Reasonable Classification Research Articles

Морозюк О. О. Національні та зарубіжні особливості обліку й оподаткування поліпшення та ремонтів необоротних активів

The purpose of the article is to systematize and substantiate the national and foreign peculiarities of accounting and taxation of the renewal and repair of non-current assets. It is proved that by investing in the renewal of non-current assets, the efficiency of economic activity of an enterprise is achieved, and compared to the acquisition or creation of new items of non-current assets, renewal and repairs require significantly less cost and are capable of increasing the productivity of economic activity of enterprises in a relatively short time and at relatively low cost. The authors propose to interpret renewal as a set of measures aimed at maintaining the items of non-current assets in working condition with elements of improvement of the initially expected economic benefits, as well as at increasing future economic benefits initially expected from the use of such assets. Such an interpretation of renewal will allow to include repairs and maintenance with elements of modification or modernization of individual parts of the item, which meets the needs of modern production and increases the objectivity of assessing the value of non-current assets that have undergone appropriate improvement. The authors substantiate both the national and foreign peculiarities of accounting and taxation of the renewal and repairs of non-current assets. The main problems of accounting and taxation of the renewal and ongoing repairs of non-current assets are identified, among which the main ones are as follows: arbitrary interpretation of the term «renewal» and diverse and inconsistent classification of types of renewal of the non-current assets in accounting and taxation practice in the absence of a clear disclosure of this concept in the national or international regulations; outdated primary documents; lack of specially designated accounts for accounting for the costs of repairing non-current assets and insufficient analytical orientation of subaccounts to the account 15 «Capital investments» to reflect the improvement of non-current assets – owing to the lack of a systematic classification of such costs; unregulatedness in legal acts of the process of determining cases when the implementation of a particular type of repair or renewal leads, or does not lead to an increase in future economic benefits originally expected from the use of the item, which complicates the procedure for accounting and defining the amount of possible changes in the valuation of non-current assets. A list of the main directions of development of digital socioeconomic relations in the context of global cooperation is proposed. To improve the system of accounting and taxation of the renewal and repair of non-current assets, it is proposed the following: reworking of regulatory legal acts in terms of disclosure of the concept of of «renewal» and providing a single reasonable classification of its types, which will contribute to an adequate presentation of information on the costs of the renewal of non-current assets and more fully meet the information needs of the analysis; definition of basic accounts and guidelines for accounting for these costs, especially taking into account the types of renewal, which will avoid possible erroneous accounting records and facilitate the formation of an analysis information base. The considered problems of accounting and taxation of the renewal and repair of non-current assets and proposed recommendations for their solution create the basis for continuing research in the direction of improving the methodology and organization of accounting and taxation of economic activity of enterprises in conditions of limited financial and material resources.

Оптимизация модели взаимодействия с клиентами в отделе телемаркетинга на основе выявления зон роста менеджеров

The article describes a method of search of sales manager growth zones based on the assumption that the client's refusal after prior consent to purchase occurs most often due to improper negotiations. Based on the author's research of the main directions of improving sales efficiency and the principles of organizing professional training of sales managers, an original method of searching growth zones of telemarketing department specialists is proposed. The author presented classification of typical reasons for potential customers' refusal to purchase at the final stage of the negotiation process and developed recommendations for their elimination.

Parkinsonian daytime sleep-wake classification using deep brain stimulation lead recordings

Excessive daytime sleepiness is a recognized non-motor symptom that adversely impacts the quality of life of people with Parkinson's disease (PD), yet effective treatment options remain limited. Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is an effective treatment for PD motor signs. Reliable daytime sleep-wake classification using local field potentials (LFPs) recorded from DBS leads implanted in STN can inform the development of closed-loop DBS approaches for prompt detection and disruption of sleep-related neural oscillations. We performed STN DBS lead recordings in three nonhuman primates rendered parkinsonian by administrating neurotoxin 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP). Reference sleep-wake states were determined on a second-by-second basis by video monitoring of eyes (eyes-open, wake and eyes-closed, sleep). The spectral power in delta (1–4 Hz), theta (4–8 Hz), low-beta (8–20 Hz), high-beta (20–35 Hz), gamma (35–90 Hz), and high-frequency (200–400 Hz) bands were extracted from each wake and sleep epochs for training (70% data) and testing (30% data) a support vector machines classifier for each subject independently. The spectral features yielded reasonable daytime sleep-wake classification (sensitivity: 90.68 ± 1.28; specificity: 88.16 ± 1.08; accuracy: 89.42 ± 0.68; positive predictive value; 88.70 ± 0.89, n = 3). Our findings support the plausibility of monitoring daytime sleep-wake states using DBS lead recordings. These results could have future clinical implications in informing the development of closed-loop DBS approaches for automatic detection and disruption of sleep-related neural oscillations in people with PD to promote wakefulness.

Using Deep Neural Network Approach for Multiple-Class Assessment of Digital Mammography.

According to the Health Promotion Administration in the Ministry of Health and Welfare statistics in Taiwan, over ten thousand women have breast cancer every year. Mammography is widely used to detect breast cancer. However, it is limited by the operator's technique, the cooperation of the subjects, and the subjective interpretation by the physician. It results in inconsistent identification. Therefore, this study explores the use of a deep neural network algorithm for the classification of mammography images. In the experimental design, a retrospective study was used to collect imaging data from actual clinical cases. The mammography images were collected and classified according to the breast image reporting and data-analyzing system (BI-RADS). In terms of model building, a fully convolutional dense connection network (FC-DCN) is used for the network backbone. All the images were obtained through image preprocessing, a data augmentation method, and transfer learning technology to build a mammography image classification model. The research results show the model's accuracy, sensitivity, and specificity were 86.37%, 100%, and 72.73%, respectively. Based on the FC-DCN model framework, it can effectively reduce the number of training parameters and successfully obtain a reasonable image classification model for mammography.

The concept and classification of reasons for a court decision in economic litigation

Issues of the historical formation of the motives of a court decision as a component of the process of motivation of court decisions in the economic judiciary of Ukraine are highlighted.
 Attention is drawn to the complex nature of the motives of the court decision, to their twofold subjective-normative essence. Emphasis is placed on the importance of motivational reasons, judicial reasoning and judicial conviction, as the fundamental motives of the law enforcement activity of judges of the economic jurisdiction.
 The significant complexity of such a psychological category as a motive was noted, even regardless of the procedural activity associated with the motivation of court decisions.
 The existing classification of reasons for a court decision is analyzed and the legal characteristics of the main types of reasons are provided. Attention was drawn to the inadmissibility of taking into account "political motives" in the process of judicial proceedings. The importance of the role of judges in the process of citing the motives of court decisions, taking into account moral aspects in order to endow judicial acts with the property of justice, is noted.
 Emphasis is placed on the complexity and importance of the activity of economic jurisdiction judges in providing sufficient and convincing reasons for the decision, which requires maximum professional knowledge, skills and abilities from them.
 On the basis of the obtained results, the need for further improvement of the motivational and law-enforcing activity of the court is stated, which will ensure that every person realizes the principle of the rule of law and the right to a fair hearing of his case, with the receipt of a high-quality and motivated court decision.

Analysis of ecological security evaluation and zoning threshold determination of typical energy industrial area based on PSR model

Ecological security is the main way to measure the effects of urbanization and industrialization in developing regions on the ecological environment. As an important coal chemical industry base in China, Taiyuan is located in the Loess Plateau, resulting in great pressure on the ecological environment. Based on the pressure-state-response (PSR) model, this study selected 15 evaluation indicators to evaluate the ecological security of 10 counties(districts/city) in Taiyuan City. Using the spatial principal component analysis (SPCA) method and GIS technology to simulate and analyze the ecological security distribution of this area, for the first time, the typical method and average method were used to divide the threshold of the ecological security level to achieve a more reasonable ecological security classification method. The results showed that from 2000 to 2016, the ecological security level of Taiyuan city and its surrounding areas was relatively low, mainly concentrated in Xinghualing, Yingze and Jiancaoping districts, while the ecological security level of Loufan county, Qingxu County, Gujiao city and Yangqu County was relatively high. Among these, energy mining and urbanization have had a relatively obvious impact on ecological security. It could provide more detailed ideas for the protection of ecological security in different areas and enrich the research on the ecological security of important coal chemical bases.

Improvement of accuracy of under-performing classifier in decision making using discrete memoryless channel model and Particle Swarm Optimization

In-spite of availability of wide range of algorithms for constructing multi-class classifier, there are applications like Decision support system, Game prediction in the sports, where the multi-class classifier performs relatively poor in terms of achieving the reasonable classification accuracy. In this paper, poorly performing multi-class classifier (constructed using the classical methods like Artificial Neural Network, cascade of Support Vector Machine, etc.) is treated as the discrete memoryless channel model with known transition probabilities (channel matrix) and the unknown priors. It is further used to construct M-ary Mini-Max technique based randomized decision rule to improve the performance of the multi-class classifier in terms of the classification accuracy. The prior probabilities and the probabilities associated with the M-ary randomized decision rule are further solved using the Particle Swarm Optimization. The experimental results based on the Monte-Carlo simulation using the synthetic data set and the real data set reveal the consistent improvement in the performance of the poorly performing classifier using the proposed technique.

Meninism and Preconceived Ideology with specific Indian Dimension of Human Rights in Today’s Changing Globalized Scenario: A Critical Appraisal

Men and Women both are an integral part of nature and integrant with each and another for wholeness to ensure the existence and survival of this human life in contrary time also for revitalization and revival in this mortal world. When time is changed then human conduct also changes consequently some women are abusing the law against men resultantly human rights of men are being violated very seriously in the gravest manner which is the contravention of life, personal liberty and security of men. The deprivation and endanger of life, personal liberty and security of men is in very plight conditions under exceptions in India. To prevent the abuses of law, preconceived ideology, predefined consequences against men in the protection of human rights to secure ends of justice for men is now need of hour and necessity. This research paper deals with human rights of men in today’s changing scenario. It has specially been emphasized on the preconceived ideology of people against men on this basis the discrimination and decision are taken prejudicially. Some laws also are in existence due to this preconceived ideology which is not justified in today’s changing globalized world under rule of law and democratic society, subjecting to exceptions. This preconceived ideology and its predefined consequences against men are inconsistent with and in derogation of human rights, abridging and taking away the rights of men. It reveals the Quantum of doctrine of protective discrimination for women against men, bearing in mind the principle of reasonable classification. When any person says or justifies that the particular thing is right or wrong, the same is stated on the basis of foundation root or quantum of knowledge of that person thereby the person has the understanding and sensibility accordingly and consequently, the person acts. But this research article does not include misogyny. It is not against the feminism. The history gives the evidences about the untold sorrow of women for the contravention of their human rights. This research article also is in favour of women empowerment and women human rights but no innocent men be sentenced. Everyone has inherent human rights by birth including human rights of men. Human rights for men are also available as per laws in today’s changing globalized world.

Research on commodity business value and customer value of e-commerce platforms: Based on consumer psychology and cognition.

Under the background of economic globalization and COVID-19, online shopping has gradually replaced offline shopping as the main shopping mode. In this paper, consumers’ perceptions are introduced into the traditional BCG matrix to form a new BCG matrix, and according to it, the small gifts of a gift e-commerce platform are classified. We then performed a robustness test comparing the BCG matrix with K-means clustering. We found that new BCG matrix can objectively reflect the value of small gifts and provide suggestions for the e-commerce platform to make subsequent product decisions. Then we judge the customer value of the platform based on the improved RFM model and K-means++ clustering, and provide a reasonable customer value classification method for the e-commerce platform. Finally, we comprehensively consider the relationship between the commodity value and customer value, and analyze the preferences of different types of customer groups for different types of small gifts. Our research result shows that small gifts can be divided into 4 categories according to commodity value, namely “stars,” “cash cows,” “questions marks,” and “dogs.” These four categories of small gifts can be converted into each other through marketing ploys. Customers can be divided into important retention customers, key loyal customers and general development customers according to their values. Faced with different types of customers, managers can adopt different strategies to extract customer value. However, consumer psychology will affect consumer cognition, and different types of consumers prefer different types of small gifts, so the precise implementation of marketing strategies will effectively improve the profitability of the gift e-commerce platform. Compared with the traditional classification method, the commodity business value classification method proposed in this paper uses management analysis and planning methods, and introduces consumer psychological factors into the commodity and customer classification, so that the classification results are more credible. In addition, we jointly analyze the results of commodity value classification and customer value classification, and analyze in detail the preferences of different valued customer groups for different types of commodities, so as to provide directions for subsequent research on customer preference.

Exploring relationship between emotion and probiotics with knowledge graphs.

Researchers have identified gut microbiota that interact with brain regions associated with emotion and mood. Literature reviews of those associations rely on rigorous systematic approaches and labor-intensive investments. Here we explore how knowledge graph, a large scale semantic network consisting of entities and concepts as well as the semantic relationships among them, is incorporated into the emotion-probiotic relationship exploration work. We propose an end-to-end emotion-probiotics relationship exploration method with an integrated medical knowledge graph, which incorporates the text mining output of knowledge graph, concept reasoning and evidence classification. Specifically, a knowledge graph for probiotics is built based on a text-mining analysis of PubMed, and further used to retrieve triples of relationships with reasoning logistics. Then specific relationships are annotated and evidence levels are retrieved to form a new evidence-based emotion-probiotic knowledge graph. Based on the probiotics knowledge graph with 40,442,404 triples, totally 1453 PubMed articles were annotated in both the title level and abstract level, and the evidence levels were incorporated to the visualization of the explored emotion-probiotic relationships. Finally, we got 4131 evidenced emotion-probiotic associations. The evidence-based emotion-probiotic knowledge graph construction work demonstrates an effective reasoning based pipeline of relationship exploration. The annotated relationship associations are supposed be used to help researchers generate scientific hypotheses or create their own semantic graphs for their research interests.

Selection of a Transparent Meta-Model Algorithm for Feasibility Analysis Stage of Energy Efficient Building Design: Clustering vs. Tree

Energy Efficient Building (EEB) design decisions that have traditionally been made in the later stages of the design process now often need to be made as early as the feasibility analysis stage. However, at this very early stage, the design frame does not yet provide sufficient details for accurate simulations to be run. In addition, even if the decision-makers consider an exhaustive list of options, the selected design may not be optimal, or carefully considered decisions may later need to be rolled back. At this stage, design exploration is much more important than evaluating the performance of alternatives, thus a more transparent and interpretable design support model is more advantageous for design decision-making. In the present study, we develop an EEB design decision-support model constructed by a transparent meta-model algorithm of simulations that provides reasonable accuracy, whereas most of the literature used opaque algorithms. The conditional inference tree (CIT) algorithm exhibits superior interpretability and reasonable classification accuracy in estimating performance, when compared to other decision trees (classification and regression tree, random forest, and conditional inference forest) and clustering (hierarchical clustering, k-means, self-organizing map, and Gaussian mixture model) algorithms.

High-Performance and Robust Binarized Neural Network Accelerator Based on Modified Content-Addressable Memory

The binarized neural network (BNN) is one of the most promising candidates for low-cost convolutional neural networks (CNNs). This is because of its significant reduction in memory and computational costs, and reasonable classification accuracy. Content-addressable memory (CAM) can perform binarized convolution operations efficiently since the bitwise comparison in CAM matches well with the binarized multiply operation in a BNN. However, a significant design issue in CAM-based BNN accelerators is that the operational reliability is severely degraded by process variations during match-line (ML) sensing operations. In this paper, we proposed a novel ML sensing scheme to reduce the hardware error probability. Most errors occur when the difference between the number of matches in the evaluation ML and the reference ML is small; thus, the proposed hardware identified cases that are vulnerable to process variations using dual references. The proposed dual-reference sensing structure has >49% less ML sensing errors than that of the conventional design, leading to a >1.0% accuracy improvement for Fashion MNIST image classification. In addition, owing to the parallel convolution operation of the CAM-based BNN accelerator, the proposed hardware achieved >34% processing-time improvement compared with that of the digital logic implementation.

1102P Genomic alterations correlated with the expression of PD-L1 based on the next-generation sequencing in Chinese non-small cell lung cancer

Blockade of programmed cell death protein-1 (PD1)/programmed cell death ligand-1 (PD-L1) interaction is one of important immunotherapies in lung cancers. Current research also focused on the relationship between genomic alterations and the expression of PD-L1. Therefore, we analyzed the molecular characteristics and its correlation with the expression of PD-L1, which may help to establish a reasonable tumor classification strategy to improve immunotherapy. Tissue specimens were obtained from 391 cases suffered from lung cancer. Frequencies of gene alterations were detected and analyzed using next-generation sequencing(152 or 603 genes). All specimens were also performed for PD-L1 (22C3) by immunohistochemistry (IHC) with PD-L1 tumor proportion score (TPS) reported for further analysis. In 391 lung cancer samples, 113 (28.9%) cases had comutations in both TP53 and EGFR, 110 (28.1%) cases had alterations in EGFR only, 94 (24.0%) cases had alterations in TP53 only, and 74(18.9%) cases were wild-type of TP53 and EGFR. Two major alterated genes were TP53 (57.0%) and EGFR (52.9%), both of whose frequencies were significantly higher than the other tested genes (P < 0.05). Groups with alterations in EGFR, APC (3.3%) or RBM10 (4.1%) were significantly associated with the decreased expression of PD-L1 (P < 0.05), while alterations in TP53 were significantly associated with the increased expression of PD-L1 (P < 0.05). Comparative analysis revealed that alterations (single or compound mutation) occured in either EGFR or TP53 affect the PD-L1 expression in this Chinese cohort. Although alterations in either EGFR or TP53 has independent and synergistic effects on the PD-L1 expression, there is limited evidence for those alterations serving as predictive biomarkers for the PD-L1 status. Furthermore, whether patients with both high PD-L1 expression and alterations in EGFR or TP53 can benefit from immunotherapies is still in question, and this subgroup is still worthy of clinical molecular recognition by further study.

Machine Learning-Based Classification and Evaluation of Regional Ethnic Traditional Sports Tourism Resources

Traditional sports in ethnic minority regions are a valuable cultural heritage. Regional ethnic traditional sports are not only a sports business but also a tourism resource. The construction of a reasonable regional sports tourism resource classification model is fundamental to the development of sports tourism resources. However, the existing sports tourism resources classification is mostly constructed manually based on the national standard tourism resources classification system. The efficiency and accuracy of the traditional manual classification are poor and cannot reflect the characteristics of regional ethnic traditional sports tourism. In order to solve the above problems, a machine learning-based classification method for regional ethnic traditional sports tourism resources is proposed. Firstly, the relevant concepts and characteristics of traditional sports tourism resources are introduced. Then, taking the development of traditional sports of ethnic minorities in Yunnan Province as the research object, SWOT analysis, literature, interview, questionnaire, and mathematical statistics are used to investigate and analyse the overall status of the development of regional ethnic traditional sports. Secondly, a classification evaluation method based on an optimised back-propagation (BP) neural network is proposed. Finally, the optimised BP neural network model is applied to the classification of traditional sports tourism resources. The experimental results show that the optimised BP model performs well in the classification of traditional sports tourism resources, verifying its effectiveness.

Automated Pixel-Level Deep Crack Segmentation on Historical Surfaces Using U-Net Models

Crack detection on historical surfaces is of significant importance for credible and reliable inspection in heritage structural health monitoring. Thus, several object detection deep learning models are utilized for crack detection. However, the majority of these models are powerful at most in achieving the task of classification, with primitive detection of the crack location. On the other hand, several state-of-the-art studies have proven that pixel-level crack segmentation can powerfully locate objects in images for more accurate and reasonable classification. In order to realize pixel-level deep crack segmentation in images of historical buildings, this paper proposes an automated deep crack segmentation approach designed based on an exhaustive investigation of several U-Net deep learning network architectures. The utilization of pixel-level crack segmentation with U-Net deep learning ensures the identification of pixels that are important for the decision of image classification. Moreover, the proposed approach employs the deep learned features extracted by the U-Net deep learning model to precisely describe crack characteristics for better pixel-level crack segmentation. A primary image dataset of various crack types and severity is collected from historical building surfaces and used for training and evaluating the performance of the proposed approach. Three variants of the U-Net convolutional network architecture are considered for the deep pixel-level segmentation of different types of cracks on historical surfaces. Promising results of the proposed approach using the U2−Net deep learning model are obtained, with a Dice score and mean Intersection over Union (mIoU) of 71.09% and 78.38% achieved, respectively, at the pixel level. Conclusively, the significance of this work is the investigation of the impact of utilizing pixel-level deep crack segmentation, supported by deep learned features, through adopting variants of the U-Net deep learning model for crack detection on historical surfaces.

Research on the Application of Multimodal-Based Machine Learning Algorithms to Water Quality Classification

With the development of society and the accelerated industrialization, the problem of water pollution has become increasingly prominent. In order to stop the gathering and diffusion of harmful substances in water bodies, leading to further deterioration of water quality and more serious environmental problems, environmental management departments have developed a series of pollutant discharge standards to prevent water pollution in real time. Common testing methods are the colorimetric method and TDS (total dissolved solids) value testing method, which are mostly through water bodies that contain acid, alkali, salt, and other indicators of the concentration test, to produce an assessment of water quality. However, the traditional methods of water quality testing, whether in the measurement time or in the accuracy of the test, are certain defects. In order to be able to quickly detect the concentration of water quality indicators in water bodies, timely response and treatment of highly polluted water bodies are urgently needed. In this paper, we propose a water quality detection classification model based on multimodal machine learning algorithm. Firstly, we preprocessed and analyzed the collected water quality dataset and determined the reasonable and perfect water quality classification influencing factors. Then, we successively built 15 kinds of classification models based on machine learning algorithms for water quality detection. At the same time, we evaluated the performance of each model. From the four evaluation indexes of precision, recall rate, F1 value, and accuracy, respectively, the real value is compared with the predicted value of each model. The experimental results show that sulfate, pH, solids, and hardness are the important influencing factors to perform water quality testing. And the three models XGBoost (Extreme Gradient Boosting), CatBoost (Categorical Boosting), and LGBM (Light Gradient Boosting Machine) have better performances in conducting water quality testing. Finally, we further optimized the classification models based on XGBoost, CatBoost, and LGBM by using two major tools: cross-validation and hyperparameter tuning.

Types of Maintenance Based on Uncertain Data Envelope Analysis

Nowadays, one of the main challenges of marine maintenance is how to select an optimum maintenance strategy for each component of the complex ship machinery system. The uncertainty of the parameters is one of the main difficulties encountered. For example, engineers and experts are always questioning the credibility and integrity of the data collected, and historical maintenance records may also be lost during maintenance. The above data asymmetry will also lead to parameter uncertainty, which directly affects the accuracy of the prediction results. This paper proposes a method for determining maintenance types of ship equipment based on uncertainty theory and the Data Envelopment Analysis (DEA) model. Firstly, this paper constructs an uncertain maintenance optimization model (UMOM) based on the classic Data Envelopment Analysis model. Then, we converted the UMOM into an equivalent deterministic model for easy calculation by the uncertainty theory. Finally, a case study is given to verify this model. The results will conclude that the UMOM can meet the need for a reasonable classification of maintenance types of mechanical equipment in a marine system and provide valuable information for systemic management and storage.

Influence of auxiliary soil variables to improve PXRF-based soil fertility evaluation in India

Portable X-ray fluorescence (PXRF)spectrometry has already been established as a rapid and cost-effective tool for predicting various soil physicochemical properties. This study used PXRF in combination with physiographic, agro-climatic, soil parent-material, and physicochemical attributes (pH, electrical conductivity (EC), loss on ignition organic matter, and organic carbon) as auxiliary properties to predict multiple soil fertility indicators [available K, Ca, Mg, Fe, Cu, Zn, Mn, B, K/Mg ratio, total exchangeable bases (TEB), and sulfur availability index (SAI)] via four machine-learning algorithms (random forest, support vector regression, stepwise multiple linear regression, and an averaged model). Principal component analysis (PCA) indicated the links between PXRF-reported elements, agro-climatic zones, and soil parent materials. Although no universal prediction model can be selected to suit all 11 soil fertility parameters, three parameters (available Ca, Fe, and TEB) produced reasonable model performance with an R2 > 0.50 for most prediction model-dataset combinations. Concatenation of auxiliary soil parameters with PXRF data showed relative improvement in model accuracy compared to PXRF in isolation. Notably, the agro-climatic zone appeared influential for predicting available K, Mg, Zn, Fe, Mn, B, K/Mg ratio, and TEB. For potential fertilizer recommendation, six parameters (available K, Ca, Mg, Cu, Mn, and B) produced reasonable classification performance via the averaged model using all auxiliary predictors (κ > 0.30). The same categorical model was used, as an instance, for delineating a conceptualized framework for (PXRF+ auxiliary properties)-based fertilizer recommendation facilitating site-specific nutrient management. More research is needed to enhance model prediction/classification accuracy by including a well-balanced dataset and other relevant auxiliary variables with PXRF. Nevertheless, the importance of adding auxiliary soil properties with PXRF elemental data for cost-effective and accessible nutrient management in resource-poor countries seems promising.

ANALYSIS OF THE MOST COMMON DAMAGES IN REINFORCED CONCRETE STRUCTURES: A REVIEW

Reinforced concrete structures are often subjected to various negative environmental influences, reducing their reliability and durability. Main engineering tasks include extension of their life cycle, assessment of durability, reliability and residual service life. This requires reliable assessment of existing damages due to negative environmental impacts. Deterioration of RC structures is complex issue, which should be considered with the account of various factors. Damages and defects should be assessed, according to different criteria: degradation degree, type, time and cause of formation, etc. Article provides detailed analysis of the most common damages in RC structures on the basis of thorough literature review of this issue. Also, the classification of reasons for decrease of bearing capacity is proposed. Additionally, are discussed corrosion mechanisms and specifics of stress-strain state in corroded RC structures.

A robo-advisory framework for Islamic and Environmental, Social and Governance (ESG) compliance – A benchmark study on the S&amp;P 500 stock index

The last decade has brought a revolution in the financial sector, enabling more individuals and institutions to access the stock market. Islamic finance has similarly gained considerable interest from the growing Muslim population that is looking for Shariah-compliant investment options. Furthermore, a growing number of investors are looking for investment options that are environmentally sustainable and adhere to ethical values. While there are various Shariah advisory services, these services differ widely in their assessments of whether a company is Shariah-compliant. Given the cost of these services, it is typically only available for large institutional investors. The objective of this article is to address the challenges of providing individualized Shariah-compliant investment services automatically while providing a comparison with Environmental, Social and Governance (ESG) compliance to assist both investors who are seeking Shariah and ESG compliant investments. Hence, the study presents a new unsupervised learning framework for the determination of Shariah compliance and evaluates it with ESG compliance. The framework first filters the companies based on whether they have any exclusionary activities and then performs a clustering approach in order to classify them into compliant and non-compliant stocks. The framework was evaluated on the S&amp;P 500 stock index and delivered acceptable and reasonable classifications. This Robo-Shariah advisor framework allows automatized real-time Shariah compliance evaluation based on a data-driven approach. The practical implication of this new framework is the enablement of objective, data-driven Shariah-compliant investment recommendations that can be easily integrated with Shariah expert information. The social implication is the empowerment of retail investors in being able to easily set up their own Shariah-compliant portfolio.