the hybrid approach offers a more versatile framework for solving IK, resulting in superior overall performance in terms of solving complexity, computational efficiency, and accuracy among the three methods. [ABSTRACT FROM AUTHOR]

The real-time cucurbit fruit detection algorithm in complex environment of greenhouse is associated with challenges. Leaves occlusion, overlapping fruits, back light, front light among others, are some of these challenges. Meanwhile, this fruit detection algorithm is expected to be robust for generalization, lightweight in size, accurate and fast. For these purposes, this paper proposed an improved YOLO series detection algorithm and compared with YOLOv4, and YOLOv5 algorithms. The Backbone residual block arrangement of YOLOv4 and YOLOv5 were respectively improved from 1,2,8,8,4 to 2,3,4,3,2 and F,3,9,9,3 to F,3,4,3,2 using Neck type Path Aggregation Network (PANet) and Feature Pyramid Network (FPN). The detection performance of the improved Backbone was more accurate and faster than the Backbone of YOLOv4 and YOLOv5. Accuracy of the Neck added PANet is greater than FPN, but detection time of FPN is less than PANet. Among the tested improved algorithms, the obtained results of YOLOv4RPANet having the accuracy of 91.5% and detection time of 5.0 ms completely outperformed YOLOv4 and YOLOv5. Moreover, the YOLO series detection algorithms are lightweight in size, can better generalize against fruit detection challenges, and applicable for real-time fruit detection. [ABSTRACT FROM AUTHOR]

The aim of this paper is to evaluate the completeness, accuracy and credibility of the medical information found online about hypertension and to compare the quality of the Romanian and English websites. This observational, cross-sectional study included 25 Romanian and 25 English websites. Pre-established inclusion and exclusion criteria were used for sample selection. Credibility was assessed using 12 criteria based on the eEurope 2002 expert recommendations. Completeness and accuracy of information were evaluated using a quality standard based on information from literature and guidelines. The credibility, completeness and accuracy scores were rated on a scale from 0 to 10. Student t test and Mann-Whitney U were used to compare the two languages subsamples. Spearman and Pearson's tests were applied to test the correlations. The threshold for statistical significance was set at 0.05. The average credibility score was 4.7 for the Romanian websites and 5.6 for English websites (p=0.0686). The average completeness score was 3.7 for the Romanian websites and 5.3 for English websites (p=0.0007). The average accuracy score was 3.88 for the Romanian websites and 3.62 for English websites (p=0.5215). Significant moderate intensity correlation was found between the Google rank and the accuracy of the Romanian websites (r=-0.5438, p=0.0050). The overall quality scores of the Romanian and English websites presenting information about hypertension were modest. This study found a correlation between the Google rank and the accuracy score of the Romanian websites but since this was the only significant correlation, the findings are not sufficient to infer general recommendations. The study suggests that knowing both Romanian and English doesn't offer a significant advantage in accessing high-quality online information about hypertension. The Google rank cannot be used as a quality indicator since there is no consistent correlation between it and the quality scores. [ABSTRACT FROM AUTHOR]

Parallel implementations of Krylov subspace methods often help to accelerate the procedure of finding an approximate solution of a linear system. However, such parallelization coupled with asynchronous and out-of-order execution often makes more visible the non-associativity impact in floating-point operations. These problems are even amplified when communication-hiding pipelined algorithms are used to improve the parallelization of Krylov subspace methods. Introducing reproducibility in the implementations avoids these problems by getting more robust and correct solutions. This paper proposes a general framework for deriving reproducible and accurate variants of Krylov subspace methods. The proposed algorithmic strategies are reinforced by programmability suggestions to assure deterministic and accurate executions. The framework is illustrated on the preconditioned BiCGStab method and its pipelined modification, which in fact is a distinctive method from the Krylov subspace family, for the solution of non-symmetric linear systems with message-passing. Finally, we verify the numerical behavior of the two reproducible variants of BiCGStab on a set of matrices from the SuiteSparse Matrix Collection and a 3D Poisson's equation. [ABSTRACT FROM AUTHOR]

In this paper, we advocate and expand upon a previously described monitoring strategy for efficient and robust estimation of disease prevalence and case numbers within closed and enumerated populations such as schools, workplaces, or retirement communities. The proposed design relies largely on voluntary testing, which is notoriously biased (e.g. in the case of coronavirus disease 2019) due to nonrepresentative sampling. The approach yields unbiased and comparatively precise estimates with no assumptions about factors underlying selection of individuals for voluntary testing, building on the strength of what can be a small random sampling component. This component enables the use of a recently proposed "anchor stream" estimator, a well-calibrated alternative to classical capture-recapture (CRC) estimators based on 2 data streams. We show that this estimator is equivalent to a direct standardization based on "capture," that is, selection (or not) by the voluntary testing program, made possible by means of a key parameter identified by design. This equivalency simultaneously allows for novel 2-stream CRC-like estimation of general mean values (e.g. means of continuous variables like antibody levels or biomarkers). For inference, we propose adaptations of Bayesian credible intervals when estimating case counts and bootstrapping when estimating means of continuous variables. We use simulations to demonstrate significant precision benefits relative to random sampling alone. [ABSTRACT FROM AUTHOR]

This paper explores discussions centred on the activities of the British Board of Longitude to consider the ways in which some men of science, instrument makers and others thought about questions of precision and accuracy, both in principle and in terms of what was possible in practice when making observations at sea. It considers firstly the terminology used in some eighteenth- and early nineteenth-century texts, highlighting the concept of exactness, which was more commonly used to describe one of the desirable qualities of instruments and methods. It then looks at some of the discussions and debates in which the Board of Longitude was involved from the 1760s to think about different actors' expectations of what levels of exactness might be either desirable or possible for day-to-day navigation. The focus is on the ability to make accurate shipboard observations and on the question of what degree of exactness might have been accepted as good enough for routine navigational purposes when at sea. [ABSTRACT FROM AUTHOR]

Static analyses of bridge structures are currently performed using the finite element method (FEM). Depending on the geometry of the structure and the technically required accuracy of calculations, different levels of discretization of these structures are used in their design. In the design process, beam grillage models (denoted e¹, p²), shell models (denoted e², p²) or shell-beam models (denoted e¹+ e², p³) are often used. Solid models (denoted e³+ p³) are mostly used in advanced analyses, having frequently a scientific character. It is shown that there is an impact of the applied types of the numerical model (i.e., degree of complexity, degree of discretization, accuracy of the model) of the road bridge on the calculated values of bending moments and displacements, which indirectly affects the global safety coefficient of the designed bridge structure. The main purpose of the calculations is to examine the discrepancies of analyzed internal forces and displacements depending of the type of numerical model used. The calculated values are referred to the results taken from the field tests of the existing bridge denoted MS 03, which is a continuous beam structure with the three spans 37:50 + 46:75 + 37:50 m made of prestressed concrete and with variable beam depth. On the basis of numerical simulations, the paper provides author's recommendations for computer modeling of similar bridges. [ABSTRACT FROM AUTHOR]

The aim of this paper is to investigate the usefulness of examples that show typical learner errors in online pedagogical dictionaries of English for the accuracy of error correction as well as immediate and delayed retention of usage. The optimal positioning of examples of errors in entries is also researched. In an online experiment, participants did a sentence correction exercise with the help of purpose-built monolingual dictionary entries, where the provision and positioning of examples showing errors were controlled. Two test versions were created, which differed only in the presence of examples of errors in the entries. Usage retention was observed immediately after the test and two weeks later. The results indicate that it is worthwhile to include examples of errors in online learners' dictionaries because they contribute greatly to the retention of usage in the long run. They also help to rectify errors, though the effect is not statistically significant. The positioning of examples showing errors in entries has no influence on error correction accuracy or usage retention. The study reveals examples of errors to be a valuable induction-oriented stand-alone dictionary component placed outside warning boxes, which typically include explicit grammar rules and promote deduction. [ABSTRACT FROM AUTHOR]

In CRYPTO 2019, Gohr first introduced a neural differential distinguisher for round-reduced SPECK32/64 to distinguish ciphertext pairs with a specific input difference from random data, indicating a promising approach to the cryptanalysis of lightweight ciphers. This paper proposes a new neural differential distinguisher model for GIFT and ASCON, both of them are the finalists of the NIST Lightweight Cryptography Competition with ASCON being announced as the winner in February 2023. In our model, we utilize the score distribution of multiple ciphertext differences instead of a single ciphertext pair for classification, which is different from Gohr's model. First, we construct neural distinguishers to evaluate the scores for ciphertext differences. Then we train neural networks to classify the distribution of scores, which is more efficient than the traditional statistical tests such as the Kolmogorov–Smirnov test. Based on the proposed model, we improve the prediction accuracy from 55.42% to 99.36% and from 50.69% to 69.25% for 7-round GIFT-128 and 4-round ASCON-PERMUTATION respectively. Compared with the previous neural distinguisher for ASCON, our distinguisher covers an additional round and attains higher accuracy. The experimental results show that better results can be obtained with the lower hamming weight input difference, and the method presented in this paper can improve the effectiveness of cryptanalysis with neural networks. [ABSTRACT FROM AUTHOR]

Prognostics is used in predictive maintenance to estimate the remaining time to the end of the life of a system or component. Among the many challenges of prognostics is the need for model verification and validation. Over the years, several objective metrics have been utilized by the community. Some of these metrics came from statistics, others from forecasting, and others have been proposed specifically for prognostics. A single "perfect" metric has not yet been put forward. Finding one metric that can excel in all evaluation dimensions and case studies is an open question. In this review, we analyze the most important metrics of prognostics. A set of 19 metrics is subject to analysis and implementation. The metrics are implemented on a public GitHub project. Our analysis focuses only on metrics for deterministic predictions. Stochastic predictions are out of the scope. The paper describes properties, advantages, disadvantages, and industrial applicability of each metric. We also discuss potential modifications to the existing metrics and the development of new metrics. A final table summarizes the main properties of the metrics. Our goal is to raise awareness about prognostics metrics and help establish a common evaluation procedure. Code available at: MetricsForPrognostics. [ABSTRACT FROM AUTHOR]

Rolling bearings are essential core components in mechanical equipment, and accurately predicting their remaining useful life (RUL) is crucial for equipment maintenance. However, the accuracy and interpretability of prediction results from existing end-to-end methods are questioned, due to limitations in sample size and the black-box mechanism. To address these issues, this paper proposes the fractional derivative kernel recursive generalized maximum correntropy algorithm (FrKRGMC). By utilizing the memory capacity of fractional derivative for nonstationary signals and its improved adaptability, a more efficient gradient descent is established for kernel recursive generalized maximum correntropy algorithm (KRGMC). Meanwhile, the convergence of the mean error and mean square error (MSE) for the proposed method, as well as the time complexity of the algorithm, are demonstrated, providing evidence for the practicality of the algorithm at a theoretical level. Finally, the superiority of the proposed method is demonstrated by comparing it with other kernel adaptive filtering (KAF) methods and deep learning methods, in terms of the RUL prediction of rolling bearings. This method offers an interpretable and easy-to-deploy RUL prediction scheme for practical applications. It addresses the limitations of end-to-end methods when data is insufficient, enhances the accuracy and reliability of RUL prediction, and introduces a new perspective to this field. • The fractional derivative is applied to the field of kernel adaptive filtering for the first time, and fractional derivative kernel recursive generalized maximum correntropy (FrKRGMC) is proposed. • The FrKRGMC algorithm is used to predict the RUL of rolling bearings for the first time. • The convergence and time complexity of the proposed FrKRGMC algorithm are analyzed, including mean error analysis and mean square error analysis. • The proposed FrKRGMC algorithm provides a reliable, interpretable and easy-to-deploy option for field applications. [ABSTRACT FROM AUTHOR]