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The purpose of this paper is to develop an appropriate machine learning model for predicting soil compaction degree while also examining the contribution rates of three influential factors: moisture content, electrical conductivity and temperature, towards the prediction of soil compaction degree.

Taking fine-grained soil A and B as the research object, this paper utilized the laboratory test data, including compaction parameter (moisture content), electrical parameter (electrical conductivity) and temperature, to predict soil degree of compaction based on five types of commonly used machine learning models (19 models in total). According to the prediction results, these models were preliminarily compared and further evaluated.

The Gaussian process regression model has a good effect on the prediction of degree of compaction of the two kinds of soils: the error rates of the prediction of degree of compaction for fine-grained soil A and B are within 6 and 8%, respectively. As per the order, the contribution rates manifest as: moisture content > electrical conductivity >> temperature.

By using moisture content, electrical conductivity, temperature to predict the compaction degree directly, the predicted value of the compaction degree can be obtained with higher accuracy and the detection efficiency of the compaction degree can be improved.

The purpose of this paper is to examine the rice consumption by rice grain types under the rising income scenario in Bangladesh. Generally, with an increase in income, households tend to consume more food items that are high-value, enriched foods and protein, such as meat and fish, by substituting for cereals. However, consumers also substitute when it comes to grain quality. For example, cereals, such as rice, are available in a range of qualities from the ordinary type (coarse-grain) to the premium type (fine grain). The authors postulate that as household incomes increase, households may consume more premium-type rice (or fine-grain rice), while overall consuming less rice or fewer carbohydrates.

Using the Bangladesh Household Income and Expenditure Survey 2000, 2005, and 2010, and applying multivariate probit and seemingly unrelated regression estimation procedures, this study quantifies the impact of income, household demographics, and urbanization on rice consumption by rice grain types (coarse-grain, medium-grain, and fine-grain types).

The results show that urban, wealthy households and, households headed by educated heads and spouses, are more likely to consume fine-grain rice than their counterparts.

After yield, grain type is the second most important factor for farmers when considering the adoption of a new variety. The price of rice and other cereals is highly associated with the grain type. This study concludes that plant breeding programs of major cereals, such as rice and wheat, should take into account the consumer grain-type preferences when developing new varieties.

This study aims to use porous concrete and mineral adsorbents (additives) for reducing the quantity and improving the quality of urban runoff.

The effects of adding mineral adsorbents and fine grains to porous concrete is tested for increasing its performance in improving the quality of urban runoff. Two levels of sand (10 and 20 per cent) and 5, 10 and 15 per cent of zeolite, perlite, LECA and pumice were added to the porous concrete. Unconfined compressive strength, hydraulic conductivity (permeability) and porosity of the porous concrete specimens were measured. Some of the best specimens were selected for testing the improvement of runoff quality. A rainfall simulator was designed and the quality of the runoff was investigated for changes in electrical conductivity (EC), total suspended solids (TSS), total dissolved solids (TDS) and chemical oxygen demand (COD).

The results of this study showed that compressive strength of the porous concrete was increased by adding fine grains to the concrete mixture. Fine grains decreased the permeability and porosity of the samples. Zeolite had the highest compressive strength. Samples having pumice own maximum permeability. Samples which had perlite, had the least compressive strength and permeability. Because of the fast flow of runoff water in the porous slab and its low thickness, sufficient time was not provided for effective functioning of the additives, and the removal percentage of the pollution parameters was low.

Porous concrete can ameliorate both quantity and quality of the urban runoff.

Corporate social responsibility (CSR) is significant in the financial market. Despite plenty of existing research on CSR, few studies have quantified the fine-grained aspects of CSR and examined how diverse CSR aspects are associated with firms' trade credit. Based on the released CSR reports, this paper strives to measure the CSR fulfillment of firms and examine the relationships between CSR and trade credit in terms of textual features presented in these reports.

This research proposes a natural language processing-based framework to extract the overall readability and the sentiment of fine-grained aspects from CSR reports, which can signal the performance of firms' CSR in diverse aspects. Furthermore, this paper explores how the textual features are associated with trade credit through partial dependence plots (PDPs), and PDPs can generate both linear and nonlinear relationships.

The study’s results reveal that the overall readability of the reports is positively associated with trade credit, while the performance of the fine-grained CSR aspects mentioned in the CSR reports matters differently. The performance of the environment has a positive impact on trade credit; the performance of creditors, suppliers and information disclosure, shows a U-shaped influence on trade credit; while the performance of the government and customers is negatively associated with trade credit.

This study expands the scope of research on CSR and trade credit by investigating fine-grained aspects covered in CSR reports. It also offers some managerial implications in the allocation of CSR resources and the presentation of CSR reports.

The correlations between mechanical behaviour, tensile strength, and rock parameters of metasedimentary rock samples in Karak, Pahang’s New Austrian Tunnelling Method (NATM) were statistically evaluated from the rock mechanic laboratory works at the selected sections around 2,000 m of the tunnel (named as NATM-1). According to a statistical analysis, lithotypes, geological structures, and region geology have a significant impact on the mechanical behaviour of the metasedimentary rock. In the Brazilian test, the fracture behaviour of the disc specimens was highly related to the reliability and precision of the experimental data by validations of methods. In this work, the impact of different loading methods and rock lithotypes on the failure mechanism of Brazilian discs was examined utilising five different metasedimentary rock types and three different loading methods. During the loading operation, the strain and displacement fields of the specimens were recorded and evaluated using a computerised strain gauge system. The rock types, according to experimental data, have a significant impact on the peak load and deformation properties of Brazilian discs. With the method below, tensile strength point of a disc specimen is clearly regulated by the material stiffness and tensile–compression ratio. Seismic occurrences have had a substantial impact on changing the rock and exerting forces that may affect its mechanical characteristics as well as its vulnerability to weathering effects or discontinuities. As a result, the goal of this study is to look into the connection between rock mechanics and metasedimentary rock stress analysis in NATM-1, Karak, Pahang.

Data protection requirements heavily increased due to the rising awareness of data security, legal requirements and technological developments. Today, NoSQL databases are increasingly used in security-critical domains. Current survey works on databases and data security only consider authorization and access control in a very general way and do not regard most of today’s sophisticated requirements. Accordingly, the purpose of this paper is to discuss authorization and access control for relational and NoSQL database models in detail with respect to requirements and current state of the art.

This paper follows a systematic literature review approach to study authorization and access control for different database models. Starting with a research on survey works on authorization and access control in databases, the study continues with the identification and definition of advanced authorization and access control requirements, which are generally applicable to any database model. This paper then discusses and compares current database models based on these requirements.

As no survey works consider requirements for authorization and access control in different database models so far, the authors define their requirements. Furthermore, the authors discuss the current state of the art for the relational, key-value, column-oriented, document-based and graph database models in comparison to the defined requirements.

This paper focuses on authorization and access control for various database models, not concrete products. This paper identifies today’s sophisticated – yet general – requirements from the literature and compares them with research results and access control features of current products for the relational and NoSQL database models.

The purpose of the study is to address the problems of low accuracy and missed detection of occluded pedestrians and small target pedestrians when using the YOLOX general object detection algorithm for pedestrian detection. This study proposes a multi-level fine-grained YOLOX pedestrian detection algorithm.

First, to address the problem of the original YOLOX algorithm in obtaining a single perceptual field for the feature map before feature fusion, this study improves the PAFPN structure by adding the ResCoT module to increase the diversity of the perceptual field of the feature map and divides the pedestrian multi-scale features into finer granularity. Second, for the CSPLayer of the PAFPN, a weight gain-based normalization-based attention module (NAM) is proposed to make the model pay more attention to the context information when extracting pedestrian features and highlight the salient features of pedestrians. Finally, the authors experimentally determined the optimal values for the confidence loss function.

The experimental results show that, compared with the original YOLOX algorithm, the AP of the improved algorithm increased by 2.90%, the Recall increased by 3.57%, and F1 increased by 2% on the pedestrian dataset.

The multi-level fine-grained YOLOX pedestrian detection algorithm can effectively improve the detection of occluded pedestrians and small target pedestrians.

The authors introduce a multi-level fine-grained ResCoT module and a weight gain-based NAM attention module.

Current segmentation systems almost invariably focus on linear segmentation and can only divide text into linear sequences of segments. This suits cohesive text such as news feed but not coherent texts such as documents of a digital library which have hierarchical structures. To overcome the focus on linear segmentation in document segmentation and to realize the purpose of hierarchical segmentation for a digital library’s structured resources, this paper aimed to propose a new multi-granularity hierarchical topic-based segmentation system (MHTSS) to decide section breaks.

MHTSS adopts up-down segmentation strategy to divide a structured, digital library document into a document segmentation tree. Specifically, it works in a three-stage process, such as document parsing, coarse segmentation based on document access structures and fine-grained segmentation based on lexical cohesion.

This paper analyzed limitations of document segmentation methods for the structured, digital library resources. Authors found that the combination of document access structures and lexical cohesion techniques should complement each other and allow for a better segmentation of structured, digital library resources. Based on this finding, this paper proposed the MHTSS for the structured, digital library resources. To evaluate it, MHTSS was compared to the TT and C99 algorithms on real-world digital library corpora. Through comparison, it was found that the MHTSS achieves top overall performance.

With MHTSS, digital library users can get their relevant information directly in segments instead of receiving the whole document. This will improve retrieval performance as well as dramatically reduce information overload.

This paper proposed MHTSS for the structured, digital library resources, which combines the document access structures and lexical cohesion techniques to decide section breaks. With this system, end-users can access a document by sections through a document structure tree.

The purpose of this paper is to solve the problems existing in topic popularity prediction in online social networks and advance a fine-grained and long-term prediction model for lack of sufficient data.

Based on GM(1,1) and neural networks, a co-training model for topic tendency prediction is proposed in this paper. The interpolation based on GM(1,1) is employed to generate fine-grained prediction values of topic popularity time series and two neural network models are considered to achieve convergence by transmitting training parameters via their loss functions.

The experiment results indicate that the integrated model can effectively predict dense sequence with higher performance than other algorithms, such as NN and RBF_LSSVM. Furthermore, the Markov chain state transition probability matrix model is used to improve the prediction results.

Fine-grained and long-term topic popularity prediction, further improvement could be made by predicting any interpolation in the time interval of popularity data points.

The paper succeeds in constructing a co-training model with GM(1,1) and neural networks. Markov chain state transition probability matrix is deployed for further improvement of popularity tendency prediction.

To meet the emerging demand for fine-grained annotation and semantic enrichment of cultural heritage images, this paper proposes a new approach that can transcend the boundary of information organization theory and Panofsky's iconography theory.

After a systematic review of semantic data models for organizing cultural heritage images and a comparative analysis of the concept and characteristics of deep semantic annotation (DSA) and indexing, an integrated DSA framework for cultural heritage images as well as its principles and process was designed. Two experiments were conducted on two mural images from the Mogao Caves to evaluate the DSA framework's validity based on four criteria: depth, breadth, granularity and relation.

Results showed the proposed DSA framework included not only image metadata but also represented the storyline contained in the images by integrating domain terminology, ontology, thesaurus, taxonomy and natural language description into a multilevel structure.

DSA can reveal the aboutness, ofness and isness information contained within images, which can thus meet the demand for semantic enrichment and retrieval of cultural heritage images at a fine-grained level. This method can also help contribute to building a novel infrastructure for the increasing scholarship of digital humanities.