Identification Stage Research Articles

Membangun Kemandirian Perempuan Melalui Keterampilan Kewirausahaan untuk Meningkatkan Kesejahteraan Keluarga

Poverty is a social problem that is defined as a situation where a person is unable to maintain himself according to the standard of living of the group and is also unable to utilize his mental and physical energy within the group. This poverty does not just happen. Many factors cause poverty, including (a) individual factors, (b) social factors, and (c) cultural factors. The impact of poverty is clearly felt by many parties, including married women, their situation is very diverse, there are women who are divorced by their husbands, or even left by their husbands, or they have husbands but their husbands do not support them and their families, so they choose to struggle alone to support themselves and their families. This problem must be addressed immediately, how to prepare PRSE to be able to meet the needs of their families, through various empowerment efforts in order to prepare them to become independent women who carry out multiple roles. Based on this phenomenon, educational solutions are needed, including training programs in the form of project Based Learning (PBL) to build women's independence through entrepreneurial skills to improve family welfare in Legok Kidul Village, Paseh District, and Sumedang Regency. The training program was implemented through the stages of identification of needs, coordination of activities, development of program design, program action, and reporting and publication.

Determinants of Dutch public health professionals' intention to use digital contact tracing support tools: A cross-sectional online questionnaire study.

Contact tracing (CT) can be a resource intensive task for public health services. To alleviate their workload and potentially accelerate the CT-process, public health professionals (PHPs) may transfer some tasks in the identification, notification, and monitoring of contacts to cases and their contacts themselves, using 'digital contact tracing support tools' (DCTS-tools). In this study, we aimed to identify determinants of PHPs' intention to use DCTS-tools. Between February and April 2022, we performed a cross-sectional online questionnaire study among PHPs involved in CT for COVID-19 in the Netherlands. We built three random forest models to identify determinants of PHPs' intention to use DCTS-tools for the identification, notification, and monitoring of contacts, respectively. The online questionnaire was completed by 641 PHPs. Most respondents had a positive intention towards using DCTS-tools for the identification (64.5%), notification (58%), and monitoring (55.2%) of contacts. Random forest models were able to correctly predict the intention of 81%, 80%, and 81% of respondents to use DCTS-tools for the identification, notification, and monitoring of contacts, respectively. Top-determinants of having a positive intention are the anticipated effect of DCTS-tools on the feasibility and efficiency of CT (speed, workload, difficulty), the degree to which PHPs anticipated that cases and contacts may find it pleasant and may be willing to participate in CT using DCTS-tools, and the degree to which PHPs anticipated that cases and contacts are sufficiently supported in CT when using DCTS-tools. Most PHPs have a positive intention to involve cases and their contacts in the identification, notification, and monitoring stages of the CT-process through DCTS-tools. The identified top-determinants should be prioritized in the (future) development and implementation of DCTS-tools in public health practice. Citizens' perspectives on the use of DCTS-tools should be investigated in future research.

Compositional Deep Probabilistic Models of DNA-Encoded Libraries.

DNA-encoded library (DEL) has proven to be a powerful tool that utilizes combinatorially constructed small molecules to facilitate highly efficient screening experiments. These selection experiments, involving multiple stages of washing, elution, and identification of potent binders via unique DNA barcodes, often generate complex data. This complexity can potentially mask the underlying signals, necessitating the application of computational tools, such as machine learning, to uncover valuable insights. We introduce a compositional deep probabilistic model of DEL data, DEL-Compose, which decomposes molecular representations into their monosynthon, disynthon, and trisynthon building blocks and capitalizes on the inherent hierarchical structure of these molecules by modeling latent reactions between embedded synthons. Additionally, we investigate methods to improve the observation models for DEL count data, such as integrating covariate factors to more effectively account for data noise. Across two popular public benchmark data sets (CA-IX and HRP), our model demonstrates strong performance compared to count baselines, enriches the correct pharmacophores, and offers valuable insights via its intrinsic interpretable structure, thereby providing a robust tool for the analysis of DEL data.

Ship Formation Identification with Spatial Features and Deep Learning for HFSWR

Ship detection has been an area of focus for high-frequency surface wave radar (HFSWR). The detection and identification of ship formation have proven significant in early warning, while studies on the formation identification are limited due to the complex background and low resolution of HFSWR. In this paper, we first establish a spatial distribution model of ship formation in HFSWR. Then, we propose a cascade identification algorithm of ship formation in the clutter edge. The proposed algorithm includes a preprocessing stage and a two-stage formation identification stage. The Faster R-CNN is introduced in the preprocessing stage to locate the clutter regions. In the first stage, we propose an extremum detector based on connected regions to extract suspicious regions. The suspicious regions contain ship formations, single-ship targets, and false targets. In the second stage, we design a network connected by a convolutional neural network (CNN) and an extreme learning machine (ELM) to identify two densely distributed ship formations from inhomogeneous clutter and single-ship targets. The experimental results based on the factual HFSWR background demonstrate that the proposed cascade identification algorithm is superior to the extremum detector combined with the classical CNN algorithm for ship formation identification. Meanwhile, the proposed algorithm performs well in weak formation and deformed formation identification.

Связь типов видеоигр и коммуникативных навыков игроков юношеского возраста: систематический обзор литературы с использованием инструмента PRISMA

<p style="text-align: justify;">The article presents a systematic review of studies on the effects of different types of video games on the communication skills of adolescent and young adult gamers. To conduct the systematic review, the PRISMA methodology was used, which offers an algorithm that makes the literature review as objective as possible. This methodology involves searching for articles of interest in databases based on keywords. Of the 1,953 articles retrieved from 2018—2023, 9 studies were included in the qualitative synthesis after going through the identification, screening, and eligibility stages. Based on the results of analysis of data from these studies, video games have the potential to develop communication skills in adolescents and young adults. The most pronounced effect is demonstrated by massively multiplayer online video games (MMOG), since during the game the participants actively interact with each other using oral and written communication. MMOGs motivate players to learn English, its grammar and vocabulary, and the ability to communicate confidently increases the players’ self-esteem and provides skills in intercultural interaction. Further development of issues related to the practical use of MMOGs within the framework of foreign language training courses seems promising.</p>

PENGELOLAHAN RISIKO RANTAI PASOK PRODUK OLAHAN BANDENG PADA UD. ARSHAINDO MENGGUNAKAN METODE HOUSE OF RISK

ABSTRACT
 Milkfish is one of the types that dominates the total production of this pond culture because it is one of the most potential fish species to be cultivated because of the fish's viability, which is 10,208.23 tons based on data from the East Java Province Fisheries and Marine Service in the last 207 updated in 2019 The increase in the aquaculture yield has triggered the owners of UKM (Small and Medium Enterprises) which are developing in Gresik Regency to process the milkfish. Opportunities for processing milkfish into other products make this product a higher selling value compared to fresh milkfish which is sold directly to consumers. The large number of parties involved in the process or supply chain causes risk to arise, therefore it is necessary to identify the risk of SMEs and design a handling strategy for the risks that exist in the related SMEs. The House Of Risk method is used in identifying risks and providing risk management strategies for the dominant risk causes. This method consists of 2 stages, namely the risk identification stage and the causes of risk or called the House Of Risk Phase 1 and the risk management phase or the House Of Risk Phase 2.The House Of Risk 1 phase shows that in the related SMEs there are 24 types of risk and 20 types of risk causes. which exists. In calculating the ARP and using the Pareto diagram, the results show that there are 5 causes of dominant risk in these UKM. The House Of Risk 2 phase results in 5 risk management strategies for 20 causes of dominant risk.
 Keywords :House Of Risk, Milkfish, Risk Identification, Risk Management

Risk Management for the Self-Help Home Stimulant Assistance Program, Southeast Maluku Regency

The Self-Help Home Stimulant Assistance Program (BSRS) in Southeast Maluku Regency for the fiscal year 2023 faces potential risks that necessitate preventive measures to avert adverse consequences. This research aimed to identify, assess, and control the risks associated with the BSRS Program. The study employed literature reviews, interviews, and distributed questionnaires to 30 program stakeholders. The collected data underwent analysis using standard quantitative methods, encompassing risk identification, assessment, and control stages. Out of 19 risk variables, seven were categorized as Very High-Risk, five as High-Risk, and six as Moderate Risk. The research findings emphasize the imperative need for targeted control measures, such as enhancing coordination and technical activities, to mitigate and avoid identified risks in the BSRS Program implementation. The implications of this study extend beyond risk management, providing valuable insights for program improvement and sustainability. By addressing the identified risks, the BSRS Program can not only safeguard against potential negative outcomes but also enhance its effectiveness and long-term success, contributing positively to community welfare in Southeast Maluku Regency. This research serves as a foundation for informed decision-making, fostering a more resilient and robust implementation of stimulant assistance programs in the region.

Analisis Optimasi Penjadwalan dengan Metode Precedence Diagram Method pada Proyek Konstruksi Pembangunan Rumah Sakit Regina Maris Medan

Good project management in the process of implementing private residential buildings, private buildings, and infrastructure buildings intended for the public interest, is one of the factors for the progress of a country. The study focused on the construction of the Regina Maris hospital in Medan, which is one of the developments in the complex category. The purpose of this study is to provide the results of an analysis of the development in order to achieve goals or targets that can provide efficient use of time, cost and quality which are considered to be the main supporting factors in a development.The research was carried out with three main activity stages, namely the identification stage, the data compilation stage and problem analysis and the use of the Precedence Diagram Method (PDM) method to analyze the optimization of work implementation schedules. The identification stage was carried out in the study area, beginning with observing the condition of the construction implementation, and deepening the identification of environmental conditions or internal/external factors that could hinder work. The results of the identification stage, accompanied by identification of problems and factors that can hinder work, are then used as initial data from the preparation stage of optimizing work implementation schedules using the Precedence Diagram Method

A Low-Latency Approach for RFF Identification in Open-Set Scenarios

Radio frequency fingerprint (RFF) identification represents a promising technique for lightweight device authentication. However, current research on RFF primarily focuses on the close-set recognition assumption. Moreover, the high computational complexity and excessive latency during the identification stage represent an intolerable burden for Internet of Things (IoT) devices. In this paper, we propose a deep-learning-based RFF identification framework in relation to open-set scenarios. Specifically, we leverage a simulated training scheme, in which we strategically designate certain devices as simulated unknowns. This allows us to fine-tune our extractor to better handle open-set recognition. Additionally, we construct an exemplar set that only contains representative RFF features to further reduce time consumption in the identification stage. The experiments are carried out on a hardware platform involving LoRa devices and using a USRP N210 software-defined radio receiver. The results show that the proposed framework can achieve 90.23% accuracy for rogue device detection and 93.85% accuracy for legitimate device classification. Furthermore, it is observed that using an exemplar set consisting of half the total data size can reduce the time overhead by 58% compared to using the entire dataset.

Modeling of Machine Learning-Based Extreme Value Theory in Stock Investment Risk Prediction: A Systematic Literature Review.

The stock market is heavily influenced by global sentiment, which is full of uncertainty and is characterized by extreme values and linear and nonlinear variables. High-frequency data generally refer to data that are collected at a very fast rate based on days, hours, minutes, and even seconds. Stock prices fluctuate rapidly and even at extremes along with changes in the variables that affect stock fluctuations. Research on investment risk estimation in the stock market that can identify extreme values is nonlinear, reliable in multivariate cases, and uses high-frequency data that are very important. The extreme value theory (EVT) approach can detect extreme values. This method is reliable in univariate cases and very complicated in multivariate cases. The purpose of this research was to collect, characterize, and analyze the investment risk estimation literature to identify research gaps. The literature used was selected by applying the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and sourced from Sciencedirect.com and Scopus databases. A total of 1107 articles were produced from the search at the identification stage, reduced to 236 in the eligibility stage, and 90 articles in the included studies set. The bibliometric networks were visualized using the VOSviewer software, and the main keyword used as the search criteria is "VaR." The visualization showed that EVT, the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models, and historical simulation are models often used to estimate the investment risk; the application of the machine learning (ML)-based investment risk estimation model is low. There has been no research using a combination of EVT and ML to estimate the investment risk. The results showed that the hybrid model produced better Value-at-Risk (VaR) accuracy under uncertainty and nonlinear conditions. Generally, models only use daily return data as model input. Based on research gaps, a hybrid model framework for estimating risk measures is proposed using a combination of EVT and ML, using multivariable and high-frequency data to identify extreme values in the distribution of data. The goal is to produce an accurate and flexible estimated risk value against extreme changes and shocks in the stock market. Mathematics Subject Classification: 60G25; 62M20; 6245; 62P05; 91G70.

The collective action dilemma and its solution in the governance of urban community public space—an empirical analysis from Tianjin

The governance of community public space is one of the most concerned issue of people’s livelihood, and its effectiveness determines the residents’ living experience and even the stability of the grassroots political authority. In order to explore the factors of the effectiveness of community public space governance and the path to enhance the effectiveness of community public space governance, this analysis combines the influencing factors with the three stages of identification, management and confirmation in public value management theory, and constructs an indicator system for measuring and analyzing the effectiveness of community public space governance. The fsQCA3.0 is used to analyze the data of 300 residents in Tianjin, China, after which several aims that affect the effectiveness of community public space governance are obtained. The results show that the paths of “resident trust, property management is authoritative” and “customer oriented, democratic consultation” that bring higher governance efficiency have a consistency of 0.874761 and a coverage of 0.480994, which explains well what combination of antecedent factors should be present in communities with high public space governance effectiveness. Based on this, in order to improve the governance efficiency of community public space, improving residents’ living experience and promote the modernization of China’s urban construction, the government, property management company, owners’ committee and neighborhood committee should increase investment in enhancing policy support, establishing service concepts, introducing technology and equipment, enhancing residents’ trust and establishing dispute coordination agencies.

Combing dynamic parameter identification and singular value decomposition adaptive unscented Kalman filter for predictive state of charge of lithium-ion batteries

The development of a secure battery management system (BMS) for electric vehicles depends heavily on the correct assessment of the online state-of-charge (SOC) of Li-ion batteries. The ternary lithium battery is used as the research object in this paper, and a second-order RC equivalent circuit model is developed to characterize the dynamic operating characteristics of the battery. In order to solve the problem that the adaptive unscented Kalman filter (AUKF) algorithm is easy to fail SOC estimation because the error covariance matrix is not positively definite due to the incomplete accuracy of the equivalent circuit model, a corresponding solution is proposed. Considering the poor real-time battery SOC estimate caused by the battery model’s fixed parameters, therefore we propose the Variable Forgetting Factor Recursive Least Squares (VFFRLS) algorithm for joint estimation of Li-battery SOC and the Singular Value Decomposition-AUKF (SVD-AUKF) algorithm. The SVD-AUKF algorithm can accurately estimate the SOC of the battery when the error covariance is negative. The algorithm can be adaptively adjusted in both the parameter identification and SOC estimation stages, which can effectively solve the problem of poor estimation accuracy caused by fixed parameters. According to experiments, under two separate dynamic operating situations, the joint estimation algorithm’s error is less than 2%, and its stability has also been greatly enhanced. At the same time, when the initial SOC value is set incorrectly, the convergence time of the algorithm proposed in this paper can reach within 2.1 seconds for BBDST and DST conditions, which can be well adapted to complex working conditions.

Leveraging Digital Marketing For Traditional Grocery Businesses

This study examines the implementation of digital marketing strategies in a traditional grocery distributor in Malang, Indonesia, operating with conventional methods. The research, conducted from June to September 2023, follows an action research methodology encompassing identification, design, implementation, and evaluation stages. The initial identification phase, through interviews and observations, highlighted the distributor's limited customer reach, necessitating a digital marketing approach for market expansion. The subsequent design phase involved creating tailored content for social media platforms like TikTok and Instagram, including product photos, videos, and storytelling in captions. The strategy implementation stage saw these plans executed, with significant content posted across platforms and the successful integration of an e-catalogue and Google Business for enhanced online visibility. The project evaluation revealed increased customer engagement on social media, indicating effective strategy implementation. However, challenges in quantitatively assessing direct sales impact due to the distributor's traditional operation model were noted. The study suggests that while immediate sales increases were not definitively measured, the strategy laid the groundwork for future growth and market expansion. The research highlights the importance of digital marketing for traditional businesses in adapting to the digital era and underscores the necessity of establishing clear KPIs for effective strategy evaluation..

An Inexact Sequential Quadratic Programming Method for Learning and Control of Recurrent Neural Networks.

This article considers the two-stage approach to solving a partially observable Markov decision process (POMDP): the identification stage and the (optimal) control stage. We present an inexact sequential quadratic programming framework for recurrent neural network learning (iSQPRL) for solving the identification stage of the POMDP, in which the true system is approximated by a recurrent neural network (RNN) with dynamically consistent overshooting (DCRNN). We formulate the learning problem as a constrained optimization problem and study the quadratic programming (QP) subproblem with a convergence analysis under a restarted Krylov-subspace iterative scheme that implicitly exploits the structure of the associated Karush-Kuhn-Tucker (KKT) subsystem. In the control stage, where a feedforward neural network (FNN) controller is designed on top of the RNN model, we adapt a generalized Gauss-Newton (GGN) algorithm that exploits useful approximations to the curvature terms of the training data and selects its mini-batch step size using a known property of some regularization function. Simulation results are provided to demonstrate the effectiveness of our approach.

How to conceptualize the connotation of “resilience – risk” for critical infrastructures? An exploratory theoretical framework

Critical infrastructures (CIs) play an important role in urban economic development and the maintenance of social stability. In recent years, with the increase in natural and man-made risk events affecting CI operations, CI risk management is facing severe challenges and gradually transitioning to CI resilience management. However, the connotation of “resilience - risk” for CIs is still not clearly conceptualized. To address this problem, an exploratory theoretical framework is constructed, including three stages of system resilience identification, resilience impact analysis and connotation element extraction. Subsequently, based on the constructed framework, probability, consequence, and resilience-based scenario that align with the resilience characteristics and impact analysis results are extracted as connotation elements to define a triplet spatial-temporal evolution function of “resilience - risk”. This study can provide a theoretical reference for better understanding the relationship between CI resilience and CI risk.

First concrete documentation for presence of Aedes (Stegomyia) albopictus in Bolivia: dispelling previous anecdotes.

The presence of Aedes albopictus in Bolivia has been a subject of controversy, with a lack of concrete documentation. This study aimed to provide evidence of Ae. albopictus presence in Bolivia. Larval habitats were sampled in Rosario del Yata and San Agustín, Guayaramerín Municipality, Beni Department, northern Bolivia. Collected mosquito larvae were reared to the L4 and adult stages for morphological identification, with some specimens sequenced for confirmation. Aedes albopictus was identified in multiple larval habitats within peridomestic areas, such as buckets, canisters, and cut plastic bottles used as flower vases in both localities, confirming its establishment in the area. This represents the first concrete documentation of the species in Bolivia. The collections (larvae and adults) have been deposited in the Medical Entomology Laboratory of the Universidad Mayor de San Simón in Cochabamba, Bolivia, and the Laboratory of Entomology of the Instituto Nacional de Laboratorios de Salud of the Ministry of Health in La Paz, Bolivia. Given its role as a vector for arboviruses such as dengue and Chikungunya, Ae. albopictus should be incorporated into the Bolivian National Programme of Vector Control for monitoring.

Relative age effect across the talent identification process of youth female soccer players in the United States: Influence of birth year, position, biological maturation, and skill level.

The aims of the study were to examine the relative age effect (RAE) in youth female soccer players in the United States (US) and the influence of birth year, playing position, estimated maturation and skill ratings. The sample consisted of 3,364 youth female soccer players who were active in the 2021-2022 US soccer season across three main stages of the talent identification (TID) process for Youth National Team (YNT) players (i.e., Club, TID Center, and YNT). A prevalent RAE for players born in Q1 was present in the full sample. A significant prevalence for Q1 players were identified for both Club and TID Center, but not YNT. A significant RAE prevalence for Q1 players was identified for most of the age groups from U13-U18 at Club (except U18) and TID Center (except U17). Significant RAEs prevalence for players born in Q1 were found in Goalkeepers, Center Backs, Midfielders, and Center Forwards at Club and TID Center (except Wide Forwards). The data identified a consistent RAE prevalence for Q1 players in early and on-time-maturers across all levels. An even birthdate spread was evident in YNT with a prevalence for Q4 players and a higher percentage of late-maturers than elsewhere in the TID process. Results reinforce evidence indicating RAEs still exist in soccer, yet show for the first time within a youth female soccer TID process, the influence of contextual factors on the prevalence of RAE. This information can be used to advance TID and development across the US soccer landscape.

Explaining the concepts and examples of conflict of interest and its management in the medical laboratory system of Iran: A scoping review.

The occurrence of conflict of interest (CoI) is assumed to be a big challenge facing all healthcare sectors, including the medical laboratory system (MLS). Therefore, this study aimed to explain the concepts and examples of CoI and its management within this system. This scoping review was fulfilled in 2023 in Iran. To this end, the Persian and English keywords of this study were searched on the Persian and English databases based on the Medical Subject Headings (MeSH) and the Arksey and O'Malley methodological framework. All retrieved articles were then imported into EndNote X8, and their quality was evaluated via the Mixed Methods Appraisal Tool (MMAT). In total, 104 articles, of 6875 sources extracted, published from 2000 to 2023, were recruited following the identification and screening stages. Most articles had reported the maximum number of CoI examples in the MLS in diverse nations, including Iran, particularly in terms of structural, financial, objective, and subjective issues and primary and secondary interests among policymakers and higher-ranking managers of the healthcare system. Currently, CoI in healthcare systems, including the MLS, is unavoidable, but the best strategy, in preference to removing this situation, is to manage it under potential conditions before its occurrence and conversion into corruption. In view of that, it is recommended to provide effective training programs, increase transparency at work, facilitate compliance with professional guidelines, reform organizational structures and processes in the public sector, and establish policies best suited to manage CoI in the MLS of Iran.

Research the adaptive neural networks using possibility in control systems for cycle chemistry at thermal power plants

The study is devoted to the analysis of the learning possibilities of adaptive neural networks in the context of water-chemical regime management systems. During a series of experiments on a laboratory stand with a disturbing effect, transient characteristics describing changes in the controlled value were obtained. The obtained mathematical models using the TunePID program served as the basis for the analysis and research of adaptive neural networks. Adaptive neural networks are a powerful tool capable of learning from the data obtained and dynamically changing their structure and parameters to accurately predict and control the behavior of the system. Unlike classical mathematical models, adaptive neural networks have the ability to process nonlinear dependencies and adapt to changing conditions without the need for manual reconfiguration. The stages of identification of disturbances and adaptation of compensator settings were described and illustrated. The study included an analysis of the work of a neural network with a different number of hidden neurons and various activation functions. The results showed that neural networks trained on data using an adaptive approach are able to predict and control the system with an accuracy comparable to the calculated parameters of the control coefficients calculated manually.

Development and validation of Russian olfactory test

To develop and validate the national olfactory test on healthy volunteers. The development and validation of the olfactory test included several stages. First, the development of an olfactory test was carried out, the selection of aromas to assess the threshold and identification ability of olfaction. 25 dilutions of n-butanol were used for the assessment of the threshold olfactory ability. For the stage of assessing the identification ability of the sense of smell, in our previous study, an assessment of the recognition of odor names in the territory of the Russian Federation was carried out. A total of 3.000 people from 8 federal districts of the Russian Federation were interviewed. During the development of the test, 20 names of flavors with the highest rating were used. By the 8th, the selection of monocomponent substances was carried out. Commercially available certified food and perfume flavorings have been used for fragrances whose equivalent in the test cannot be a monocomponent substance. A group of 25 healthy volunteers selected a flavor or a monocomponent for each of the 20 positions. To carry out the identification stage of testing, a booklet was developed with answer options for each fragrance, including 80 images associated with the smell. A methodology for conducting diagnostics has been created. Next, the validation of the developed olfactory test was carried out on 150 healthy volunteers. The study included an assessment of the threshold and identification ability of the sense of smell using the developed test and conducting a comparative analysis with a set of flavors and descriptors corresponding to the Sniffin' Sticks test. The developed test includes: 2 panels - panel 1 to assess the threshold ability of smell, panel 2 to assess the identification ability of smell, a booklet with 80 images and captions to them. The norms of threshold and identification olfactory abilities were also determined in the developed test. The domestic test was validated against the relative foreign Sniffin' Sticks test. Spearman's correlation between the accuracy values of the domestic test (17-20; 85.00-100.00%) and the values of the foreign test (11-16; 68.75-100.00%) did not reveal statistically significant differences (rs=0.065, p=0.432), which confirms the equally effective assessment of olfactory ability by the domestic olfactory test in comparison with its foreign counterpart. In this work, a methodology for the use of Russian olfactory test was developed and validated on healthy volunteers. The features of the developed test are an assessment of the threshold and identification ability of smell, an adapted set of odors for the Russian population, the use of paper blotters when applying flavor and visual images of descriptors. Despite the wide variety of psychophysical tests, this problem requires further study and comparative analysis of olfactory tests available In Russia and foreign analogues in order to obtain a universal and effective diagnostic method for the populations of our country.