Typification of Forest Areas by Natural-Production Conditions Based on Cluster Analysis

2021 ◽  
pp. 120-137
Author(s):  
Ilya R. Shegelman ◽  
◽  
Pavel V. Budnik
Keyword(s):  
Cluster Analysis ◽  
Data Set ◽  
Polar Urals ◽  
Natural Production ◽  
Wide Range ◽  
Murmansk Region ◽  
The Republic ◽  
Negative Environmental Impact ◽  
Machine Systems ◽  
Production Conditions

The effectiveness of harvesting machines, their reliability, and the level of negative environmental impact depends on the degree of adaptation of the equipment to natural-production conditions (NPC). To choose the equipment it is necessary to allocate groups of areas with close NPC. The purpose of the study is to form methodological tools for forest industry typification of forest areas by NPC. It is proposed to carry out the typification of forest areas based on cluster analysis. For this purpose, a methodology has been developed, including: setting the goal of typing areas by NPC; data collection on NPC; conducting cluster analysis; decision making on typification of areas by NPC. The task of cluster analysis is to divide, on the basis of a certain set of data, the set of forest areas into groups with similar NPCs. It is proposed to use Euclidean distances as a measure of belonging to one of the groups, and to determine the data set by indicators describing the NPC. The proposed methodology has been tested on the example of the European North of Russia (ENR). The study showed that three zones can be distinguished in ENR: zone A, including the Murmansk region; zone B, including the Republic of Karelia, the Republic of Komi and the Arkhangelsk region; zone C, including the Vologda region. Additionally, two subzones are distinguished in zone B: the West Karelian Upland and the territories belonging to the Northern, Subpolar and Polar Urals. The proposed methodology allows to increase the degree of formalization and convenience of the typification process of forest areas by NPC, to take into account a wide range of various aspects of natural-production conditions, their probabilistic nature, as well as to flexibly carry out the typification of areas for specific purposes. The research results may be applicable in solving problems of searching for effective technologies and rational parameters of logging machine systems.

scholarly journals Analysis of natural-production conditions for timber harvesting in European North of Russia

2019 ◽  
Vol 65 (2) ◽  
pp. 81-91
Author(s):  
Ilya Shegelman ◽  
Pavel Budnik ◽  
Vyacheslav Baklagin ◽  
Oleg Galaktionov ◽  
Ivan Khyunninen ◽  
...  
Keyword(s):  
Decision Making ◽  
Cluster Analysis ◽  
Forest Management ◽  
Timber Harvesting ◽  
Decision Making Process ◽  
Small Enterprises ◽  
Natural Production ◽  
European North ◽  
Different Levels ◽  
Production Conditions

Abstract Natural-production conditions determine operational efficiency of logging machines. This influence needs to be taken into account at different levels of forest management. It is necessary to allocate areas with similar natural-production conditions for effective forest management. It allows simplifying the decision making process for selecting logging technology and machines. The purpose of this study was to establish areas with similar natural and production conditions in the European North of Russia (ENR). In addition, for small enterprises, we recommend logging technologies and logging machines that can be used in established areas. We determined the indicators of the natural-production conditions of ENR regions and compared them. Cluster analysis was used to compare the indicators. We found that ENR can be divided into three main zones A, B, C and two subzones B1 and B2 with similar natural-production conditions. In the zones A, B and the subzones B1 and B2, small logging enterprises should use a harvester and a forwarder. In the zone C, the enterprises can use a logging system including a harvester and a forwarder or a logging system including a feller buncher, a skidder and a processor. The logging system should be based on the light class of logging machines for the zone A, the medium class or the heavy class for the zones B, C and the subzones B1, B2, the heavy class of machines for the zone C.

scholarly journals THE TECHNIQUE OF FORMATION OF SYSTEM OF FORESTRY MACHINES

2017 ◽  
Vol 7 (1) ◽  
pp. 111-117 ◽  
Author(s):  
Мамматов ◽  
Vladimir Mammatov ◽  
Мохирев ◽  
Aleksandr Mokhirev
Keyword(s):  
Expert Knowledge ◽  
Stock Selection ◽  
Screening Criteria ◽  
Natural Production ◽  
Wide Range ◽  
Major Operation ◽  
Market Engineering ◽  
The Right ◽  
Production Conditions ◽  
Selection Of

A variety of natural-production conditions, which is necessary to conduct logging activities largely complicates the choice of forest machines for each individual company in the industry having logging sites in its composition. Depending on many natural and production conditions such as: slope, bearing capacity of soils, ambient temperature, maximum investments, etc., and the wide range of forestry equipment on the market, engineering divisions of the company solving a problem about the purchase of new or replacement of lost its performance machines are more and more difficult to make the right choice. This work presents a methodology for forming the system of forest machines with the natural and production conditions in a convenient form, not requiring special expert knowledge from the field of mathematical modeling or other areas of science. According to the methodology the first step is selection of basic machine and performing the first major operation in felling trees, then switch to the auxiliary machines which are linked to the underlying performance, to reduce the number of in-process stock. Selection of basic and auxiliary machines is provided that candidates and excludes important indicators. If in the process of selecting the logging machine does not meet the screening criteria for excluding indicators then it does not participate in the selection. Significant index can be neglected in case of discrepancy of candidates to stated requirements. The performance of the model was tested on the example of harvesting sites of the enterprises of the Krasnoyarsk region in spring 2016. In the course of the experiment the system of forest machines, meeting the required selection criteria, was obtained.

Structure Guided Molecular Docking Assisted Alignment Dependent 3DQSAR Study on Steroidal Aromatase Inhibitors (SAIs) as Anti-breast Cancer Agents

2019 ◽  
Vol 16 (7) ◽  
pp. 808-817 ◽  
Author(s):  
Laxmi Banjare ◽  
Sant Kumar Verma ◽  
Akhlesh Kumar Jain ◽  
Suresh Thareja
Keyword(s):  
Breast Cancer ◽  
Molecular Docking ◽  
Aromatase Inhibitors ◽  
Direct Synthesis ◽  
3D Qsar ◽  
Chemotherapeutic Agents ◽  
Modeling Tools ◽  
Data Set ◽  
Wide Range ◽  
Steroidal Aromatase Inhibitors

Background: In spite of the availability of various treatment approaches including surgery, radiotherapy, and hormonal therapy, the steroidal aromatase inhibitors (SAIs) play a significant role as chemotherapeutic agents for the treatment of estrogen-dependent breast cancer with the benefit of reduced risk of recurrence. However, due to greater toxicity and side effects associated with currently available anti-breast cancer agents, there is emergent requirement to develop target-specific AIs with safer anti-breast cancer profile. Methods: It is challenging task to design target-specific and less toxic SAIs, though the molecular modeling tools viz. molecular docking simulations and QSAR have been continuing for more than two decades for the fast and efficient designing of novel, selective, potent and safe molecules against various biological targets to fight the number of dreaded diseases/disorders. In order to design novel and selective SAIs, structure guided molecular docking assisted alignment dependent 3D-QSAR studies was performed on a data set comprises of 22 molecules bearing steroidal scaffold with wide range of aromatase inhibitory activity. Results: 3D-QSAR model developed using molecular weighted (MW) extent alignment approach showed good statistical quality and predictive ability when compared to model developed using moments of inertia (MI) alignment approach. Conclusion: The explored binding interactions and generated pharmacophoric features (steric and electrostatic) of steroidal molecules could be exploited for further design, direct synthesis and development of new potential safer SAIs, that can be effective to reduce the mortality and morbidity associated with breast cancer.

Integrative Data Analysis from a Unifying Research Synthesis Perspective

2018 ◽  
Author(s):  
Eun-Young Mun ◽  
Anne E. Ray
Keyword(s):  
Data Analysis ◽  
Large Scale ◽  
Research Synthesis ◽  
Alcohol Intervention ◽  
Data Set ◽  
Integrative Data Analysis ◽  
Level Data ◽  
Model Complex ◽  
Wide Range ◽  
Individual Participant

Integrative data analysis (IDA) is a promising new approach in psychological research and has been well received in the field of alcohol research. This chapter provides a larger unifying research synthesis framework for IDA. Major advantages of IDA of individual participant-level data include better and more flexible ways to examine subgroups, model complex relationships, deal with methodological and clinical heterogeneity, and examine infrequently occurring behaviors. However, between-study heterogeneity in measures, designs, and samples and systematic study-level missing data are significant barriers to IDA and, more broadly, to large-scale research synthesis. Based on the authors’ experience working on the Project INTEGRATE data set, which combined individual participant-level data from 24 independent college brief alcohol intervention studies, it is also recognized that IDA investigations require a wide range of expertise and considerable resources and that some minimum standards for reporting IDA studies may be needed to improve transparency and quality of evidence.

scholarly journals A Self-Spatial Adaptive Weighting Based U-Net for Image Segmentation

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 348
Author(s):  
Choongsang Cho ◽  
Young Han Lee ◽  
Jongyoul Park ◽  
Sangkeun Lee
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Medical Image Segmentation ◽  
Feature Maps ◽  
Data Set ◽  
Feature Map ◽  
Adaptive Weighting ◽  
Spatially Adaptive ◽  
Wide Range ◽  
Decoder Architecture

Semantic image segmentation has a wide range of applications. When it comes to medical image segmentation, its accuracy is even more important than those of other areas because the performance gives useful information directly applicable to disease diagnosis, surgical planning, and history monitoring. The state-of-the-art models in medical image segmentation are variants of encoder-decoder architecture, which is called U-Net. To effectively reflect the spatial features in feature maps in encoder-decoder architecture, we propose a spatially adaptive weighting scheme for medical image segmentation. Specifically, the spatial feature is estimated from the feature maps, and the learned weighting parameters are obtained from the computed map, since segmentation results are predicted from the feature map through a convolutional layer. Especially in the proposed networks, the convolutional block for extracting the feature map is replaced with the widely used convolutional frameworks: VGG, ResNet, and Bottleneck Resent structures. In addition, a bilinear up-sampling method replaces the up-convolutional layer to increase the resolution of the feature map. For the performance evaluation of the proposed architecture, we used three data sets covering different medical imaging modalities. Experimental results show that the network with the proposed self-spatial adaptive weighting block based on the ResNet framework gave the highest IoU and DICE scores in the three tasks compared to other methods. In particular, the segmentation network combining the proposed self-spatially adaptive block and ResNet framework recorded the highest 3.01% and 2.89% improvements in IoU and DICE scores, respectively, in the Nerve data set. Therefore, we believe that the proposed scheme can be a useful tool for image segmentation tasks based on the encoder-decoder architecture.

scholarly journals Prediction of Wide Range Two-Dimensional Refractivity Using an IDW Interpolation Method from High-Altitude Refractivity Data of Multiple Meteorological Observatories

2021 ◽  
Vol 11 (4) ◽  
pp. 1431
Author(s):  
Sungsik Wang ◽  
Tae Heung Lim ◽  
Kyoungsoo Oh ◽  
Chulhun Seo ◽  
Hosung Choo
Keyword(s):  
High Altitude ◽  
Korean Peninsula ◽  
Interpolation Method ◽  
Atmospheric Condition ◽  
Propagation Path ◽  
Two Dimensional ◽  
Data Set ◽  
Wide Range ◽  
Atmospheric Data ◽  
Terrain Surface

This article proposes a method for the prediction of wide range two-dimensional refractivity for synthetic aperture radar (SAR) applications, using an inverse distance weighted (IDW) interpolation of high-altitude radio refractivity data from multiple meteorological observatories. The radio refractivity is extracted from an atmospheric data set of twenty meteorological observatories around the Korean Peninsula along a given altitude. Then, from the sparse refractive data, the two-dimensional regional radio refractivity of the entire Korean Peninsula is derived using the IDW interpolation, in consideration of the curvature of the Earth. The refractivities of the four seasons in 2019 are derived at the locations of seven meteorological observatories within the Korean Peninsula, using the refractivity data from the other nineteen observatories. The atmospheric refractivities on 15 February 2019 are then evaluated across the entire Korean Peninsula, using the atmospheric data collected from the twenty meteorological observatories. We found that the proposed IDW interpolation has the lowest average, the lowest average root-mean-square error (RMSE) of ∇M (gradient of M), and more continuous results than other methods. To compare the resulting IDW refractivity interpolation for airborne SAR applications, all the propagation path losses across Pohang and Heuksando are obtained using the standard atmospheric condition of ∇M = 118 and the observation-based interpolated atmospheric conditions on 15 February 2019. On the terrain surface ranging from 90 km to 190 km, the average path losses in the standard and derived conditions are 179.7 dB and 182.1 dB, respectively. Finally, based on the air-to-ground scenario in the SAR application, two-dimensional illuminated field intensities on the terrain surface are illustrated.

Prodromal Dementia With Lewy Bodies: Evolution of Symptoms and Predictors of Dementia Onset

2021 ◽  
pp. 089198872110235
Author(s):  
Kathryn A. Wyman-Chick ◽  
Lauren R. O’Keefe ◽  
Daniel Weintraub ◽  
Melissa J. Armstrong ◽  
Michael Rosenbloom ◽  
...  
Keyword(s):  
Clinical Features ◽  
Dementia With Lewy Bodies ◽  
Clinical Symptoms ◽  
Neuropsychiatric Symptoms ◽  
Lewy Bodies ◽  
Rem Sleep Behavior Disorder ◽  
Data Set ◽  
Wide Range ◽  
Prodromal Dementia ◽  
Dementia Onset

Background: Research criteria for prodromal dementia with Lewy bodies (DLB) were published in 2020, but little is known regarding prodromal DLB in clinical settings. Methods: We identified non-demented participants without neurodegenerative disease from the National Alzheimer’s Coordinating Center Uniform Data Set who converted to DLB at a subsequent visit. Prevalence of neuropsychiatric and motor symptoms were examined up to 5 years prior to DLB diagnosis. Results: The sample included 116 participants clinically diagnosed with DLB and 348 age and sex-matched (1:3) Healthy Controls. Motor slowing was present in approximately 70% of participants 3 years prior to DLB diagnosis. In the prodromal phase, 50% of DLB participants demonstrated gait disorder, 70% had rigidity, 20% endorsed visual hallucinations, and over 50% of participants endorsed REM sleep behavior disorder. Apathy, depression, and anxiety were common prodromal neuropsychiatric symptoms. The presence of 1+ core clinical features of DLB in combination with apathy, depression, or anxiety resulted in the greatest AUC (0.815; 95% CI: 0.767, 0.865) for distinguishing HC from prodromal DLB 1 year prior to diagnosis. The presence of 2+ core clinical features was also accurate in differentiating between groups (AUC = 0.806; 95% CI: 0.756, 0.855). Conclusion: A wide range of motor, neuropsychiatric and other core clinical symptoms are common in prodromal DLB. A combination of core clinical features, neuropsychiatric symptoms and cognitive impairment can accurately differentiate DLB from normal aging prior to dementia onset.

scholarly journals Securitisation gaps: Towards ideational understandings of state weakness

2021 ◽  
pp. 1-20
Author(s):  
Kevork Oskanian
Keyword(s):  
The State ◽  
Collective Knowledge ◽  
State Action ◽  
Role Of The State ◽  
Wide Range ◽  
State And Society ◽  
Interpretive Approach ◽  
Recent Developments ◽  
The Republic

Abstract This article contributes a securitisation-based, interpretive approach to state weakness. The long-dominant positivist approaches to the phenomenon have been extensively criticised for a wide range of deficiencies. Responding to Lemay-Hébert's suggestion of a ‘Durkheimian’, ideational-interpretive approach as a possible alternative, I base my conceptualisation on Migdal's view of state weakness as emerging from a ‘state-in-society's’ contested ‘strategies of survival’. I argue that several recent developments in Securitisation Theory enable it to capture this contested ‘collective knowledge’ on the state: a move away from state-centrism, the development of a contextualised ‘sociological’ version, linkages made between securitisation and legitimacy, and the acknowledgment of ‘securitisations’ as a contested Bourdieusian field. I introduce the concept of ‘securitisation gaps’ – divergences in the security discourses and practices of state and society – as a concept aimed at capturing this contested role of the state, operationalised along two logics (reactive/substitutive) – depending on whether they emerge from securitisations of the state action or inaction – and three intensities (latent, manifest, and violent), depending on the extent to which they involve challenges to state authority. The approach is briefly illustrated through the changing securitisation gaps in the Republic of Lebanon during the 2019–20 ‘October Uprising’.

Relationship Between Gas Emissions and Dry Matter Intake in Mature Angus Cows

2021 ◽  
Vol 99 (Supplement_2) ◽  
pp. 32-33
Author(s):  
Amanda Holder ◽  
Megan A Gross ◽  
Alexi Moehlenpah ◽  
Paul Beck
Keyword(s):  
Dry Matter ◽  
Significant Positive Correlation ◽  
Crossover Design ◽  
Dry Matter Intake ◽  
Data Set ◽  
Gas Emissions ◽  
Positive Correlation ◽  
Wide Range ◽  
Carbon Dioxide Co2 ◽  
Second Period

Abstract The objective of this study was to examine the effects of diet quality on greenhouse gas emissions and dry matter intake (DMI). We used 42 mature, gestating Angus cows (600±69 kg; and BSC 5.3±1.1) with a wide range in DMI EPD (-1.36 to 2.29). Cows were randomly assigned to 2 diet sequences forage-concentrate (FC) or concentrate-forage(CF) determined by the diet they consumed in each period (forage or concentrate). The cows were adapted to the diet and the SmartFeed individual intake units for 14 d followed by 45 d of intake data collection for each period. Body weight was recorded on consecutive weigh days at the beginning and end of each period and then once every two wk for the duration of a period. Cows were exposed to the GreenFeed Emission Monitoring (GEM) system for no less than 9 d during each period. The GEM system was used to measure emissions of carbon dioxide (CO2) and methane (CH4). Only cows with a minimum of 20 total >3-m visits to the GEM were included in the data set. Data were analyzed in a crossover design using GLIMMIX in SASv.9.4. Within the CF sequence there was a significant, positive correlation between TMR DMI and CH4 (r=0.81) and TMR DMI and CO2 (r=0.69), however, gas emissions during the second period on the hay diet were not correlated with hay intake. There was a significant, positive correlation between hay DMI and CO2 (r=0.76) and hay DMI and CH4 (r=0.74) when cows first consumed forage (FC). In comparison to the CF sequence, cows on the FC sequence showed a positive correlation between CO2 and TMR DMI during the second period. There was also a significant positive correlation between hay and TMR DMI when assessed across (r=0.43) or within sequence (FC r=0.41, CF r=0.47).

Query Processing of RFID Data with Object Trajectory in Manufacture and Logistics

2009 ◽  
Vol 16-19 ◽  
pp. 1043-1047
Author(s):  
Sun Wei ◽  
Li Hua Dong ◽  
Yao Hua Dong
Keyword(s):  
Pervasive Computing ◽  
Radio Frequency Identification ◽  
Object Identification ◽  
Data Set ◽  
Wide Range ◽  
Storage Scheme ◽  
Rfid Data ◽  
Frequency Identification ◽  
Application Data ◽  
Path Queries

In the domain of manufacture and logistics, Radio Frequency Identification (RFID) holds the promise of real-time identifying, locating, tracking and monitoring physical objects without line of sight due to an enhanced efficiency, accuracy, and preciseness of object identification, and can be used for a wide range of pervasive computing applications. To achieve these goals, RFID data has to be collected, filtered, and transformed into semantic application data. However, the amount of RFID data is huge. Therefore, it requires much time to extract valuable information from RFID data for object tracing. This paper specifically explores options for modeling and utilizing RFID data set by XML-encoding for tracking queries and path oriented queries. We then propose a method which translates the queries to SQL queries. Based on the XML-encoding scheme, we devise a storage scheme to process tracking queries and path oriented queries efficiently. Finally, we realize the method by programming in a software system for manufacture and logistics laboratory. The system shows that our approach can process the tracing or path queries efficiently.

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