Error Approach Research Articles

Analisis Pengaruh Penempatan Shearwall Pada Gedung Hotel Shafira

Hotel Shafira is a flat-shaped building with 10 floors, 12 meters wide and 72 meters long. These structures are prone to significant horizontal irregularities, reducing the overall efficiency of the building. The irregularity is partly due to the uneven distribution of the mass, as seen from the difference in the span of the portal between the back and front of the building which produces large cavities. Analysis with the ETABS program revealed that the eccentricity between the center of self and the center of mass reached 5 meters in the direction of the strong axis. This study overcomes the uneven load distribution by repositioning the shearwall through a trial and error approach, reducing eccentricity from 4 meters to 1.3 meters. This change not only increases the building's capacity, but also reduces the need for reclamation by 13.9% and shows the results of the pushover analysis in the Immediate Occupancy category. The placement of shearwalls has a significant influence on the capacity and stability of the building structure.

Assessing the impact of three emission (3E) parameters on environmental quality in Canada: A provincial data analysis using the quantiles via moments approach

ABSTRACT Existing studies rarely examine the simultaneous effects of three emitting indicators (3E) – full emitting non-renewable, non-emitting renewable, and low-emitting nuclear - on three specific greenhouse gases: CO2, CH4, and N2O. We investigate the impact of three energy types on environmental quality, using Canadian data from 1990-2022. It incorporates macroeconomic policies, economic uncertainty, geopolitical risks, and eco-innovation, and employs the quantiles via moments method to explore the evolving relationships among these factors, considering provincial variances. Findings reveal that non-renewable energy sources deteriorate environmental quality by increasing CO2, CH4, and N2O emissions across all quantiles (from q.5 to q.95), while renewable and nuclear energies, along with eco-innovation initiatives, have a beneficial effect by reducing greenhouse gas emissions across all quantiles. Economic policy uncertainty is a contributing factor to greenhouse gas emissions across all quantiles, whereas geopolitical risks primarily impact the middle to upper quantiles (from q.50 to q.95). To counteract the lack of cross-sectional dependence in the quantiles via moments methodology, this paper employs Driscoll and Kraay’s standard errors approach to fortify its findings’ reliability. It concludes with policy suggestions promoting renewable energy and eco-innovation through increased investment, vibrant long-term policies, provincial collaboration, and adoption of green technologies.

Towards achieving inclusive energy in SSA: The role of financial inclusion and governance quality

Energy poverty is a serious impediment to achieving Sustainable Development Goal 7 (SDG-7), thereby compelling policymakers and the scientific community to make collaborative commitments to address energy issues worldwide. While there is growing debate on the prevalent determinants of energy poverty, the empirical scrutiny of the contributing role of financial inclusion and governance quality remains elusive in existing literature. Therefore, this study investigates the impact of financial inclusion and governance quality on energy poverty in 29 sub-Saharan Africa (SSA) spanning 2004–2022. The empirical analysis was accomplished using the Augmented Mean Group (AMG) and Common Correlated Effects Mean Group (CCEMG) techniques with the Driscoll-Kraay standard error (DKSE) approach as a robustness check. The empirical outcomes indicate that a well-developed financial system that fosters easy and quick access to credit, especially among marginalized households, leads to the affordability of modern energy resources, thereby ameliorating energy poverty in the region. Additionally, the findings demonstrate the undeniable role of good governance in fostering a sound and inclusive financial system that enables households to access energy resources, ultimately reducing energy poverty in the region. Based on these outcomes, this study proposes important policy insights for the design and implementation of inclusive financial and energy policies.

Reentry vehicle fixed-time terminal guidance and attitude control with impact angle constraints

The motivation of this paper is to handle the terminal guidance and attitude control problem for the unpowered reentry vehicle in the diving phase, which is crucial for interception to the target. The most important objectives of this study are to intercept the maneuvering target with the miss distance and impact angle error as small as possible. First, the second-order derivatives of the aerodynamic angles are derived with the fin deflections as control input, which is objective to avoid the tracking of reentry vehicle body angle rate command in the traditional methods. Second, a novel fixed-time time-varying parameter nonsingular terminal sliding mode guidance law is proposed to improve the convergence speed of line-of-sight angle rate, which can avoid control input singularity without switching to the polynomial form when the error approaches the origin. Thirdly, a new global time-varying sliding mode is developed to design the attitude controller, with the predefined fixed convergence time and analytical solution. The system states are proved to be fixed-time convergence based on the Lyapunov stability theory. Six-degree-of-freedom flight simulations are conducted to validate the superiority of the proposed algorithm in terms of miss distance, impact angle error, intercepting time and control input energy.

How energy transition and fintech influence income inequality in Sub-Saharan Africa: Evidence from a panel-corrected standard error approach

This study explores the role of financial technology (FinTech) and the energy transition in shaping income inequality across 45 sub-Saharan African economies (SSA). By employing the panel-corrected standard errors approach, the analysis takes into account contextual and institutional factors, as well as regional specificities, for the period extending from 2000 to 2021. This research elucidates the connections among energy transition, FinTech, and income inequality, emphasizing their significance for economic progress and clean energy access. The findings reveal significant inequality in the outcomes of SSA countries, with an average Gini index of 43.197, indicating that most countries are unequal. This study reveals a notable connection between transitioning to renewable energy sources and a reduction in income inequality, with a one-unit increase in the energy transition indicator resulting in a 5.10% decrease in inequality. Moreover, FinTech is also found to contribute to a reduction in income inequality, with a 1.31% decrease observed for every unit increase in the FinTech indicator. The analysis also revealed a weak, yet statistically significant, link between population density and income inequality, with a one-unit increase in inequality of 0.00632%. Notably, industrial value added is found to be an influential factor with a statistically significant coefficient of 0.129. The key implications of the study emphasize the importance of redistributive policies, financial inclusion, and the promotion of renewable energy sources in constructing more inclusive economic systems. Furthermore, the study underscores the significant value of accurate energy production prediction for informed decision-making in energy planning and policy-making on regional and global scales.

MALEXNET-EUNET: MULTI-CLASS LIVER CANCER CLASSIFICATION AND SEGMENTATION USING A HYBRID DEEP LEARNING SYSTEM

Background and objective: One of the most prevalent and significant causes of cancer-related mortality worldwide is considered to be liver cancer. Techniques for automatically segmenting and classifying liver tumors are crucial for supporting medical professionals during the tumor diagnostic process. Classifying liver tumors is challenging due to noise, nonhomogeneity, and the significant appearance diversity observed in tumor tissue. Also, in recent years, most of the research has performed binary classification, but there is still a lack of research on multi-class liver cancer classification. Therefore, we perform a multi-class liver cancer classification and segmentation in this research. Methods: We propose a hybrid deep learning-based multi-class liver cancer classification and segmentation system in this research. The collected CT images are pre-processed in four stages: contrast enhancement, noise filtering, smoothing and sharpening, and liver region segmentation. Next, the binary, texture, histogram, and rotational, scalability, and translational (RST) features are extracted from the pre-processed images. Then, the average correlation (AC) and probability of error (POE) approaches are applied to selecting relevant features and excluding less significant features. After feature selection, a modified AlexNet (MAlexNet) model is used to classify the multi-class classification of liver tumors. Finally, the identified liver tumor regions are segmented using the enhanced U-Net (EUNet) model. Results: Identified liver tumors are accurately classified into three different categories using the proposed classification model: hemangioma (HEM), hepatocellular carcinoma (HCC), and metastatic carcinoma (MET), with an average accuracy of 99.19%. We have collected the multi-class liver cancer CT images from real time patients. The results of the experiments show that proposed hybrid system provides adequate overall accuracy, is less noise-sensitive, and outperforms other state-of-the-art techniques such as SVM, Faster RCNN, Mask RCNN, and SAR-U-Net on a wide range of CT images. Conclusion: Radiologists and doctors can identify liver tumors more accurately using the suggested innovative framework.

A review on recent applications of machine learning in mechanical properties of composites

AbstractComposites are undergoing extensive research and utilization due to their excellent mechanical properties, driven by human needs. Traditionally, the research methods in materials science predominantly rely on empirical theory or experimental trial and error approaches. However, the increased complexity of composite materials results in a greater intricacy in their mechanical behavior. Consequently, the utilization of traditional research methods may not achieve sufficient efficiency. Materials science is rapidly transitioning into a data‐driven era, with machine learning (ML) emerging as a potent tool to expedite materials development and enhance properties prediction. Significant advancements have been achieved in the application of ML to the study of composite mechanics. In this review article, we elucidate various ML methods employed in the construction of constitutive models for isotropic and anisotropic composites, and delve into the research on construction ML models that leverage input data derived from composite processes, structures, and environmental conditions to predict material mechanical properties. Additionally, we summarize recent noteworthy ML applications in composite design and optimization. Finally, possible prospective viewpoints are proposed for future development, with the aim of providing essential scientific guidance for advancing material science and technology through ML.Highlights Machine learning can address complexity in constitutive model of the anisotropic composites. Machine learning predicts mechanical properties of composites well by process and structure. Machine learning enhances efficiency in inverse design to optimize composites. Limitations, challenges, development trends of ML in composites.

Light-Activated Molecular Purification (LAMP) of Recombinant Proteins.

The production of recombinant proteins has become a focal point in biotechnology, with potential applications in catalysis, therapeutics, and diagnostics. Before their application, these proteins undergo cumbersome downstream processing, including multiple resin-based chromatography steps (ion exchange or affinity-based) to isolate the protein of interest from host cell proteins, which are more abundant. These methods often involve (1) nonspecific binding of host cell proteins onto the resin, (2) a trial and error approach in determining elution conditions for the protein of interest, and (3) complex functionalization of the resin. These processes are also further supplemented with additional processing steps including buffer exchange through dialysis or desalting. Despite the prevalence and need for proteins, challenges persist in optimizing elution conditions and minimizing downstream processing steps, which contribute to the overall cost, impeding their translation into the market. To address these challenges, there has been a growing interest in stimuli-responsive purification systems, which allow for precise control and modulation of the purification process for protein recovery by altering their properties or behavior in response to specific external conditions, such as heat, light, or chemicals. We have developed a light-activated molecular purification (LAMP) system, a stimuli-responsive chromatography technique where the purification of recombinant proteins is triggered by light. We employed a photocleavable protein (PhoCl1) that binds specifically to Ni-NTA resin through a hexa-histidine tag at its N-terminus. We harnessed the ability of PhoCl1 to undergo photocleavage into two fragments for the development of LAMP. To demonstrate LAMP, the protein of interest (POI) is genetically fused to the C-terminus of PhoCl1. The exposure to 405 nm light (1.5 mW cm-2 for 12 h) results in the release of POI into the supernatant. We showcased the potential of LAMP by purifying highly charged green fluorescent proteins and an enzyme, riboflavin kinase. Our custom-built violet light LED setup achieved more than 50% light-induced photocleavage of the fusion constructs, resulting in the release of more than 30% of the POI into the supernatant, with the remainder retained within the resin. All the proteins purified using LAMP were more than 90% pure. Moreover, the comparison of the riboflavin kinase purified through LAMP and the traditional chromatography (Ni-NTA affinity method) revealed no significant changes in the activity levels. These highlight the broad potential of LAMP in providing a facile, yet robust stimuli-responsive protein purification technique, which leverages the potential of light to purify the proteins and overcome the limitations of current conventional chromatography systems.

Machine learning for screening and predicting the best surface modifiers for a rational optimization of efficient perovskite solar cells

The key to keep the rising slope of perovskite solar cell performances is to reduce non-radiative losses by minimizing defect density. To this end, a large variety of strategies have been adopted spanning from the use of interfacial layers, surface modifiers, to interface engineering. Although winning concepts have been demonstrated, they result from a mere trial and error approach, which is time consuming and operator-dependent. To face this challenge, in this work, we propose the use of a machine learning approach for an educated and rational material screening with optimal characteristics in terms of surface passivation. In particular, we applied Shapley additive explanation to extract the specific chemical features of the passivator, which directly impact the device parameters, specifically the open circuit voltage (Voc). By monitoring the different material parameters as input, we were able to list the most promising passivators and directly test them in working solar cells. By comparing the device performances with the results of the modeling and with additional optical and morphological characterization, we retrieved the most significant material properties linked to the highest efficiency, which are (i) the presence of chlorine and its strong binding capacity to positively charged defects on perovskite surface, reducing the non-radiative recombination and (ii) an increased flexibility of the molecule, resulting in better coverage of the surface. Finally, we tested the predictive power of the ML algorithm proposing a new passivator, which, implemented in a working device, leads to the predicted high Voc confirming the results of the modeling.

Nurses' perspectives and experiences on medical errors: A qualitative study.

Medical errors are among the most important factors that threaten patient safety. Therefore, nurses' perspectives and experiences about medical errors are important for this manner. The aim of this study was to determine in depth the perspectives and experiences of nurses related to how they define medical error, as well as its causes, management and reporting. This descriptive, exploratory study involved a qualitative design. A total of 15 clinical nurses from eleven provinces were reached by snowball sampling method. In the study, nurses' perspectives on medical errors were obtained through semistructured in-depth online interviews conducted based on phenomenological methods. Descriptive analysis was used in the analysis of the data obtained from the interviews. The research was conducted following the COnsolidated criteria for REporting Qualitative checklist. The resulting 26 codes were categorized under seven themes: "Definition of medical error", "Experience of medical errors", "Frequency of medical errors", "Causes of medical errors", "Reporting of medical errors", "Medical error approach" and "Prevention of medical errors". The results show that it is still necessary to increase the awareness of nurses about medical errors and the importance of error reporting. In addition, it reveals the need for leadership to eliminate the negative approach to medical errors and shows that nurse managers should be empowered accordingly. It is the first step of quality and safe care to reveal nurses' perspectives on medical errors, their thoughts about medical errors and their experiences about errors in their institutions. In this study, nurses' knowledge, opinions and experiences regarding medical errors were revealed in this qualitative study. Therefore, this study offers important clues to nursing services, hospital managers and policy makers for clinical and institutional arrangements.

Highly stable silicon oxycarbide all-solid-state batteries enabled by machined learning accelerated screening of oxides and sulfides electrolytes

Traditional trial–error approach severely limits and restricts rapid development of high-performance anode and electrolytes materials, searching huge parameters space of various anode-solid electrolyte interfaces in an effective and efficient way is the key issue. Here, a novel computational strategy combining machine learning and first-principles is proposed to achieve efficient high-throughput screening of oxides and sulfides electrolytes for highly stable silicon oxycarbide all-solid-state batteries. First-principles calculations demonstrate significant compact of material type and elemental doping on interfacial compatibility between silicon oxycarbide and various electrolytes. By proposing several novel descriptors including interfacial adhesion and formation energies of frozen system with low computation cost, the amounts of demanded trainings data are significantly reduced. Gradient-boosted regression tree model shows low mean absolute errors of 0.09 and high R2 value of 0.99 for the prediction of interface formation energy, demonstrating ultrahigh accuracy and reliability of the algorithm. The present work discovers a series of uninvestigated stable anode-solid electrolytes interfacial couples for further experimental preparation.

Method Development and Validation of an Aerosol Sampling Technique for the Analysis of Nicotine in Electronic Cigarette Aerosols.

Because of the increasing popularity of e-cigarettes, monitoring the e-cigarette market has become important for national health authorities to guarantee safety and quality. In the EU, the Tobacco Products Directive requires emission studies for e-cigarette products. The absence of industry guidelines for studying these emissions and the lack of proper validation in the literature led us to develop and validate a method using the total error approach for the determination of nicotine in e-cigarette aerosols. A commercial vaping device was used to generate aerosols, which were then collected on Cambridge filter pads and measured for nicotine concentration by UHPLC-DAD after extraction. The method was successfully validated by generating accuracy profiles, which show that the β-expectation tolerance intervals remained below the acceptance limits of ±20%. Within-run repeatability and intermediate precision were considered acceptable since the highest RSD value obtained was below 5%. The method was applied to 15 commercial e-liquids. A complete validation of a method for the analysis of e-cigarette emissions is presented, including several parameters that impact the accuracy and reproducibility. Similar systematic approaches for method development and validation could be used for other e-cigarette emission analysis methods to ensure the reliability of the measurements.

A parameter uniform numerical method on a Bakhvalov type mesh for singularly perturbed degenerate parabolic convection–diffusion problems

AbstractWe are focused on the numerical treatment of a singularly perturbed degenerate parabolic convection–diffusion problem that exhibits a parabolic boundary layer. The discretization and analysis of the problem are done in two steps. In the first step, we discretize in time and prove its uniform convergence using an auxiliary problem. In the second step, we discretize in space using an upwind scheme on a Bakhvalov-type mesh and prove its uniform convergence using the truncation error and barrier function approach, wherein several bounds derived for the mesh step sizes are used. Numerical results for a couple of examples are presented to support the theoretical bounds derived in the paper.

Superior electroadhesion force with permittivity-engineered bilayer films using electrostatic simulation and machine learning approaches

Electroadhesive forces are crucial in various applications, including grasping devices, electro-sticky boards, electrostatic levitation, and climbing robots. However, the design of electroadhesive devices relies on speculative or empirical error approaches. Therefore, we present a theoretical model comprising predictive coplanar electrodes and protective layers for analyzing the electrostatic fields between an object and electroadhesive device. The model considers the role of protective layer and the air gap between the electrode surface and the object. To exert a higher electroadhesive force, the higher permeability of the protective layer is required. However, a high permeability of the protective layer is hard to withstand high applied voltage. To overcome this, two materials with different permeabilities were employed as protective layers—a low-permeability inner layer and a high-permeability outer layer—to maintain a high voltage and generate a large electroadhesive force. Because a low-permeability inner layer material was selected, a more permeable outer layer material was considered. A theoretical analysis revealed complex relationships between various design parameters. The impact of key design parameters and working environments on the electroadhesion behavior was further investigated. This study reveals the fundamental principles of electroadhesion and proposes prospective methods to enhance the design of electroadhesive devices for various engineering applications.

Computational modeling of cell motility and clusters formation in enzyme-sensitive hydrogels

In this paper, we propose an extension of a previous model of cell motility in tissue engineering applications recently developed by the authors. Achieving large-scale production of neo-tissue through biofabrication technologies remains challenging owing to the need of thoroughly optimizing all the relevant process variables, a task hardly attainable through solely trial and error approaches. Therefore, the present work is intended to provide a valid and effective computational-based support for neo-tissue formation, with a specific focus on the preliminary phase of such process, in which cells move through a polymeric scaffold (hydrogel) and then compact into clusters. Cell motility is modeled by resorting to the phase-field method, and by incorporating diffusion of nutrients from the external culture bath as well as the expression by cells of chemoattractant substances that bias the random path they otherwise would follow. The previous model has been enriched by additionally encompassing the secretion of enzymes by cells that cleave the crosslinks between the hydrogel polymer chains. As such, in the present model hydrogel degradation exhibits spatio-temporal variations in its chemo-physical properties related to the local amount of enzymes, which deeply affects cell motility. Numerical results showcase the pivotal importance of the cells micro-environment properties for their crawling in hydrogel scaffolds, opening towards the development of a predictive computational-aided optimization tool for neo-tissue growth in bioprinted scaffolds.

Optimal design of large-scale nonlinear Bayesian inverse problems under model uncertainty

We consider optimal experimental design (OED) for Bayesian nonlinear inverse problems governed by partial differential equations (PDEs) under model uncertainty. Specifically, we consider inverse problems in which, in addition to the inversion parameters, the governing PDEs include secondary uncertain parameters. We focus on problems with infinite-dimensional inversion and secondary parameters and present a scalable computational framework for optimal design of such problems. The proposed approach enables Bayesian inversion and OED under uncertainty within a unified framework. We build on the Bayesian approximation error (BAE) approach, to incorporate modeling uncertainties in the Bayesian inverse problem, and methods for A-optimal design of infinite-dimensional Bayesian nonlinear inverse problems. Specifically, a Gaussian approximation to the posterior at the maximum a posteriori probability point is used to define an uncertainty aware OED objective that is tractable to evaluate and optimize. In particular, the OED objective can be computed at a cost, in the number of PDE solves, that does not grow with the dimension of the discretized inversion and secondary parameters. The OED problem is formulated as a binary bilevel PDE constrained optimization problem and a greedy algorithm, which provides a pragmatic approach, is used to find optimal designs. We demonstrate the effectiveness of the proposed approach for a model inverse problem governed by an elliptic PDE on a three-dimensional domain. Our computational results also highlight the pitfalls of ignoring modeling uncertainties in the OED and/or inference stages.

Inequality in Africa: Does injustice matter?

AbstractThe following study aims to investigate the impact of injustice on income inequality in Africa between 2000 and 2020. Both male and female injustice levels are evaluated using gender‐specific scores to indicate the injustice suffered by both genders. The study utilized the Gini and Palma ratios as proxies for income distribution inequality. After addressing error term‐related problems with the Newey‐West Standard Corrected Error approach, the findings indicate that injustice is a significant contributor to inequality in Africa, especially for females, with little evidence of male injustice. The results are consistent for both middle and low‐income countries that adopted the French civil law and English common law systems to govern their institutions. Additionally, the study found that educational development and basic welfare maintenance contribute to reducing inequality in Africa, which is exacerbated by many barriers to political inclusion. To address the gender bias in income distribution, policymakers in Africa should design policies that provide equal access to justice for both male and female populations. The study also suggests adopting policies that enhance human capital accumulation and political inclusion in‐state activities to create a safe, just, and equal environment.

The role of natural resources, and economic development in evaluating the environmental effects of biofuel and waste energy generation in the group of BRICS

The green energy sources such as biomass conversions and waste energy have shown great potential in environmental management. This article aims to examine the changes brought about by the production of biofuel and the management of solid fossil waste over the past forty years, and to analyze the significant roles that natural resources and economic factors have played in the BRICS countries (Brazil, Russia, India, China, and South Africa) from 1990 to 2021. To achieve accurate and feasible results, second-generation econometric methodologies were applied. The Driscoll-Kraay standard errors (DKSE) approach revealed a significant negative relationship between biofuel production and environmental damage through waste management. Thus, an increase in biofuel production is associated with a reduction in environmental degradation in the region. Furthermore, the results indicate that environmental degradation (ED), the dependent variable, is still positively correlated with natural resources, urbanization, trade, and economic growth. As the financial sector struggles to address environmental threats, stringent monitoring of financial control measures and provisions for environmental issues is necessary. To achieve sustainable development, BRICS member countries need to address the issues of natural resource exhaustion and over-exploitation. The findings of this research suggest policy support for sustainable redevelopment, which should be goal-oriented and backed by the central authorities of the respective countries.

Development and Validation of a Near Infra-Red (NIR) Hand-held Spectrophotometric Method Using PCA Approaches and Chemometric Tools: Application for Qualitative and Quantitative Determination of Tadalafil Marketed in Kinshasa—D.R. Congo

A hand-held NIR spectrophotometric method was developed, validated, and applied for the determination of tadalafil in tablets. The aim of our work was to develop analytical methods based on vibrational techniques using low-cost portable equipment. Based on different chemometric modeling, we attempted to validate the method, which gave encouraging results from the principal component analysis (PCA), DD-SIMCA, and PLS modeling. Following this, we optimized the method using an appropriate experiment plan. For validation, we used the total error approach with acceptance limits set at ±10% with a risk level of 5%. The method showed that it was possible to perform both qualitative and quantitative analysis of pharmaceutical products using low-cost portable NIR systems with chemometric tools. The developed approach enabled the completion of the first step in implementing an NIR method for quality control of tadalafil-based drugs in the DRC. Validation difficulties of the PLS method resulted from the lack of information about inter-day serial variations of spectral responses. It would be interesting to extend the study to a larger calibration interval in order to correct uncertainties that may result from the variability observed under different conditions and to verify robustness. These are the limitations of this work, but the results are nevertheless very encouraging.

A novel dynamic finite-time prescribed performance control strategy with constraint and robustness

This paper is devoted to the design of adaptive nonlinear prescribed performance controllers for balancing constraint and robustness. Firstly, a shift function is introduced to enhance robustness in the initial stage. Secondly, a new finite-time performance function and a regulation of dynamic adjustments are developed for the first time. Due to the regulation, it can be prevented by the proposed scheme that tracking errors approach or exceed performance boundaries during operation, which ensures both robustness and constraint, and the performance function is differentiable of order n, which can be used in backstepping design for higher-order systems without approximators. Finally, the designed controller is more robust in both transient and stable states, whose effectiveness and superiority are validated by stability analysis, matlab simulation, and an actual test on a real robotic arm.