Correction Mechanism Research Articles

Research on the Establishment and Improvement of Fault Tolerance and Error Correction Mechanism

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Preclinical lentiviral hematopoietic stem cell gene therapy corrects Pompe disease-related muscle and neurological manifestations

Pompe disease, a rare genetic neuromuscular disorder, is caused by a deficiency of acid alpha-glucosidase (GAA), leading to an accumulation of glycogen in lysosomes, and resulting in the progressive development of muscle weakness. The current standard treatment, enzyme replacement therapy (ERT), is not curative and has limitations such as poor penetration into skeletal muscle and both the central and peripheral nervous systems, a risk of immune responses against the recombinant enzyme, and the requirement for high doses and frequent infusions. To overcome these limitations, lentiviral vector-mediated hematopoietic stem and progenitor cell (HSPC) gene therapy has been proposed as a next-generation approach for treating Pompe disease. This study demonstrates the potential of lentiviral HSPC gene therapy to reverse the pathological effects of Pompe disease in a preclinical mouse model. It includes a comprehensive safety assessment via integration site analysis, along with single-cell RNA sequencing analysis of central nervous tissue samples to gain insights into the underlying mechanisms of phenotype correction.

To evaluate the impact of watching a video sequence in a virtual reality helmet and on a TV screen on a person’s postural stability

The paper presents an analysis of changes in postural stability when a person is presented with a video sequence in a virtual reality helmet and from a TV screen. Postural stability was assessed using a computer stabilometer complex. Changes in the stabilometric indicators compared with control tests (before viewing) were shown for both cases (watching videos on the screen and in a virtual reality helmet). Besides, viewing a video sequence in a virtual reality helmet had a greater impact on the instability. While watching a video from a TV screen and in a virtual reality helmet, the contribution of visual information to maintaining balance in the sagittal plane decreased. However, while watching from the TV screen, the contribution of vestibular information for posture control increased. When viewed with virtual reality helmet, the contribution of somatosensory information and the cerebellum increased. The results may suggest that virtual reality requires more conscious corrective mechanisms to stabilize posture.

Improving non-invasive trajectory decoding via neural correlates of continuous erroneous feedback processing

Objective. Over the last decades, error-related potentials (ErrPs) have repeatedly proven especially useful as corrective mechanisms in invasive and non-invasive brain-computer interfaces (BCIs). However, research in this context exclusively investigated the distinction of discrete events intocorrectorerroneousto the present day. Due to this predominant formulation as a binary classification problem, classical ErrP-based BCIs fail to monitor tasks demanding quantitative information on error severity rather than mere qualitative decisions on error occurrence. As a result, fine-tuned and natural feedback control based on continuously perceived deviations from an intended target remains beyond the capabilities of previously used BCI setups.Approach.To address this issue for future BCI designs, we investigated the feasibility of regressing rather than classifying error-related activity non-invasively from the brain.Main results.Using pre-recorded data from ten able-bodied participants in three sessions each and a multi-output convolutional neural network, we demonstrated the above-chance regression of ongoing target-feedback discrepancies from brain signals in a pseudo-online fashion. In a second step, we used this inferred information about the target deviation to correct the initially displayed feedback accordingly, reporting significant improvements in correlations between corrected feedback and target trajectories across feedback conditions.Significance.Our results indicate that continuous information on target-feedback discrepancies can be successfully regressed from cortical activity, paving the way to increasingly naturalistic, fine-tuned correction mechanisms for future BCI applications.

Identifying Key Causal Factors of “Outsourcing Failure” in Government Procurement of Public Service Based on Interval Type‐2 Fuzzy DEMATEL‐Prospect Theory

ABSTRACTAccurately identifying the key causal factors of “outsourcing failure” in government procurement of public service (GPPS) is the focus when constructing its corrective mechanism. The research process and contributions of this paper unfold in two parts: First, based on the structure‐process‐outcome (SPO) model, from the three dimensions of structure, process and outcome, constructing a library of factors causing “outsourcing failure” in GPPS. Then aiming at the ambiguity, uncertainty, and risk preference of decision makers in the decision‐making process, this study proposes a method to identify the key causal factors of “outsourcing failure” in GPPS based on interval type‐2 fuzzy decision‐making trial and evaluation laboratory (DEMATEL)‐prospect theory. We take government procurement of home‐based care services in Jiangsu Province as an example. The results show that nine key causal factors were identified. Further, targeted correction suggestions are proposed to provide strong support and reference for improving the effect of GPPS outsourcing.

Fairness-Aware Federated Learning with Real-Time Bias Detection and Correction

Federated Learning (FL) enables collaborative model training across decentralized devices while preserving user data privacy. However, disparities in data distributions among clients can lead to biased models that perform unfairly across different demographic groups. This paper proposes a fairness-aware Federated Learning framework equipped with real-time bias detection and correction mechanisms. Our approach adjusts model updates to address biases detected at local client levels before aggregating them at the central server. We demonstrate the effectiveness of our method through empirical evaluations on multiple datasets, showcasing significant improvements in fairness and model accuracy. Our proposed framework involves a multi-tiered approach to ensure fairness in the model training process. Firstly, it employs local bias detection techniques at the client level to identify disparities in model performance across different groups. Clients then utilize bias correction mechanisms to adjust their model updates, addressing any detected biases before sending updates to the central server. The central server aggregates these bias-corrected updates, ensuring that the global model benefits from equitable learning while maintaining overall performance.

Refined Prior Guided Category-Level 6D Pose Estimation and Its Application on Robotic Grasping

Estimating the 6D pose and size of objects is crucial in the task of visual grasping for robotic arms. Most current algorithms still require the 3D CAD model of the target object to match with the detected points, and they are unable to predict the object’s size, which significantly limits the generalizability of these methods. In this paper, we introduce category priors and extract high-dimensional abstract features from both the observed point cloud and the prior to predict the deformation matrix of the reconstructed point cloud and the dense correspondence between the reconstructed and observed point clouds. Furthermore, we propose a staged geometric correction and dense correspondence refinement mechanism to enhance the accuracy of regression. In addition, a novel lightweight attention module is introduced to further integrate the extracted features and identify potential correlations between the observed point cloud and the category prior. Ultimately, the object’s translation, rotation, and size are obtained by mapping the reconstructed point cloud to a normalized canonical coordinate system. Through extensive experiments, we demonstrate that our algorithm outperforms existing methods in terms of performance and accuracy on commonly used benchmarks for this type of problem. Additionally, we implement the algorithm in robotic arm-grasping simulations, further validating its effectiveness.

Globalization, Migration, and Economic Growth in Nigeria

The study aimed to empirically examine the effect of globalization and migration on Nigeria's economic growth. The study covered the period 33 years (1990 – 2022). The variables used in the study are foreign direct investment, trade openness, foreign exchange rate, remittances, and gross domestic product. The ARDL (autoregressive distributed lag) model was employed for the study and a long-run relationship was established. Findings revealed that remittance had a significantly positive effect on Nigeria's economic growth in the long run. The finding of the error correction mechanism revealed a speed of adjustment to equilibrium of 31.1%. The study suggested that the government, as a key player, should take immediate action to formulate policies that bolster and optimize the advantages of remittance inflows. This is crucial as remittances have been found to positively and significantly affect economic growth in the short and long run. These measures may encompass reducing transaction expenses, improving financial literacy among recipients, and encouraging investments to amplify the developmental effects of remittances.

Hubungan Jangka Pendek dan Jangka Panjang antara Pembiayaan Perbankan Syariah dan GDP di Indonesia dengan menggunakan metode Vector Error Correction Model

This research analyzes the relationship between Islamic bank financing and economic growth as measured by Gross Domestic Product (GDP) using the Vector Error Correction Model (VECM) to explain the short-term and long-term relationship dynamics between these two variables. The results of this research found that there is a long-term negative relationship between Islamic bank financing and GDP. These findings indicate that, although Sharia financing is expected to support economic growth through Sharia-based financial principles, the reality shows that the direct effect on overall economic growth is more complex and not always unidirectional. Apart from that, this research also finds that in the short term, both sharia financing and GDP adjust back to long-term balance after experiencing deviations, which means a strong economic correction mechanism exists. The stable VAR stability test results validate the model's reliability in describing the accurate relationship between sharia financing and economic growth in Indonesia. These findings provide important insights for policymakers and Islamic banking practitioners in developing more effective strategies to encourage inclusive economic growth.

Energy efficiency and greenhouse gas emissions: A panel ARDL model of top five emitters in Africa

The Paris Agreement has mandated drastic decarbonization by developed and developing economies. Many of the top African emitters are now exploring drastic restructuring of the production processes in a way that promotes energy utilization efficiency. This is critical for ensuring an environmentally benign growth process. This study presents a unique addition to the body of existing knowledge by analysing the effect of some indicators of energy efficiency on three indicators of environmental quality among the top five emitters of GHG in Africa. The data were from the World Development Indicators (WDI), covering the 1990–2020 periods for South Africa, Egypt, Nigeria, Algeria, and Libya. The panel Autoregressive Distributed Lag (ARDL) model was used for data analysis. The results showed that all the included series were stationary at first difference. The optimum lag lengths were determined independently in STATA 17 software, and the most frequent lags across the countries were selected. The Hausman test revealed that the pooled mean group (PMG) estimation was preferred to other estimators. The results showed that in the long-run, particulate emission damages, CO2 intensity, and energy use intensity positively impacted GHG emissions, while GDP per capita and renewable energy utilization produced mixed results. In the short run, the error correction mechanism parameters were all significant (p < 0.01) in Libya for all emission indicators, while Algeria showed significance (p < 0.01) in total GHG and CO2 per capita models. The coefficient of particulate emission damages significantly reduced (p < 0.01) total GHG emissions in Libya and Nigeria and renewable energy significantly increased (p < 0.01) GHG emissions in South Africa but reduced them in Nigeria. Also, CO2 emission intensity significantly (p < 0.01) and positively impacted GHG emissions in South Africa, Egypt, Libya, and Nigeria, while electricity power transmission losses increased total emissions and CO2 emissions in Libya and Algeria. It was concluded that initiatives to reduce GHG emissions should focus on developing countries’ sensitivity to emission damages, and promotion of initiatives for using renewable energies. Also, promotion of energy efficiency across industries offers a prospect for ensuring environmental sustainability.

Multi-Objective energy management of Solar-Powered integrated energy system under forecast uncertainty based on a novel Dual-Layer correction framework

Integrated energy systems (IESs) are increasingly pivotal in the global shift towards sustainable energy frameworks. Within IESs, the energy management system (EMS) plays a critical role, tasked with optimizing energy allocation to achieve objectives like grid stability, energy reliability, and cost-efficiency. A significant challenge for EMS is the inherent unpredictability of renewable energy. Given this, a model predictive control (MPC)-based real-time energy management framework is proposed, which aims to mitigate the impacts of radiation forecast uncertainties in solar-powered IES. A dual-layer correction mechanism is proposed to quantify forecast uncertainty, resulting in uncertain intervals inferred from the hidden Markov model (HMM). Then, the uncertain intervals are adeptly managed within the MPC decision-making framework through the constraint-tightening method. A case study on solar-powered electricity, heating, and hydrogen IES demonstrates the validity of the proposed framework. Five different sets of energy management strategies, such as deterministic analysis, distributionally robust optimization, and the proposed HMM-MPC are subjected to long-term simulations. Comparative analysis reveals that the proposed method enhances various aspects of the performance metrics, including demand response, energy reliability, and system self-sufficiency.

Automated External Bone Fixation Robot Design and Orthopedic Analysis

Background: Bone external fixation technology is an important therapeutic approach for limb correction, primarily involving a class of external fixation correction mechanisms. It has achieved good results in limb lengthening and correcting deformities to restore limb function. In clinical practice, existing correction mechanisms face challenges such as high resistance between components, complex installation procedures, and high precision requirements for installation, requiring manual adjustment by physicians, which limits precise control and quantification of correction parameters. Objective: To address these issues, an automated correction bone external fixation robot was designed based on traditional Ilizarov external fixation devices(TIEFD). Methods: By increasing the number of unidirectional hinge connections, the robot can effectively reduce motion resistance. Subsequently, the robot was combined with the tibia for correction motion simulation, obtaining the motion parameters θ of the deformed bones during the correction process based on the trajectory of the tibia's point mass. Finally, correction motion experiments and finite element analysis(FEA) were conducted on the robot combined with a control system. Results: The results showed that the robot could precisely control correction parameters, with the error range for rotational correction not exceeding 0.5 radians and the error range for traction correction not exceeding 0.2 mm. FAE, based on corrective force data, concluded that compared to (TIEFD), the robot could reduce bone stress by 16 MPa during the correction process. Conclusion: The robot effectively reduces patient discomfort, this provides a reliable theoretical basis for bone external fixation technology and holds positive significance for the treatment of skeletal deformities.

Prediction of remaining life of bearings based on integral correction and global attention mechanism

Prediction of remaining life of bearings based on integral correction and global attention mechanism

Globalization, Urbanization, Energy Consumption, Economic Expansion and Industrialization: Pathway to Tanzania’s Environmental Sustainability Agenda 2030

This study delves into a comprehensive analysis of interconnectedness between globalization, urbanization, energy consumption, industrialization, and economic expansion as the pathway towards Tanzania’s 2030 environmental sustainability agenda utilizing time series data spanning from 1990 to 2022. This data was sourced from the World Bank and KOF Swiss Institute. The autoregressive distributed lag (ARDL) model was utilized in this study. Unit root test, ARDL bound cointegration test, Akaike Information Criterion, Serial correlation test, Heteroscedasticity test, and Jarque-Bera test were used for data analysis. The study revealed that the lag of environmental sustainability, industrialization, and economic expansion affects CO2 emissions in the short term by 0.516%, 0.153%, and -0.179% respectively. The study further shows that urbanization increases CO2 emissions by 1.02 percent in the long run, while globalization reduces CO2 by 0.298 percent, which consequently improves environmental sustainability. ECM (Error Correction Mechanisms) suggests 54% speed adjustment of environmental sustainability within one year after shocks on explanatory variables. The study further recommends an appropriate policy that will encourage conservation of the environment, monitoring and evaluation, technology and innovation of green-friendly energy sources, workshops, and community inclusion on matters concerning the environment

Multi-granularity attribute similarity model for user alignment across social platforms under pre-aligned data sparsity

Cross-platform User Alignment (UA) aims to identify accounts belonging to the same individual across multiple social network platforms. This study seeks to enhance the performance of UA tasks while reducing the required sample data. Previous research has focused excessively on model design, lacking optimization throughout the entire process, making it challenging to achieve performance without heavy reliance on labeled data. This paper proposes a semi-supervised Multi-Granularity Attribute Similarity Model (MGASM). First, MGASM optimizes the embedding process through multi-granularity modeling at the levels of characters, words, articles, structures, and labels, and enhances missing data by leveraging adjacent text attributes. Next, MGASM quantifies the correlation between attributes of the same granularity by constructing Multi-Granularity Attribute Cosine Distance Distribution Vectors (MA-CDDVs). These vectors form the basis for a binary classification similarity model trained to calculate similarity scores for user pairs. Additionally, an attribute reappearance score correction (ARSC) mechanism is introduced to further refine the ranking of candidate users. Extensive experiments on the Weibo-Douban and DBLP17-DBLP19 datasets demonstrate that compared to state-of-the-art methods, The hit-precision of the MGASM series has significantly improved by 68.15% and 27.02%, almost reaching 100% precision. The F1 score has increased by 37.6% and 21.4%.

A market correction mechanism regulation as a consequence of the 2021–2023 energy crisis

A market correction mechanism regulation as a consequence of the 2021–2023 energy crisis

Residential demand response online optimization based on multi-agent deep reinforcement learning

Demand side management in the context of smart grids exploits the value stream of load flexibility, promoting stable and economic operation of the power grid. To address multi-uncertainties such as device characteristics, distributed renewable generation, and residential electricity demand, this paper proposes a model-free framework based on multi-agent deep reinforcement learning to achieve efficient online optimization of demand response strategies. Firstly, the demand side management is formulated as a partially observable Markov game. Then, a multi-agent soft actor-critic algorithm is proposed, combining a temporal electricity price feature extraction technology to improve learning efficiency. Reward correction mechanism is designed to avoid new charging and discharging peaks in the demand response. Finally, case studies demonstrate the effectiveness of the proposed method in reducing the electricity cost and aggregate peak demand of residents. The proposed method reduces the daily average cost by 21.40%, 8.09%, and 5.88% compared to MAPPO, MADDPG, and SUMADRL, and exhibits the highest training efficiency and scalability against the benchmarks.

Domestic Demand and Export Performance in CEMAC: An Assessment with Endogeneity-related Model

Based on several alternative approaches there is by now some evidence suggesting that there is a negative relationship between exports and domestic sales. But in the majority of cases this regularity has focused on industrialized countries. While the external trade of African countries faces numerous challenges, with the low level of intra regional trade and the concentration of exports in some raw materials. Thus the aim of this paper to assess this new export channel for African countries. We then estimate a dynamic panel model with an error correction mechanism of the export market share (the difference between exports and foreign demand) for the six economies of the CEMAC zone between 1974 and 2021. Through the use of instrumental variables (2SLS) we address the role of endogeneity. According to the results, such link is prominent for African countries such the CEMAC countries where domestic demand impacts effectively negatively exports in CEMAC with a negative elasticity of around 53 %. When allowing for an asymmetric relationship between domestic sales and exports the results remain unchanged suggesting that a significant relationship between domestic demand and exports performance still holds.

Change point detection via Gaussian mixture model

Change point detection aims to find abrupt changes in time series data. These changes denote substantial modifications to the process; these can be modeled as a change in the distribution (in location, scale, or trend). Traditional changepoint detection methods often rely on a cost function to assess if a change occurred in a series. Here, change point detection is investigated in a mixture-model-based clustering framework and a novel change point detection algorithm is developed using a finite mixture of regressions with concomitant variables. Through the introduction of a label correction mechanism, the unstructured clustering-based labels are treated as ordered and distinct segment labels. This approach can detect change points in both univariate and multivariate time series, and different kinds of change can be captured using a parsimonious family of models. Performance is illustrated on both simulated and real data. Journal of Statistical Research 2024, Vol. 58, No. 1, pp. 197-219.

Evidence that Femto-H2 Exists Leads to the Correct Mechanism of Cold Fusion (Cold Fusion is Caused by Femto-D2)

Femto-H2 is generated by compression of H- H bond of H2 confined at expandable T site, and authors’ mechanism of Cold fusion is that femto-D2 created at expandable T site cause fusion of D+D due to the shielding of coulomb repulsive force because femto- D2 has so dense electron between d-d that coulomb repulsive force is shielded to cause Fusion. One of the controversial heat generation reactors is QHe (Quantum Hydrogen Energy) developed by clean-planet, who explains that QHe is a heat-generating technology with hydrogen quantum diffusion. The diffusion is induced by heating a small amount of hydrogen saturated in nano-sized nickel-based composite material. Although they did not describe it cold fusion, it is commonly understood to be cold fusion. Therefore, this kind of Cold fusion debate is so confusing that no common standard theory has been established. Therefore, I would like to explain the mechanism of QHe based on author’s Mechanism of Cold Fusion. with H2 loading into multi-stacked Ni/Cu which has large number of reaction site of grain-boundary, where the hydrogen segregates to enhance the reaction of femto- H2 generation. And femto-H2 can fuse to Cu or Ni to be mainly Ze to generate 15~17MeV, which is comparable of energy by D+D=4He+24MeV.Because femto-H2 is so small to fuse to the metal nucleus at room temperature, and at high temperature, the nucleus vibrates at high speed and femto-H2 can fuse with the metal nucleus and once the heat generation start transmutation continues. Author also discovered phenomena that seems to be related to femto-H2, which are transmutation reactor by Dr. Ohmasa and car on H2O (Water Fuel CELL) by Stanly A Meyer. Ohmasa’s transmutation reactor uses Pd coating plate and loaded hydrogen by electrolysis generate femto-H2 which enter into H2O to transmute H2O to be helium-3 and oxygen-18 clusters. Water Fuel Cell can generate huge heat by the transmutation of metal with femto-H2. Therefore, author would like to request clean-planet to study the transmutation with femto-H2 in QHe reactor to prove author’s mechanism hypo, and Cold fusion community to start the discussion on the standard theory of Cold Fusion because author’s mechanism is inconsistent with nucleus model.