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Computer-aided behavior observation is gradually supplanting paper-and-pencil approaches to behavior observation, but there is a dearth of evidence on the relative accuracy of paper-and-pencil versus computer-aided behavior observation formats in the literature. The current study evaluated the accuracy resulting from paper-and-pencil observation and from two computer-aided behavior observation methods: The Observer XT® desktop software and the Big Eye Observer® smartphone application. Twelve postgraduate students without behavior observation experience underwent a behavior observation training protocol. As part of a multi-element design, participants recorded 60 real clinical sessions randomly assigned to one of the three observation methods. All three methods produced high levels of accuracy (paper-and-pencil,.88 ±.01; The Observer XT,.84 ±.01; Big Eye Observer,.84 ±.01). A mixed linear model analysis indicated that paper-and-pencil observation produced marginally superior accuracy values, whereas the accuracy produced by The Observer XT and Big Eye Observer did not differ. The analysis suggests that accuracy of recording was mediated by the number of recordable events in the observation videos. The implications of these findings for research and practice are discussed. [ABSTRACT FROM AUTHOR]

Background The Medical Certificate of Stillbirth (MCS) records data about a baby's death after 24 weeks of gestation but before birth. Major errors that could alter interpretation of the MCS were widespread in two UK-based regional studies. Methods A multicentre evaluation was conducted, examining MCS issued 1 January 2018 to 31 December 2018 in 76 UK obstetric units. A systematic case-note review of stillbirths was conducted by Obstetric and Gynaecology trainees, generating individual 'ideal MCSs' and comparing these to the actual MCS issued. Anonymized central data analysis described rates and types of error, agreement and factors associated with major errors. Results There were 1120 MCSs suitable for assessment, with 126 additional submitted data sets unsuitable for accuracy analysis (total 1246 cases). Gestational age demonstrated 'substantial' agreement [K = 0.73 (95% CI 0.70–0.76)]. Primary cause of death (COD) showed 'fair' agreement [K = 0.26 (95% CI 0.24–0.29)]. Major errors [696/1120; 62.1% (95% CI 59.3–64.9%)] included certificates issued for fetal demise at <24 weeks' gestation [23/696; 3.3% (95% CI 2.2–4.9%)] or neonatal death [2/696; 0.3% (95% CI 0.1–1.1%)] or incorrect primary COD [667/696; 95.8% (95% CI 94.1–97.1%)]. Of 540/1246 [43.3% (95% CI 40.6–46.1%)] 'unexplained' stillbirths, only 119/540 [22.0% (95% CI 18.8–25.7%)] remained unexplained; the majority were redesignated as either fetal growth restriction [FGR: 195/540; 36.1% (95% CI 32.2–40.3%)] or placental insufficiency [184/540; 34.1% (95% CI 30.2–38.2)]. Overall, FGR [306/1246; 24.6% (95% CI 22.3–27.0%)] was the leading primary COD after review, yet only 53/306 [17.3% (95% CI 13.5–22.1%)] FGR cases were originally attributed correctly. Conclusion This study demonstrates widespread major errors in MCS completion across the UK. MCS should only be completed following structured case-note review, with particular attention on the fetal growth trajectory. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • Hyper spectral imaging was used to get many spectra in a short time. • The identification of eight paper types was done by using machine learning. • Even papers with very similar spectra could be distinguished with high accuracy. • For 68,400 spectra per sample, the accuracy was 87.8%. Abstract Paper is non-uniform; thus, it is difficult to ascertain whether the slight differences in near-infrared absorption spectra are caused by the difference in paper type or the paper's non-uniformity. We used near-infrared hyperspectral imaging to measure the spectra of many different points quickly for eight different paper samples. Next, we analysed the data using machine learning and constructed a model to ascertain whether the differences in the spectra are due to the difference in paper type or the paper's non-uniformity. Even papers with very similar spectral shapes were distinguished with high accuracy with this method. As the number of spectra used to construct the classification model increased, the accuracy of the classification model increased. With 6840 spectra per sample, the accuracy exceeded 85%, and with 68,400 spectra per sample, the accuracy for identification was 87.8%. [ABSTRACT FROM AUTHOR]

In the paper we present an approach to the automatic detection and identification of important elements in paper documents. This includes stamps, logos, printed text blocks, signatures and tables. Presented approach consists of two stages. The first one includes object detection by means of AdaBoost cascade of weak classifiers and Haar-like features. Resulting image blocks are, at the second stage, subjected to verification based on selected features calculated from recently proposed low-level descriptors combined with certain classifiers representing current machine-learning approaches. The training phase, for both stages, uses bootstrapping, i.e., integrative process, aiming at increasing the accuracy. Experiments performed on large set of digitized paper documents showed that adopted strategy is useful and efficient. [ABSTRACT FROM AUTHOR]

(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]

Whether recruiters accurately infer personality from resumes is currently a topic of great interest in the light of technological advancements and new types of resume formats (like video resumes) that are increasingly being used. Based on predictions from the realistic accuracy model and the media richness theory, we investigated whether real recruiters ( N = 296) are able to infer applicants' Big Five personality characteristics more accurately from video and audio resumes than from less information-rich resume formats (like paper resumes) and whether applicants' perceived attractiveness (physical/vocal) affects accuracy judgments. As expected and with the exception of extraversion, personality was not accurately judged from paper resumes. Interestingly, information-rich resume formats and applicants' perceived attractiveness did not affect accuracy judgments. Despite recruiters' stubborn reliance on their capacity to infer personality from resume information, study findings clearly showed that different types of resumes –also the new, more information-rich ones– are no valid tools to infer candidates' personality from. [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]

Adherence and accuracy of self-monitoring of dietary intake influences success in weight management interventions. Information technologies such as computers and smartphones have the potential to improve adherence and accuracy by reducing the burden associated with monitoring dietary intake using traditional paper-based food records. We evaluated the acceptability and accuracy of three different 7-day food record methods (online accessed via computer, online accessed via smartphone, and paper-based). Young women (N=18; aged 23.4±2.9 years; body mass index 24.0±2.2) completed the three 7-day food records in random order with 7-day washout periods between each method. Total energy expenditure (TEE) was derived from resting energy expenditure (REE) measured by indirect calorimetry and physical activity level (PAL) derived from accelerometers (TEE=REE×PAL). Accuracy of the three methods was assessed by calculating absolute (energy intake [EI]–TEE) and percentage difference (EI/TEE×100) between self-reported EI and TEE. Acceptability was assessed via questionnaire. Mean±standard deviation TEE was 2,185±302 kcal/day and EI was 1,729±249 kcal/day, 1,675±287kcal/day, and 1,682±352 kcal/day for computer, smartphone, and paper records, respectively. There were no significant differences between absolute and percentage differences between EI and TEE for the three methods: computer, –510±389 kcal/day (78%); smartphone, –456±372 kcal/day (80%); and paper, –503±513 kcal/day (79%). Half of participants (n=9) preferred computer recording, 44.4% preferred smartphone, and 5.6% preferred paper-based records. Most participants (89%) least preferred the paper-based record. Because online food records completed on either computer or smartphone were as accurate as paper-based records but more acceptable to young women, they should be considered when self-monitoring of intake is recommended to young women. [ABSTRACT FROM AUTHOR]

• Multiple evaluation methodologies used in retinal vessel segmentation identified. • Published scores in 100 papers categorized by the methodology they are based on. • The scores are numerically adjusted to eliminate the biases for comparability. • A new baseline ranking of segmentation techniques provided. • The comparison of incomparable scores is an ongoing phenomenon in the literature. [Display omitted] In the last 15 years, the segmentation of vessels in retinal images has become an intensively researched problem in medical imaging, with hundreds of algorithms published. One of the de facto benchmarking data sets of vessel segmentation techniques is the DRIVE data set. Since DRIVE contains a predefined split of training and test images, the published performance results of the various segmentation techniques should provide a reliable ranking of the algorithms. Including more than 100 papers in the study, we performed a detailed numerical analysis of the coherence of the published performance scores. We found inconsistencies in the reported scores related to the use of the field of view (FoV), which has a significant impact on the performance scores. We attempted to eliminate the biases using numerical techniques to provide a more realistic picture of the state of the art. Based on the results, we have formulated several findings, most notably: despite the well-defined test set of DRIVE, most rankings in published papers are based on non-comparable figures; in contrast to the near-perfect accuracy scores reported in the literature, the highest accuracy score achieved to date is 0.9582 in the FoV region, which is 1% higher than that of human annotators. The methods we have developed for identifying and eliminating the evaluation biases can be easily applied to other domains where similar problems may arise. [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]

The white blood cell (WBC) plays an instrumental role in protecting the human body from different infectious diseases and unknown biological invaders. Their count and cellular morphology drastically change when the body suffers from any such disease or abnormal conditions. This paper demonstrates the detection of five types of WBC present in human blood smear images. Several authors proposed WBC detection using only one color space. But this paper addresses WBC-type detection using four popular color spaces – RGB (red, green, and blue), HSV (hue, saturation, and intensity), YCbCr (luminance, chroma blue, and chroma red), and L*a*b* (luminosity, chromaticity layer “a*”, and chromaticity layer “b*”). The proposed methodology involves color thresholding, K-means clustering, nuclear region analysis, and the implementation of a feedforward neural network. The unsupervised machine learning algorithm, K-means clustering is used to segment nuclei from the blood smear images. The detection of WBC-type using four color spaces is compared in terms of accuracy, sensitivity, specificity, and positive predictive value. It is observed that HSV-based WBC-type detection methodology outperforms methodologies based on RGB, YCbCr, and L*a*b* color spaces. The same methodology mentioned above is then compared with the works proposed by other authors. [ABSTRACT FROM AUTHOR]

Nowadays, the volume of online information is growing and it is difficult to find the required information. Effective strategies such as recommender systems are required to overcome information overload. Collaborative filtering is a widely used type of recommender system in e-commerce environments and can simply provide suggestions for users. Recently, deep learning approaches were applied in collaborative filtering to tackle some drawbacks. This systematic review aims to provide a comprehensive review of recent research efforts on deep learning-based collaborative filtering recommender systems. We explain the research methodology and paper selection process and the search query. 102 papers are selected out of 719 papers that were published between 2019 and May 2023. Furthermore, the approaches in the selected papers are classified into two main categories: memory-based and model-based techniques. The main ideas, advantages, disadvantages, used tools, type of neural network, applications, and evaluation parameters of each selected paper are also discussed in detail. It was found that CNN (Convolutional Neural Network), AE (Autoencoder), DNN (Deep neural network), and Hybrid networks are the four mostly used neural networks in recommender systems. Also, Python, MATLAB, and Java are the most frequently used tools in the reviewed papers. Regarding the applications of the recommender systems in the reviewed papers, movies, products, and music recommendation are three most frequent applications. We point out the open issues and future research directions. Some key challenges such as cold start, data sparsity, scalability, and accuracy are still open to be addressed. [ABSTRACT FROM AUTHOR]

In recent years, unmanned aerial vehicles (UAVs) have become increasingly important as a tool for quickly collecting high-resolution (spatial and spectral) imagery of the Earth's surface. The final products are highly dependent on the choice of values for various parameters in flight planning, the type of sensors, and the processing of the data. In this paper ground control points (GCPs) were first measured using the Global Navigation Satellite System (GNSS) Real-Time Kinematic (RTK) method, and then due to the low height accuracy of the GNSS RTK method all points were measured using a detailed leveling method. This study aims to provide a basic assessment of quality, including four main aspects: (1) the difference between an RGB sensor and a five-band multispectral sensor on accuracy and the amount of data, (2) the impact of the number of GCPs on the accuracy of the final products, (3) the impact of different altitudes and cross flight strips, and (4) the accuracy analysis of multi-altitude models. The results suggest that the type of sensor, flight configuration, and GCP setup strongly affect the quality and quantity of the final product data while creating a multialtitude model does not result in the expected quality of data. With its unique combination of sensors and parameters, the results and recommendations presented in this paper can assist professionals and researchers in their future work. [ABSTRACT FROM AUTHOR]

The appearance and development of new construction materials, technology and accurate geodetic instruments have led to the necessity of their inevitable use in the health monitoring, maintenance, and restoration of civil structures and special structures such as high-rise cooling towers. This paper presents an accurate practical and analytical method for determining the structural deformation and axis inclination of high rise cooling towers using terrestrial laser scanner (TLS) observations through the projection of tower surface points coordinates on a vertical plane. Four cooling towers in El-Mahla El-Kubra city, Egypt are observed and studied. Two of them with height 56 m, and the others were 36 m height. The studied four towers have different cross-section diameters along the tower height. Each tower has a cone shape with a curved wall. The equation of the tower wall is determined using TLS observations and least squares adjustment techniques. The equations of cone projection with a curved wall are derived and presented in this paper. From TLS observations, the radii and accuracy of each 2 m tower height are determined with center coordinates, and then the inclination of the tower axis is calculated and drawn. From the results of TLS observations, data processing, and analysis using the suggested techniques, it is deduced that there is a deformation in tower walls with small values. The specified technique for observations collection and TLS data analysis through projection on a vertical plane is significant and valuable for determining the structural deformation of circular high rise buildings and towers. From the results, there are obvious deformation values in some cooling towers, so maintenance work must be included. The towers also must be checked and monitored several times at brief intervals. [ABSTRACT FROM AUTHOR]

Background: At present, the evidence regarding digital impressions' accuracy recorded by using digital scanners is lacking. This systematic review aimed to evaluate whether the type of scanning (intraoral/extraoral) affects the Accuracy of Digital Impressions. Method: Two independent reviewers performed a systematic search in the database both electronically and manually (PubMed, Ebsco HOST, the Cochrane Library, and Google Scholar) for articles published from 1 January 2010 to 1 December 2022. This study was registered with the International Prospective Register of Systematic Reviews (PROSPERO CRD42020188765) and followed the PRISMA statement. The question in focus was as follows: Does the type of scanning (intraoral or extraoral) affect the accuracy of digital impression? Results: A total of 449 papers were obtained by searching electronically and manually. In total, 15 complete-text papers qualified for assessment based on eligibility criteria. After reading the full-text articles, five studies were excluded. Ten studies were selected for the qualitative analysis. The qualitative data reported that the accuracy of both types of scanners (intraoral and extraoral) lies within the range of clinical acceptability. Nevertheless, the intraoral scanners seem to be more accurate when compared to the extraoral scanners for a partial arch situation. Conclusions: Scanning type affects the accuracy of the digital impression. Various factors influence the scanning ability. Intraoral scanners seem to be more accurate compared to extraoral scanners for a partial arch situation. More studies comparing the accuracy of the intraoral scanner and extraoral scanner for a complete arch scan and in an in vivo study setting are needed. [ABSTRACT FROM AUTHOR]

This paper assesses the fault-detection capabilities of modern deep-learning models. It highlights that a naive deep-learning approach optimized for accuracy is unsuitable for learning fault-detection models from time-series data. Consequently, out-of-the-box deep-learning strategies may yield impressive accuracy results but are ill-equipped for real-world applications. The paper introduces a methodology for estimating fault-detection delays when no oracle information on fault occurrence time is available. Moreover, the paper presents a straightforward approach to implicitly achieve the objective of minimizing fault-detection delays. This approach involves using pseudo-multi-objective deep optimization with data windowing, which enables the utilization of standard deep-learning methods for fault detection and expanding their applicability. However, it does introduce an additional hyperparameter that needs careful tuning. The paper employs the Tennessee Eastman Process dataset as a case study to demonstrate its findings. The results effectively highlight the limitations of standard loss functions and emphasize the importance of incorporating fault-detection delays in evaluating and reporting performance. In our study, the pseudo-multi-objective optimization could reach a fault-detection accuracy of 95% in just a fifth of the time it takes the best naive approach to do so. [ABSTRACT FROM AUTHOR]

Algorithms for machine learning have found extensive use in numerous fields and applications. One important aspect of effectively utilizing these algorithms is tuning the hyperparameters to match the specific task at hand. The selection and configuration of hyperparameters directly impact the performance of machine learning models. Achieving optimal hyperparameter settings often requires a deep understanding of the underlying models and the appropriate optimization techniques. While there are many automatic optimization techniques available, each with its own advantages and disadvantages, this article focuses on hyperparameter optimization for well-known machine learning models. It explores cutting-edge optimization methods such as metaheuristic algorithms, deep learning-based optimization, Bayesian optimization, and quantum optimization, and our paper focused mainly on metaheuristic and Bayesian optimization techniques and provides guidance on applying them to different machine learning algorithms. The article also presents real-world applications of hyperparameter optimization by conducting tests on spatial data collections for landslide susceptibility mapping. Based on the experiment's results, both Bayesian optimization and metaheuristic algorithms showed promising performance compared to baseline algorithms. For instance, the metaheuristic algorithm boosted the random forest model's overall accuracy by 5% and 3%, respectively, from baseline optimization methods GS and RS, and by 4% and 2% from baseline optimization methods GA and PSO. Additionally, for models like KNN and SVM, Bayesian methods with Gaussian processes had good results. When compared to the baseline algorithms RS and GS, the accuracy of the KNN model was enhanced by BO-TPE by 1% and 11%, respectively, and by BO-GP by 2% and 12%, respectively. For SVM, BO-TPE outperformed GS and RS by 6% in terms of performance, while BO-GP improved results by 5%. The paper thoroughly discusses the reasons behind the efficiency of these algorithms. By successfully identifying appropriate hyperparameter configurations, this research paper aims to assist researchers, spatial data analysts, and industrial users in developing machine learning models more effectively. The findings and insights provided in this paper can contribute to enhancing the performance and applicability of machine learning algorithms in various domains. [ABSTRACT FROM AUTHOR]

The article presents the results of research on the development of a method for improving the positioning accuracy of an UAV equipped with a single-frequency GPS receiver for determining the linear elements of exterior orientation in aerial photogrammetry. Thus, the paper presents a computational strategy for improving UAV position determination using the SPP code method and the products of the IGS service. The developed algorithmswere tested in two independent research experiments performed with theUAVplatform on which an AsteRx-m2 UAS single-frequency receiver was installed. As a result of the experiments, it was shown that the use of IGS products in the SPP code method made it possible to improve the accuracy of the linear elements to the level of about ±2.088 m for X coordinate, ±1.547 m for Y coordinate, ±3.712 m for Z coordinate. The paper also shows the trend of changes in the obtained accuracy in determining linear elements of exterior orientation in the form of a linear regression function. Finally, the paper also applies the SBAS corrections model for the improvement of UAV position calculation and determination of linear elements of exterior orientation. In this case, the improvement in the accuracy of determining the linear elements of exterior orientation is about ±1.843 m for X coordinate, ±1.658 m for Y coordinate, ±7.930 m for Z coordinate. As the obtained test results show, the use of IGS products and SBAS corrections in the SPP code method makes it possible to improve the determination of UAV positions for the use in aerial photogrammetry. [ABSTRACT FROM AUTHOR]

Instrument transformers are largely used to measure voltages and currents in public electricity networks. Their performance is essential for Power Quality measurements. Tests and procedures to estimate the effect of single influence factors are performed according to current standards. However, the instrument transformers are exposed simultaneously to more than one factor. The standardized procedures for their characterization with combined influence factors are under construction in Europe. The work in this paper is focused on the development of a new set-up for the determination of the accuracy and frequency response of medium-voltage transformers within the combined influence factors of temperatures and vibrations. The test set-up includes a platform for the combination of temperature and vibrations, a generating system for harmonics up to 9 kHz superposed with 35 kV, 50 Hz voltage and a reference measuring system. The results of several tests carried out on a 35 kV/100 V/50 Hz voltage transformer in the presence of these influence factors are discussed in this paper. They show that the accuracy and frequency response of the voltage transformers are more influenced by temperature than by vibrations. [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]