bluuebunny/crossref_metadata_2025 huggingface dataset:
What is strategic management research?
Authors: Rozanna Mira
Published: March 2024

Purpose: The aims of this study is to describe the strategic in management research
Methods: A literature analysis method was used, in which researchers conduct conceptual studies and review strategic and strategic management theories, research results, and expert opinions. Furthermore, the researchers grouped, allocated, organized, and used a variety of literature in specific fields.
Results: Strategic management is a concept of building capabilities that enable companies to create value, shareholders, and society when operating in a competitive market. This can result in studies of decisions and actions taken by top executives/TMT so that companies gain competitive advantage.
Limitations: This study used a qualitative approach with a limited level of generalization; therefore, so in the future it may also be necessary to review it with other approaches and wider research locations.
Contribution: This study provides views on the revitalization of tourism destinations in terms of synergy between stakeholders so that it can be the basis for developing development policies, especially in the aspect of local communities.
Big Data Analytics and market competitiveness of selected firms in Lagos State, Nigeria
Authors: Chike Nwosu Kanayo , Mbamalu Euphemia Ifunanya , Oguanobi Chimezie Alex , Egbunike Chinedu Francis
Published: March 2024

Purpose: This study specifically evaluates the effect of Intangible Big Data Analytics Resources (IBDAR) and Tangible Big Data Analytics Resources (TBDAR) on a firm’s market competitiveness in manufacturing firms. The authors used RBV as the main theoretical framework to investigate this.
Research methodology: This study used a survey research design. The study employed a non-probability sample and a final sample of seventy-two employees selected from manufacturing firms in Lagos State, Nigeria.
Results: The hypotheses were tested using multiple linear regressions. The empirical results showed that the organizational use of TBDAR has a significant effect on Market Competitiveness (MCOM), and that the organizational use of IBDAR has a significant effect on MCOM.
Limitations: First, the sample is restricted to only the Nigerian setting; to draw broader and deeper implications, it could be useful to take diverse samples from different contexts and sectors. Second, this study does not utilize the PLS-SEM technique to model mediators and moderators.
Contribution: This study has significant policy implications for practitioners and is an original study based on primary data from Nigerian manufacturing firms.
Reflection of Local Cultural Values behind Loss Accounting Practices by Ilabulo Sellers
Authors: Thalib Mohamad Anwar
Published: March 2024

the practice of loss accounting by ilabulo traders.
Research methodology: This research uses the Islamic paradigm. The approach used is Islamic ethnomethodology. There are five data analysis stages: charity, knowledge, faith, revelation information, and courtesy.
Results: The study results show that traders avoid losses by distributing the leftover ilabulo to the local community for free. This accounting practice is conditional on the value of patience. In the Islamic culture of Gorontalo, the elders often internalize the value of patience through lumadu "mopo'o tanggalo duhelo" The meaning of this expression is the whole of patience. The implication of this research is to present the concept of loss accounting based on local wisdom values.
Limitations: The limitation of this research lies in the informants who have yet to provide information from the cultural experts.
Contribution: The contribution of this research is that it can provide deeper insights into the local cultural values that influence the accounting practices of ilabulo traders. This can help us understand how culture plays a significant role in local-level accounting decision-making. Furthermore, the results of this research can be used to develop an accounting model that is more in line with the culture and local context of ilabulo traders. This can assist in designing more relevant and sustainable accounting practices.
Villages SDG's response to mental health: Better late than never
Authors: Sijabat Saut Gracer
Published: March 2024

Purpose: This article presents village-based mental health policies in response to sustainable development goals.
Research methodology: A qualitative research approach was used in conjunction with library research.
Results: According to the findings of this study, the focus of development goals, namely living a healthy and affluent life, has not been on mental health. Furthermore, there are findings on advocacy, research and innovation, partnership, and integration of mental health and other SDGs.
Contribution: Based on the findings of this study, several recommendations were made, including developing wise mental health policies in rural areas, increasing mental health commitments and budgets, mobilizing communities to respond to rural mental health policies, and improving treatment facilities and mental health response services.
Novelty: Although the goals of sustainable development place a special emphasis on healthy and prosperous lives, insufficient data on mental health have been collected, and mental health policies based on village wisdom have not become a theme that many central and regional governments have implemented.
FACILIDADES E DIFICULDADES NO EXPERIENCIAR DA SEXUALIDADE DURANTE A GESTAÇÃO
Authors: Freitas Ana Vitória Pereira de , Soares Daniele Pereira , Casimiro Maria Raquel Antunes , Nascimento Maria Berenice Gomes , Freitas Fabiana Ferraz Queiroga , Fernandes Marcelo Costa
Published: March 2024

Este estudo teve como objetivo analisar as facilidades e dificuldades no experienciar da sexualidade durante a gestação. Trata-se de estudo descritivo com abordagem qualitativa, realizado com 14 gestantes em acompanhamento pré-natal, em uma Unidade Básica de Saúde da cidade de Cajazeiras, Paraíba, Brasil. Para a coleta de dados foi utilizada entrevista semiestruturada, após a aprovação do Comitê de Ética e Pesquisa. Utilizou-se do Discurso do Sujeito Coletivo para organização e análise dos resultados. Identificou-se, como facilidade, no decorrer dos discursos, o aumento do desejo sexual e os sentimentos positivos em relação à autoimagem. Como dificuldades, as transformações físicas como empecilho para o ato sexual e os sentimentos negativos em relação à autoimagem. É importante que a mulher possa ter um espaço de vínculo e de diálogo com o profissional de saúde, de modo a expor sua experiência e tirar suas dúvidas, para que se empodere e vivencie a sua sexualidade de maneira plena.
A Collaborative Data Library for Testing Prognostic Models
Authors: Sikorska Joanna , Hodkiewicz Melinda , D’Cruz Ashwin , Astfalck Lachlan , Keating Adrian
Published: March 2024

A web-based data management system for use by researchers and industry around the world to access suitable datasets for testing prognostic models is developed. The value of the project is in the provision of, and access to, real-world data for asset failure prediction work. In practice, it is difficult for researchers to obtain data from industrial equipment. Industry datasets are rarely shared and hardly ever published. When such data is made available, very little meta-data about the underlying asset is provided. This restricts the number and type of models that can be applied.The solution is a data management system for three groups: researchers needing datasets, industry and academics with datasets. This paper identifies the data being sought, the system requirements and architecture, and discusses how the design is being implemented using an Agile development approach. Crucially, meta-data is stored in the database and accessed using a secure web-based front-end so as to maximize the available information, whilst obfuscating any corporate-sensitive material. The success of this prognostics data library depends on the support of the prognostic community to contribute and use the data; similar projects have been successful in the Machine Learning and Big Data communities.
Machine Learning Strategy for Fault Classification Using Only Nominal Data
Authors: Nicchiotti Gianluca , Fromaigeat Luc , Etienne Lianel
Published: March 2024

Machine learning methods are increasingly used for rotating machinery monitoring. Usually at set up, only data associated to an engine in a good state, the so called nominal data, are available for the machine learning phase. Nevertheless a classifier requires faulty data to be trained at identifying the causes of the anomalies and this fact has generally limited the usage of data driven approaches to fault detection tasks. The paper suggests a strategy to use machine learning methods even for fault classification purposes and diagnostics. Within the proposed framework three different machine learning methods, Gaussian Mixture Model (GMM), Support Vector Machines (SVM) and Auto Associative Neural Networks (AANN) have been implemented, tested and compared. The idea is to take into account some ‘a priori’ knowledge about the faults to be classified, to drive the behavior of the machine learning methodology (SVM or AANN or GMM) to be more or less reactive to the different faults. The indicators (features) more sensitive to each kind of fault are firstly selected on the basis of expert knowledge. For each different fault, a set of indicators is defined and computed from nominal data only. Each set is then used to produce training data for one specific fault. Such data sets are then used to train one instance of each method for each different fault. The underlying logic is that fault tuned input data is able to produce fault tuned instances of the methods. For example the instance trained with the indicators associated to a fault ‘A’ reacts more powerfully in presence of the fault ‘A’ than the others. Once an anomaly is detected, the comparison among the reactions of the different ‘fault tuned’ instances allows classifying the fault, not just to detect it. The results show best detection performances for SVM whilst AANN outperforms the other two methods for classification.
Improved Railway Track Irregularities Classification by aModel Inversion Approach
Authors: Schenkendorf René , Dutschk Beate , Lüddecke Katrin , Groos Jörn C.
Published: March 2024

Over time railway networks have become complex systems characterized by manifold types of technical components with a broad range of age distribution. De facto, about 50 percent of the life cycle costs of railway infrastructures aremade up by direct and indirect maintenance costs. A remedy can be provided by a condition based preventive maintenance strategy leading to an optimized scheduling of maintenance actions taking the actual aswell as the expected future infrastructure condition into account. A prerequisite is, however, that the thousands of kilometers of railway tracks are almost continuously monitored. Thus, a promising approach is the usage of low-cost sensors, e.g. accelerometers and gyroscopes, which can be installed on common in-line freight and passenger trains. Due to ambiguous data records a credible classification of railway track irregularities directly from these data is challenging. Alternatively to this pure data-driven approach, in this paper a novel hybrid approach is presented. To this end, a simplified vehicle suspension model is applied for the purpose of railway track condition monitoring by analyzing the dynamic railway track - train interactions. The inversion of the model can be used to recalculate the actual inputs (irregularities) of the monitored system (rail surface) which have caused recorded system responses (dynamic vehicle reactions and acceleration data, respectively). These recalculated inputs are a sound basis of subsequent data-driven condition monitoring analyses.In this preliminary study, a classification algorithm is implemented to identify a simulated railway track irregularity automatically.
Diagnosis of Tidal Turbine Vibration Data through Deep Neural Networks
Authors: Galloway Grant S. , Catterson Victoria M. , Fay Thomas , Robb Andrew , Love Craig
Published: March 2024

Tidal power is an emerging field of renewable energy, harnessing the power of regular and predictable tidal currents. However, maintenance of submerged equipment is a major challenge. Routine visual inspections of equipment must be performed onshore, requiring the costly removal of turbines from the sea bed and resulting in long periods of downtime. The development of condition monitoring techniques providing automated fault detection can therefore be extremely beneficial to this industry, reducing the dependency on inspections and allowing maintenance to be planned efficiently.This paper investigates the use of deep learning approaches for fault detection within a tidal turbine’s generator from vibration data. Training and testing data were recorded over two deployment periods of operation from an accelerometer sensor placed within the nacelle of the turbine, representing ideal and faulty responses over a range of operating conditions. The paper evaluates a deep learning approach through a stacked autoencoder network in comparison to feature-based classification methods, where features have been extracted over varying rotation speeds through the Vold-Kalman filter.
A Comparison of Data-driven Techniques for Engine Bleed Valve Prognostics using Aircraft-derived Fault Messages
Authors: Baptista Marcia , Medeiros Ivo P. de , Malere Joao P. , Prendinger Helmut , Nascimento Jr. Cairo L. , Henriques And Elsa
Published: March 2024

Prognostics plays an increasingly important role in preventive maintenance and aircraft safety. An approach that has recently become popular in this field is the data-driven technique. This approach consists in the use of past data and advanced statistics to derive estimates for the reliability of an equipment without relying on any physics or engineering principle. Data-driven models have been based on two types of historical data: past failure times and health monitoring data. A kind of health monitoring data rarely used in data-driven models are aircraft-derived maintenance messages. These data consist of fault messages derived from the aircraft onboard systems to notify any unexpected events or abnormal behavior as well as to send warning signals of equipment degradation. Fault messages have not received much attention in aircraft prognostics mostly due to its asynchronous and qualitative nature that often causes difficulties of interpretation. The main goal of this paper is to show that data-driven models based on fault messages can provide better prognostics than traditional prognostics based on past failure times. We illustrate this comparison in an industrial case study, involving a critical component of the engine bleed system. The novelty of our work is the combination of new predictors related to fault messages, and the comparison of datadriven methods such as neural networks and decision trees. Our experimental results show significant performance gain compared to the baseline approach.
A Framework for Aircraft Maintenance Strategy including CBM
Authors: Adhikari Partha Pratim , Buderath Matthias
Published: March 2024

Maintenance Planning plays a vital role in optimizing the benefits of Integrated Vehicle Health Management (IVHM). The challenge is to identify the right combinations of different types (Preventive, CBM and Run-to-Fail) of maintenance tasks for different subsystems or components of complex systems like an aircraft to achieve the most optimized solution in terms of availability, cost and safety. Maintenance Strategy plans most cost effective maintenance type for each fault of a sub-system in such a way that availability and safety are optimized. Also, the strategy should satisfy the important goals viz. technical feasibility and certifiability of the solution. This study presents a RCM based maintenance strategy framework with some modifications over the existing guidelines. The framework has been implemented and is demonstrated with a case study of EPGDS (Electrical Power Generation and Distribution System). The results with arbitrary costing for each task are outlined with the objective of demonstrating the effectiveness of the framework.
Echo State Network for the Remaining Useful Life Prediction of a Turbofan Engine
Authors: Rigamonti Marco , Baraldi Piero , Zio Enrico , Roychoudhury Indranil , Goebel Kai , Poll Scott
Published: March 2024

Among the various data-driven approaches used for RUL prediction, Recurrent Neural Networks (RNNs) have certain prima facie advantages over other approaches because the connections between internal nodes form directed cycles, thus creating internal states which enables the network to encapsulate dynamic temporal behavior and also to properly handle the noise affecting the collected signals. However, the application of traditional RNNs is limited by the difficulty of optimizing their numerous internal parameters and the significant computational effort associated with the training process. In this work, we explore the use of the Echo State Network (ESN), a relatively new type of Recurrent Neural Network (RNN). One of the main advantages of ESN is the training procedure, which is based on a simple linear regression. Unlike traditional RNNs, ESNs can be trained with fairly little computational effort, while still providing the generalization capability characteristic of RNNs. In this paper, we use Differential Evolution (DE) for the optimization of the ESN architecture for RUL prediction of a turbofan engine working under variable operating conditions. A procedure for pre-processing of the monitored signals and for identification of the onset of acceleration of degradation (i.e., the so-called elbow point in the degradation trend) will be shown. The datasets used to validate the approach have been taken from the NASA Ames Prognostics CoE Data Repository. These datasets were generated using a turbofan engine simulator, based on a detailed physical model that allows input variations of health-related parameters under variable operating conditions and records values from some specific sensor measurements. The results obtained on these data confirm the ESN’s capability to provide accurate RUL predictions.
Dynamic Vector Model Applied to Wind Speed Prognosis for Eolic Generation
Authors: Perez Aramis , Cornejo Francisco , Orchard Marcos , Silva Jorge
Published: March 2024

Dynamic characterization of energy availability profiles is paramount for an adequate incorporation of Non-Conventional Renewable Energies. This fact is particularly significant for sizing and design of eolic energy parks. The integration of eolic parks with interconnected systems requires accurate and precise knowledge on maximum and minimum power availability, as well as the moments in which you should expect the aforementioned conditions. Prognosis tools can help to determine the wind speed with a certain degree of reliability, in order to forecast energy availability. In this regard, this article aims at designing and implementing a methodology to generate a dynamic vector-autoregressive-based models for wind speed prognosis. This methodology makes use of techniques such as data clustering, time series statistical analysis and its characterization through time-variant parametric models, for a medium term horizon. The proposed method is able to prognosticate wind speed for a complete day in just one step, instead of classic approaches that repeat several one-step ahead transitions to obtain similar results. The employed methodology facilitates the identification of periodical components of the wind, including daily and seasonal, facilitating the differentiation of data clusters with similar behaviors or tendencies. In order to perform the clustering, seasonal patterns are distinguishable through the use of similar probability distributions. Kullback-Leibler divergence is used as a measure of the difference between the probability distributions, while the K-means algorithm is used for clustering. Finally, for the validation of the design two common methods are implemented: Nielsen Reference Model and an ARMA-GARCH model. Our comparative analysis shows that the proposed method greatly improves the precision and accuracy of the resulting wind forecasting.
Condition-based Maintenance: Determination of optimal deterioration levels to perform preventive activities by using a multi-objective evolutionary algorithm
Authors: Goti Aitor , Sanchez Ana I.
Published: March 2024

Several authors insist on the fact that maintenance is a key activity in the manufacturing industry, because of its economic consequences. Within maintenance, Condition-Based Maintenance programs can provide significant advantages to industrial plants. This paper is focused on the problem of Condition-Based Maintenance optimization in an industrial environment, with the objective of determining both the critical age level to perform preventive maintenance activities and the amount of this type of activities to be executed before upgrading or substituting components. For this purpose, a mathematical model who jointly considers the evolution in quality and production speed along with condition based, corrective and preventive maintenance is presented. The cost and profit optimization process using a Multi-Objective Evolutionary Algorithm is applied to an industrial case.
Analysis of two modeling approaches for fatigue estimation and remaining useful life predictions of wind turbine blades
Authors: Sanchez Hector , Sankararaman Shankar , Escobet Teresa , Puig Vicenc¸ , Frost Susan , Goebel Kai
Published: March 2024

Wind turbines components are subject to considerable stresses and fatigue due to extreme environmental conditions to which they are exposed, especially those located offshore. With thisaim, the present work explores two different approaches on fatigue damage estimation and remaining useful life predictions of wind turbine blades. The first approach uses the rainflow counting algorithm. The second approach comes from a fatigue damage model that describes the propagation of damage at a microscopic scale due to matrix cracks which manifests in a macroscopic scale as stiffness loss. Both techniques have been tested using the information provided by the blade root moment sensor signal obtained from the well known wind turbine simulator FAST (Fatigue, Aerodynamics, Structures and Turbulence).
A Methodology for Fast Deployment of Condition Monitoring and Generic Services Platform Technological Design
Authors: Fernandez Santiago , Mozzati Christophe , Arnaiz Aitor
Published: March 2024

Maintenance is a research field that has recently been gaining importance in business and where the study and development of monitoring and predictive technologies has been very active, as the role of these technologies is key in enabling predict and prevent maintenance strategies. Moreover, by means of monitoring features of processes and components, an impact in lifecycle value can be achieved. However, challenges remain in structuring the condition monitoring offer and the technological platform due, in particular, to the variety of potential domains of application, the characteristics of the existing information and the final goals of the monitoring activities. These challenges may impact in the deployment time of a condition monitoring solution. In order to limit these challenges, a methodology for fast deployment of condition monitoring and a technological service platform is presented. The methodology has been obtained from research and analysis of several use cases in the context of product-service systems. The focus is on methodological and technological results, which are presented in a general manner such that they can be applicable to the deployment of condition monitoring and services in various domains. Finally, application of the methodology is presented in two different scenarios.
Pattern Detection in Status Codes as an Optimization Tool in Offshore Wind Farms
Authors: Doro P , Scerri L , Guillame P , Helsen J
Published: March 2024

Renewable, sustainable energy is an evolving field and will soon become a desirable necessity to sustain an ever-growing growing population. One viable source is wind power which is trending towards large farms with many involved turbines. The sheer size of the analytical data derived from these sizeable wind farms poses both a challenge and an opportunity for farm level optimization. Data driven analytics and machine learning are making the larger and more useful data sets available to be analyzed. One method based on these techniques, pattern detection, is already used very successfully in fraud detection and many other big data industries. One source of ascertaining the state of a turbine is through the appropriate understanding of its status codes. Such codes can indicate a myriad of outcomes from operational events to alarm conditions. It is expected that these codes follow consistent patterns and being able to extract these patterns from the data can help us understand how certain sequences relate to the turbine behavior and subsequently analysis of historically linked patterns can aid in predicting certain events. For example if codes A and B and C tend strongly to occur within the same time window then following an A-B pattern one could confidently predict a corresponding C event within the time window. Such an understanding enables the error codes to reveal more than simply a snippet of information, but a productivity- enhancing, cost-beneficial operational regime. These could then be used to track and anticipate failure events. For such high level computing to occur you must take the data into a parallelized environment making it scalable to an entire wind farm over the course of several years. In this study the effects of a varied time window on how the patterns manifested themselves was analyzed by frequency of occurrence and subsequently validated by physical insights into turbine behavior. This approach and the results extracted are based on real data of a full offshore wind farm and could be harnessed as a simple yet powerful tool for large scale wind farm optimization.
Multi-physics based Simulations of a Shock Absorber Sub-system for PHM
Authors: L S Krishna , K M M Siddiqui , U Vanam
Published: March 2024

The multi-physics involve Mechanical, Thermal, Hydraulic, and Pneumatic based modeling and simulation of an oleo-pneumatic shock absorber with fault capabilities presented in this paper. The fault simulated in this model is leakage due to eccentricity. The one-dimensional shock absorber system models render loads at different sink velocities. These load values, used in the structural model to do static stress analysis. By using these loads directly from the system model eliminates the error in load computation from the loads group, thereby eliminating the time and cost involved in this activity. The models and static stress analyses are done with both 1-D and 3-D elements. The 3-D landing gear model meshed with using both auto and manual mesh generation options. The consequences of both 1-D, and 3-D models mesh generation are discussed in this paper. The static stress analysis, compared with the experimental results and it is found that the results are within 5% deviation. Based on the static stress analysis computed the life of a landing gear.
A Data-Driven Health Assessment Method for Electromechanical Actuation Systems
Authors: Isturiz Aitor , Aizpurua Jose Ignacio , Hernández Fidel E. , Iturrospe Aitzol , Muxika Eñaut , Viñals Javier
Published: March 2024

The design of health assessment applications for the electromechanical actuation system of the aircraft is a challenging task. Physics-of-failure models involve non-linear complex equations which are further complicated at the system-level. Data-driven techniques require run-to-failure tests to predict the remaining useful life. However, components are not allowed to run until failure in the aerospace engineering arena. Besides, when adding new monitoring elements for an improved health assessment, the airliner sets constraints due to the increased cost and weight. In this context, the health assessment of the electromechanical actuation system is a challenging task. In this paper we propose a data-driven approach which estimates the health state of the system without runto- failure data and limited health information. The approach combines basic reliability theory with Bayesian concepts and obtained results show the feasibility of the technique for asset health assessment.
ІННОВАЦІЇ У ВІТЧИЗНЯНОМУ WELLNESS ТУРИЗМІ
Authors: Бабушко Світлана , Попович Сергій
Published: March 2024

Статтю присвячено інноваціям у wellness туризмі. Для їх характеристики було проаналізовано діяльність і пропозиції вітчизняних санаторно-курортних закладів. Зясовано, що 70% закладів не орієнтовані на wellness туризм, а пропонують традиційні форми роботи з гостями. Зміни у діяльності решти оздоровчих закладів є інноваційними: впроваджуються нові технології, диверсифікуються послуги, формуються цікаві пропозиції оздоровлення, розширюються категорії гостей, створюються інноваційні програми оздоровлення з психологічними техніками, йогою, різними естетичними процедурами, спрямованими на догляд за тілом, та раціональним збалансованим харчуванням. Констатовано, що  велнес туризм має гарні шанси на динамічний розвиток завдяки впровадженню інноваційних пропозицій для оздоровлення.
КОНЦЕПЦІЯ ОЦІНКИ ЗА СПРАВЕДЛИВОЮ ВАРТІСТЮ - ОСНОВНИЙ ПРИНЦИП ПІДГОТОВКИ ФІНАНСОВОЇ ЗВІТНОСТІ ЗА МСФЗ
Authors: Михалків Алла
Published: March 2024

Розкрито сутність справедливої вартості з точки зору міжнародних стандартів фінансової звітності та стандартів оцінки. Досліджено виникнення та розвиток концепції справедливої вартості в бухгалтерському обліку . Розглянуто концепцію оцінки за справедливою вартістю як складової частини концепції звітності та обліку. Проаналізовано основні відмінності між концепцією історичної вартості та концепцією оцінки за справедливою вартістю.  З'ясовано сучасні підходи до застосування концепції оцінки за справедливою вартістю.
РОЗПОДІЛ ВИТРАТ МАЙБУТНІХ ПЕРІОДІВ ЗА МЕТОДОМ НА ОСНОВІ ПОДАТКУ НА ПРИБУТОК ВІД ОПЕРАЦІЙНОЇ ДІЯЛЬНОСТІ
Authors: Сук Петро
Published: March 2024

Розглянуто особливості відображення витрат майбутніх періодів в бухгалтерському обліку і фінансовій звітності.

Визначено можливість використання податку на прибуток від операційної діяльності як основи для розподілу витрат майбутніх періодів.

Доведено доцільність застосування методу на основі податку на прибуток від операційної діяльності для розподілу витрат майбутніх періодів.

Використання методу розподілу витрат майбутніх періодів на основі податку на прибуток від операційної діяльності розглянуто на прикладі.

Запропоновано два способи розрахунку методу розподілу витрат майбутніх періодів на основі податку на прибуток від операційної діяльності: 1) від початкової суми; 2) від залишкової суми.
SPECIFIC ASPECTS OF THE LEGAL AND REGULATORY FRAMEWORK FOR CYBERSECURITY IN DIFFERENT COUNTRIES OF THE WORLD IN THE CONTEXT OF THE INTERNATIONAL SECURITY SYSTEM
Authors: Horlichenko Serhii
Published: March 2024

Cybersecurity is emerging as a critical issue in contemporary international law, with significant implications for the security of nations. The use of information and communication technologies poses potential threats to various aspects of societal and state functioning, prompting the international community to explore the establishment of a comprehensive legal framework for cooperative efforts in the area of cybersecurity.

The article describes the evolution of regulatory and legal mechanisms dedicated to securing cyberspace. The narrative emphasises that the establishment of rules governing activities in cyberspace requires careful consideration of cybersecurity, the protection of personal data, and the fight against cybercrime and cyberterrorism.

Looking to the future, the analysis of international legislation identifies areas for improvement in the area of cyberspace security under current conditions.
БЮДЖЕТНІ ПОВНОВАЖЕННЯ МІСЦЕВОГО САМОВРЯДУВАННЯ У КОНСТИТУЦІЯХ ЄВРОПЕЙСЬКИХ ДЕРЖАВ: ПОРІВНЯЛЬНО-ПРАВОВИЙ АСПЕКТ
Authors: Коротич Олег , Севастьяненко Олена
Published: March 2024

У статті здійснено порівняльно-правовий аналіз конституційних норм, що регулюють бюджетні повноваження місцевого самоврядування, деяких європейських держав. Визначено фінансові повноваження місцевого самоврядування та їх поділ на підгрупи з можливими рівнями їх нормативного закріплення. За результатами проведеного дослідження висловлено пропозиції щодо оптимального шляху конституційного закріплення здійснення бюджетних повноважень місцевого самоврядування органами державної влади в Україні.
ПРИНЦИПИ РЕАЛІЗАЦІЇ ПРАВА НА ОСВІТУ НЕПОВНОЛІТНІМИ: АДМІНІСТРАТИВНО-ПРАВОВИЙ АСПЕКТ
Authors: Новіцька Ірина , Максименко Наталія
Published: March 2024

Право на освіту є основоположною, невід’ємною та гарантованою державою можливістю осіб здобувати знання та навички у порядку, встановленому законом. Однак, саме статус неповнолітніх в освітній сфері потребує особливого захисту, адже неповнолітні особи потребують самореалізації, яка відбувається шляхом отримання базових знань у закладах освіти різного рівня.
ОСОБЛИВОСТІ УПРАВЛІННЯ ВОГНЕМ ЧАСТИНАМИ ТА ПІДРОЗДІЛАМИ ВІЙСЬК ПРОТИПОВІТРЯНОЇ ОБОРОНИ СУХОПУТНИХ ВІЙСЬК
Authors: Кудряшов Віктор , Коломійцев Олексій , Кулєшов Олександр , Воронін Віктор , Третяк Вячеслав , Балабуха Олексій , Клівець Сергій , Качуровський Георгій , Кудря Ілля , Кітов Вадим , Кулєшова Тетяна
Published: March 2024

Розкрито проблематику щодо удосконалення управління вогнем частинами та підрозділами протиповітряної оборони, яка полягає у вирішенні протиріччя між вже досягнутими та й усе зростаючими можливостями вогневих засобів – з одного боку, та, з іншого боку – з можливостями системи управління вогнем по використанню вогню зенітних засобів для надійного прикриття і захисту військ та об'єктів критичної інфраструктури .
ПОРІВНЯЛЬНИЙ АНАЛІЗ МОРФОЛОГІЧНОГО ПОЛІМОРФІЗМУ Vibrio Cholerae У МАЗКАХ ЕЛЕКТИВНОГО ПОЖИВНОГО СЕРЕДОВИЩА
Authors: Прилуцький Сергій , Коркоц Анна
Published: March 2024

Стаття присвячена роботі з проведення порівняльного аналізу морфологічно-структурної будови збудника холери у мазках досліджуваної сировини та у поживному середовищі. Було визначено основні причини та наслідки такої мінливості, вплив на патогенність холерного вібріону в умовах його морфологічної мінливості в залежності від середовища початкового існування або культивування.
ВЕРИФІКАЦІЯ РОЗРАХУНКОВИХ МОДЕЛЕЙ ВИЗНАЧЕННЯМ АДЕКВАТНОСТІ ТА ТОЧНОСТІ
Authors: Нікулін Олександр , Наконечна Тетяна , Нікулін Олексій , Бяков Андрій
Published: March 2024

Верифікацію розрахункових моделей процесу прокатування можна розглядати, як практичне оцінювання якості математичного моделювання процесів. Актуальна прикладна верифікація пов’язана з ефективністю інженерних рішень щодо вдосконалення виробничих процесів, яке спирається на адекватність і точність розрахунків. Для конкретизації методики розглянуті моделі визначення опору деформації сталі 45 при гарячій обробці тиском за підходами В.І. Зюзіна та інших, а також Андреюка-Тюленева. При оцінюванні адекватності та точності розрахунків  використані аналітичні залежності, які також зручні для моделювання в системах проектування і керування технологією прокатування. На основі отриманих оцінок зроблено висновок про адекватність і припустиму точність розрахунків за формулами, тобто успішну верифікацію моделей.
ANALYSIS OF MENTAL HEALTH RESEARCH
Authors: Babkova Nadiia , Huliieva Dina , Kochuieva Zoia , Ugolnikova Nataliia
Published: March 2024

In 2022 there are more than 150 million people only in Europe who have mental health problems. The availability of services for population has decreased, deteriorating economic conditions, stress, military conflict make our mental health vulnerable. At the same time, the use of artificial intelligence (AI) makes possible revolutionary breakthroughs in healthcare and medicine. AI technologies are being considered as a new tool for planning, monitoring and identifying health services at level of populations and individuals. AI-powered tools could be used like digitized healthcare data, including electronic records, images and handwritten notes, to automate tasks, make clinicians' jobs easier, and understand the causes of complex diseases. At the same time, the use of AI-based solutions often involves the use of complex statistical and mathematical tools and multi-dimensional data, which raises the possibility of errors and misinterpretation of results: researchers sometimes tend to overtrust AI. There is also concern about the lack of open reporting of the use of AI models, which limits the ability to replicate results. The study showed that data and models used are most often not publicly available, and there is little collaboration between researchers.
ФАКТОРИ, ЯКІ ВПЛИВАЮТЬ НА РОБОТУ ТРАНСПОРТУ В ЗАЛЕЖНОСТІ ВІД ДОРОЖНІХ УМОВ
Authors: Давидчик Олександр , Коваленко Андрій , Коваленко Віталій
Published: March 2024

Дослідження проводилися на ділянці автомобільної дороги Т0441 Кам'янське – Лобойківка Н31 – Петреківка – Царичанка Н31. Довжина маршруту – 54.3 км.

Розглянуті сучасні методи оцінки ефективності використання палива Основним вимірником паливної економічності автомобіля в нашій країні і в більшості європейських країн є витрата палива в літрах на 100 км пройденого шляху (шляхова витрата).

Проведена порівняльна лінійна залежність і логарифмічна. Із отриманих результатів, визначино що коефіцієнт кореляції кращий для логарифмічної залежності R=0.82 (для лінійної залежності R=0.7).
EXTERNAL BISECTOR OF A TRIANGLE IN CONSTRUCTION PROBLEMS
Authors: Hetmanenko Liudmyla
Published: March 2024

In this paper, the author thoroughly examines the external bisector of a triangle as an important element in construction problems involving triangles. The author proposes original methods for solving both classic constructive problems and original problems based on new dependencies.
ФУНКЦІОНАЛЬНО-СЕМАНТИЧНЕ ПОЛЕ ОПТАТИВНОЇ МОДАЛЬНОСТІ
Authors: Біціллі Анна , Лимаренко Олена
Published: March 2024

Стаття присвячена особливостям організації функціонально-семантичного поля оптативної модальності в англійській мові. Виділено наступні семантичні різновиди оптативної модальності: власне бажання, бажання-намір, гіпотетичне бажання, бажання, спрямоване на отримання певної інформації, бажання-порада. Представлені також функціонально-семантичні поля кожного з підвидів оптативної модальності, а саме лексико-граматичні засоби їх вираження. Основний акцент в роботі зроблено на порівнянні лексико-морфологічних засобів висловлення оптативної модальності. Доведено, що власне бажання як ядро функціонально-семантичного поля оптативної модальності позбавлене додаткових семантичних нашарувань, є кістяком оптативності. Інші семантичні підвиди ґрунтуються на семантичному значенні власне бажання із включенням додаткової конотації.
CULTURAL DIVERSITY IN NONVERBAL COMMUNICATION: COMPARING THE MEANING OF HUNGARIAN-RUSSIANAMERICAN NONVERBAL SIGNS
Authors: Toth Alexandra
Published: March 2024

The aim of my research was to conduct a comparative analysis of nonverbal communication cues between Hungarian, Russian and American cultures, with a focus on the cultural specificities and overlaps of nonverbal cues used to express different emotions and states, such as aggressiveness, uncertainty and interest. The study involved an extensive literature review and a questionnaire survey with participants from different cultures to understand how these nonverbal signals are interpreted and perceived in different cultural contexts.

In the analysis, I paid particular attention to the different interpretations of body language, facial expressions, gestures and touch, and spatial relations cues. The results of the research have highlighted that although there are some common forms of nonverbal communication, there are also a number of significant cultural differences that fundamentally influence the encoding and decoding of messages.

For example, while in American culture direct eye contact and open posture can be signs of sincerity and confidence, in Hungarian and Russian cultures these signals can have different contexts and intensities. The meaning and acceptability of touch also varies; what is seen as a friendly gesture in one culture may suggest excessive closeness and inappropriateness in another.

The research also highlighted the intercultural communication difficulties and misunderstandings that can arise from different interpretations of nonverbal signals. This underlines the need to better understand and respect the communication customs and norms of different cultures in order to communicate effectively and efficiently.

At the end of my research, I have formulated recommendations for those involved in cross-cultural communication, whether in business, educational or personal contexts. I have stressed the importance of cultural sensitivity, avoiding prejudices and active listening, which can be key to successful and meaningful intercultural dialogues.
TOPOGRAPHIC ANATOMY OF KIDNEYS AND CHANGES IN THE SIZE AND POSITION OF A SINGLE KIDNEY
Authors: Pivtorak Volodymyr , Monastyrskiy Volodymyr , Pivtorak Kateryna , Bulko Iryna
Published: March 2024

The article examines the influence of macroscopic and microscopic changes in the kidney on function and the development of pathological processes. Kidney volume has been shown to be the optimal parameter for predicting kidney function. It has been established that weight gain often leads to nephroptosis. The variability of the renal vessels in terms of number, sources of departure from the main vessels, method of branching, spatial relationships of the branches was noted.
TRANSCRANIAL NEAR-INFRARED THERAPY FOR COGNITIVE PERFORMANCE AND NEUROLOGICAL STATUS ENHANCEMENT
Authors: Borsuk Daria , Bondarenko Maryna , Zaytseva Olga
Published: March 2024

There is a growing interest in non-invasive treatment options in the field of neurotherapy with transcranial near-infrared (tNIR) light demonstrating promising results across a broad spectrum of neurological disorders. This paper explores the therapeutic potential of tNIR and its efficacy in stimulating cellular functions to improve outcomes in neurodegenerative diseases such as dementia, Parkinson’s disease, Alzheimer’s disease as well as traumatic brain injury, stroke recovery, neuroinflammatory conditions, depression, and BDNF stimulation. Through the direct transcranial application of low-level wavelengths of red or near-infrared light, tNIR stimulation activates neural tissue metabolism, modulates brain function, enhances cognitive performance, and alleviates chronic brain inflammation. This study synthesizes current research findings to illustrate the mechanisms underlying tNIR's action, evaluates its potential across various neurotherapeutic applications, and presents an overview of its current therapeutic implementations. Studies demonstrate the tNIR's capacity to penetrate the skull, stimulate neural tissues, enhance mitochondrial function, and increase ATP production. tNIR has been shown to improve cognitive functions and reduce neuroinflammation, offering a novel approach to treating neurodegenerative conditions. tNIR’s application extends to stroke recovery, where it has been shown to reduce infarct zones as well as nerve regeneration through promoting synaptogenesis and BDNF stimulation.
Case Report: The Diaphragmatic Hernia: A Rare Cause of Intestinal Obstruction
Authors: Naimi S. , Faiz S. , Bouktib Y. , Elhejjami A. , Boutakioute B. , Idrissi M. Ouali , Cherif N. , Ganouni Idrissi El
Published: March 2024

We present the case of a 22-year-old patient, with congenital diaphragmatic hernia, diagnosed at an advanced stage due to an occlusive syndrome observed on the CT scan, revealing colonic distension upstream of a strangulated Bochdalek's diaphragmatic hernia. The patient underwent laparotomy, where the hernia was reduced, and the diaphragmatic rupture was closed. The purpose of this article is to highlight a rare cause of intestinal obstruction, which is strangulated diaphragmatic hernia, when the diagnosis and detection of complications are confirmed through CT scan, allowing for early management.
The Prevalence and Pattern of Psychosocial Disorders among Secondary School-Going Students in Urban Areas of Bangladesh
Authors: Paul Nibedita , Sarkar Probir Kumar , Talukder Mohammad Abu Sayeed , Ali Shayda , Islam Md. Tohidul , Rahman Azamgir Hasibur , Biswas Suvasish Kumar , Paul Tapesh Kumar
Published: March 2024

Background: Adolescent mental health is a growing concern globally, with anxiety disorders being particularly prevalent. This study aims to explore the prevalence and patterns of anxiety among secondary school-going students by examining the interplay of various psychosocial, familial, and health-related factors. Methods: This cross-sectional study was conducted with 600 secondary school students using the Generalized Anxiety Disorder-7 (GAD-7) scale. The study assessed the prevalence of anxiety and its correlation with demographic factors (age, gender), living situation, academic performance, health and lifestyle factors (BMI, chronic health conditions, sleep patterns), and family dynamics. Results: The analysis revealed that 52.67% of participants exhibited none to minimal anxiety symptoms, while 36.33% experienced mild anxiety, 8.00% moderate anxiety, and 3.00% severe anxiety. Age and gender showed no significant correlation with overall anxiety levels. However, living situation, academic performance, and health factors like BMI and sleep patterns were significantly associated with anxiety levels. Family dynamics, including income level and parenting style, also showed a strong correlation with anxiety. Students with severe anxiety were more likely to have lower academic performance, experience psychosocial challenges, and come from less supportive family environments. Conclusion: The study highlights the multifaceted nature of anxiety disorders in adolescents, which is influenced by a complex interplay of individual, familial, and environmental factors. These findings underscore the need for holistic approaches in addressing adolescent anxiety, involving family-based interventions, academic support, and health promotion strategies. The study contributes valuable insights into adolescent mental health, emphasizing the importance of early identification and comprehensive management of anxiety disorders.
Moderasi Beragama : Analisis Kebijakan dan Strategi Penguatan di Kementerian Agama Republik Indonesia
Authors: Rofiqi Rofiqi , Firdaus Mohammad , Salik Mohamad , Zaini Achmad
Published: March 2024

Religious moderation is currently a very interesting discussion, not only among academics but also among the public. The emergence of the phenomenon of intolerance and radicalism certainly becomes asbabun nuzul from moderation so the Ministry of Religion of the Republic of Indonesia feels the need to make religious moderation a policy focus. This paper aims to reveal policy analysis and strategies for strengthening religious moderation in the context of education in Indonesia. The research method used is qualitative with the type of library research using the note-taking technique. The results of this study indicate that the policy of strengthening religious moderation is contained in the 2020-2024 RPJMN which promotes strengthening religious moderation through 5 (five) strategies to strengthen religious moderation including; religious broadcasting, education system, management of houses of worship, management of public spaces, and Islamic boarding schools. However, the focus and findings in this study lead to the implementation of the education system in Islamic Higher Education, in which houses of religious moderation are formed at State Islamic Universities in Indonesia.