Classical Error Research Articles

Tourism development, environmental regulations, and natural resource management: Evidence from G20 countries

The rapid development in the tourism sector across the globe has posed significant challenges for the conservation and management of natural resources, necessitating the implementation of effective environmental regulations. In this perspective, to add empirical evidence in the academic literature, this study aims to investigate the role of tourism development and environmental regulations in the management of natural resources in the G20 countries. To serve the objective, this paper takes the data of G20 countries over the period of 1995–2020 and employ FGLS and PCSE econometric techniques for empirical estimations. The results have shown that tourism development, environmental regulations, environmental protection technologies, and government regulatory quality interventions have a positive impact on natural resources management. However, natural resource depletion has a negative impact on the protection of natural resources. The results were significant when both a common AR(1) model and a panel-specific AR(1) model were used. Although the slope coefficients vary slightly, the estimates are comparable using the Prais-Winsten and Driscoll-Kraay standard error regressions. The findings of the study hold promising insights for policy formation in G20 countries for developing environmental and tourism policies to manage the natural resources for the future generations.

Improving table detection for document images using boundary

Locating tables in document images is the first step to extracting table information, and high location precision is required. The dominant approach of table detection is based on an object detection algorithm, and the detector defines the prediction task as a regression problem, which inevitably leads to positioning errors. To address this issue, this paper presents an approach called Border Line Correction (BLC) to refine the rough prediction results of the original detector through the table boundary lines extracted from the document image. Our approach transforms the regression task into a classification problem, thus avoiding the inherent regression error of the object detection algorithm. Traditional annotation methods are inadequate for table detection tasks as they fail to capture the completeness and purity of the detection results. Therefore, this study treats the correct position of a table as a tolerance region. Additionally, to overcome the limitations of existing datasets in the materials domain, we collected 1183 samples from scientific literature in the materials field and created the MatTab dataset, annotating the tables with tolerance regions. This paper use Cascade RCNN with Swin Transformer as baseline models, and BLC is utilized to optimize the detection results. Experimental results demonstrate significant improvements with BLC at an IOU of 0.95 on the MatTab, ICDAR2019, and ICDAR2017 datasets. In MatTab, the percentage of correctly detected complete and pure tables increased from 72.3% to 82.1%.

Title the Effect of Financing Distribution, Restructuring and Portfolio at Risk (PAR) on PNM Surabaya Branch Office Revenue During the Covid-19 Pandemic

This research was conducted at PT PNM Surabaya Branch for the period 2019-2021 aims to examine the influence of the Effect of Financing Distribution, Restructuring, and Portfolio at Risk (PAR) on PNM Surabaya Branch Office Revenue during the Covid-19 Pandemic. Independent variables are Financing Distribution, Restructuring and Portfolio at Risk (PAR). The dependent variable is PNM Surabaya Branch office Revenue. This research design is quantitative research with use of SPSS version 22, secondary data type. The regression analysis testing, anova test and Residuals test. The results show, based on the anova test (T-test), that partially the distribution of financing, restructuring and portfolio at risk (PAR) has a simultaneous effect on revenue. While based on the Multicollinearity Test, Financing Distribution and Portfolio at Risk (PAR) have no significant effect on revenue of PNM Surabaya Branch office. while restructuring has a very significant effect on the revenue of PNM Surabaya branch office

A computational stochastic procedure for solving the epidemic breathing transmission system

This work provides numerical simulations of the nonlinear breathing transmission epidemic system using the proposed stochastic scale conjugate gradient neural networks (SCGGNNs) procedure. The mathematical model categorizes the breathing transmission epidemic model into four dynamics based on a nonlinear stiff ordinary differential system: susceptible, exposed, infected, and recovered. Three different cases of the model are taken and numerically presented by applying the stochastic SCGGNNs. An activation function ‘log-sigmoid’ uses twenty neurons in the hidden layers. The precision of SCGGNNs is obtained by comparing the proposed and database solutions. While the negligible absolute error is performed around 10–06 to 10–07, it enhances the accuracy of the scheme. The obtained results of the breathing transmission epidemic system have been provided using the training, verification, and testing procedures to reduce the mean square error. Moreover, the exactness and capability of the stochastic SCGGNNs are approved through error histograms, regression values, correlation tests, and state transitions.

Are ride-hailing services safer than taxis? A multivariate spatial approach with accommodation of exposure uncertainty

Despite many research efforts on ride-hailing services and taxis, limited studies have compared the safety performance of the two modes. A major challenge is the need for reliable mode-specific exposure data to model their safety outcomes. Moreover, crash frequencies of the two modes by injury severities tend to be spatially and inherently correlated. To fully address these issues, this study proposes a novel multivariate conditional autoregressive model considering measurement errors in mode-specific exposures (MVCARME). More specially, a classical measurement error structure accommodates the uncertainty of estimated mode-specific exposures, and a multivariate spatial specification is adopted to capture potential spatial and inherent correlations. The model estimation is accelerated by an integrated nest Laplace approximation method. The census tracts in the city of Chicago are set as the spatial analysis unit. The mode-specific exposures (vehicle-mile-traveled) in each census tract are estimated by trip assignments using ride-hailing and taxi trip data in 2019. The modeling results indicate that both ride-hailing crashes and taxi crashes are positively associated with transportation factors (e.g., vehicle-mile-traveled, mode-specific vehicle-mile-traveled, and traffic signal numbers), land use factors (i.e., number of educational and alcohol-related sites), and demographic factors (e.g., median household income, transit ratio, and walk ratio). By comparison, the proposed model outperforms the others (i.e., negative binomial models and multivariate conditional autoregressive model) by yielding the lowest deviance information criterion (DIC), Watanabe-Akaike information criterion (WAIC), mean absolute error (MAE), and root-mean-square error (RMSE). According to the results of t-tests, ride-hailing services are found to be prone to a higher risk of minor injury crashes compared with taxis, despite no significant difference between the risks of severe injury crashes. Methodologically, this study adds a robust safety evaluation approach for comparing crash risks of different modes to the literature. At the same time, practically, it provides researchers, practitioners, and policy-makers insights into the safety management of various mobility alternatives.

Determinants of credit default swap spread changes: The sell-side perspective

This study revisits the credit spread puzzle using credit default swap spread changes between 2002 and 2020. We find that credit-related structural variables account for only 13.8% of the variation in credit default swap spread changes. There exists a single dominant common component that explains 49.8% of the regression residuals. Sell-side risk-bearing constraints are closely related to the unknown common component and significantly improve the explanatory power of credit default swap spread changes, particularly the commonality between them.

Size and shape of human mandible: Sex differences and influence of age on sex estimation accuracy

The degree of sexual dimorphism expressed by human bones is of primary importance for the development of accurate methods for sex estimation. The objective of the present study was to investigate sex differences in shape and size of the mandible using geometric morphometric methods. The study also aimed to examine the impact of age on the sex classification ability of the size and shape of the mandible. Computed tomography images of 190 Bulgarians (98 males and 92 females) were used in the study. Polygonal surface models of the skulls were generated and used for digitizing 45 landmarks located on the mandible. The raw three-dimensional coordinates of the landmarks were processed via generalized Procrustes superimposition. The sex differences in mandibular size and shape were evaluated for statistical significance. Multivariate regression was applied for correction of the allometric effect. Principal component analysis, discriminant analysis, and canonical variate analysis were also used in the study. Mandibular size differed significantly between males and females and achieved a sex classification accuracy of 87%. The significance of the sex differences in mandibular shape depended on the type of shape variables used in the analysis. The shape variables provided different classification accuracy: 78% using the Procrustes coordinates and 53% using the regression residuals. The male and female mandibles differed significantly in size and shape, including the allometric component. Mandibular size is a more effective sex indicator than shape. Age has an ambiguous effect on the classification accuracy of the size and shape variables of the mandible.

A generalisation of the method of regression calibration

There is direct evidence of risks at moderate and high levels of radiation dose for highly radiogenic cancers such as leukaemia and thyroid cancer. For many cancer sites, however, it is necessary to assess risks via extrapolation from groups exposed at moderate and high levels of dose, about which there are substantial uncertainties. Crucial to the resolution of this area of uncertainty is the modelling of the dose–response relationship and the importance of both systematic and random dosimetric errors for analyses in the various exposed groups. It is well recognised that measurement error can alter substantially the shape of this relationship and hence the derived population risk estimates. Particular attention has been devoted to the issue of shared errors, common in many datasets, and particularly important in occupational settings. We propose a modification of the regression calibration method which is particularly suited to studies in which there is a substantial amount of shared error, and in which there may also be curvature in the true dose response. This method can be used in settings where there is a mixture of Berkson and classical error. In fits to synthetic datasets in which there is substantial upward curvature in the true dose response, and varying (and sometimes substantial) amounts of classical and Berkson error, we show that the coverage probabilities of all methods for the linear coefficient alpha are near the desired level, irrespective of the magnitudes of assumed Berkson and classical error, whether shared or unshared. However, the coverage probabilities for the quadratic coefficient beta are generally too low for the unadjusted and regression calibration methods, particularly for larger magnitudes of the Berkson error, whether this is shared or unshared. In contrast Monte Carlo maximum likelihood yields coverage probabilities for beta that are uniformly too high. The extended regression calibration method yields coverage probabilities that are too low when shared and unshared Berkson errors are both large, although otherwise it performs well, and coverage is generally better than these other three methods. A notable feature is that for all methods apart from extended regression calibration the estimates of the quadratic coefficient beta are substantially upwardly biased.

Identification and validation of disulfidptosis-associated molecular clusters in non-alcoholic fatty liver disease.

Objective: Non-alcoholic fatty liver disease (NAFLD) is the most prevalent liver disease in the world, and its pathogenesis is not fully understood. Disulfidptosis is the most recently reported form of cell death and may be associated with NAFLD progression. Our study aimed to explore the molecular clusters associated with disulfidptosis in NAFLD and to construct a predictive model. Methods: First, we analyzed the expression profile of the disulfidptosis regulators and immune characteristics in NAFLD. Using 104 NAFLD samples, we investigated molecular clusters based on differentially expressed disulfidptosis-related genes, along with the related immune cell infiltration. Cluster-specific differentially expressed genes were then identified by using the WGCNA method. We also evaluated the performance of four machine learning models before choosing the optimal machine model for diagnosis. Nomogram, calibration curves, decision curve analysis, and external datasets were used to confirm the prediction effectiveness. Finally, the expression levels of the biomarkers were assessed in a mouse model of a high-fat diet. Results: Two differentially expressed DRGs were identified between healthy and NAFLD patients. We revealed the expression profile of DRGs in NAFLD and the correlation with 22 immune cells. In NAFLD, two clusters of molecules connected to disulfidptosis were defined. Significant immunological heterogeneity was shown by immune infiltration analysis among the various clusters. A significant amount of immunological infiltration was seen in Cluster 1. Functional analysis revealed that Cluster 1 differentially expressed genes were strongly linked to energy metabolism and immune control. The highest discriminatory performance was demonstrated by the SVM model, which had a higher area under the curve, relatively small residual and root mean square errors. Nomograms, calibration curves, and decision curve analyses were used to show how accurate the prediction of NAFLD was. Further analysis revealed that the expression of three model-related genes was significantly associated with the level of multiple immune cells. In animal experiments, the expression trends of DDO, FRK and TMEM19 were consistent with the results of bioinformatics analysis. Conclusion: This study systematically elucidated the complex relationship between disulfidptosis and NAFLD and developed a promising predictive model to assess the risk of disease in patients with disulfidptosis subtypes and NAFLD.

Accelerating the computational process for analyzing dynamic thermal behavior of an innovatively designed longitudinally-graded sandwich plate: Deep-learning approach

Employing the solutions through artificial intelligence (AI) to solve mechanical engineering problems can pave the way toward accelerating the computation process, especially for those engaged with the dynamic nature of the time-variable systems. In this regard, this article attempts to employ deep neural networks (DNN) for the coupled thermoelasticity assessment of the longitudinally sandwiched plates exposed to intensive thermal shock. The left and right face sheets of the sandwich plate are enriched with graphene platelets (GPLs) along the longitudinal direction. The thermal shock is considered as the instantaneous heat flux applied in the axial direction. Elasticity theory is employed for deriving the governing equations in an exact template and differential quadrature approach (DQA) in conjunction with the Laplace transform plays the role of solution approaches to attain the thermoelastic reaction of the sandwich system at training points. Then, the DNN utilizes these points to adjust a predictor mechanism for analyzing the behavior of the sandwich system in every load-exertion circumstances. Additionally, the DNN would be optimized by a brand-new metaheuristic algorithm according to some error estimation criteria to minimize the error of regression. The mentioned solution is validated by comparing its performance with the outcomes of the published literature. This study provides a novel platform to use the great potential of AI-based methods in solving dynamic thermoelastic problems in the world of solid structures.

The impact of natural resource management, innovation, and tourism development on environmental sustainability in low-income countries

Natural resource depletion and the effects of global warming on the environment have emerged as urgent worldwide challenges. Many countries have responded by implementing plans to manage their natural resources and deal with environmental issues in the post-COVID-19 era. However, innovative abilities are frequently limited in low-income countries (LICs), which makes it difficult for them to successfully offset environmental consequences. It is crucial to comprehend the possible contributions that innovation, tourism, and the availability of natural resources can make to resolving these issues. This study focuses on examining how innovation, tourism, and environmental degradation relate to LICs richness of natural resources. To establish the reliability of the results, empirical analysis utilizing data from 2000 to 2020 of LICs was carried out using the Feasible GLS, Prais-Winsten Regression, Driscoll-Kraay standard error regression, and Non-Parametric Kernel regression models. The findings show that natural resources have a beneficial effect on environmental degradation in LICs. Natural resource-dependent sectors are thought to use energy-intensive procedures, which contribute to greater emissions, in countries where the share of natural resource rent in GDP is higher. Higher levels of innovation may not always result in environmental sustainability in LICs, as the study also demonstrates a positive association between innovation and environmental degradation. Although there is a link between tourism and environmental deterioration, it is possible that the growth of tourism in LICs will encourage the transition to a less carbon-intensive service-based economy.

Immunological characterization and diagnostic models of RNA N6-methyladenosine regulators in Alzheimer's disease

Alzheimer's disease (AD) is the most prevalent form of dementia, and it displays both clinical and molecular variability. RNA N6-methyladenosine (m6A) regulators are involved in a wide range of essential cellular processes. In this study, we aimed to identify molecular signatures associated with m6A in Alzheimer's disease and use those signatures to develop a predictive model. We examined the expression patterns of m6A regulators and immune features in Alzheimer’s disease using the GSE33000 dataset. We examined the immune cell infiltration and molecular groups based on m6A-related genes in 310 Alzheimer's disease samples. The WGCNA algorithm was utilized to determine differently expressed genes within each cluster. After evaluating the strengths and weaknesses of the random forest model, the support vector machine model, the generalized linear model, and eXtreme Gradient Boosting, the best machine model was selected. Methods such as nomograms, calibration curves, judgment curve analysis, and the use of independent data sets were used to verify the accuracy of the predictions made. Alzheimer's disease and non-disease Alzheimer's groups were compared to identify dysregulated m6A-related genes and activated immune responses. In Alzheimer's disease, two molecular clusters linked to m6A were identified. Immune infiltration analysis indicated substantial variation in protection between groups. Cluster 1 included processes like the Toll-like receptor signaling cascade, positive regulation of chromatin binding, and numerous malignancies; cluster 2 included processes like the cell cycle, mRNA transport, and ubiquitin-mediated proteolysis. With a lower residual and root mean square error and a larger area under the curve (AUC = 0.951), the Random forest machine model showed the greatest discriminative performance. The resulting random forest model was based on five genes, and it performed well (AUC = 0.894) on external validation datasets. Accuracy in predicting Alzheimer's disease subgroups was also shown by analyses of nomograms, calibration curves, and decision curves. In this research, we methodically outlined the tangled web of connections between m6A and AD and created a promising prediction model for gauging the correlation between m6A subtype risk and AD pathology.

An Analysis of the Effect of Naira Devaluation on Nigerian Balance Of Trade

This study investigates the effect of naira devaluation on Nigerian balance of trade using time series data from 1986 to 2021. The study examined the long run and short-run equilibrium relationship between devaluation and trade balance over the study period. The study used unrestricted Vector Auto Regression (VAR), Co-integration and Vector Error Correction Mechanism (VECM) technique in the analysis of data. The variables currency devaluation (CDV), Export rate (EXPT), Import rate (IMPT) and Trade balance are found to be 1(1) variables, while Interest rate (INTR) is found to be 1(0) variables. Based on the VAR Co- integration test result, it indicates long-run equilibrium relationship among the variables. The error correction term was correctly signed and statistically significant. The estimates from the VAR model showed both positive and negative shocks running from naira devaluation to trade balance, export rate, import rate and currency devaluation based on the past and current values. The study concluded that, naira devaluation have positive and significant impact on trade balance of Nigeria. The study recommended that, Government should improve this indigenous innovation to meet up with international standard as a means to reduce dependence on import and encourage production of competitive goods and services, as export becomes cheaper after devaluation, it is expected that Nigeria as a country will immensely improve on its exported goods and increase the output of exportable goods which will leads to favorable balance of trade among others.

Five Centuries of Groundwater Elevations Provide Evidence of Shifting Climate Drivers and Human Influences on Water Resources in North Central Florida

AbstractGroundwater depletion is a concern around the world with implications for food security, ecological resilience, and human conflict. Long‐term perspectives provided by tree ring‐based reconstructions can improve understanding of factors driving variability in groundwater elevations, but such reconstructions are rare to date. Here, we report a set of new 546‐year tree‐ring chronologies developed from living and remnant longleaf pine (Pinus palustris) trees that, when combined with existing bald cypress (Taxodium distichum) tree‐ring chronologies, create a set of nested reconstructions of mean annual groundwater elevation for North Central Florida that together explain 66% of the variance in instrumental measurements and span 1498–2015. Split calibration confirms the skill of the reconstructions, but coefficient of efficiency metrics and significant autocorrelation in the regression residuals indicate a weakening relationship between tree growth and groundwater elevation over recent decades. Comparison to data from nearby groundwater wells suggests extraction of groundwater is likely contributing to this weakening signal. Periodicity within the reconstruction and comparison with global sea surface temperatures (SST) highlight the role of El Niño‐Southern Oscillation (ENSO) in driving groundwater elevations, but the strength of this role varies substantially over time. Atlantic and Pacific SST modulate ENSO influences, and comparisons to multiple proxy‐based reconstructions indicate an inconsistent and weaker influence of ENSO prior to the 1800s. Our results highlight the dynamic influence of ocean‐atmospheric phenomena on groundwater resources in North Central Florida and build on instrumental records to better depict the long‐term range of groundwater elevations.

The influence of chemical etching on the surface quality of phase components

Large-aperture fused silica phase optical components such as continuous phase plate (CPP) are widely used in large-scale laser devices to achieve beam homogenization and improve beam quality. However, under the action of high-energy lasers, their lower damage threshold seriously restricts their service life and increases cost of using. Compared with other fused silica components, chemical processing technology with hydrofluoric acid solution (HF) is lacking in the processing of phase components because the residual root mean square value (RMS) of phase elements is very high, and it can not guarantee this. Therefore, it is necessary to carry out research on the influence of chemical treatment on phase components. In this paper, we improved the chemical treatment process to achieve the change of residual RMS value not more than 3 nm, and improved the ability of resisting laser damage that the damage threshold of 29J/cm2 was obtained under the test conditions 351nm@3ns. Finally, we successfully mastered the chemical control process of phase components and applied it to the CPPs as other fused quartz materials engineering production process.

Sliding Window Network Coding Enables NeXt Generation URLLC Millimeter-Wave Networks

Ultra-reliability and low-latency are pivotal requirements of the emerging 6th generation of communication systems (xURLLC). The transition in millimeter-wave (mmWave) technology, from omni-directional to highly directional antennas, has been seen as an enabler for high bandwidth communications, still susceptible to high loss and high latency variation. Classical error recovery approaches cannot close the rising gap between high throughput and low delay in such systems. In this work, we incorporate effective sliding window network coding solutions in mmWave communications. While legacy systems such as rateless codes improve the delay, cross-layer results show that they do not provide Low Latency Communications (LLC), due to the lossy behaviour of mmWave channel and the lower-layers’ retransmission mechanisms. On the other hand, fixed sliding window random linear network coding (RLNC) is able to achieve LLC, and even better, adaptive sliding window RLNC obtains Ultra-Reliable LLC (URLLC) in mmWave backhaul networks.

Automated Electrodermal Activity and Facial Expression Analysis for Continuous Pain Intensity Monitoring on the X-ITE Pain Database.

This study focuses on improving healthcare quality by introducing an automated system that continuously monitors patient pain intensity. The system analyzes the Electrodermal Activity (EDA) sensor modality modality, compares the results obtained from both EDA and facial expressions modalities, and late fuses EDA and facial expressions modalities. This work extends our previous studies of pain intensity monitoring via an expanded analysis of the two informative methods. The EDA sensor modality and facial expression analysis play a prominent role in pain recognition; the extracted features reflect the patient's responses to different pain levels. Three different approaches were applied: Random Forest (RF) baseline methods, Long-Short Term Memory Network (LSTM), and LSTM with the sample-weighting method (LSTM-SW). Evaluation metrics included Micro average F1-score for classification and Mean Squared Error (MSE) and intraclass correlation coefficient (ICC [3, 1]) for both classification and regression. The results highlight the effectiveness of late fusion for EDA and facial expressions, particularly in almost balanced datasets (Micro average F1-score around 61%, ICC about 0.35). EDA regression models, particularly LSTM and LSTM-SW, showed superiority in imbalanced datasets and outperformed guessing (where the majority of votes indicate no pain) and baseline methods (RF indicates Random Forest classifier (RFc) and Random Forest regression (RFr)). In conclusion, by integrating both modalities or utilizing EDA, they can provide medical centers with reliable and valuable insights into patients' pain experiences and responses.

Classical and Bayesian error analysis of the relativistic mean-field model for doubly magic nuclei

Classical and Bayesian error analysis of the relativistic mean-field model for doubly magic nuclei

Impact of eco-innovation and financial efficiency on renewable energy – Evidence from OECD countries

The research on the driving factors of renewable energy consumption has attracted a lot of attention, but the impact of eco-innovation and financial efficiency on it is rare, therefore, we launched this study to explore the impact of financial efficiency and ecological innovation on renewable energy consumption in OECD countries between 2001 and 2018 using fully modified ordinary least square (FMOLS) and Driscoll-Kraay standard error regression. Empirical results show that both financial efficiency and eco-innovation have long-term positive correlations with renewable energy consumption. In addition, we analyzed the heterogeneity of the impact of selected factors on renewable energy consumption through fixed-effects panel quantile regression, and found that with the increase of quantile, the promotion effect of financial efficiency and ecological innovation on renewable energy consumption is decreasing. Finally, we suggest that OECD countries increase and expand their total financial resources, while promoting healthy competition among financial institutions to improve financial efficiency; promote ecological innovation in energy production, transmission and distribution, in order to improve the use of renewable energy.

Agrivoltaic system for energy-food production: A symbiotic approach on strategy, modelling, and optimization

Agrivoltaic system is a symbiotic approach to minimize land use conflicts for energy-food production. This production is dynamically affected by environmental parameters. Hence, an efficient system design is needed by adopting a suitable optimization model. The objective of this study is to discuss the application of artificial neural network and genetic algorithm to an experimental 0.675 kWp agrivoltaic system with turmeric crops for optimum production. Turmeric is a medicinal crop used primarily as spices and traditional medicine. The input parameters for the model are solar radiation, temperature, humidity, and rainfall, while the output parameters are energy and food. After training, validation, and testing of the model, the error has been found as ­0.03711 and ­0.00494 for energy and food systems respectively. For system performance, the established model generates a minimum mean squared error and maximum regression correlation coefficient indicating a favourable relationship between measured and predicted values. The predicted energy and food production has been obtained at optimal conditions as 104.9097 kWh and 9.0955 kg, respectively. The performance indicators such as land equivalent ratio and payback period have been found as 1.73 and 9.49 respectively. Thus, the system is techno-economically viable and feasible worldwide satisfying the targets of sustainable development goals.