Identifying Important Attributes for the Siberian Forests Management Using Rough Sets Analysis

In this paper, we propose the extended technique from the usual rough set analysis by considering the dominance relations to the quantitative data, and propose the criterion to select the rule. Proposed expert system using rough set analysis applies a most likelihood to estimate mapping from attributes of product to Kansei word. Usual estimation of interval efficiency has been developed by linear regression, however, our system can estimate interval efficiency by nonlinear mapping. As the experimental result, we analyze about the influence to user's psychology from the front design of a car and extraction of relationship among Kansei words by expert system using rough set analysis.

Convergence of information and communication on technology changed the life of a common man over the last few years. At the same time it increases threats to a common man. In order to overcome these situations lots of research is carried out in the direction network and internet security. In addition identification of various attacks is of great challenge before taking any security measures. Identification of phishing attacks such as fake e-mails or websites is one such critical attack in internetworking. It is an illegal trick game that collects personal information from a legitimate user. The fake e-mails or website looking like a true one and even in the web pages where users are asked to send their personal information may look like a legitimate. Identification of such websites, e-mails is one of great challenge today. To this end, in this paper, we propose a model for identification of phishing attacks and chief attributes that make an object phishing object using rough set and formal concept analysis. The concept is explained with an illustration and followed by a case study. The results obtained show the viability of the proposed research.

During the analysis of stability heat conduction in the composite pipes, firstly, when the heat equation contained fuzzy and random uncertain parameters, interval equations of the heat conduction are presented in the rough set. Secondly, the error expecting of heat conduction equation is presented. Finally, with upper (lower) approximation in rough set, a new method of the rough set analysis to deal with the stability heat conduction is presented.

Rough sets have been applied to many areas where multi-attribute data is needed to be analyzed to acquire knowledge for decision making. Web-based Support Systems (WSS) are a new research area that aims to support human activities and extend human physical limitations of information processing with Web technologies. The applications of rough set analysis for WSS is looked at in this article. In particular, our focus will be on Web-Based Medical Support Systems (WMSS). A WMSS is a support system that integrates medicine practices (diagnosis and surveillance) with computer science and Web technologies. We will explore some of the challenges of using rough sets in a WMSS and detail some of the applications of rough sets in analyzing medical data.

The Indonesian government has not been able to realize sustainable forest management under the 1945 constitutional mandate, causing much forest damage. Indonesia urgently needs good forest governance. Therefore, it is essential to give a more significant role to local communities in managing forests in the future. The primary objective of this research project is to find sustainable forest management practices based on the customary law of the Bayan community. This research uses the literature review and case study method. An additional objective of this research is to study the problems that occur due to the absence of legal protection for local communities with regard to forest use and conflicts. This research demonstrates that the Indonesian government provides extensive support to the private forestry sector (for example through award of permits) and has ignored the rights of people who live around the forest. Forest management through customary law, such as Awiq-awiq in Bayan village, can provide great economic, social, and cultural benefits to local communities. Awiq-awiq regulates prohibitions, sanctions, and the procession of the customary assembly in association with forest management, and has been an effective way to use forests sustainably. The local community is highly compliant with awiq-awiq because they have been directly involved in drafting and reviewing this regulation. The customary law approach used by the Bayan community is an example of sustainable forest management practices that run well; and could be a valuable model for the Indonesian state and other forest conservation managers to consider.

This work aims to develop alternative classifications for teams in a Championship. Data from the 2005 Brazilian National Soccer Championship are analyzed. Rough Sets Theory (RST) is employed in this analysis. By evaluating the quality of the approximation in terms of probabilities of concordance and discordance between the classification by the set of decision attributes and by the set of condition attributes of a randomly chosen pair of objects as discernible or indiscernible, the modification of RST employed allows to consider antisymmetric and intransitive relations. The balance between the numbers of goals scored by the pairs of clubs in direct confrontations is one such relation.

Nowadays, power systems play an important role in the whole electric industry. Failures of such systems should result in serious social and economical damages. Therefore, the power systems should be highly reliable. This paper presents the method to build a new type of failure diagnosis system based on rough set theory. The testing data of power systems for their failure conditions are based on experts' evaluations with uncertainty, especially little knowledge and human experiences are available on power system failure diagnosis. The rough set theory plays a vital role in handling them.

ABSTRACTThe main aim of the article is to assess the consequences of newly proposed legislative initiative on introducing intensive forest management practices in Russia. Implementation of norms and its effect on sustainable forest management practices have been analysed in this study on one enterprise operating in the Republic of Karelia. This meant modelling of forest growth, clear cuts and regeneration within 100 km radius from the mill for two alternative management scenarios with fixed demand of wood based on current norms and decreasing harvesting age to half from the current. Wood demand of the enterprise, structure and accessibility of forest resources, i.e. forest road infrastructure were taken into account in the analysis. Both forest management scenarios decreased the total growing stock significantly, and therefore considered as non-sustainable practices. In addition, forest age structure was more uneven for both scenarios at the end of the simulation period. Comparison of two alternative management practices showed that the implementation of intensive forest management in Russia requires new norms that would be based on principles of sustainable forest management.

Background and Objectives: Teak (Tectona grandis) is one of Thailand's most valuable economic tree species, prized for its exceptional qualities such as durability, aesthetic appeal, and resistance to pests and diseases. Its popularity spans both domestic and international markets, making accurate estimation of merchantable teak volume essential for forest management and the timber industry. Precise volume estimation facilitates accurate economic valuation and minimizes resource losses associated with traditional destructive tree-felling methods. Recent advancements in technology, light detection and ranging or LiDAR, have provided opportunities to enhance the accuracy and efficiency of forest resource data collection. Among these tools, the Terrestrial Laser Scanner (TLS) stands out for its ability to generate high-resolution three-dimensional (3D) models of individual trees and forest stands in a non-destructive method. Unlike traditional methods, TLS enables data collection without damaging trees, offering a sustainable and efficient alternative for forestry applications. This study aimed to develop a methodology for utilizing TLS to construct 3D models of teak trees to estimate merchantable wood volume. The results were compared with the traditional felling method, which is considered the most accurate but destructive. In addition, the study attempts to establish predictive equations and volume tables for estimating merchantable teak volume. These outputs aimed to support sustainable forest management practices, reduce resource wastes, and facilitate the economic valuations of teak for the timber industry. Methods: The study was conducted in the Mae Chang forest plantation, located in Mae Mo district, Lampang province, Thailand. The stratified random sampling was employed to establish 12 sample plots, each sizing 40 m × 40 m. The plots were divided into two age groups based on plantation year: 18 years old (planted in 2006) and 39 years old (planted in 1985), with six plots in each group. Data collection involved using a Faro Focus S150 TLS to scan individual trees in each plot. The TLS data were processed to create detailed 3D tree models for further analysis. Merchantable timber volume was calculated using two methods: the stem curve method and quantitative structure models (QSM), both implemented through the 3D forest software. The TLS-derived volume estimates were validated by felling selected sample trees. The diameters and merchantable lengths of these felled trees were measured, and their volumes were calculated using Huber’s formula. Statistical analysis was performed to compare TLS-derived volumes with traditional felling-based volumes using paired t-tests. The accuracy of TLS-derived estimates was evaluated using root mean square error (RMSE). Additionally, predictive equations for merchantable volume estimation were developed using regression analysis. Two types of equations were created: one using diameter at breast height (DBH) as the sole variable and the other incorporating both DBH and merchantable height (HM). A volume table was constructed based on these equations, enabling non-destructive volume estimation for teak trees of varying DBH and HM ranges. Results: The 3D models generated using TLS provided merchantable volume estimates that closely aligned with volumes derived from felled trees. Statistical analysis revealed no significant differences between TLS-derived volumes and traditional felling-based volumes (p > 0.05). The RMSE values for TLS-derived estimates were 0.02 and 0.03 cubic meters when using the stem curve and QSM methods, respectively, indicating high accuracy. Two predictive equations for merchantable volume estimation were developed. The first equation utilized DBH as a single predictor variable and demonstrated a coefficient of determination (R²) of 0.966. The second equation incorporated both DBH and HM, achieving a higher R² value of 0.988. Validation of these equations using data from felled trees showed mean absolute percentage errors (MAPE) of 17.81% and 11.01%, respectively, confirming their suitability for practical applications. The most accurate equation was subsequently used to construct a merchantable volume table, which estimating based on DBH and HM ranges without requiring tree felling. This table provides a practical tool for forest managers and timber industry stakeholders to assess teak volume efficiently and sustainably. Conclusion: The results of this study demonstrate that TLS is a reliable and effective tool for non-destructive estimation of merchantable teak volume. The 3D models generated using TLS data enable precise volume estimation through methods such as stem curve and QSM, producing results that are statistically comparable to traditional destructive felling methods. The acceptable error margins and high accuracy of TLS-derived estimates highlight its potential as a sustainable alternative to conventional volume estimation techniques. By minimizing the need for destructive sampling, TLS-based methods contribute to sustainable forest management practices, preserving valuable tree resources while maintaining economic productivity. Furthermore, the development of predictive equations and merchantable volume tables offers practical solutions for forest managers and timber industry professionals, streamlining the assessment of merchantable volume and economic valuation. Overall, this study underscores the importance of integrating advanced technologies such as TLS into forestry practices. By improving the accuracy and sustainability of resource assessments, these innovations can support long-term forest conservation and enhance the economic potential of Thailand's teak plantations.

The task of modeling the distribution of a large number of earthquake events with frequent tremors detected prior to a main shock presents us unique challenges to model a robust classifier tool for rapid responses are needed in order to address victims.We have designed using a relational database for running a geophysical modeling application after connecting database record of all clusters of foreshock events from (1998-2010) for a complete catalog of seismicity analysis for the Himalayan basin.by Nath et al,2010.This paper develops a reduced rough set analysis method and implements this novel structure and reasoning process for foreshock cluster forecasting.In this study, we developed a reusable information technology infrastructure, called Efficient Machine Readable for Emergency Text Selection(EMRETS).The association and importance of precursory information in reference to earthquake rupture analysis is found out through attribute reduction based on rough set analysis.Secondly, find the importance of attributes through information entropy is a novel approach for high dimensional complex polynomial problems predominant in geo-physical research and prospecting.Thirdly, we discuss the reducible indiscernible matrix and decision rule generation for a particular set of geographical co-ordinates leading to the spatial discovery of future earthquake having prior foreshock.This paper proposes a framework for extracting, classifying, analyzing, and presenting semi-structured catalog data sources through feature representation and selection.

Probably the distinguishing concept in incomplete information analysis is that of “boundary”: in fact a boundary is precisely the region that represents those doubts arising from our information gaps. In the paper it is shown that the rough set analysis adequately and elegantly grasps this notion via the algebraic features provided by co-Heyting algebras.KeywordsBoolean AlgebraApproximation SpaceHeyting AlgebraStone AlgebraBounded Distributive LatticeThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

As climate change accelerates, forests play a pivotal role in global carbon dynamics by providing both carbon sequestration and ecosystem resilience. This review synthesizes two decades (2004-2024) of research on the interrelationships between forest carbon sequestration, carbon credit mechanisms, and the economic dimensions of sustainable forest management (SFM). Despite substantial progress, notable gaps persist in the economic valuation of forest carbon credits and their integration into SFM strategies. Drawing on systematic analysis of Web of Science data, it examines four core interconnected research themes: (i) global trends and international collaboration in forest carbon research, (ii) economic valuation of forest ecosystem services to mitigate deforestation and enhance sequestration, (iii) the influence of forest management practices, species diversity, and climate zones on soil organic carbon and its economic value, and (iv) the interplay between economic, ecological, and policy drivers shaping carbon credit markets. Findings reveal a 12.16% annual growth rate in publications, strong global collaboration (39.11% co-authorship), and priority research themes such as aboveground biomass, climate change, carbon storage, and carbon credits. Recent emphasis has shifted toward advanced tools (e.g., LiDAR), sustainable management approaches, and interdisciplinary strategies, reflecting a broader focus on climate resilience and policy integration. SFM practices, such as agroforestry, selective logging, and afforestation, emerge as effective for enhancing carbon stocks while generating economic returns. Persistent challenges include policy inconsistencies, insufficient community participation, and a lack of standardized methods for carbon measurement and valuation. Regional disparities in carbon values complicate international trading and climate agreements. Overall, the review offers practical insights for policymakers, researchers, and practitioners by connecting forest management, carbon sequestration, and economic incentives. Moreover, it underscores the need for long-term, socio-economically integrated studies, strengthened policy frameworks, and enhanced cooperation to align SFM with global sustainability objectives.

The study examined the effect of market access on the adoption of sustainable soil management practices by farmers in the study area. Specifically, the study described the socio-economic characteristics of the farmers; identified the sustainable soil management practices adopted by the farmers; determined factors influencing the choice of sustainable soil management practices used; determined the relationship between market access and the adoption intensity of sustainable soil management practices and identified constraints faced in adopting sustainable soil management practices. Multistage sampling procedure was employed to collect data from one hundred and fifty (150) farmers. Thereafter, data collected were analyzed using descriptive statistics and multivariate probit regression. The findings revealed that the mean age of the respondents was 45 years, with a mean household size of about 6 members. Majority (69.6%) of the respondents had formal education and a mean year of experience of about 18. The study also revealed that all the respondents had access to one form of market or the other for purchase of inputs and sales of their products. In addition, the mean distance covered from farm to home was less than 5 kilometres for most of the respondents while the distance from home to major market and farm to major market was between 5 and 10 kilometres. The most popular sustainable soil management practices adopted by the farmers were the use of chemical fertilizers, crop rotation and intercropping. Furthermore, the multivariate probit regression model showed that age of the respondents, educational level, farm size, household size, farming experience, farm income, awareness of sustainable soil management practices, average distance to the input market, average distance to the output market, average price of product, average price of input for each practice, subsidies on input for each practice, significantly influenced the adoption of sustainable soil management practice by farmers. Also, it was found by the study that inadequate fund is the major constraint faced by the respondents in adopting sustainable soil management practices.

This paper highlights the significance of Artificial Intelligence (AI) in the realm of drone applications in forestry. Drones have revolutionized various forest operations, and their role in mapping, monitoring, and inventory procedures is explored comprehensively. Leveraging advanced imaging technologies and data processing techniques, drones enable real-time tracking of changes in forested landscapes, facilitating effective monitoring of threats such as fire outbreaks and pest infestations. They expedite forest inventory by swiftly surveying large areas, providing precise data on tree species identification, size estimation, and health assessment, thus supporting informed decision-making and sustainable forest management practices. Moreover, drones contribute to tree planting, pruning, and harvesting, while monitoring reforestation efforts in real-time. Wildlife monitoring is also enhanced, aiding in the identification of conservation concerns and informing targeted conservation strategies. Drones offer a safer and more efficient alternative in search and rescue operations within dense forests, reducing response time and improving outcomes. Additionally, drones equipped with thermal cameras enable early detection of wildfires, enabling timely response, mitigation, and preservation efforts. The integration of AI and drones holds immense potential for enhancing forestry practices and contributing to sustainable land management. In the future explainable AI (XAI) improves trust and safety by providing transparency in decision-making, aiding in liability issues, and enabling precise operations. XAI facilitates better environmental monitoring and impact analysis, contributing to efficient forest management and preservation efforts. If a drone's AI can explain its actions, it will be easier to understand why it chose a particular path or action, which could inform safety procedures and improvements.

Family forest lands represent a vitally important economic, environmental, and social resource in the U.S. A study of family forest owners was conducted in Virginia in 2007 to determine the relationship between attendance at Extension Service educational programs and the adoption of sustainable forest management practices. A mail survey was conducted to 3435 randomly selected forest owners, with a usable response rate of 32%. Participation in educational programs was shown to be significantly related to higher levels of adoption for all seven categories of sustainable forest management practices studied. For example, in the woodland management category, participants in workshops offered through the Virginia Forest Landowner Education Program (VFLEP) adopted one or more specific practices at a rate of 94%, significantly greater than 83% for forest owners who attended other general educational programs, which in turn was significantly higher than the 75% adoption rate for forest owners who did not attend any educational programs. Two key indicators of sustainable forest management are the preparation and use of a forest management plan, and the use of professional technical assistance providers. For both of these categories participants in the VFLEP adopted at significantly higher rates, 41% and 73%, respectively.