Detailed Categories Research Articles

Age Prediction from Korean Speech Data Using Neural Networks with Diverse Voice Features

A person’s voice serves as an indicator of age, as it changes with anatomical and physiological influences throughout their life. Although age prediction is a subject of interest across various disciplines, age-prediction studies using Korean voices are limited. The few studies that have been conducted have limitations, such as the absence of specific age groups or detailed age categories. Therefore, this study proposes an optimal combination of speech features and deep-learning models to recognize detailed age groups using a large Korean-speech dataset. From the speech dataset, recorded by individuals ranging from their teens to their 50s, four speech features were extracted: the Mel spectrogram, log-Mel spectrogram, Mel-frequency cepstral coefficients (MFCCs), and ΔMFCCs. Using these speech features, four deep-learning models were trained: ResNet-50, 1D-CNN, 2D-CNN, and a vision transformer. A performance comparison of speech feature-extraction methods and models indicated that MFCCs + ΔMFCCs was the best for both sexes when trained on the 1D-CNN model; it achieved an accuracy of 88.16% for males and 81.95% for females. The results of this study are expected to contribute to the future development of Korean speaker-recognition systems.

Global heterogeneous graph convolutional network: from coarse to refined land cover and land use segmentation

ABSTRACT The abundant details embedded in very-high-resolution remote sensing images establish a solid foundation for comprehending the land surface. Simultaneously, as spatial resolution advances, there is a corresponding escalation in the required granularity of land cover and land use (LCLU) categories. The coarse classes identified necessitate further refinement into more detailed categories. For instance, the ‘built-up’ class can be subdivided into specific categories such as squares, stadiums, and airports. These refined LCLU classifications are better equipped to support diverse domains. Nonetheless, most studies simply adopt methods initially designed for coarse LCLU when addressing the challenging refined LCLU segmentation. Few studies have considered the inherent relationships between coarse and refined LCLU, overlooking the potential exploitation of the numerous recently released LCLU products. To better leverage this prior knowledge, we propose the Global Heterogeneous Graph Convolutional Network (GHGCN). The GHGCN introduces a heterogeneous graph and excels in establishing relationships between coarse and refined LCLU, which can extract long-distance dependencies more effectively than convolutional neural networks. Furthermore, this model is performed end-to-end, eliminating the necessity for presegmentation and facilitating training acceleration. GHGCN exhibits competitive performance compared to state-of-the-art models, indicating its effective design in exploiting coarse LCLU data, especially for categories with limited samples. The source code is released at: https://github.com/Liuzhizhiooo/GHGCN.

A long-term analysis, modeling and drivers of forest recovery in Central Mexico

This study aims to evaluate the changes in forest cover from 1994 to 2015, identify the key drivers of forest recovery, and predict future trends. Using high-resolution remote sensing data, we mapped forest canopy density into detailed categories (closed > 50%, open 10–50%, and deforested < 10%) to differentiate processes like degradation, deforestation, densification, reforestation, and afforestation. A multinomial logistic regression was used to explore the relationship between the forest processes and socioeconomic, proximity, planning, and policy potential drivers. Future trends were modeled using the Land Change Modeler. The analysis showed that 81.5% of the area remained unchanged, 14% experienced recovery, and 4.5% faced disturbances. Factors such as elevation, proximity to roads, and participation in payment for environmental services (PES) programs significantly influenced recovery trends. Predictive modeling for 2035 suggests forest cover will increase by 7%, reaching 77% coverage of the study area, and closed forest areas will rise by 12% compared to 1994. The findings underscore the effectiveness of conservation efforts and natural regeneration in enhancing forest cover, offering valuable insights for global forest management and policy-making efforts.

Gender Diversity, Gender Dysphoria/Incongruence, and the Intersection with Autism Spectrum Disorders: An Updated Scoping Review.

In several ways, 2018 represents a global pivot point in research and care related to the intersection of gender diversity and autism, reflected by the increase in the number of studies on the intersection and broadening of topics. We conducted a scoping review of the literature published between September 2018 and January 2024 to summarize recent research in the field. We compared and contrasted research trends post September 2018 to research that was summarized in previous scoping reviews (Nordahl-Hansenet al. in J Autism Develop Disord 49:1745-1748, 2019; Øien et al. J Autism Develop Disord 48(12):4028-4037, 2018. https://doi.org/10.1007/s10803-018-3843-z ). We also summarized systematic reviews and meta-analyses published after 2018. Compared to previous reviews, we included more comprehensive search terms to focus on gender diversity broadly, rather than just gender dysphoria or gender identity disorder, and we included more detailed coding categories to uniquely identify shifts and continuities in research priorities over time. A total of 99 empirical research studies were published after August 2018 (the end of the search in the previous review) and through January 2024 on autism, gender diversity, and related topics were reviewed. There has been growth in the quantity and quality of studies on the intersection of autism and gender diversity, although methodological improvements are still needed and described in the current review. Future research should include more collaborations with stakeholders to set research priorities that are reflective of the communities being described.

Detail categories for the flange-to-web weld detail in corrugated web girders

Corrugated web girders have gained popularity in both buildings and bridges due to their efficient use of materials. Previous research work has demonstrated that stainless steel corrugated web offers a promising solution for composite road bridges. However, to utilize these girders Efficiently in bridges, it is crucial to understand their fatigue performance. Notably, EN1993–1-9 does not include a detail category for the flange-to-web weld detail in girders with corrugated webs made of either carbon or stainless steel. To address this gap, numerous tests have been conducted and reported in the literature on carbon steel girders with various corrugation geometries. This paper aims at deriving detail categories for the flange-to-web weld detail in girders with corrugated webs. As a first step, it focuses on carbon steel, compiling, analyzing, and evaluating 86 fatigue test results available in the literature. The results show that the corrugation angle is the most important geometric parameter influencing the fatigue strength of the studied details. Based on this work, the following detail categories are proposed for various corrugation angles: DC125 for angles smaller than 30°, DC112 for angles between 30° and 40°, DC100 for angles between 40° and 45°, and DC90 for angles between 45° and 60°. Furthermore, the impact of other influencing geometrical parameters, such as bend radius and flange thickness, on fatigue strength is explored. The risk for root cracking in the weld between the flange and corrugated web is also evaluated through effective notch stress analysis.

Evolution and Research Trends in Physical Culture. A Longitudinal Analysis of Student Diploma Theses from the Didactic Team of Physical Culture Sciences, formerly the Department of Physical Culture, Faculty of Earth Sciences and Spatial Management, 2017-2024

Objective: The aim of this study was to conduct a comprehensive analysis of diploma theses produced in the Department of Physical Culture at the Faculty of Earth Sciences and Spatial Management between 2017-2024, to identify main research trends, thematic evolution, and assess the impact of external factors on research directions in the field of physical culture. Material and Methods: The analysis included 95 diploma theses (82 bachelor's and 13 master's). Mixed research methods were applied, combining quantitative and qualitative analysis. Content analysis, statistical analysis (including chi-square tests, analysis of variance, linear regression), and advanced data analysis methods (LDA topic modeling, social network analysis, SARIMA time series analysis) were conducted. Additionally, measures of thematic diversity and supervisor specialization indicators were applied. Results: A statistically significant upward trend in the number of theses (β = 1.1875, p = 0.0134) and significant seasonality of defenses were found. Five main thematic areas were identified: physical activity and health (29.5%), tourism (23.2%), sport (21.1%), health (15.8%), and recreation (10.5%). LDA analysis revealed more detailed categories, indicating growing interdisciplinarity of research. A significant impact of the COVID-19 pandemic on thesis topics was observed in 2020-2021. Social network analysis showed a key role of certain supervisors in shaping research directions (highest eigenvector centrality = 1.00). Conclusions: The study revealed dynamic development of the physical culture discipline, with growing emphasis on interdisciplinarity and health aspects. Adaptation of research topics to current social and technological challenges was observed. Results indicate the need for further expansion of the thematic scope of theses, particularly in areas of new technologies and long-term effects of physical activity. The analysis provides valuable information for strategic planning of study program development and research directions in the field of physical culture, tourism, and recreation.

An AAT technique based fatigue evaluation of composite corrugated steel web girders under coupled flexural load and temperature gradient

Medium-to-long spanned composite corrugated steel web (CSW) girders have recently been extensively constructed in the hot-humid terrains. Their degraded structural behavior in service has been concerning due to the presence of surface cracks under vehicle load-induced fatigue and repeated temperature fluctuation as coupled loads. This paper presents an experimental coupled loading study for such a structure with a CSW dimensional scale factor comparable to contemporary research work. An innovative artificial acceleration testing (AAT) technique was developed to fulfill the fatigue experimental test under controlled periodic variation of temperature. The structural deterioration modes during the whole fatigue loading progress have been analyzed. A new equation was proposed for the calculation of the coupled stress field incorporating the compatible strain condition at the steel-concrete interface under coupled loads. The resultant S-N relations based on fracture mechanics are demonstrated to be reasonably consistent with codified detail categories. The stress intensity factor allowing for crack geometry and the heat flux due to the prescribed temperature difference was developed and justified for the analytical remaining fatigue life analysis. The results can provide a reference for the development of AAT technology and the basic theory of thermal-structural analysis for these girders in the future.

A Survey of Automated Data Augmentation for Image Classification: Learning to Compose, Mix, and Generate.

Data augmentation is an effective way to improve the generalization of deep learning models. However, the underlying augmentation methods mainly rely on handcrafted operations, such as flipping and cropping for image data. These augmentation methods are often designed based on human expertise or repeated trials. Meanwhile, automated data augmentation (AutoDA) is a promising research direction that frames the data augmentation process as a learning task and finds the most effective way to augment the data. In this survey, we categorize recent AutoDA methods into the composition-, mixing-, and generation-based approaches and analyze each category in detail. Based on the analysis, we discuss the challenges and future prospects as well as provide guidelines for applying AutoDA methods by considering the dataset, computation effort, and availability of domain-specific transformations. It is hoped that this article can provide a useful list of AutoDA methods and guidelines for data partitioners when deploying AutoDA in practice. The survey can also serve as a reference for further study by researchers in this emerging research area.

Investigating the Future of Freight Transport Low Carbon Technologies Market Acceptance across Different Regions

Fighting climate change has become a major task worldwide. One of the key energy sectors to emit greenhouse gases is transportation. Therefore, long term strategies all over the world have been set up to reduce on-road combustion emissions. In this context, the road freight sector faces significant challenges in decarbonization, driven by its limited availability of low-emission fuels and commercialized zero-emission vehicles compared with its high energy demand. In this work, we develop the Mobility and Energy Transportation Analysis (META) Model, a python-based optimization model to quantify the impact of transportation projected policies on freight transport by projecting conventional and alternative fuel technologies market acceptance as well as greenhouse gas (GHG) emissions. Along with introducing e-fuels as an alternative refueling option for conventional vehicles, META investigates the market opportunities of Mobile Carbon Capture (MCC) until 2050. To accurately assess this technology, a techno-economic analysis is essential to compare MCC abatement cost to alternative decarbonization technologies such as electric trucks. The novelty of this work comes from the detailed cost categories taken into consideration in the analysis, including intangible costs associated with heavy-duty technologies, such as recharging/refueling time, cargo capacity limitations, and consumer acceptance towards emerging technologies across different regions. Based on the study results, the competitive total cost of ownership (TCO) and marginal abatement cost (MAC) values of MCC make it an economically promising alternative option to decarbonize the freight transport sector. Both in the KSA and EU, MCC options could reach greater than 50% market shares of all ICE vehicle sales, equivalent to a combined 35% of all new sales shares by 2035.

On the statistical assessment of the EN 1993-1-9 detail categories for hot dip galvanised steel

This paper provides the final developments regarding the fatigue life prediction of hot dip galvanised (HDG) steel details. The background information that has led to the definition of new S-N curves based on fatigue tests is firstly discussed, in accordance with the third draft revision of the EN 1993-1-9 and comparative guidelines. Using relevant statistical distributions and probability of survival levels, a statistical evaluation of all data retrieved from the literature is carried out. The two-parameter Weibull distribution, often used for reliability analysis in different areas of expertise, is also discussed. Finally, the overall impact of this assessment on the latest design rules from EN 1993-1-9 is discussed and guidance is given for an accurate determination of S-N curves for HDG steel details, based on the lessons learned.

A Comprehensive Review of Hardware Acceleration Techniques and Convolutional Neural Networks for EEG Signals.

This paper comprehensively reviews hardware acceleration techniques and the deployment of convolutional neural networks (CNNs) for analyzing electroencephalogram (EEG) signals across various application areas, including emotion classification, motor imagery, epilepsy detection, and sleep monitoring. Previous reviews on EEG have mainly focused on software solutions. However, these reviews often overlook key challenges associated with hardware implementation, such as scenarios that require a small size, low power, high security, and high accuracy. This paper discusses the challenges and opportunities of hardware acceleration for wearable EEG devices by focusing on these aspects. Specifically, this review classifies EEG signal features into five groups and discusses hardware implementation solutions for each category in detail, providing insights into the most suitable hardware acceleration strategies for various application scenarios. In addition, it explores the complexity of efficient CNN architectures for EEG signals, including techniques such as pruning, quantization, tensor decomposition, knowledge distillation, and neural architecture search. To the best of our knowledge, this is the first systematic review that combines CNN hardware solutions with EEG signal processing. By providing a comprehensive analysis of current challenges and a roadmap for future research, this paper provides a new perspective on the ongoing development of hardware-accelerated EEG systems.

ToxDAR: A Workflow Software for Analyzing Toxicologically Relevant Proteomic and Transcriptomic Data, from Data Preparation to Toxicological Mechanism Elucidation.

Exploration of toxicological mechanisms is imperative for the assessment of potential adverse reactions to chemicals and pharmaceutical agents, the engineering of safer compounds, and the preservation of public health. It forms the foundation of drug development and disease treatment. High-throughput proteomics and transcriptomics can accurately capture the body's response to toxins and have become key tools for revealing complex toxicological mechanisms. Recently, a vast amount of omics data related to toxicological mechanisms have been accumulated. However, analyzing and utilizing these data remains a major challenge for researchers, especially as there is a lack of a knowledge-based analysis system to identify relevant biological pathways associated with toxicity from the data and to establish connections between omics data and existing toxicological knowledge. To address this, we have developed ToxDAR, a workflow-oriented R package for preprocessing and analyzing toxicological multi-omics data. ToxDAR integrates packages like NormExpression, DESeq2, and igraph, and utilizes R functions such as prcomp and phyper. It supports data preparation, quality control, differential expression analysis, functional analysis, and network analysis. ToxDAR's architecture also includes a knowledge graph with five major categories of mechanism-related biological entities and details fifteen types of interactions among them, providing comprehensive knowledge annotation for omics data analysis results. As a case study, we used ToxDAR to analyze a transcriptomic dataset on the toxicology of triphenyl phosphate (TPP). The results indicate that TPP may impair thyroid function by activating thyroid hormone receptor β (THRB), impacting pathways related to programmed cell death and inflammation. As a workflow-oriented data analysis tool, ToxDAR is expected to be crucial for understanding toxic mechanisms from omics data, discovering new therapeutic targets, and evaluating chemical safety.

Performance of laser metal deposition on hot-rolled stainless steel for hybrid steel structures

The construction sector is increasingly recognising the potential of metal additive manufacturing (AM) for fabricating complex steel details with enhanced performance and reduced resource usage. In the coming years, the application of AM technologies is expected to advance, leading to the development of new structural elements and improved performance of existing ones. This progression involves the integration of conventional construction steel with AM techniques, such as laser metal deposition (LMD), to create hybrid systems that incorporate efficient details. LMD offers the advantage of overcoming size limitations associated with laser powder bed fusion (LPBF) while leveraging its geometric capabilities. In this study, the mechanical behavior of hybrid steel components, fabricated using LMD on AISI316L stainless steel plates, was investigated through an extensive experimental campaign involving 40 samples. Axial tests, fatigue tests, and material characterisation were conducted to evaluate strength criteria and long-term behavior. Results demonstrate that LMD-produced AISI316L exhibits mechanical properties compliant with EN1993's "Design of steel structures" encompassing elastic and plastic behavior. Furthermore, both LMD samples and hybrid specimens exhibit promising fatigue performance, aligning with the highest fatigue detail category (detail category 160) for conventional steel details.

Birth defects among offspring of California firefighters, 2007–2019

PurposeLimited research examines birth defects from maternal or paternal firefighting exposure. This study aims to assess if maternal or paternal occupational exposure to firefighting during periconception is associated with offspring birth defects. MethodsData from California birth certificates (2007–2019) were linked to maternal / offspring hospitalization records. Occupation during the periconceptional period was categorized from vital statistics as the following: paternal non-firefighting (n = 4,135,849), paternal firefighting (n = 22,732), maternal non-firefighting (n = 3,332,255) and maternal firefighting (n = 502). Birth defects were identified using ICD codes, grouped by anatomical regions. Adjusted odds ratios were estimated, and sensitivity analyses explored police officer reference groups and detailed birth defect categories. ResultsOffspring of paternal firefighters had lower odds of circulatory defects (aOR = 0.9, 95% CI 0.8, 1.0), oral clefts (aOR = 0.6, 95% CI 0.4, 0.8) and respiratory defects (0.7, 95% CI 0.6, 0.9) compared to paternal non-firefighters. Associations between maternal firefighting and offspring birth defects were imprecise. Substituting police officers as the reference group attenuated findings. ConclusionsOffspring of paternal firefighters may have similar or slightly lower birth defect odds compared to offspring of non-firefighters. Limited data was available for assessing maternal firefighting outcomes. Future studies should prioritize studies using occupational exposure matrices to limit misclassification of exposure.

Motivational aspects of wintering in polar stations

For over one hundred years, humans have been actively exploring polar regions. At present, each year, several thousand people work at polar stations in the Arctic and Antarctica. Polar stations have unique workplace characteristics; the conditions are classified as isolated, confined, and extreme. This leaves the important question of what motivates people to carry out professional tasks in such a context, particularly during wintering over in a polar station. The current study was designed to answer that question. Eighty-six winterers (22 women and 64 men) from ten different countries described their motivation to winter over. The qualitative approach, thematic analysis, was applied to data analysis. Two coding cycles were applied: initial coding and theming of the data. Fifty-six detailed categories were identified and then grouped thematically, resulting in 17 main categories. The three main motivation categories were challenge and adventure (N = 36), unique experience (N = 16), and performance of the job (N = 15). There were also differences in motivation between explorers from different countries, from Arctic and Antarctica polar stations, from civilian and military polar stations, and between female and male polar expeditioners. Obtained results can be applied to improve selection procedures and better support expeditioners during their stay in the polar station.

Optimization and fatigue assessment of girders with large-dimension corrugated steel webs

In recent years, large-dimension Corrugated Steel Webs (CSW) has increasingly become an available solution for long-span bridges to reduce deadweight and improve prestressing efficiency. Compared to conventional CSW used in small-span bridge girders, girders with large-dimension CSW in long-span bridges are more susceptible to fatigue cracking under repetitive traffic loads. However, the specifications aimed for small-dimension CSW may resulted in unsafe fatigue detail categories. This paper investigated the fatigue performance of large-dimension CSW girders for bridges. The reasonable corrugation angles of girders with CSW were obtained using Grey Relational analysis. The fatigue detail category corresponding to optimized corrugation angles was suggested based on the Theory of Critical Distance (TCD). The Finite Element Analysis (FEA) results indicate that the hot spot stress concentration factor (Ks) on the web-flange weld toe increases with the corrugation angle and decreases with the bend radius. When the corrugation angle and the bend radius are fixed, Ks increases with the corrugation wavelength. By developing a balanced fatigue and shear buckling performance scheme, the optimized corrugation angles of wavelength 1000 ∼ 2000 mm are between 30° and 45°. There is a negative correlation between the fatigue detail categories of CSW girders and corrugation wavelengths. In general, the fatigue detail category of wavelength 1600 ∼ 2000 mm under four-point bending can be classified into a range between 50 and 71 for Eurocode 3, Category D and Category E′ design curves for AASHTO, FAT 50 and FAT 71 for IIW respectively.

The Influence of YOLOv5 Hyperparameters for Construction Details Detection

Computer vision has become a fundamental area of interest in recent decades. Each area has unique data which object detection methods can analyse. However, it is important to find the most suitable parameters for the model that detects different object groups. In this research has been investigated the influence of pre-trained YOLOv5 (nano (n), small (s), medium (m), large (l), extralarge (x)) models, hyperparameters (learning rate, momentum, and weight decay) and different image augmentation (hsv_h, degrees, translate, flipud, mosaic, mixup, shear, perspective) efficiency for similar construction details detection. A newly collected dataset with twenty-two labelled categories of construction details was prepared. A total of 270 models were trained and evaluated. Every model was evaluated with 3,300 test images which backgrounds were mixed, neutral, and white backgrounds. The most accurate model was YOLOv5l with learning rate – 0.001, momentum – 0.950 and weight decay – 0.0001. This model achieved – 0.5015 (50.15%) accuracy.

Data-Driven Policy Learning Methods from Biological Behavior: A Systematic Review

Policy learning enables agents to learn how to map states to actions, thus enabling adaptive and flexible behavioral generation in complex environments. Policy learning methods are fundamental to reinforcement learning techniques. However, as problem complexity and the requirement for motion flexibility increase, traditional methods that rely on manual design have revealed their limitations. Conversely, data-driven policy learning focuses on extracting strategies from biological behavioral data and aims to replicate these behaviors in real-world environments. This approach enhances the adaptability of agents to dynamic substrates. Furthermore, this approach has been extensively applied in autonomous driving, robot control, and interpretation of biological behavior. In this review, we survey developments in data-driven policy-learning algorithms over the past decade. We categorized them into the following three types according to the purpose of the method: (1) imitation learning (IL), (2) inverse reinforcement learning (IRL), and (3) causal policy learning (CPL). We describe the classification principles, methodologies, progress, and applications of each category in detail. In addition, we discuss the distinct features and practical applications of these methods. Finally, we explore the challenges these methods face and prospective directions for future research.

Fatigue performance of stud connectors in joints between top slabs and steel girders with corrugated webs subjected to transverse bending moments

Steel-concrete composite box girders with corrugated steel webs (CSWs) are broadly used in bridge engineering, in which the joints between top RC slabs and steel girders with CSWs are usually subjected to transverse bending moments caused by off-center vehicle loads. Stud connectors arranged in the joints usually bear repeated pull-out forces in this case and are prone to fatigue failure. Therefore, two reduced scale joints between top slabs and weathering steel girders with CSWs were designed and tested, on which variable amplitude fatigue loads were applied to investigate the fatigue performance of studs incorporated in the joints. The two joints presented similar fatigue behavior, i.e., cracks of not more than 0.26 mm in width appeared on the surface of the top slab during the fatigue loading, and the separation of the top RC slabs and the top girder flanges was observed; meanwhile, minute cracks developed along the welds connecting the CSW with the bottom girder flange, and the stiffness of the joints gradually decreased due to the accumulation of fatigue damage. The two joints underwent 2,650,000 and 2,103,600 load cycles respectively before fatigue failure occurred. After partially demolishing the RC slabs, several welded head studs in the joints were observed fractured at the roots and detached from the top girder flange. Whereafter, finite element (FE) models referring to the test joints were developed and analyzed, and the effect of the wave height (WH) of the CSW and the transverse row spacing (TRS) of studs on the distribution of pull-out forces among studs was investigated. It showed that the pull-out force bore by each stud in the joint was distributed unevenly even in the same row. Then, a modification factor α was introduced to revise the pull-out forces of studs calculated in the theoretical method. An equation to determine the factor was fitted, considering the effect of the WH of the CSW and the TRS of studs. Finally, the detail category for studs bearing pull-out forces in the joints was discussed based on the experimental results and the collected test data, and it was preliminarily recommended studs subjected to pull-out forces can be classified into detail category 45 specified by Eurocode 3 in fatigue design.

A two-stage consensus model based on personalized individual semantics under linguistic preference relations with self-confidence

Linguistic preference relations with self-confidence (LPRs-SC) are the preference relation that can reflect the decision maker’s (DM) confidence psychology and has received widespread attention for their simple form and multiple information. Currently, arithmetic studies of LPRs-SC are conducted separately for preference relations and self-confidence. In addition, personalized individual semantics (PIS) is an important tool in large-scale decision-making to reflect the differences in the semantic understanding of DMs. However, the confidence level in LPRs-SC limits the preference relation to a certain extent and the linguistic representations of these two components are usually different. This means that it is not only necessary to propose an arithmetic rule that can express the restrictive relationship between the two but also to construct a model that can extract the PIS of preference relation and confidence respectively. Besides, we constructed a two-stage consensus reaching process (CRP) based on the specificity of the LPRs-SC structure when enhancing group harmony. The process takes self-confidence as an independent source of information, delineates the adjusted categories in detail, and builds an adjustment model accordingly. Finally, the example and comparative analyses verify the merits of the proposed PIS in terms of consistency enhancement and CRP in terms of speed and accuracy harmonization.