bluuebunny/crossref_metadata_2025 huggingface dataset:
Biodiversity Conservation and Ecosystem Services in the Hindu Kush Himalaya
Authors: Sharma Eklabya , Chettri Nakul
Published: June 2021

With 22% global land surface area, the mountains ecosystem is home to 13%of the total human populations. Evolved as a unique ecosystem, the mountainadds value with diverse ecosystems; tradition and culture as well as ecosys-tem services for socio-economic development in the mountain areas andbeyond. As it caters half of the humanity with its diverse types of ecosystemservices, the realization of its significance are still limited in national, regionaland global discourses. In the conservation front, there has been a significantprogress in the concept and practices from species focussed interventions tohabitat and ecosystem/landscape conservation approaches. The Hindu KushHimalaya (HKH), known as the highest mountain ecosystems in the world isalso the water tower for the region often referred as the third pole. This uniqueecosystem is an important repository of biological and cultural diversities andsource of varied ecosystems services to 240 million people living within andabout a one third of global population living downstream. The region hasbeen in spotlight for being part of the 36 ‘Global Biodiversity Hotspot’ aswell as climate change hotspot. However, our understanding the dynamics ofchanging landscapes and climate and its linkage to people, mostly challengedby poverty are limited. International Centre for Integrated Mountain Devel-opment (ICIMOD), an inter-governmental regional knowledge and enabling centre, has been instrumental in developing knowledge about the dynam-ics of these fragile ecosystems and support its regional member countriesthrough science based integrated approaches. Since its inception, ICIMODhas been engaged in developing knowledge and supporting policies for moun-tain development focusing on socio-economic, ecological and environmentaldimensions. In this paper, we present the retrospect of our understandingand learnings in the HKH through transboundary landscape managementand regional cooperation mostly focused on conservation of biodiversity andecosystem services perspectives. The paper reflects on changing paradigmand complex process for strengthening regional cooperation in the HKH.
Changing Outlook on Harnessing Biodiversity Values – A Special Focus on Indian Himalaya
Authors: Rawal Ranbeer , Negi Vikram S. , Bhatt Indra D.
Published: June 2021

The Himalaya, as provider of goods and range of ecosystem services, is vitalfor sustaining life of millions of people in uplands and billions in lowlands.Indian National Action Plan on Climate Change has recognized the regionvital for ecological security of the country. However, growing demand forbio-resources, combined with inadequate appreciation for values of goodsand services provided by Himalayan biodiversity, has led to the unsustainableuse of these resources, leading to environmental degradation in the region.The paper also looks into the scenario as to how recent pandemic of COVID19 has brought increased focus of people on bio-resources of the region.More importantly it has suggested conservation and sustainable utilizationof biodiversity to ensure for sustaining the continuous flow of ecosystemservices and goods. Biodiversity in the region provides several opportunities,however, under changing scenario, we need to think afresh and differently onpossibilities of finding better and optimal livelihood options, and the issuesof conservation in the region. In this context, larger focus on conservationeducation has been stressed as a way forward. This paper attempts to builda case for the biodiversity in Indian Himalayan Region through a SWOT assessment. Certain cases have been elaborated wherein the outlook haschanged to harness the values.
The Evolution of Global Energy Governance: Scenario Analysis with a Focus on the G20
Authors: Shin Sang Yoon
Published: June 2021

This study addresses global energy governance and its future evolution. Whilegovernance plays an essential role in securing the sustainability of energyand the environment, the current global energy governance has limitations:fragmentation and failure to reflect changes in the global energy landscape.This study provides future scenarios on how global energy governance willevolve, focusing on the G20. Then, each scenario is presented with itsstrengths, weaknesses, opportunities, and threats. The G20 has promotedglobal energy cooperation to address challenges such as the energy trilemmasince 2009 and is increasing its contribution as one of the most influentialplatforms for energy cooperation. Until now, the G20 has positioned itselfbetween a symbolic and substantial platform. Nevertheless, this group willincrease its influences on global energy governance and its contribution toglobal energy cooperation.
Medical Image of the Month: Metastatic Spindle Cell Carcinoma of the Breast
Authors:   University of Arizona, Tucson, AZ USA, Kusupati Vinita , Natali Stefano
Published: June 2021

No abstract available. Article truncated after 150 words. Clinical Scenario: A 71-year-old woman with primary malignancy of the breast in remission post bilateral mastectomy in 2005 and 2008, presented to the emergency room with progressive shortness of breath for the past 6 months. Upon arrival to the emergency room, she described localized sharp chest pain along the right thoracic wall which had gradually worsened over the past three months. The pain was exacerbated with movement and with deep inspiration. She also endorsed significant hemoptysis, expectorating approximately 500 ml of bloody sputum on the morning of her presentation. Pertinent vitals revealed that she was both tachycardic and tachypneic, saturating 94% on room air with an increased work of breathing. Physical examination was significant for coarse breath sounds and diminished right sided lung sounds. Initial labs demonstrated a normal troponin and an unremarkable EKG. A chest radiograph demonstrated a large left mediastinal and hilar mass with numerous parenchymal nodules bilaterally. …
Shape reconstruction based on a multicore optical fiber array with temperature self-compensation
Authors: Zhu Weitao , Sun Guangkai , He Yanlin , Zhuang Wei , Zhou Kangpeng , Zhai Wenjing
Published: June 2021

Temperature variations affect the accuracy of fiber-optic shape sensors; thus, temperature compensation is particularly important. This study developed a temperature self-compensation algorithm and verified the measuring accuracy of shape sensors after temperature compensation. A multicore fiber Bragg grating (FBG) sensor array was calibrated to confirm the consistency of sensor characteristics, and the relationship between the curvature and wavelength shift of FBGs was studied. A variable-temperature experiment revealed the temperature sensitivity of the FBG sensors, and these results were used by the temperature self-compensation algorithm. Further, shape reconstruction before and after temperature compensation was studied. The deformed shapes of the multicore FBG sensor array under different bending conditions were reconstructed. The results obtained after temperature compensation show that the average error between the measured and the theoretical coordinate values as less than 0.33 mm, the maximum error as less than 5.61 mm, and the relative error as less than 3.50%. The proposed temperature self-compensation algorithm has excellent prospects for application to flexible structures.
Performance improvement of FSO communication systems using hybrid-ARQ protocols
Authors: Verma Gyan Deep , Mathur Aashish
Published: June 2021

In release 16 of the third-generation project partnership, it has been suggested that hybrid-automatic repeat request (H-ARQ) protocols can be used to ensure robust communications. In this paper, we incorporate H-ARQ in the free-space optical (FSO) communication systems to improve their performance and communication link reliability. We derive novel, to the best of our knowledge, closed-form expressions for the outage probability of the FSO systems under the combined impact of gamma–gamma distributed atmospheric turbulence and pointing errors (PEs) for heterodyne detection and intensity modulation and direct detection techniques. This is accomplished using various H-ARQ protocols, such as, at least once, chase combing, and incremental redundancy (INR). Further, using outage probability expressions, we calculate throughput for the FSO systems using H-ARQ protocols. Useful insights into the FSO system performance are obtained through the asymptotic analysis for the considered H-ARQ protocols and detection techniques. It is observed through the numerical results that H-ARQ with INR performs better than the other considered H-ARQ protocols. We also draw a comparison between the outage performance of an FSO system with boresight PE and without boresight PE.
Joint object classification and turbulence strength estimation using
convolutional neural networks
Authors: LeMaster Daniel A. , Leung Steven , Mendoza-Schrock Olga L.
Published: June 2021

In a recent paper, Kee et al. [Appl. Opt.
59, 9434 (2020)APOPAI0003-693510.1364/AO.405663] use a
multilayer perceptron neural network to classify objects in imagery
after degradation through atmospheric turbulence. They also estimate
turbulence strength when prior knowledge of the object is available.
In this work, we significantly increase the realism of the turbulence
simulation used to train and evaluate the Kee et al. neural network. Second, we develop a new
convolutional neural network for joint character classification and
turbulence strength estimation, thereby eliminating the prior
knowledge constraint. This joint classifier–estimator expands
applicability to a broad range of remote sensing problems, where the
observer cannot access the object of interest directly.
Regularization-parameter-free optimization approach for image deconvolution
Authors: Rajora Sunaina , Butola Mansi , Khare Kedar
Published: June 2021

Image deconvolution is often modeled as an optimization problem for a cost function involving two or more terms that represent the data fidelity and the image domain constraints (or penalties). While a number of choices for modeling the cost function and implementing the optimization algorithms exist, selection of the regularization parameter in the cost function usually involves empirical tuning, which is a tedious process. Any optimization framework provides a family of solutions, depending on the numerical value of the regularization parameter. The end-user has to perform the task of tuning the regularization parameter based on visual inspection of the recovered solutions and then use the suitable image for further applications. In this work, we present an image deconvolution framework using the methodology of mean gradient descent (MGD), which does not involve any regularization parameter. The aim of our approach is instead to arrive at a solution point where the different costs balance each other. This is achieved by progressing the solution in the direction that bisects the steepest descent directions corresponding to the two cost terms in each iteration. The methodology is illustrated with numerical simulations as well as with experimental image records from a bright-field microscope system and shows uniform deconvolution performance for data with different noise levels. MGD offers an efficient and user-friendly method that may be employed for a variety of image deconvolution tools. The MGD approach as discussed here may find applications in the context of more general optimization problems as well.
A simple, powerful diode laser system for atomic physics
Authors: Daffurn Andrew , Offer Rachel F. , Arnold Aidan S.
Published: June 2021

External-cavity diode lasers are ubiquitous in atomic physics and a wide variety of other scientific disciplines, due to their excellent affordability, coherence length, and versatility. However, for higher-power applications, the combination of seed lasers, injection-locking and amplifiers can rapidly become expensive and complex. Here we present a high-power, single-diode laser design with specifications:

>

210

m
W

, 100 ms-linewidth

(
427
±
7
)

k
H
z

,

>

99

%

mode purity, 10 GHz mode-hop-free tuning range, and 12 nm coarse tuning. Simple methods are outlined to determine the spectral purity and linewidth with minimal additional infrastructure. The laser has sufficient power to collect

10

10

87

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b

atoms in a single-chamber vapor-loaded magneto-optical trap. With appropriate diodes and feedback, the system could be easily adapted to other atomic species and diode laser architectures.
Large dynamic-range fiber Bragg grating sensor system for acoustic emission detection
Authors: Gong Zhe , Che Jiawei , Wei Heming , Krishnaswamy Sridhar
Published: June 2021

A distributed feedback (DFB) fiber laser and fiber Bragg gratings (FBGs) are configured to demodulate the wavelength shifts of FBG dynamic strain sensors. The FBG sensors act as sensing units to detect the dynamic strain and the demodulators while the DFB fiber laser only acts as a narrow-linewidth light source. As the reflective spectrum of the FBG sensor changes due to dynamic strains, the output is subsequently converted into a corresponding intensity change and detected directly by a photodetector. The 0.2 nm linewidth FBG sensor can detect the impact signal with a frequency of up to 300 kHz with a maximum of 29.17 µɛ, which is comparable with the detecting result of the piezoelectric transducer sensor. Moreover, the directional response of the FBG sensor is maximized when the direction of acoustic wave propagation is parallel to the optical fiber. The relation between the sensitivity and the FBG spectrum linewidth is presented, and the detectable strain range versus different FBG linewidths is also discussed.
Quantitative 2D thermometry in turbulent sooting non-premixed flames
using filtered Rayleigh scattering
Authors: Pu Jinpeng , Sutton Jeffrey A.
Published: June 2021

This paper demonstrates the application of a thermometry method in turbulent
sooting non-premixed flames using filtered Rayleigh scattering (FRS).
Fuel tailoring is used to establish a specific

C

2

H

2

-based fuel mixture such that
temperature can be determined accurately by a single FRS measurement
over the entirety of mixture fraction space, or equivalently, for all
relevant thermo-chemical states. Evaluation is performed in a
hierarchy of flows to establish measurement precision and accuracy.
Initial assessments in a series of heated fuel mixtures; non-sooting,
near-adiabatic flat flames; and laminar non-premixed sooting flames
show accuracy of the approach over a full range of expected
temperatures and high single-shot measurement precision (e.g.,  

65

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S
N
R

<

80

) for temperatures between 1900 and
2200 K. Subsequently, 2D temperature measurements are evaluated in a
turbulent non-premixed sooting flame. For the current fuel mixture,
the local mixture-averaged Rayleigh scattering cross section is nearly
constant over all composition space, and thus traditional laser
Rayleigh scattering (LRS) is used as a measurement standard in the
absence of soot. The results show excellent agreement between the
proposed FRS thermometry approach and LRS in non-sooting regions. In
the presence of soot, the proposed FRS-based approach shows no signs
of interference. In addition, 2D temperature imaging shows high SNR
(60–74) over all temperature conditions. Thus, we believe the proposed
methodology successfully provides a high-resolution 2D temperature
method under turbulent sooting flame conditions.
Fast-tunable femtosecond visible radiation via sum-frequency generation from a high power NIR NOPO
Authors: Binhammer Yuliya , Binhammer Thomas , Mevert Robin , Lang Tino , Rück Angelika , Morgner Uwe
Published: June 2021

We report on a high power ultra-broadband, quickly tunable non-collinear parametric oscillator with highly efficient intra-cavity sum-frequency generation. It simultaneously delivers femtosecond pulses in two synchronized output beams: up to 4.9 W tunable from 650 to 1050 nm in the near infrared and up to 1.9 W from 380 to 500 nm in the visible spectral range. The (to our knowledge) novel source is ideally suited for spectroscopy or multi-color imaging. First results of two-color functional microscopy are presented.
Multichannel left-subtract-right feature vector piston error detection method based on a convolutional neural network
Authors: Wang Peng-Fei , Zhao Hui , Xie Xiao-Peng , Zhang Ya-Ting , Li Chuang , Fan Xue-Wu
Published: June 2021

To realize the large-scale and high-precision co-phasing adjustment of synthetic-aperture telescopes, we propose a multichannel left-subtract-right feature vector piston error detection method based on a convolutional neural network, which inherits the high precision and strong noise resistance of the DFA-LSR method while achieving a detection range of (−139λ, 139λ) (λ = 720 nm). In addition, a scheme to build large training datasets was proposed to solve the difficulty in collecting datasets using traditional neural network methods. Finally, simulations verified that this method can guarantee at least 94.96% accuracy with large samples, obtaining a root mean square error of 10.2 nm when the signal-to-noise ratio is 15.
Deep-learning approach for predicting laser-beam absorptance in full-penetration laser keyhole welding
Authors: Oh Sehyeok , Kim Hyeongwon , Nam Kimoon , Ki Hyungson
Published: June 2021

Laser-beam absorptance in a keyhole is generally calculated using either a ray-tracing method or electrodynamic simulation, both physics-based. As such, the entire computation must be repeated when the keyhole geometry changes. In this study, a data-based deep-learning model for predicting laser-beam absorptance in full-penetration laser keyhole welding is proposed. The model uses a set of keyhole top- and bottom-aperture as inputs. From these, an artificial intelligence (AI) model is trained to predict the laser-energy absorptance value. For the training dataset, various keyhole geometries (i.e., top- and bottom-aperture shapes) are hypothetically created, upon which the ray-tracing model is employed to compute the corresponding absorptance values. An image classification model, ResNet, is employed as a learning recognizer of features to predict absorptance. For image regression, several modifications are applied to the structure. Five model depths are tested, and the optimal AI architecture is used to predict the absorptance with an R2 accuracy of 99.76% within 1.66 s for 740 keyhole shapes. Using this model, several keyhole parameters affecting the keyhole absorptance are identified.
Color-guided optimization model with reliable self-structure priors for depth map restoration
Authors: Chen Yumin , Hong Zhihu , Sun Dongjun , Peng Zhaoyu , Zhang Nongtao , Luo Kangshun , Liu Chong , Tian Junming , Qing Yan , Li Chunlin , Ibrahim Mostafa Mahmoud , Yang You
Published: June 2021

Depth maps captured by Kinect or time of flight (ToF) cameras have an active role in many visual applications. However, a brutal truth is that these depth maps are often contaminated with compound noise, which includes intrinsic noise and missing pixels. In addition, depth maps captured with ToF-based cameras are low in resolution. As these depth maps carry rich and critical information about 3D space, high quality post-processing is crucial for supporting subsequent visual applications. Previous works were proposed via the guiding of the registered color image and bicubic interpolation as an initialization for the up-sampling task, where challenges arose from texture coping and blurry depth discontinuities. Motivated by these challenges, in this paper, we propose a new optimization model depending on the relative structures of both depth and color images for both depth map filtering and up-sampling tasks. In our general model, two self-structure priors for depth and color images are constructed individually and used for the two tasks. For overcoming the texture coping problem, the color-based and depth-based priors are used near the depth edges and at the homogeneous regions respectively. To this end, we further propose a confidence map at every task for managing where every prior is used. Experimental results on both simulated and real datasets for Kinect and ToF cameras demonstrate that the proposed method has a superior performance than benchmarks.
The first quantitative assessment of radiocarbon chronologies for initial pottery in Island Southeast Asia supports multi-directional Neolithic dispersal
Authors: Cochrane Ethan E. , Rieth Timothy M. , Filimoehala Darby
Published: June 2021

Neolithization, or the Holocene demographic expansion of farming populations, accounts for significant changes in human and animal biology, artifacts, languages, and cultures across the earth. For Island Southeast Asia, the orthodox Out of Taiwan hypothesis proposes that Neolithic expansion originated from Taiwan with populations moving south into Island Southeast Asia, while the Western Route Migration hypothesis suggests the earliest farming populations entered from Mainland Southeast Asia in the west. These hypotheses are also linked to competing explanations of the Austronesian expansion, one of the most significant population dispersals in the ancient world that influenced human and environmental diversity from Madagascar to Easter Island and Hawai‘i to New Zealand. The fundamental archaeological test of the Out of Taiwan and Western Route Migration hypotheses is the geographic and chronological distribution of initial pottery assemblages, but these data have never been quantitatively analyzed. Using radiocarbon determinations from 20 archaeological sites, we present a Bayesian chronological analysis of initial pottery deposition in Island Southeast Asia and western Near Oceania. Both site-scale and island-scale Bayesian models were produced in Oxcal using radiocarbon determinations that are most confidently associated with selected target events. Our results indicate multi-directional Neolithic dispersal in Island Southeast Asia, with the earliest pottery contemporaneously deposited in western Borneo and the northern Philippines. This work supports emerging research that identifies separate processes of biological, linguistic, and material culture change in Island Southeast Asia.
Identification of naturally processed Zika virus peptides by mass spectrometry and validation of memory T cell recall responses in Zika convalescent subjects
Authors: Crooke Stephen N. , Ovsyannikova Inna G. , Kennedy Richard B. , Poland Gregory A.
Published: June 2021

Once an obscure pathogen, Zika virus (ZIKV) has emerged as a significant global public health concern. Several studies have linked ZIKV infection in pregnant women with the development of microcephaly and other neurological abnormalities, emphasizing the need for a safe and effective vaccine to combat the spread of this disease. Preclinical studies and vaccine development efforts have largely focused on the role of humoral immunity in disease protection. Consequently, relatively little is known in regard to cellular immunity against ZIKV, although an effective vaccine will likely need to engage both the humoral and cellular arms of the immune system. To that end, we utilized two-dimensional liquid chromatography coupled with tandem mass spectrometry to identify 90 ZIKV peptides that were naturally processed and presented on HLA class I and II molecules (HLA-A*02:01/HLA-DRB1*04:01) of an immortalized B cell line infected with ZIKV (strain PRVABC59). Sequence identity clustering was used to filter the number of candidate peptides prior to evaluating memory T cell recall responses in ZIKV convalescent subjects. Peptides that individually elicited broad (4 of 7 subjects) and narrow (1 of 7 subjects) T cell responses were further analyzed using a suite of predictive algorithms andin silicomodeling to evaluate HLA binding and peptide structural properties. A subset of nine broadly reactive peptides was predicted to provide robust global population coverage (97.47% class I; 70.74% class II) and to possess stable structural properties amenable for vaccine formulation, highlighting the potential clinical benefit for including ZIKV T cell epitopes in experimental vaccine formulations.
Path planning for the Platonic solids on prescribed grids by edge-rolling
Authors: Lam Ngoc Tam , Howard Ian , Cui Lei
Published: June 2021

The five Platonic solids—tetrahedron, cube, octahedron, dodecahedron, and icosahedron—have found many applications in mathematics, science, and art. Path planning for the Platonic solids had been suggested, but not validated, except for solving the rolling-cube puzzles for a cubic dice. We developed a path-planning algorithm based on the breadth-first-search algorithm that generates a shortest path for each Platonic solid to reach a desired pose, including position and orientation, from an initial one on prescribed grids by edge-rolling. While it is straightforward to generate triangular and square grids, various methods exist for regular-pentagon tiling. We chose the Penrose tiling because it has five-fold symmetry. We discovered that a tetrahedron could achieve only one orientation for a particular position.
Institutional narratives of the shift to media literacy: Interviewing New Zealand’s Office of Film and Literature Classification
Authors: Henry Claire
Published: June 2021

This article presents a case study of media literacy deployment in contemporary media regulation through interviews with New Zealand’s Chief Censor and other key staff members in the Office of Film and Literature Classification. The interviews offer an account of how media literacy came to be adopted as a prominent strategy within their policy remit. This article explores how the institutional narrative reconciles their media literacy approaches and core classification work, finding four key discursive and practical strategies: integrating, expanding, reconceptualizing and reasserting relevance. The analysis also reveals how the standpoint of media as a site of potential harm shapes the meaning of media literacy in this institutional context. This case study provides insight into the utility of media literacy for contemporary media regulation, examining how a classification agency navigates change and continuity in the face of a challenging and rapidly evolving media landscape.
TRANSACTION COSTS AND THEIR EFFECT ON THE SIZE OF THE ENTERPRISE AND THE STRUCTURE OF THE ECONOMY
Authors: Orekhovsky P. A.
Published: June 2021

The paper presents a discursive analysis of the use of the category of “transaction costs”. The similarity of the concepts of “transaction costs’ and “costs of the marketing” by E. Chamberlin is demonstrated. However, in the original discourse of R. Coase, transaction costs are considered as the costs of using the price mechanism associated with the market allocation of resources. The firm, as a hierarchical economic system, was an alternative allocation mechanism to the market. The factors that influence the value of transaction costs in O. Williamson’s contract theory are almost unrelated to sales and the marketing. In the discourse of J. Buchanan and M. Olson transaction costs are costs arising in the political market. For J. Buchanan, these are the “positive” costs of creating new laws that reduce the social costs associated with the production and distribution of public goods. M. Olson’s discourse addresses not only the “positive” but also the “negative” costs associated with dividing markets through government regulation. In turn, R. Coase himself, investigating the problem of social costs associated with external effects, in fact got rid of transaction costs, equating them to zero. It is this situation of “Coase’s theorem” that began to be used in the framework of the analysis methodology of the discipline “Economics and Law”. The paper proposes an interpretation of transaction costs as costs of overcoming the boundaries between heterogeneous systems, based on the discourse of E. de Soto. In this case, the prices of legality/illegality can be viewed as the transaction costs of using different economic systems.
Continuing Medical Education Questions: June 2021
Authors: Sadowski Brett W.
Published: June 2021

Article Title: Exploratory comparative effectiveness trial of green kiwifruit, psyllium, or prunes in US patients with chronic constipation
Continuing Medical Education Questions: June 2021
Authors: Howard Dominique E.
Published: June 2021

Article Title: ACG Clinical Guidelines: Prevention, Diagnosis, and Treatment of Clostridioides difficile Infections
EFFECTS OF LARGE SCALE UNLOADING ON EXISTING SHIELD TUNNELS IN SANDY GRAVEL STRATA
Authors: Hao Wu , Miao Miao , Hui Zhou , Zhi-hua Feng
Published: June 2021

The influence of large-scale unloading of soil on existing metro tunnels is a difficult problem in the operation of Metro in sandy gravel strata. In order to predict and control the tunnel deformation and ensure the safety and normal operation of the metro, three-dimensional numerical models are proposed in this study. These models based on the line 5 of Chengdu subway engineering and the adjacent unloading projects analyse the deformation and stress characteristics of existing metro tunnels under large-scale unloading of soil. In conclusion, after comprehensive reinforcement measures such as advanced tubal curtains and horizontal beams, anti-floating anchor cables, the elastic modulus of strata is improved to a certain extent, and the rebound of pit bottom soil is effectively reduced. After the excavation of the foundation pits are completed, the maximum longitudinal uplift of left and right tunnel is 8.33 mm and 9.56 mm under reinforced condition, which is less than the control standard value of 10 mm.
Information Measuring System for Correction of Working Memory Parameters in the Learning Processes
Authors:   Ural Federal University named after the first President of Russia B.N. Yeltsin, Petrenko A.A. , Kublanov V.S.
Published: June 2021

This paper presents the results of a system analysis of the information-measuring system (IMS) for correction of the working memory parameters of subjects during the learning process, in which the neuroplasticity of the brain is triggered by stimulating the nerve formations of the neck of a spatially distributed field of voltage impulses, the target of stimulation is the cervical ganglia of the sympathetic nervous system, control of functional changes in the body is carried out according to changes in the parameters of the blood supply system, and psychometric tests are used to assess working memory parameters. Aim. Development of IMS, using neuroelectro¬stimulation of peripheral nerve formations of the neck for correction of working memory parameters in the learning process. Materials and methods. The study involved 65 subjects, divided by randomization into experimental, control and placebo groups. Working memory parameters were assessed using the dual 2-back test, and the functional state was assessed using heart rate variabi¬lity (HRV) analysis. Results. The results of the study showed that the proposed structure of the IMS and the method of correction working memory allow to improve the working memory parameters and normalize state of the autonomic nervous system after three correction procedures. The obtained test results practically do not change and are saved after 2 months. Conclusion. IMS, which provides multichannel neuroelectrostimulation of peripheral nerve formations in the neck, is effective for correction of cognitive skills, which is important for solving problems in the field of education.
On One Solution of the Numerical Problem Differentiation in Calculation Vertical Speed of Aircraft
Authors:   Russian Air Force Military Educational and Scientific Center “Air Force Academy  named after Professor N.E. Zhukovsky and Y.A. Gagarin”, Panferov V.I. , Panferov S.V. , Hayutin А.М. , Trenin N.A. ,   South Ural State University
Published: June 2021

To ensure safety and improve the efficiency of flight missions, reliable information about the altitude and speed parameters of the aircraft (AC) is required. Therefore, improving the algorithm for calculating the vertical speed used as part of the algorithmic support for air signal systems (ASS) is a very urgent task. Purpose of the study. The problem of calculating the vertical speed of an aircraft in the ASS is considered. Materials and methods. The analysis of literature data on the use of numerical differentiation procedures to solve this problem is carried out, it is noted that the methods used are based on different ideas and approaches. It is indicated that two-point algorithms are significantly worse than multi-point algorithms in terms of the achieved accuracy, howe¬ver, they are characterized by significant simplicity and speed. Various versions of multipoint algorithms are used, differing in complexity, the amount of information used, and the accuracy achieved. The features of the regularizing algorithms, which are essentially filters of a low-frequency useful signal, suppressing the high-frequency component of the error in measuring the altitude signal or, what is the same, atmospheric pressure, are noted. The data on systems with hardware differentiation of the height signal are given. Results. A fairly simple four-point algorithm for numerical differen¬tiation is proposed and substantiated. Due to the averaging of both the measurement results themselves and the estimates of the derivatives, significant filtering of noise is realized, which is an important advantage of the algorithm. For greater accuracy in estimating the vertical speed, it is envisa¬ged to include a preliminary filtering algorithm in the experimental data processing scheme. The filtering algorithm is found from the solution of the optimization problem; it is shown that this algorithm is structurally similar to the filtering algorithms constructed according to the well-known approaches of R. Kalman. The results of computational experiments on the study of the features and characteristics of the proposed algorithms are presented, illustrating their advantages, performance and the possibility of further use in ASS. It is shown that preliminary filtering significantly increases the accuracy of the vertical velocity estimation. Conclusion. The developed algorithms can be used to improve the algorithmic support of the ASS.
Program and Project Approaches to Solving Large-Scale Environmental Problems
Authors:   South Ural State University, Gelrud Ya.D. , Kibalov E.B. , Malov V.Yu. ,   Institute of Economics and Industrial Engineering, Siberian Branch  of the Russian Academy of Sciences
Published: June 2021

The article discusses the federal program “Ecology of the Angara-Yenisei region (AYR)”. The description of the systemic synthesis of different approaches to the assessment of the program of restoration and preservation of the natural complex of the Yenisei, as the skeletal basis of the ecological-assimilation potential of AEP is given. The necessity of taking into account the uncertainty factor when evaluating large-scale environmental projects is substantiated. Purpose of the study. Show that the effects of large-scale projects affect the very scenario of economic development, in this regard, it is incorrect to use the primitive integration of an alternative (strategy) into the same scenario, it is shown that it is necessary to evaluate the “project – scenario” relationship. Materials and methods. The authors propose to use three levels of assessment for evaluating large-scale projects: macro level, meso level and micro level. This classification allows you to gradually reduce the level of uncertainty. The information obtained at the previous level of assessment is the source for the lower level. Results. At the first stage, the judgments of the experts were processed using computer products developed at the IEIE. At the second step, the National Project “Ecology of Russia” was analyzed, consisting of 11 federal projects, and a mathematical model was developed for the multicriteria problem of optimal cost management of the project, taking into account the uncertainty factor. The result of the third step was the creation of a hybrid model for assessing large-scale environmental projects from a logical-heuristic model based on expert information and an economic-mathematical model. Analogs of such models, created with the participation of the authors, work in the evaluation of large-scale railway projects. This refers to a family of semi-dynamic optimization models, which have been tested to varying degrees in solving meso-level problems, both in planned and market economies. Moreover, on the basis of one of the versions of this family, a medical-ecological-economic model was developed, and with its help, a scenario analysis of the development of the subjects of the Asian part of Russia was carried out. Conclusion. The article provides a brief description of the functionality and the need to use the appropriate mathematical and software tools as the stages of large-scale projects progress from concept to implementation.
Application of Fuzzy Logic to Assess Banks' Credit Risk
Authors:   Vladimir State University named after Alexander and Nicolay Stoletovs, Ozerova M.I. , Zhigalov I.E.
Published: June 2021

The banking system is a constantly evolving system. The information environment of the bank is growing, the volumes of processed information are increasing due to the growth of users and banking products. To reduce risks, banks make a financial assessment of the situation of individuals and legal entities. The aim of the work is to develop fuzzy multi-connected models designed to predict the receipt of a positive or negative decision to receive a banking product. The decision is made based on scoring. Scoring consists in assigning points for completing a certain questionnaire developed by underwriters of credit risk assessors. Based on the results of the points gained, the system automatically makes a decision on approving or refusing to issue a loan. Different banks have diffe¬rent scoring models. Purpose of the study. The paper considers the use of fuzzy models for making a decision by a bank to issue a banking product that implements the concept of “soft computing”. Methods. The use of fuzzy logic methods in credit scoring is not new, but it is not widely used in practice because it is expensive to integrate into existing systems. Each bank uses its own indicators of the client's financial reliability in scoring. Most of the indicators in banks are the same, but when deciding to issue different banking products, they have different numerical values. The data of the standard scoring methodology of a real bank were taken as the initial data. To predict a bank's decision to issue a banking product to a client, a fuzzy model was applied, production rules were proposed, and membership functions were determined. The model focused on the simultaneous processing of incoming data from multiple clients and for different banks and different scoring models. Results. The developed mathematical model for assessing the client's rating and predicting the decision to receive a banking product based on the fuzzy inference rule. The obtained results are proposed to be used in a multi-banking web-oriented system of providing banking products to corporate clients.
Modeling the Dynamics of Gross Regional Product
Authors:   Yugra State University, Kutyshkin A.V.
Published: June 2021

The gross regional product is a key macroeconomic indicator that characterizes the development of the regional socio-economic system. Modeling the dynamics of this indicator, as the final product of the economic system, also involves the development of models for changing the values of aggregated cost factors associated with its production. One of the directions of using the developed models is the formation of short-term forecasts of the direct values of the gross regional product and the factors of labor and capital costs of the regional economy that determine them. Aim. Modeling the dynamics of the value of the gross product of the regional economy of the Yamal-Nenets Autonomous Okrug on the basis of the one-sector model of R. Solow for the formation of short-term forecasts of the value of this indicator. Materials and methods. The one-sector model of economic growth by R. Solow is used, in which the functioning of the regional economy is described by two-factor neoclassical functions with a constant elasticity of substitution of factors of production costs. Production functions are identified using retrospective sequences of indices of gross regional product and cost factors of the regional economy. Short-term forecasting of the gross product of the regional economy is carried out using a production function that provides a better approximation of retrospective data of the time interval of the previous forecasting year. Results. Neoclassical two-factor production functions with constant elasticity of substitution of cost factors of the regional economy of the Yamalo-Nenets Autonomous Okrug (YNAO) for the period from 2001 to 2018 were constructed.The values of the gross regional product and the average annual number of employed in the Yamal-Nenets economy were calculated for 2017–2019, which were compared with the corresponding data published by the Federal State Statistics Service of the Russian Federation, and the forecast values published by the regional administration. Conclusion. It has been established that the used production functions give acceptable estimates of the approximation of the actual values of the gross regional product of the considered regional economy in the considered time interval. Modification of the models for changing the values of cost factors made it possible to increase the accuracy of short-term forecasts, both directly of the values of the gross regional product and the values of factors of labor and capital costs.
Application of Methods of Information Engineering for Change of Structure of Multilayer Systems of Organizational Management
Authors:   Voronezh State Technical University, Belousov V.E. , Dorofeev D.V. , Zenkova E.N.
Published: June 2021

In this work the task is formulated and the method of reconfiguration of systems of organizational management on the basis of synthesis of the functional structure having an impact on an appearance of all system considerably defining an order of its functioning, integrating in a whole of means of technical and mathematical, program and information support is offered. The research objective consists in need of increase in effective management of organizational systems on the basis of complex development, implementation and application of funds of automated management of its elements. Expert systems on the basis of declarative programming languages are applied to synthesis of similar structures. Research methods. As the tool for definition of the knowledge base about the field of restructuring of structure of management calculation of expressions and language of a predicate logic, that is a logic theory of first order is used. Then, the problem of reconfiguration of hierarchical structure of management system can be presented doubly. First, as a problem of definition of the changes of the known rational hierarchical structure providing minimum loss from the arisen functional failures. Secondly, as a problem of creation of new rational structure which provides optimum use of the resources used in the course of achievement of definite purposes in the changing conditions. Results. As a result of a research, on the basis of de Morgan's law the factors influencing a system status of organizational management in general are defined. The necessary structure of solvable tasks of all hierarchical structure is defined by extent of influence of different factors on characteristics of structure of management. Conclusion. Work of the received expert system consists in consecutive execution or failure to follow rules and transition from one status to another. In case of an impasse the expert system gives the report in the form of requirements of alternative change of these or those rules (factors). New rules are remembered. Thus, the system of knowledge is increased.
The conceptual basis of strategic and operational management of industrial enterprises in conditions of instability
Authors:   JSC “Kuznetsk Ferroalloys”, Korennaya K.A.
Published: June 2021

The problems of strategic and operational management of industrial enterprises in modern conditions of global instability cannot be effectively solved, as in the recent past. New approaches, methods and models are needed that would take into account the complexities of managing industrial enterprises in modern business conditions. Purpose of the study. It is necessary to create a new adaptive approach to the strategic and operational management of industrial enterprises, which would take into account the accelerating dynamics of international markets, as well as the factors of indirect and direct impact on the enterprise, which mainly determine its adaptability. Materials and methods. The main provisions of the conceptual basis of strategic and operational management of industrial enterprises, as well as a set of mathematical models for the implementation of this process are presented. Results. The materials formulated in the article on the preparation and adoption of managerial decisions allow the owners and managers of industrial enterprises to assess all aspects of this process in interrelation and form, in essence, a new paradigm for the functioning of industrial enterprises in conditions of military-political and socio-economic instability in the world, as well as the introduction various sanctions on the part of Western countries in relation to Russian industrial enterprises. At the same time, it becomes possible to take into account the factors of direct and indirect impact on industrial enterprises, which have become especially important in recent years. Conclusion. The approach presented in this article to the formation of the conceptual basis of strategic and operational management of industrial enterprises is, in essence, new and relevant, allowing company shareholders to form promising strategies for the development of industrial enterprises in conjunction with the procedures for operational management of the current activities of enterprises. Thus, the preparation and adoption of decisions on the management of industrial enterprises, both at the strategic and operational levels, becomes possible to carry out on the basis of a holistic algorithm based on the complex of models and methods of strategic and operational management of industrial enterprises developed by the author.
Analysis of Halal and Marketing Strategy for Broiler Chickens' Supply Chain during Covid-19 : A Case Study in Yogyakarta, Indonesia
Authors: Asih Hayati Mukti , Firmansyah Fahri , Faishal Muhammad
Published: June 2021

Corona virus has a destructive impact on the world's economy, especially in Indonesia, making a large-scale social restriction policy (SRL). Many restaurants, hotels, schools, and colleges in Yogyakarta are closed. Consumers, mostly students from outside Yogyakarta, have to return to their hometowns. This case impacts the decline in chicken prices to cover losses due to excess supply and decreasing demand. This research aims to analyze the decline in chicken prices, halal products' and the concept of marketing strategies impact on the chicken supply chain during the Covid-19 outbreak in Yogyakarta. This research used SEM to obtain accurate analysis and descriptive results to overview phenomena from society under study. The analysis using SEM shows that halal factors do not significantly affect consumer decisions in buying chicken. However, it was found that there was an increase in consumers in online shopping, which could create new strategies in marketing strategies. The application of online marketing strategies will improve consumers' services so that it is expected to be a novel value that can increase consumer buying interest during the Covid-19 outbreak in Yogyakarta, Indonesia.