Correction Mechanism Research Articles

Energy Literacy of Economics Students in Rijeka: Knowledge, Attitudes, and Behavioral Approach

The research problem of this paper is related to numerous open questions in the field of energy, its understanding, its use, and the challenges of the energy future. After the introductory part, in which a brief historical overview of energy literacy is provided, the paper focuses on energy literacy, its emergence, and the different approaches to its definition and measurement. The paper analyzes the energy literacy of students at the Faculty of Economics in Rijeka based on their cognitive, affective, and behavioral skills. Their knowledge about energy, energy processes, and the energy efficiency of the objects they encounter on a daily basis is examined. Their personal attitudes and values are examined through various forms of personal initiatives and active participation in energy sustainability projects. Finally, the habits and behavioral patterns that the respondents have in their daily lives and their tendencies to save energy resources and find energy-efficient solutions are examined. Despite some positive findings, the current results are not satisfactory and point to the creation of adequate public policies with a particular focus on education and the role of the corrective mechanisms of the state, but also the need for joint negotiations between policy makers, regulators, scientists, representatives of civil society, and the business community. The particular contribution of this work is reflected in being the first research of its kind conducted among Croatian economics students, as well as among Croatian students in general; in the creation of a research instrument that is conceptually consistent with the findings from the existing literature, but with an original set of questions within each energy literacy skill adapted to the cultural and sociological background of the respondents; and in a kind of progress from previous research by taking into account the quantitative aspect (in addition to the qualitative) assessment of the respondents’ energy literacy.

Leveraging CRC Error Detection for Enhanced Error Correction in Digital Communication Systems

In this study, CRC error detection techniques will be used to not only identify but fix single-bit errors in digital communication systems. In the past, CRC codes have been employed mainly for their ability to detect errors efficiently. However, this paper suggests a new way that will make it possible to modify CRC functions and thus correct the identified errors. It is intended that by using an optimized look-up table in Verilog hardware description language with an advanced Xilinx 14.7 toolchain, this research can increase the dependability and effectiveness of error correction mechanisms found in digital systems. The study presents how to create a detailed hardware description, optimize lookup tables for minimal resource usage and increased error correction capability; then synthesizes design with Xilinx 14.7.The conclusion makes a practical contribution towards identification and removal of single-bit errors which indicates a turning point in the use of Cyclic Redundancy Check (CRC) technology from being just about error detection to full scale error correction. This enhancement has the potential to greatly increase the durability and reliability of data transmission and storage systems, especially in critical applications requiring utmost data integrity. Keywords: Cyclic Redundancy Check (CRC), Error detection, Single-bit errors, Optimized lookup table, Xilinx 14.7 toolchain.

A Block Chain-Based Access Control Framework with Privacy Protection in Cloud

Access control is a pivotal aspect of modern computing systems, ensuring that only authorized entities can interact with sensitive data and resources. Traditional access control methods, while effective, face challenges in terms of security, transparency, and scalability. Blockchain technology has emerged as a promising solution to enhance access control in cloud computing environments. Blockchain's inherent features, such as decentralization, immutability, and cryptographic security, offer a unique framework for addressing the limitations of traditional access control systems. In this paper, we explore the integration of blockchain technology with cloud computing to establish a more secure and transparent access control mechanism. By utilizing blockchain, access control policies and permissions can be stored in a tamper-proof and transparent ledger. Scalability is an issue since processing many access control transactions on the blockchain might cause network congestion and sluggish processing. Transactions take time to upload to the blockchain, which can delay real-time access choices. It takes skill to integrate and manage blockchain and cloud technologies together. Choosing the correct consensus mechanism affects system efficiency and security. Consider the costs of establishing and maintaining such a system and the difficulty of fixing faults owing to blockchain immutability. In conclusion, this paper underscores the significance of access control in cloud computing and the limitations of traditional approaches. By harnessing the power of blockchain technology, a more secure, transparent, and scalable access control framework can be established, revolutionizing the way we manage access to sensitive data and resources in complex digital ecosystems.

Modeling the Nexus between European Carbon Emission Trading and Financial Market Returns: Practical Implications for Carbon Risk Reduction and Hedging

The carbon–financial nexus helps firms evaluate susceptibility to carbon risk more effectively. This is the first research article to model the short- and long-run co-integrating association between European financial markets, the CBOE oil price volatility index (OVZ) and the European carbon emission trading system (EU-ETS) by using the daily returns from 1 October 2013 to 1 October 2023. We utilize co-integration test followed by the ARDL framework with an error correction mechanism (ECM). Moreover, we utilize the DCC-GARCH-t copula framework to estimate the hedge ratio and to select an optimal portfolio weight for carbon risk hedging. Overall, the findings suggested that EU-ETS (OVZ) has a consistent positive (negative) short-term influence on all the equity returns of Belgium, Denmark, Finland, France, Germany, Ireland, Italy, Netherlands, Spain and the stock indices of the whole Eurozone. However, in the long term, EU-ETS has a positive (negative) effect on the stock returns of France and the Eurozone (Belgium and Spain). Belgian and Spanish companies could implement long-term carbon reduction policies. Belgian and Spanish firms should focus on the utilization of green energy resources and the internalization of carbon emission-free mechanical processes as this may offer a safeguard against the additional pressure arising from escalating carbon prices. Finally, an optimal portfolio weight selection strategy based upon the DCC-GARCH-t copula approach aims for higher hedging effectiveness (HE) than the hedge ratio strategy when adopting short-term positions in Italian and Danish equity markets to reduce the risk of long-term EU-ETS volatility.

The European energy crisis, the Dutch TTF, and the market correction mechanism: a financial markets perspective

Abstract In September 2022, the Title Transfer Facility (TTF) prices spiked at unprecedented levels. For various reasons, including the Russian invasion of Ukraine, Europe was facing an energy crisis: gas supply was significantly reduced while demand remained unchanged. Policymakers responded with a variety of measures, including regulatory interventions on the TTF market. While seeking to improve the situation in the ‘real world economy’, these measures have an important financial regulatory angle. This article investigates the potential impact of these interventions, in particular of the Market Correction Mechanism, on the wholesale gas market from a financial markets perspective. Considering, amongst others, observations from the EU Agency for the Cooperation of Energy Regulators, the European Securities and Markets Authority, and the European Central Bank, it appears that these interventions may have unintended negative consequences in light of the functioning and attractiveness of the Capital Markets Union and achieving strategic autonomy.

Statistical Analysis of Speaking Strategies among Non-Native English Speaking Graduate Students in the Masters Legal and Judicial Governance and Banking, Finance, and Insurance Programs

This study aims to investigate the speaking strategies used by non-native English-speaking Master's students enrolled in the programs of Legal and Judicial Governance (L.J.G.) and Banking, Finance, and Insurance (B.F.I.) at the Faculty of Legal, Social, and Economic Studies of Mohammedia, Hassan II University of Casablanca, Morocco. The research employs an extensive questionnaire to analyze various aspects of speaking strategies employed by Master's students, including language translation, communication priorities, error correction mechanisms, and sources of language input. Descriptive statistics are applied to analyze responses and provide insights into the prevalence and effectiveness of these speaking strategies. Additionally, a comparative analysis is conducted to juxtapose the strategies employed by both Master's students. The findings underscore the diverse approaches adopted by Master's students via a Likert scale to enhance their speaking proficiency within the specific academic disciplines of Legal and Judicial Governance (L.J.G.) and Banking, Finance, and Insurance (B.F.I.). Furthermore, the research illuminates the factors influencing the selection of speaking strategies among Master's students. This study highlights the significance of its findings for language teaching and learning at the Master's level within the Faculty of Legal, Social, and Economic Studies of Mohammedia. It discusses implications for professors aiming to facilitate language development among Master's students and provides suggestions for future research in this field.

Evaluating health information systems-related errors using the human, organization, process, technology-fit (HOPT-fit) framework.

Complex socio-technical health information systems (HIS) issues can create new error risks. Therefore, we evaluated the management of HIS-related errors using the proposed human, organization, process, and technology-fit framework to identify the lessons learned. Qualitative case study methodology through observation, interview, and document analysis was conducted at a 1000-bed Japanese specialist teaching hospital. Effective management of HIS-related errors was attributable to many socio-technical factors including continuous improvement, safety culture, strong management and leadership, effective communication, preventive and corrective mechanisms, an incident reporting system, and closed feedback loops. Enablers of medication errors include system sophistication and process factors like workarounds, variance, clinical workload, slips and mistakes, and miscommunication. The case management effectiveness in handling the HIS-related errors can guide other clinical settings. The potential of HIS to minimize errors can be achieved through continual, systematic, and structured evaluation. The case study validated the applicability of the proposed evaluation framework that can be applied flexibly according to study contexts to inform HIS stakeholders in decision-making. The comprehensive and specific measures of the proposed framework and approach can be a useful guide for evaluating complex HIS-related errors. Leaner and fitter socio-technical components of HIS can yield safer system use.

The Analysis of Important Issues Related to Trade Balance of Pakistan (2000-2023)

In this study will discuss about the important issues related to balance of trade of Pakistan by covering the time period 2000 to 2023. It is analyzed that there is positive impact of market size and infrastructure on balance of trade of Pakistan where is exchange rate is impacting negatively for balance of trade of Pakistan. The data is analyzed through correction mechanism and co-integration after testing the stationery of data. Few important policy directions are given on the basis of available data that told about core variables of economy that may impact in right direction for economic analysis of Pakistan. In addition to this the annual data is utilized for policy making in this studies.

¿Qué se ha investigado en América Latina sobre condenas e imputaciones erróneas? Una revisión sistemática de la literatura disponible en el período 2010-2023

The article seeks to know what has been researched in Latin America on wrongful convictions and prosecutions. Drawing on a systematic literature review from 2010 to July 2023 in WoS, Scopus and SciELO databases, the article identifies a universe of 50 publications. From a quantitative perspective, the paper inquiries into aspects such as years of publication, countries to which they refer, data about the scientific community that has generated this literature, and the topics they address. Then, it provides a qualitative analysis of the content of the publications, distinguishing among those that address general aspects, factors that contribute to wrongful convictions and prosecutions, correction mechanisms, and compensation mechanisms for wrongful convictions and prosecutions. Although with limitations, the findings provide an overview of research in this area in Latin America, showing that, although scholarship is still scarce compared with other latitudes, the topic has begun to attract interest in recent years in the region.

¿Qué se ha investigado en América Latina sobre condenas e imputaciones erróneas? Una revisión sistemática de la literatura disponible en el período 2010-2023

The article seeks to know what has been researched in Latin America on wrongful convictions and prosecutions. Drawing on a systematic literature review from 2010 to July 2023 in WoS, Scopus and SciELO databases, the article identifies a universe of 50 publications. From a quantitative perspective, the paper inquiries into aspects such as years of publication, countries to which they refer, data about the scientific community that has generated this literature, and the topics they address. Then, it provides a qualitative analysis of the content of the publications, distinguishing among those that address general aspects, factors that contribute to wrongful convictions and prosecutions, correction mechanisms, and compensation mechanisms for wrongful convictions and prosecutions. Although with limitations, the findings provide an overview of research in this area in Latin America, showing that, although scholarship is still scarce compared with other latitudes, the topic has begun to attract interest in recent years in the region.

A hybrid method combining Lévy process and neural network for predicting remaining useful life of rotating machinery

The accurate prediction of remaining useful life (RUL) for rotating machinery with gears and bearings at its core plays a crucial role in ensuring equipment's safe operation and preventing catastrophic accidents. Therefore, this paper focuses on the RUL issue of rotating machinery, proposing a novel RUL prediction framework. Initially, leveraging multi-domain feature extraction and self-organizing map (SOM) networks, this paper constructs the dynamic entropy weighted minimum quantization error (DEWMQE) as the initial health indicator (HI). Subsequently, employing windowed inertia smoothing and scale correction mechanisms, this paper filters anomalies in HI, ensuring trend smoothing and scale consistency. Considering the degradation process and characteristics of rotating machinery systems, this study utilizes linear multi-fractional Lévy stable motion (LMLSM) to build a degradation model. Building upon this, to effectively utilize historical data and overcome the shortcomings of random process theory requiring predefined degradation paths, this study further integrates bidirectional gated recurrent units (BGRU) and similarity transfer learning methods to devise the BGTLLM hybrid model, enabling adaptive fitting of the degradation trend. Finally, the Monte Carlo (MC) method is employed to evaluate the uncertainty in RUL prediction. The effectiveness and accuracy of the proposed hybrid prediction model in RUL forecasting are validated using heavy-duty truck axle data and the PHM2012 dataset.

Improving the professionalization level of a new generation of construction workers to reduce their unsafe behaviour: an empirical study in China

Objectives. Improvement of the professionalization level for the new generation of construction workers (NGCWs) is critical to upgrade the construction industry. It also provides a new approach to reduce their unsafe behaviour. The purpose of this study is to analyse the correction mechanism of the professionalization level on NGCWs’ unsafe behaviour. We examined the mediating role of work–family conflict and job burnout on impacting NGCWs’ unsafe behaviour. The moderation effect of the NGCWs’ perceived organizational support was also examined. Methods. The cross-sectional study was conducted based on structural equation modelling, confirmatory factor analysis and regression analysis with a sample of 496 NGCWs in China. Results. The professionalization level can effectively correct NGCWs’ unsafe behaviour. Job burnout can independently mediate the relationship between professionalization level and unsafe behaviour and act as a serial mediator for work–family conflict. Moreover, the high level of perceived organizational support will effectively suppress the effect of work–family conflict on job burnout. Conclusions. Improving the professionalization level is an effective way to correct NGCWs’ unsafe behaviour. Moreover, management should help the NGCWs balance work and family, to alleviate burnout. Additionally, it is suggested that management should ensure the NGCWs perceive the organizational support.

Enhancing process state monitoring in energy storage systems: A robust design synthesis with physics-informed generalized observer

Energy storage systems (ESSs) are crucial for managing renewable energy fluctuations. Knowing ESSs’ states is vital for thermal management. This paper presents a robust design synthesis approach, leveraging a physics-informed generalized observer (GO), for enhancing the process state monitoring in ESSs. The proposed GO takes advantage of the inherent characteristics of ESSs as interconnected subsystems with distinct physical significance. By utilizing measured inputs and outputs, the GO accurately estimates the unmeasurable and unknown process states. GO employs a physics-informed model and a tracking feedback correction mechanism for accurate state estimation. It offers high accuracy, robustness, and insensitivity to measurement noise. To validate the effectiveness of the proposed approach, two case studies are conducted on ESSs. The first involves monitoring the core temperature of lithium batteries, and the second involves an experimental study on monitoring the inner chamber temperature of heat buffer tanks. Experimental results demonstrate approximately 10% improvement compared to conventional methods. GO provides a simple design method, clear physical interpretation, and strong interpretability. Overall, GO is an effective and versatile observer for process monitoring with significant advantages and potential for various applications.

Learning-driven hybrid scaling for multi-type services in cloud

In order to deal with the fast changing requirements of container based services in clouds, auto-scaling is used as an essential mechanism for adapting the number of provisioned resources with the variable service workloads. However, the latest auto-scaling approaches lack the comprehensive consideration of variable workloads and hybrid auto-scaling for multi-type services. Firstly, the historical data based proactive approaches are widely used to handle complex and variable workloads in advance. The decision-making accuracy of proactive approaches depends on the prediction algorithm, which is affected by the anomalies, missing values and errors in the historical workload data, and the unexpected workload cannot be handled. Secondly, the trigger based reactive approaches are seriously affected by workload fluctuation which causes the frequent invalid scaling of service resources. Besides, due to the existence of scaling time, there are different completion delays of different scaling actions. Thirdly, the latest approaches also ignore the different scaling time of hybrid scaling for multi-type services including stateful services and stateless services. Especially, when the stateful services are scaled horizontally, the neglected long scaling time causes the untimely supply and withdrawal of resources. Consequently, all three issues above can lead to the degradation of Quality of Services (QoS) and the inefficient utilization of resources. This paper proposes a new hybrid auto-scaling approach for multi-type services to resolve the impact of service scaling time on decision making. We combine the proactive scaling strategy with the reactive anomaly detection and correction mechanism. For making a proactive decision, the ensemble learning model with the structure improved deep network is designed to predict the future workload. On the basis of the predicted results and the scaling time of different types of services, the auto-scaling decisions are made by a Deep Reinforcement Learning (DRL) model with heterogeneous action space, which integrates horizontal and vertical scaling actions. Meanwhile, with the anomaly detection and correction mechanism, the workload fluctuation and unexpected workload can be detected and handled. We evaluate our approach against three different proactive and reactive auto-scaling approaches in the cloud environment, and the experimental results show the proposed approach can achieve the better scaling behavior compared to state-of-the-art approaches.

Negative Pre-aware for Noisy Cross-Modal Matching

Cross-modal noise-robust learning is a challenging task since noisy correspondence is hard to recognize and rectify. Due to the cumulative and unavoidable negative impact of unresolved noise, existing methods cannot maintain a stable performance when the noise increases. In this paper, we present a novel Negative Pre-aware Cross-modal (NPC) matching solution for large visual-language model fine-tuning on noisy downstream tasks. It is featured in two aspects: (1) For noise recognition and resistance, previous methods usually directly filter out a noise subset, we propose to estimate the negative impact of each sample. It does not need additional correction mechanisms that may predict unreliable correction results, leading to self-reinforcing error. We assign a confidence weight to each sample according to its negative impact in the training process. This adaptively adjusts the contribution of each sample to avoid noisy accumulation. (2) For maintaining stable performance with increasing noise, we utilize the memorization effect of DNNs by maintaining a memory bank. Specifically, we apply GMM to select high-confident clean samples as the memory entry, where the memory entry is used to estimate the negative impact of each sample. Since clean samples are easier distinguished by GMM with increasing noise, the memory bank can still maintain high quality at a high noise ratio. Compared to the correction mechanism focusing on noise samples, memory bank-based estimation is more robust, which makes the model performance stable on noisy datasets. Extensive experiments demonstrate that our method significantly improves matching accuracy and performance stability at increasing noise ratio. Our approach also surpasses the state-of-the-art methods by a large margin. The code is available at: https://github.com/ZhangXu0963/NPC.

Impediments in attracting greenfield investment: does a stable absorption rate of the European cohesion policy make a difference?

ABSTRACT This paper seeks to investigate how the absorption rate of the European Cohesion Policy (ECP) funds and its dynamics are related to the creation of greenfield foreign direct investment (FDI) projects in the European Union (EU). A dynamic panel estimation based on 28 recipient countries in 2007–2016 reveals that a high ECP funds absorption rate alone does not ensure the creation of greenfield FDI. However, stable absorption (i.e. low dynamics in the ECP funds absorption rate) seems to promote greenfield investment. The large year-to-year changes in the ECP funds absorption rate appear to indicate a non-stable business environment that may hamper new investment, calling for corrective mechanisms to prevent the inefficient rapid withdrawal of funds after the eligibility period.

Barrier analysis to improve big data analytics capability of the maritime industry: A mixed-method approach

The maritime industry is facing increasing challenges due to decarbonization requirements, trade disruptions, and geoeconomic fragmentation, such as International Maritime Organization (IMO) sets out clear framework to reach net zero emissions by 2050, Russia-Ukraine war disrupted maritime activities in the Black and Azov seas, and increased trade tensions between the United States and China. To enhance their sustainability, operational efficiency, and competitiveness, maritime organizations are therefore very keen to build big data analytics capability (BDAC). However, various barriers, mean that only a handful are able to do so. We adopt a mixed-method approach to analyze these barriers. Thematic analysis is used to identify five categories of barriers and 16 individual barriers based on empirical data collected from 26 maritime organizations. These are then prioritized using the analytic hierarchy process (AHP), followed by total interpretive structural modelling (TISM) to understand their interrelationships. Finally, cross-impact matrix multiplications applied to classification (MICMAC) is employed to differentiate the role of each barrier based on its driving and dependence power. This paper makes several theoretical contributions. First, China's hierarchical cultural value orientation encourages competition and obedience to rules, resulting in unwillingness to share knowledge, lack of coordination, and lack of error correction mechanisms. These cultural barriers hinder BDAC development. Second, organizational learning category barriers are found to be the most important in impeding BDAC development. This study also raises practitioners' awareness of the need to tackle cultural and organizational learning barriers.

An early enriched experience drives targeted microglial engulfment of miswired neural circuitry during a restricted postnatal period.

Brain function is critically dependent on correct circuit assembly. Microglia are well-known for their important roles in immunological defense and neural plasticity, but whether they can also mediate experience-induced correction of miswired circuitry is unclear. Ten-m3 knockout (KO) mice display a pronounced and stereotyped visuotopic mismapping of ipsilateral retinal inputs in their visual thalamus, providing a useful model to probe circuit correction mechanisms. Environmental enrichment (EE) commenced around birth, but not later in life, can drive a partial correction of the most mismapped retinal inputs in Ten-m3 KO mice. Here, we assess whether enrichment unlocks the capacity for microglia to selectively engulf and remove miswired circuitry, and the timing of this effect. Expression of the microglial-associated lysosomal protein CD68 showed a clear enrichment-driven, spatially restricted change which had not commenced at postnatal day (P)18, was evident at P21, more robust at P25, and had ceased by P30. This was observed specifically at the corrective pruning site and was absent at a control site. An engulfment assay at the corrective pruning site in P25 mice showed EE-driven microglial-uptake of the mismapped axon terminals. This was temporally and spatially specific, as no enrichment-driven microglial engulfment was seen in P18 KO mice, nor the control locus. The timecourse of the EE-driven corrective pruning as determined anatomically, aligned with this pattern of microglia reactivity and engulfment. Collectively, these findings show experience can drive targeted microglial engulfment of miswired neural circuitry during a restricted postnatal window. This may have important therapeutic implications for neurodevelopmental conditions involving aberrant neural connectivity.

Corrective mechanisms of motion extrapolation.

Transmission and processing of sensory information in the visual system takes time. For motion perception, our brain can overcome this intrinsic neural delay through extrapolation mechanisms and accurately predict the current position of a continuously moving object. But how does the system behave when the motion abruptly changes and the prediction becomes wrong? Here we address this question by studying the perceived position of a moving object with various abrupt motion changes by human observers. We developed a task in which a bar is monotonously moving horizontally, and then motion suddenly stops, reverses, or disappears-then-reverses around two vertical stationary reference lines. Our results showed that participants overestimated the position of the stopping bar but did not perceive an overshoot in the motion reversal condition. When a temporal gap was added at the reverse point, the perceptual overshoot of the end point scaled with the gap durations. Our model suggests that the overestimation of the object position when it disappears is not linear as a function of its speeds but gradually fades out. These results can thus be reconciled in a single process where there is an interplay of the cortical motion prediction mechanisms and the late sensory transient visual inputs.

Structural implications of BK polyomavirus sequence variations in the major viral capsid protein Vp1 and large T-antigen: a computational study.

Type and rate of amino acid variations in BKPyV may provide important insights into BKPyV diversity in human populations and an important step toward defining determinants of BKPyV-specific immunity needed to protect vulnerable patients from BKPyV diseases. Our analysis of BKPyV sequences obtained from human specimens reveals an unexpectedly high genetic variability for this double-stranded DNA virus that strongly relies on host cell DNA replication machinery with its proof reading and error correction mechanisms. BKPyV variability and immune escape should be taken into account when designing further approaches to antivirals, monoclonal antibodies, and vaccines for patients at risk of BKPyV diseases.