Correction Mechanism Research Articles

A lightweight target tracking algorithm based on online correction for meta-learning

The traditional Siamese network based object tracking algorithms suffer from high computational complexity, making them difficult to run on embedded devices. Moreover, when faced with long-term tracking tasks, their success rates significantly decline. To address these issues, we propose a lightweight long-term object tracking algorithm called Meta-Master-based Ghost Fast Tracking (MGTtracker),which based on meta-learning. This algorithm integrates the Ghost mechanism to create a lightweight backbone network called G-ResNet, which accurately extracts target features while operating quickly. We design a tiny adaptive weighted fusion feature pyramid network (TiFPN) to enhance feature information fusion and mitigate interference from similar objects. We introduce a lightweight region regression network, the Ghost Decouple Net (GDNet) for target position prediction. Finally, we propose a meta-learning-based online template correction mechanism called Meta-Master to overcome error accumulation in long-term tracking tasks and the difficulty of reacquiring targets after loss. We evaluate the algorithm on public datasets OTB100, VOT2020, VOT2018LT, and LaSOT and deploy it for performance testing on Jetson Xavier NX. Experimental results demonstrate the effectiveness and superiority of the algorithm. Compared to existing classic object tracking algorithms, our approach achieves a faster running speed of 25 FPS on NX, and real-time correction enhances the algorithm’s robustness. Although similar in accuracy and EAO metrics, our algorithm outperforms similar algorithms in speed and effectively addresses the issues of significant cumulative errors and easy target loss during tracking. Code is released at https://github.com/ygh96521/MGTtracker.git.

Assessing ChatGPT's theoretical knowledge and prescriptive accuracy in bacterial infections: a comparative study with infectious diseases residents and specialists.

Advancements in Artificial Intelligence(AI) have made platforms like ChatGPT increasingly relevant in medicine. This study assesses ChatGPT's utility in addressing bacterial infection-related questions and antibiogram-based clinical cases. This study involved a collaborative effort involving infectious disease (ID) specialists and residents. A group of experts formulated six true/false, six open-ended questions, and six clinical cases with antibiograms for four types of infections (endocarditis, pneumonia, intra-abdominal infections, and bloodstream infection) for a total of 96 questions. The questions were submitted to four senior residents and four specialists in ID and inputted into ChatGPT-4 and a trained version of ChatGPT-4. A total of 720 responses were obtained and reviewed by a blinded panel of experts in antibiotic treatments. They evaluated the responses for accuracy and completeness, the ability to identify correct resistance mechanisms from antibiograms, and the appropriateness of antibiotics prescriptions. No significant difference was noted among the four groups for true/false questions, with approximately 70% correct answers. The trained ChatGPT-4 and ChatGPT-4 offered more accurate and complete answers to the open-ended questions than both the residents and specialists. Regarding the clinical case, we observed a lower accuracy from ChatGPT-4 to recognize the correct resistance mechanism. ChatGPT-4 tended not to prescribe newer antibiotics like cefiderocol or imipenem/cilastatin/relebactam, favoring less recommended options like colistin. Both trained- ChatGPT-4 and ChatGPT-4 recommended longer than necessary treatment periods (p-value = 0.022). This study highlights ChatGPT's capabilities and limitations in medical decision-making, specifically regarding bacterial infections and antibiogram analysis. While ChatGPT demonstrated proficiency in answering theoretical questions, it did not consistently align with expert decisions in clinical case management. Despite these limitations, the potential of ChatGPT as a supportive tool in ID education and preliminary analysis is evident. However, it should not replace expert consultation, especially in complex clinical decision-making.

Finding “fake” in the news: the relationship between social media use, political knowledge, epistemic political efficacy and fake news literacy

PurposeIn the past few years, research focusing on misinformation, referred to broadly as fake news, has experienced revived attention. Past studies have focused on explaining the ways in which people correct it online and on social media. However, fewer studies have dealt with the ways in which people are able to identify fake news (i.e. fake news literacy). This study contributes to the latter by theoretically connect people’s general social media use, political knowledge and political epistemic efficacy with individuals’ fake news literacy levels.Design/methodology/approachA diverse and representative two-wave panel survey in the United States was conducted (June 2019 for Wave 1, October 2019 for Wave 2). We performed cross-sectional, lagged and autoregressive regression analyses to examined how social media us, people’s political knowledge and political epistemic efficacy are related to their fake news literacy.FindingsResults suggest that the more people used social media, were politically knowledgeable and considered they were able to find the truth in politics (i.e. epistemic political efficacy), the more likely they were to discern whether the news is fake. Implications of helping media outlets and policy makers be better positioned to provide the public with corrective action mechanisms in the struggle against fake news are discussed.Research limitations/implicationsThe measurement instrument employed in the study relies on subjects’ self-assessment, as opposed to unobtrusive trace (big) digital data, which may not completely capture the nuances of people’s social media news behaviors.Practical implicationsThis study sheds light on how the way people understand politics and gain confidence in finding political truth may be key elements when confronting and discerning fake news. With the help of these results, journalists, media outlets and policymakers may be better positioned to provide citizens with efficient, preemptive and corrective action mechanisms in the struggle against misinformation.Originality/valueRecent literature highlights the importance of literacy education to contest fake news, but little is known about what specific mechanisms would contribute to foster and reinvigorate people’s fake news literacy. This study helps address this gap.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-03-2024-0140

Self-Internal Standard Fluorescence for Ultrasensitive Detecting of mtDNA to Evaluate Matrilineal Genetic Defect Levels.

Mitochondrial DNA (mtDNA) is a unique genetic material characterized by maternal inheritance. It possesses a circular structure devoid of histone protection and exhibits low cellular abundance, which poses great challenges for its sensitive and selective detection at the living cell level. Herein, we have designed three bis-naphthylimide probes with varying linker lengths (NANn-OH, n = 0, 2, 6), facilitating the formation of distinct twisted or folded molecular conformations in the free state. These probes emit the red fluorescence around 627 nm with different fluorescence quantum yields (ΦNAN0-OH = 0.0016, ΦNAN2-OH = 0.0136, and ΦNAN6-OH = 0.0125). When encountering mtDNA (0.4-3.4 μg/mL), these probes undergo conformational changes depending on the length of the attached C-strand and exhibit a gradually increasing fluorescence signal around 453 nm. The fluorescence intensity increased to 13.5-fold, 1.9-fold, and 8.2-fold, respectively. Notably, the red fluorescence intensities around 627 nm remain constant throughout this process, thus serving as an inherent correction mechanism for proportional fluorescence signal enhancement to improve selectivity and sensitivity. NAN0-OH, NAN2-OH, and NAN6-OH showed good linearity for mtDNA in the range of 0.4-3.4 μg/mL with detection limits of LODNAN0-OH = 1.04 μg/mL, LODNAN2-OH = 1.10 μg/mL, and LODNAN6-OH = 1.15 μg/mL. Cellular experiments reveal that NAN6-OH effectively monitors curcumin-induced mtDNA damage in HepG-2 cells while enabling monitoring of genetic mtDNA damage. We anticipate that this tool holds significant potential for the precise evaluation of maternal genetic defects, thereby enhancing hypersensitive assessment in clinical medicine.

Migrant Remittances and Financial Sector Development: New Evidence from Nigeria

This paper analyzes the impact of remittances on financial sector development in Nigeria using data for more than four decades (1980‐2022). The study applies the error correction mechanism because of its ease of implementation, power and robustness as well as its role in explaining stationarity among economic variables. The empirical analysis shows that remittances promote financial sector development during the period with evidence that policies and programmes to improve the financial sector in the previous period impact significantly on the financial sector. The result also shows that increases in export and gross national product per capita promotes financial sector development in a positive and significant manner while high inflationary trend is detrimental to financial sector development.

Macroeconomic Determinants of Remittances Inflow in Nepal

Background: The phenomenon of international migration and remittances has gained significant attention in recent years, particularly in the context of developing countries like Nepal. In 2020 alone, remittance flows to low- and middle-income countries (LMICs) reached $540 billion. In many developing nations worldwide, including Nepal, migration has become an important source of income and foreign exchange, providing critical support for households and contributing to economic growth. Objective: To investigate the effect of various macroeconomic factors on remittance inflow in Nepal. Methods: Using the ARDL approach to co-integration, the study examines Nepal's short-run and long-run macroeconomic determinants of remittances, utilising annual data from 1993 to 2021. Results: The study finds that global oil prices, nominal exchange rate, and domestic output influence remittance inflow in Nepal. None of the macroeconomic variables affect remittance inflow significantly in the short run, while the oil price is positive and significant in the long run. The study also identifies the existence of a correction mechanism in the case of remittances whenever deviations from the long-run equilibrium occur. Furthermore, a negative association between domestic GDP and remittances corroborates the common finding that remittances are countercyclical and altruistic. Implications: The study has major policy implications, highlighting that remittance inflows are affected by external factors and that there is a negative relationship between domestic GDP and remittance inflows. These findings should be considered when formulating policies related to foreign employment and remittance inflows. Paper Types: Research Paper JEL classification: B22, F24, C22

A memory interaction quadratic interpolation whale optimization algorithm based on reverse information correction for high-dimensional feature selection

Feature selection is a key technique for data dimensionality reduction, and there are many challenges in facing the exponential expansion phenomenon of high-dimensional decision space. In order to improve the quality of feature selection, we propose a memory interaction quadratic interpolation whale optimization algorithm based on reverse information correction (RQWOA). First, in order to improve the convergence capability of the Whale Optimization Algorithm (WOA) in high-dimensional space, we propose a quadratic interpolation mechanism with memory information interaction, improve the quality of the datum points used for quadratic interpolation through the proposed memory interaction mechanism, and accelerate the convergence speed and accuracy of the algorithm with the help of the quadratic interpolation on the algorithm exploitation capability. In addition, to address the invalid probability flip in the feature selection subset when mapping to the discrete space, we propose a reverse information correction mechanism to judge and correct each dimension in the feature subset by using the reverse complementary information provided by individuals with poor fitness values in the population, so as to better remove invalid or redundant features and improve the quality of feature selection. Finally, in the 15 high-dimensional feature datasets compared with the comparison methods, the accuracy of RQWOA is improved by 7.49 % on average, and the number of features is compressed by 80.42 % on average, which indicates that the RQWOA algorithm has stronger feature identification and redundancy removal capabilities and can better solve the feature selection problem.

Is the movement of the INR/USD Exchange Rate and the Indian Stock Market Linked? Fresh Evidence

Dollar exchange rate and Indian stock market are leading economic indicators. The present study investigates the relationship between the two economic indicators during the period April 2005 to December 2019. Analysis of Johansen cointegration test reveals that positive long-run cointegrating relationship exists between the variables. The vector error correction mechanism shows that INR/USD exchange rate influences the Indian stock prices negatively in the short-run. The study also observes the presence of bidirectional causality between the variables in the short-run. The variance decomposition analysis further reveals that the Indian stock market is driven to considerable extent by innovations in INR/USD exchange rate. The close relationship that the study found between INR/USD exchange rate and Indian stock market will be an important factor in the decision making of both individual and institutional investors. Furthermore, the study recommends the Indian government to adopt a cautious approach in implementing the foreign currency exchange rate policies

Intelligent identification method for pipeline leakage based on GPR time–frequency features and deep learning

ABSTRACT The detection and localization of water pipeline leakages based on ground penetrating radar (GPR) technology are gradually becoming a research hotspot. Current methods mostly focus on exploring the patterns of B-Scan images, heavily relying on the subjective experience of detection personnel, which can lead to misjudgments. Moreover, the large amount of data makes it difficult for manual processing. Therefore, a method based on wavelet transform (WT) and ResNet-50 is proposed to identify the time-frequency characteristics of GPR data, thereby achieving intelligent localization of pipeline leakages. The B-Scan images from GPR are transformed into time–frequency scale images using WT, and the features in both time and frequency domains are combined to enhance the representation of leakages. Subsequently, ResNet-50 is employed for feature extraction and leakage identification. Additionally, a deviation correction mechanism is proposed to improve the clarity of the prediction results. Experimental results demonstrate that ResNet-50 achieves an accuracy of 0.917 and a recall of 0.998 on the time-frequency dataset, almost detecting all leakages, with a detection efficiency of 0.0165 s per data trace. The comprehensive method is validated in the field, indicating its capability to accurately identify and localize pipeline leakages.

The Effects of Schooling Rates and Income Levels on Energy Consumption in Households: A Panel Data Analysis on OECD Countries

AbstractWhile energy consumption affects many different areas, it is also affected by many different factors. Therefore, policies aiming to reduce energy consumption gain a multidimensional feature. Income level and education play an important role in the success of these policies. Because as the income and education levels of individuals increase, the success rate of policies aiming to reduce energy consumption is higher. In this way, while energy consumption is reduced or used more efficiently, environmental problems are prevented. In this study, the effects of average schooling rate and income level on energy consumption in residences were investigated. For this purpose, the panel data analysis was used within the scope of the annual data of 19 OECD member countries for the 1990–2019 period. As a result of the analysis, a cointegration relationship was detected between the variables and long-term coefficients and error correction coefficient and short-term coefficients were obtained with the Augmented Mean Group (AMG) estimator. The findings show that the long-term average schooling rate has a negative effect on energy consumption in households, but income level has a positive effect on a panel basis. On the other hand, it was also found that the error correction mechanism works and that the income level has a positive effect on the energy consumption in the households in the short term, but the average schooling rate does not have a significant effect on the energy consumption in the households in the short term.

Enhanced Target Localization in the Internet of Underwater Things through Quantum-Behaved Metaheuristic Optimization with Multi-Strategy Integration

Underwater localization is considered a critical technique in the Internet of Underwater Things (IoUTs). However, acquiring accurate location information is challenging due to the heterogeneous underwater environment and the hostile propagation of acoustic signals, especially when using received signal strength (RSS)-based techniques. Additionally, most current solutions rely on strict mathematical expressions, which limits their effectiveness in certain scenarios. To address these challenges, this study develops a quantum-behaved meta-heuristic algorithm, called quantum enhanced Harris hawks optimization (QEHHO), to solve the localization problem without requiring strict mathematical assumptions. The algorithm builds on the original Harris hawks optimization (HHO) by integrating four strategies into various phases to avoid local minima. The initiation phase incorporates good point set theory and quantum computing to enhance the population quality, while a random nonlinear technique is introduced in the transition phase to expand the exploration region in the early stages. A correction mechanism and exploration enhancement combining the slime mold algorithm (SMA) and quasi-oppositional learning (QOL) are further developed to find an optimal solution. Furthermore, the RSS-based Cramér–Raolower bound (CRLB) is derived to evaluate the effectiveness of QEHHO. Simulation results demonstrate the superior performance of QEHHO under various conditions compared to other state-of-the-art closed-form-expression- and meta-heuristic-based solutions.

Insurance Sector Performance and the Nigeria’s Economic Growth

The study examines insurance industry’s contribution to economic growth in Nigeria. For estimation analysis, the study employed Autoregressive Distributive Lag (ARDL) bond test, Error Correction Mechanism (ECM) and Diagnostic test.). The findings revealed that at 5% level of significance in the short run, life premium (LP) related positively with growth rate (GR) in an insignificant manner while non-life premium (NLP) contributed negatively and insignificantly to growth rate. However, in the long run, life and nonlife insurance premium influences the growth of the Nigerian economy. Therefore, the study concludes that without insurance coverage, substantial emergency funds would be looked-for to protect firms against risks. Based on the findings, the study recommends among others that life insurance companies should come up with life products mainly designed for the low-paid earners to enhance penetration, deepen the market and to create more awareness to improve the participation of product industry and firms as this will intensify the activities of the insurance industry in Nigeria. Also, insurance companies should increase diversification of insurance products mostly in non-life businesses being embarked upon.

Hierarchical few-shot learning based on top-down correction mechanism with stop strategy

Hierarchical few-shot learning based on top-down correction mechanism with stop strategy

Analyzing Data Transmission Reliability in Mobile Ad-Hoc Networks under Dynamic Scenarios

Mobile ad hoc networks (MANETs) face inherent challenges in maintaining reliable data transmission because of their dynamic and unpredictable nature. This research conducts a comprehensive reliability analysis of data transmission in MANETs, emphasizing the influence of dynamic conditions such as node mobility, changing network topologies, and fluctuating channel conditions. Through the utilization of mathematical models and simulations, the study assesses the overall reliability of data transmission, considering scenarios with diverse node densities, mobility patterns, and network sizes. Existing routing protocols, error correction mechanisms, and adaptive transmission strategies are examined for their effectiveness in reducing the impact of dynamic conditions on reliability. The research not only examines the current state of protocols but also explores potential enhancements and introduces novel approaches to enhance data transmission reliability under dynamic conditions. The findings offer a nuanced understanding of the challenges and opportunities related to reliable communication in MANETs. This research provides crucial insights for designing resilient communication systems in dynamic and mobile network scenarios, offering valuable guidance to researchers, network designers, and practitioners involved in optimizing the performance of MANETs in real-world applications.

On the justification and validity of causality in classical electromagnetism

Determining the causality in electromagnetism is more difficult than determining the causality in classical mechanics. Newton’s second law and the laws of Faraday and of Ampère are very different in character. Whereas Newton’s second law holds for a point in space, the laws of Faraday and of Ampère hold for a region in space. The ultimate aim of this article is to delve into the correct mechanism of propagation of electromagnetic waves. And, in this article, we investigate the wrong reasoning of other authors. It is concluded that an electric field neither generates nor induces a magnetic field and a magnetic field neither generates nor induces an electric field.

Journey over destination: dynamic sensor placement enhances generalization

Reconstructing complex, high-dimensional global fields from limited data points is a challenge across various scientific and industrial domains. This is particularly important for recovering spatio-temporal fields using sensor data from, for example, laboratory-based scientific experiments, weather forecasting, or drone surveys. Given the prohibitive costs of specialized sensors and the inaccessibility of certain regions of the domain, achieving full field coverage is typically not feasible. Therefore, the development of machine learning algorithms trained to reconstruct fields given a limited dataset is of critical importance. In this study, we introduce a general approach that employs moving sensors to enhance data exploitation during the training of an attention based neural network, thereby improving field reconstruction. The training of sensor locations is accomplished using an end-to-end workflow, ensuring differentiability in the interpolation of field values associated to the sensors, and is simple to implement using differentiable programming. Additionally, we have incorporated a correction mechanism to prevent sensors from entering invalid regions within the domain. We evaluated our method using two distinct datasets; the results show that our approach enhances learning, as evidenced by improved test scores.

NetHD: Neurally Inspired Integration of Communication and Learning in Hyperspace

The 6G network, the next‐generation communication system, is envisaged to provide unprecedented experience through hyperconnectivity involving everything. The communication should hold artificial intelligence‐centric network infrastructures as interconnecting a swarm of machines. However, existing network systems use orthogonal modulation and costly error correction code; they are very sensitive to noise and rely on many processing layers. These schemes impose significant overhead on low‐power internet of things devices connected to noisy networks. Herein, a hyperdimensional network‐based system, called , is proposed, which enables robust and efficient data communication/learning. exploits a redundant and holographic representation of hyperdimensional computing (HDC) to design highly robust data modulation, enabling two functionalities on transmitted data: 1) an iterative decoding method that translates the vector back to the original data without error correction mechanisms, or 2) a native hyperdimensional learning technique on transmitted data with no need for costly data decoding. A hardware accelerator that supports both data decoding and hyperdimensional learning using a unified accelerator is also developed. The evaluation shows that provides a bit error rate comparable to that of state‐of‐the‐art modulation schemes while achieving 9.4 faster and 27.8 higher energy efficiency compared to state‐of‐the‐art deep learning systems.

Modern Power Problems Training of Military Officers of Special Purpose Units

Topicality. The growing requirements for the level of functional capabilities of the body of servicemen of special purpose units require scientists, together with specialists from the Armed Forces of Ukraine, to develop effective mechanisms for improving the system of military training. The Goal of the Work. Studying the problem of the feasibility of implementing the results of modern research on adaptive changes in the body in the conditions of different load regimes into the process of military training of military personnel of special purpose units. Research Methods. The work uses a theoretical analysis and generalization of scientific literature and a sociological survey method. 25 physical training instructors in the Armed Forces and 84 servicemen of special purpose units (SPUs) took part in the study. The key issues were related to military training in the Armed Forces. A comparative analysis of the answers of instructors and servicemen with the results of research by leading scientists studying modern mechanisms for improving power capabilities was carried out. Research Results. It was established that the results of the survey of physical training instructors in the Armed Forces and military personnel of the Ukrainian Armed Forces in matters related to the use of control methods for adaptive changes in the body and load correction mechanisms do not fully correspond to the data presented by modern, mostly foreign scientists. Thus, in the modern system of force training of foreign armies, physiological, morphofunctional and biochemical methods of controlling adaptive changes in the body are actively used, and in the Armed Forces of Ukraine, as the results of our survey showed, only the heart rate indicator. Also, the Armed Forces do not use methods of quantitative assessment of force load parameters, which does not allow for the development of optimal load regimes for each category of servicemen. Conclusions. On the basis of the conducted research, it can be stated that the instructors and servicemen of the PSP who participated in the survey only minimally realize the potential that can be obtained in the process of military training. The absence of a mechanism for developing optimal load modes, their correction, and integral control of adaptive changes in the body will not allow to selectively increase the maximum strength or strength endurance indicators of the relevant muscle groups necessary for the implementation of combat tasks in a short period of time.

Public finance instruments and output growth in Nigeria

This study investigates the empirical relationship between the aggregated and disaggregated components of fiscal policy variables and output growth in Nigeria using the vector error correction mechanism for the period of 1981 to 2021. This formulation was an improvement over previous empirical studies of the impact of public finance instruments on output growth in Nigeria. Our findings suggest that fiscal policy instruments (total expenditure, capital expenditure, recurrent expenditure, total revenue, primary fiscal balance, domestic debt, external debt, oil tax revenue, and non-oil tax revenue) exert significant impacts on output growth and most of the empirical results obtained support the hypothesised relationships between public finance instruments and output growth in Nigeria. However, aggregate federal government expenditure exerts a significant negative impact on output growth, while on the disaggregated scale, capital expenditure exerts a significant positive influence on output growth whereas recurrent expenditure exerts a significant negative impact on output growth. Given these findings, we recommend that the government of Nigeria, through its fiscal authorities, should adopt growth-enhancing fiscal policies that would engender macroeconomic stability and will be potent in refocusing recurrent expenditure towards ensuring productivity growth.

Characterization and Calibration of a Photodiode Based Pyranometer for Solar Energy Applications

<p>In the domain of solar science, a pyranometer assumes a pivotal position, functioning as an indispensable tool for appraising solar irradiance across the abbreviated wavelengths of the solar spectrum, spanning from 300 to 3000 nanometers. Conventional pyranometers typically employ intricate and labour intensive thermopiles. These devices rely on arrays of thermocouples interconnected either serially or in parallel and necessitate rare earth minerals for efficient operation. In this investigation, we advocate for an innovative methodology employing photodetectors, specifically photodiodes and phototransistors, to surmount the limitations linked with traditional pyranometers. Photodiodes, commonly utilized for this purpose, proffer numerous benefits, including accelerated response times, customizable spectral range selection, and cost-effectiveness. Nonetheless, they cannot entirely supplant thermopiles owing to impediments such as the generation of dark current in the absence of illumination, temperature-dependent functionalities, and saturation at elevated irradiance levels. To counteract these constraints, we have devised a pioneering design of a photodiode-based pyranometer that incorporates a data acquisition system and correction mechanism. This innovative approach furnishes a feasible resolution for autonomous solar radiation measurement, furnishing enhanced precision and dependability.</p>