Research Tasks Research Articles

Processing large amounts of data in Python for the purposes of applied socio-economic research: advantages and current issues

The article is devoted to the study of the features of processing large amounts of data using the Python programming language. Unlike tabular processors or finished software products, programming languages offer the user a flexible toolkit for the implementation of tasks. At the same time, this creates certain risks associated with the effectiveness of using appropriate tools and optimising the operation of the programme. The purpose of the article is to study the features of processing large amounts of data in Python on the examples of immediate research tasks. The relevance of the topic and purpose of the article is due to the existing scientific gap related to a comprehensive consideration of the technical aspects of the use of programming languages and associated tools for socio-economic research. Thus, many authors who use programming languages in their works rarely provide information regarding the advantages of certain algorithms or approaches. Within the framework of the article, the author examines the procedures and algorithm of processing a large array of data on the example of specific research tasks. The conclusions are drawn about the features and advantages of Python when working with large amounts of data as well as about the prospects for the development of the relevant scientific topics.

Music Arrangement in Kurdish songs in Suleimani

Performed music is an important and unique part of the world's music center and its different situations are analyzed through several scientific theories. The Kurdish Music Center has its own efforts in the field of composing and distributing music and can be classified into different categories and by resorting to these scientific principles can be treated scientifically. Since the beginning of its establishment, Sulaymaniyah has been a city full of artistic products and various innovations. Therefore, working on this entity is an important task of scientific research and excavating the history of this invention requires more scientific research, especially since the state of scientific research in the field of music and analysis of production situations is not at the necessary level. This research is an examination of the musical works that have been distributed in the city of Suleimani. It also classifies and distinguishes works in terms of stage and style. Along with these is the analysis of each of the songs sampled by the researcher in terms of form, harmony, distribution of words and use of musical instruments

Analysis of the Suitability of Ultrasonic Testing for Verification of Nonuniform Welded Joints of Austenitic-Ferritic Sheets.

The purpose of the presented research was to determine the suitability of using ultrasonic testing (UT) to inspect heterogeneous, from a material point of view, welded joints on the example of the joints of a ferritic steel element with elements made of an austenitic steel. The echo technique with transverse (SEK) and longitudinal wave heads (SEL) addressed this issue. Due to the widespread use of 13CrMo4-5 and X2CrNiMo17-12-2 steel grades in the energy industry, they were selected as the test materials for the study. The objects of the presented research were welded joint specimens with thicknesses of 8, 12, and 16 mm and dimensions of 300 × 300 mm, made using the 135 metal active gas (MAG) process with the use of the Lincoln 309LSi wire-a ferritic-austenitic filler material. The stages of the research task were (1) making distance-amplitude curve (DAC) patterns from the test materials; (2) preparation of specimens of welded joints with artificial discontinuities in the form of through-holes; (3) performing UT tests on welded joints with artificial discontinuities using heads with 60° and 70° angles for the transverse wave and angle heads for longitudinal waves with similar beam insertion angles; (4) selection, by radiographic testing (RT), of welded joint specimens with natural discontinuities in the form of a lack of sidewall fusion; (5) performing UT tests on welded joints with natural discontinuities, using heads as welded joints with artificial discontinuities. It was found that (1) the highest sensitivity of discontinuity detection was obtained by performing tests on the ferritic steel side, which is due to the lower attenuation of the ultrasonic wave propagating in ferritic steel compared to austenitic steel; (2) the best detection of discontinuities could be obtained using a longitudinal ultrasonic wave; (3) there is a relationship between the thickness of the welded elements, the angle of the ultrasonic beam introduction, and the effectiveness of discontinuity detection.

Increasing the damping properties of the magnetorheological actuator of the vehicle suspension control system

Introduction. In accordance with one of the ways of solving the problem of increasing the smoothness of the vehicles, a controlled suspension is proposed, which is created on the basis of the use of «smart» materials – magnetorheological elastomers, the mechanical properties of which, in particular, damping, can be changed with the help of a controlling magnetic field. This is implemented with the help of the magnetorheological actuator of the suspension control system, which has the form of an elastic bushing of the suspension arm, consisting of several electrically connected in series toroid-like coils (with a core of magnetorheological elastomer). The device is powered by current, the value of which is controlled by the operator, or automatically, depending on the road profile and driving mode. Magnetorheological actuators (elastic bushings) are placed in the holes of the suspension levers instead of standard rubber ones and combined with a controlled current source. Thus, the suspension becomes controllable, which makes it possible to set the necessary vibration damping of the vehicle body to increase its smoothness. Problem. The disadvantage of the previous designs of the magnetorheological actuator is the insufficient amount of the magnetic flux density and the unevenness of its distribution within the elastic bushings. As a result, the damping properties of such controlled suspensions become insufficiently effective, which reduces the possibility of increasing the smoothness of the vehicles. The purpose of the work is to increase the damping properties of the magnetorheological actuator of the vehicle suspension control system, which will increase the control efficiency. The task is to improve the design of the performing magnetorheological device, to carry out calculations and develop a calculation scheme of the study, to determine the average magnetic flux density value and its distribution across the cross-section of the device, to calculate the dependence of the device damping indicator on the magnetic flux density, to compare the damping indicators of the improved device with previously known ones. Methodology. Research tasks were solved on the basis of magnetic field analysis using methods of magnetic field theory and SOLIDWORKS® and FEMM software packages, as well as analysis of the dependence of the damping properties of bushings from magnetorheological elastomers on magnetic flux density. A description of the design and principle of operation of the magnetorheological actuator of the vehicle suspension characteristics control system is given, based on which the calculation scheme was developed. Results. The results of research calculations showed that the average value of magnetic flux density in the proposed design of the device reached 0.85 T, its distribution became fairly uniform, and there were no zones where it was abnormally small. For the first time, the dependence of the damping index on the magnetic flux density of the controlling magnetic field has signs of scientific novelty. It was found that this indicator for the proposed design of the device increased by 22 % compared to previous other designs, which will increase the efficiency of the control system and the smoothness of the vehicle. A positive result was achieved due to the following features of the proposed design of the suspension actuator: the elastic sleeve consists of several coaxially located actuators made of anisotropic magnetorheological elastomer, in which the conglomerates of the ferromagnetic filler during the manufacturing process are located collinear to the direction of the angular deformations of the sleeve and the control magnetic field flux density vector, and the devices have control coils located on their surfaces, which are made of conductive elastic elastomer and electrically connected in a series circuit. Originality. The control method, previous designs and construction of this controlled suspension are protected by patents of Ukraine. Practical value. The direction of further research is to optimize the parameters of the control coils in order to reduce the energy consumption for them and to protect them from overheating. References 20, figures 10.

Entropy-extreme model for predicting the development of cyber epidemics at early stages

The approaches used in biomedicine to analyze epidemics take into account features such as exponential growth in the early stages, slowdown in dynamics upon saturation, time delays in spread, segmented spread, evolutionary adaptations of the pathogen, and preventive measures based on universal communication protocols. All these characteristics are also present in modern cyber epidemics. Therefore, adapting effective biomedical approaches to epidemic analysis for the investigation of the development of cyber epidemics is a promising scientific research task. The article is dedicated to researching the problem of predicting the development of cyber epidemics at early stages. In such conditions, the available data is scarce, incomplete, and distorted. This situation makes it impossible to use artificial intelligence models for prediction. Therefore, the authors propose an entropy-extreme model, defined within the machine learning paradigm, to address this problem. The model is based on estimating the probability distributions of its controllable parameters from input data, taking into account the variability characteristic of the last ones. The entropy-extreme instance, identified from a set of such distributions, indicates the most uncertain (most negative) trajectory of the investigated process. Numerical methods are used to analyze the generated set of investigated process development trajectories based on the assessments of probability distributions of the controllable parameters and the variability characteristic. The result of the analysis includes characteristic predictive trajectories such as the average and median trajectories from the set, as well as the trajectory corresponding to the standard deviation area of the parameters’ values. Experiments with real data on the infection of Windows-operated devices by various categories of malware showed that the proposed model outperforms the classical competitor (least squares method) in predicting the development of cyber epidemics near the extremum of the time series representing the deployment of such a process over time. Moreover, the proposed model can be applied without any prior hypotheses regarding the probabilistic properties of the available data.

Benchmarking analysis at establishing a culture of wellness

This article aims to establish trends in motor development through basketball activities as a prerequisite for building a wellness culture, revealing the impact of basketball exercises on motor activity in junior high school. The interests of the research are aimed at following the dynamics in the motor potential and establishing and evaluating the presence of wellness within the framework of school physical education. More specifically, the creation of a culture of wellness in the junior high school stage of the educational degree. A reliable and objective toolkit for diagnosing the level of motor potential in 12–14-year-old students was selected and applied, specifying the empirical data from the conducted research. The purpose of the research is to follow the dynamics of the motor potential in the two stages of research and to establish a wellness culture in the junior high school stage of the educational degree in the lesson of physical education and sports, based on a comparative analysis of the dynamics of motor indicators in the two cognitive stages of the study. Through legislative changes in school education, the direction “school health”, in particular the expected competences, is regulated. Key competence #9 requires knowledge and skills for sustainable development, implementation of a healthy lifestyle, and sports in the Bulgarian school. To achieve the goal, the following research tasks are solved: experimentation of a basketball training program aimed at the development of motor qualities, as a result of which the dynamics in the development of motor qualities of students studying basketball in physical education and sports classes. Based on the benchmarking analysis, it can be concluded that the results achieved at baseline show an improvement in the motor condition of the studied contingent. Motor indicators show intense dynamics in 12–14-year-old students, modeling their motor potential and confirming the presence of a wellness culture in the Bulgarian school. It can be summarized that a benchmarking analysis was carried out to establish dynamics in the indicators and the manifestation of motor skills between the students of the 4th and 5th grade, allowing the individualization of the differences in the motor potential between the empirical data in the cognitive stages of the study.

MLC-DKT: A multi-layer context-aware deep knowledge tracing model

As a fundamental research task for intelligent education, knowledge tracing (KT) aims to trace learners’ dynamic knowledge states and predict their future performance based on their historical response data. However, learners’ cognitive processes cannot be studied in isolation from the context; nevertheless, existing knowledge tracing models mainly use knowledge topology as auxiliary information but do not deeply explore the contextual information of exercises. Thus, an in-depth exploration of multi-layer contextual information in learning scenarios is required, focusing on the cognitive differences among different learners. Furthermore, knowledge tracing models exhibit limited interpretability compared with cognition diagnosis methods integrating educational priors. To address these issues, we propose a multi-layer context-aware deep knowledge tracing (MLC-DKT) model. Specifically, we first present the multi-layer contextual representation method involving knowledge concepts and exercises. In the knowledge state tracing module, we incorporate the learners’ knowledge priors into the attention mechanism to capture the evolution of the learners’ knowledge. Then, we develop a calculation method for the similarity effect among the contextual information, which helps estimate the learners’ knowledge states. In the learning performance prediction module, we introduce the guessing and slipping factors from the cognition diagnosis model to optimize the MLC-DKT model, further improving the performance of the KT model. Finally, we conduct extensive experiments on real-world datasets, demonstrating that the MLC-DKT model provides improved predictions of learner performance. Moreover, the MLC-DKT model realizes contextual awareness from knowledge concepts and exercises, making the predictions more interpretable.

Construction of a Fine Extraction Process for Seismic Methane Anomalies Based on Remote Sensing: The Case of the 6 February 2023, Türkiye–Syria Earthquake

Identifying seismic CH4 anomalies via remote sensing has been verified as a legitimate method. However, there are still some problems, such as unknown reliability due to the complex characteristics of seismic anomalies. In this study, a multi-dimensional and multi-scale methane seismic anomaly extraction process for remote sensing was constructed with the Robust Satellite Technique (RST) based on the Atmospheric Infrared Sounder (AIRS) CH4 data and then applied to the 2023 Türkiye–Syria earthquake. This study obtained the two-dimensional temporal–spatial distribution of methane anomalies and temporal variation in the anomaly index. Based on this, the three-dimensional profile structure of the 8-day methane anomaly was extracted to determine the reliability of the anomaly. Finally, based on the daily methane anomaly, combined with atmospheric circulation and backward trajectory analysis as auxiliary tools, the influence of air mass migration was excluded to enhance the accuracy of CH4 anomaly determination. The results show that the three-dimensional anomalous structure is consistent with the geological characteristics of tectonic activities, and it appears as a “pyramid” or “inverted pyramid” type in a three-dimensional space. The anomalies caused by air mass migration can be eliminated by combining them with synoptic-scale circulation motion. The time series calculated at the epicenter or a certain point in a region may not accurately reflect the influence of regional or specific tectonic activity in the atmosphere. Thus, the optimal determination of the range and magnitude of atmospheric anomalies caused by tectonic activities is a difficult task for future research.

A Literature Survey of Transition Metal Boride, Carbide, Pnictide, and Chalcogenide Water Oxidation Electrocatalysts

Transition metal borides, carbides, pnictides, and chalcogenides (X-ides) are a new class of oxygen evolution reaction (OER) electrocatalysts, which have become a relevant topic for electrocatalytic energy conversion and storage research. This is because of their earth abundance, high electrical conductivity, and resultant high OER performance. Furthermore, some X-ide electrocatalysts demonstrate various degrees of oxidation resistance under OER potentials due to their differences in chemical composition, crystal structure, and morphology. Interestingly, there are three possible post-OER electrocatalyst models: a fully oxidized oxide/(oxy)hydroxide material, a partially oxidized core@shell structure, and an unoxidized material.1 In the past ten years (from 2013 to 2022), over 890 peer-reviewed research papers have focused on X-ide OER electrocatalysts. Recently, several topical reviews have been published. However, typically these literature reviews have provided limited conclusions targeted at only certain X-ides. Additionally, certain reviews omit key points, especially regarding the “catalytically active species” in X-ide OER electrocatalysts.In this work, we provide a comprehensive summary of (i) the experimental parameters (e.g., substrates, electrocatalyst loading amounts, electrocatalyst shapes, geometric overpotentials, Tafel slopes, etc.) and (ii) the details of the electrochemical stability tests and following post analyses used in all the X-ide OER electrocatalyst publications from 2013 (the birth of this research area) to 2022.2 In particular, based on the reported post-OER analytical results, we attempt to exhaustively classify all the X-ide electrocatalysts into four groups: no oxidation, partial oxidation, full oxidation, and unknown, which is relevant to the nature of the “catalytically active species” in X-ide OER electrocatalysts. After investigating the simpler X-ide systems, transition metal-based polyanion compounds/composites are also surveyed. Finally, we introduce the reported novel and special analytical techniques employed for observing X-ide reconstruction (i.e., oxidation and other phase transformations) during OER testing, which can be useful in selecting the analytical technique of maximum utility for the researchers’ purpose. Additionally, for each material sub-group (e.g., borides, carbides, nitrides, phosphides, sulfides, selenides, and tellurides), we also point out further challenges to be addressed and propose further questions yet to be answered, which may provide clues pointing to future research tasks. References B. R. Wygant, K. Kawashima, and C. B. Mullins, ACS Energy Lett., 3, 2956–2966 (2018).K. Kawashima et al., Chem. Rev., 123, 12795–13208 (2023).

Критерии оценки навыков информационно безопасного поведения подростков

The purpose of the article is to consider the relevance of the formation of skills of information-safe behavior of adolescents as behavior that ensures their safety in organizing the search, collection, processing, analysis and codification, use and storage of information using various information sources that do not harm others. The author solves one of the research tasks related to the description of technology for the formation of skills of information-safe behavior of adolescents in an educational organization, involving the implementation of several stages, each of which is associated with the formation of components of information-safe behavior of a teenager: the stage of actualization and familiarization – with the formation of a value-motivational component in relation to information security; the stage of understanding and transformation – with the formation of the cognitive component; the stage of application and acceptance – with the formation of the activity component; the stage of analysis and synthesis involves the formation of an analytical component; the reflexive stage involves the formation of a creative component. The effectiveness of the implementation of the stages of technology for the formation of information-safe behavior skills of adolescents is presented based on the goals of its implementation, according to knowledge (cognitive), motivational-activity, reflexive criteria.

Public policies and right to sexuality of the elderly in Chile

People over 65 years of age will constitute the majority of the world's population in the short term, but in precarious living conditions: more years in a worse condition of vulnerability and fragility. Societies and development models would not be prepared, generating high personal, family and collective costs. In Chile, fragility would be highly prevalent in this population, impacting the full development of their lives; with sexuality as one of the aspects that are invisible and little studied. This work makes a critical approach, based on the review and analysis of context, public policies and legislation in force in Chile, evidencing atomization and biomedical orientation of public policies, collaborating in the understanding of the relationship between fragility and sexuality in old people; and revealing pending training and research tasks for the generation of public policies for an active and healthy life.

MENTORING IN THE HEALTHCARE SYSTEM: PROBLEMS AND PROSPECTS

ANNOTATION Rationale. During the period of rapid development of science and medical technologies, the professional and working environment is faced with the urgent task of finding mechanisms and tools for increasing the level of professional competencies and practical skills of medical specialists in the process of professional socialization. The sociological study conducted by the authors was focused on identifying factors of successful/unsuccessful professional socialization of young medical specialists, one of which is mentoring. Purpose of the study. Determining the role of the mentoring institution as a factor influencing the success/failure of professional socialization of medical specialists. Materials and methods. A survey was conducted among clinical residents of the Far Eastern State Medical University. A total of 184 respondents were interviewed using a questionnaire using a Google form. To solve the research problems, methods of analysis, comparison and generalization of the obtained data, analysis of regulatory documents, survey results and secondary data from sociological research were used. At the level of data processing and work with source material, descriptive statistics methods provided by SPSS and EXCEL software were used. Results. In the course of a sociological study conducted by the authors, the most pressing problems of introducing and implementing mentoring practices in the healthcare system were identified. The role of the mentoring institution as a socializing factor influencing the success/failure of professional socialization of medical specialists is also determined. The study formulates tasks for further research into the process of professional socialization of medical specialists from the point of view of identifying factors of a positive and destructive nature, and the role of socialization institutions in this process. Conclusion. Problems in the implementation of mentoring practices in healthcare related to the low mentoring activity of practicing medical specialists have been identified. A special need for the implementation of mentoring practices was identified at the stage of postgraduate training (clinical residency) by 83% of respondents and at the stage of starting independent professional work by 61% of respondents. Respondents expressed a positive attitude towards the introduction of mentoring in healthcare: 90% of respondents indicated that having a mentor when obtaining a medical specialty is mandatory.

Research on the Method for Recognizing Bulk Grain-Loading Status Based on LiDAR.

Grain is a common bulk cargo. To ensure optimal utilization of transportation space and prevent overflow accidents, it is necessary to observe the grain's shape and determine the loading status during the loading process. Traditional methods often rely on manual judgment, which results in high labor intensity, poor safety, and low loading efficiency. Therefore, this paper proposes a method for recognizing the bulk grain-loading status based on Light Detection and Ranging (LiDAR). This method uses LiDAR to obtain point cloud data and constructs a deep learning network to perform target recognition and component segmentation on loading vehicles, extract vehicle positions and grain shapes, and recognize and make known the bulk grain-loading status. Based on the measured point cloud data of bulk grain loading, in the point cloud-classification task, the overall accuracy is 97.9% and the mean accuracy is 98.1%. In the vehicle component-segmentation task, the overall accuracy is 99.1% and the Mean Intersection over Union is 96.6%. The results indicate that the method has reliable performance in the research tasks of extracting vehicle positions, detecting grain shapes, and recognizing loading status.

6E analysis and particle swarm optimization of a novel ultra-high temperature solar cogeneration system fusing thermochemical energy storage and multistage direct heat transfer

The reshaping of the energy development model depends on the penetration of renewable energy on the input side and the substitution of electrical energy on the supply side. Therefore, this research proposes a novel solar cogeneration system in which the Brayton cycle possesses triple pressure and can be flexibly coupled directly to each subsystem under supercriticality without intermediate exchangers. Firstly, a series of research tasks are executed sequentially, including model establishment, cycle potential comparison, operating parameter analysis, and multi-objective particle swarm optimization. Secondly, the exergy flow rate, consumption rate, and 6E (energy, exergy, exergoeconomic, exergoenvironmental, emergoeconomic, and emergoenvironmental) performances of each subsystem and subcomponent are thoroughly discussed. Lastly, the operating performance, economic benefits, and regional characteristics of each subsystem and the cogeneration system are explored. After optimization, the exergy efficiency, average advanced exergy-emergy factor, and advanced exergy-emergy impact difference of the system are 22.72 %, 56.17 %, and 23.97 %. Compared with similar research, the energy efficiency of the system is improved by 8.2 %, which can repay the capital cost in 8.83 years, and the economic benefit reaches $5,503,086 for the entire service year. The system embodies excellent 6E performance, aligns with the goals of reshaping the energy development model, and provides guidelines for the development of ultra-high temperature solar cogeneration.

Assessing molecular genetic and immunological predictors of COVID-19 course in healthcare worker risk group

Relevance. Studying features of innate and adaptive mechanisms of immune response in medical workers (MW), the most vulnerable social group with a high risk of infection, is an urgent research task. The aim of the study: Comprehensive study of innate and adaptive immune mechanisms and analyzing relationships between clinically significant polymorphisms (SNPs) in TLR2, TLR4 genes, TLR2 expression level on peripheral blood monocytes, peripheral blood cytokine profile (IL-1β, IL-10, IL-6, IFNγ, platelet activation marker) and SARS-CoV-2-specific humoral immune response in medical workers (MW) at a temporary infectious disease hospital in early and late COVID-19 convalescence. Materials and methods. immunologic, cytofluorimetric and molecular-genetic research methods were applied. Adaptive immune response in medical workers — COVID-19 convalescent subjects. Results. Early post-COVID-19 convalescence period in MW was linked to higher TLR2 monocyte expression; the mean fluorescence intensity was significantly elevated by 1.5-fold compared to control group. Late convalescence period (7 months post-COVID-19) was characterized by lowered serum IFNγ level. A decline in IFNγ production was significant: decreased by 82-fold in MR that was markedly stronger compared to control group (59 times). The imbalance of cytokines controlling antiviral innate and adaptive immune response was revealed in MW with identified combination of polymorphisms rs5743708 and rs4986790 in TLR2, TLR4 genes with the rate not exceeding 6.7%. It was found that 7 months after COVID-19 there was a markedly decreased IFNγ, IL-1β and IL-10 levels. The studies indicate both altered innate and adaptive immune mechanisms and a need to optimize therapeutic and prophylactic measures aimed at increasing patient-intrinsic resistance, protection of respiratory tract mucosal barriers and identification of genetic predictors of defects in innate and adaptive immune response in medical workers — COVID-19 convalescent subjects.

Announcement effect of tender offer share buyback around turmoil period – evidence from India

The announcement of a buyback informs the market about the company’s decision to repurchase its own shares. This announcement highlights the company’s price valuation and the inefficiencies that exist in the market. This study examines the share buyback announcement effect during the COVID-19 period. The study considered the stocks listed in the National Stock Exchange (NSE) that offered share buyback under tender offer mode during the pre-pandemic period between April 2016 and February 2020 and the post-pandemic period between March 2020 and March 2022. 75 firms in the pre-pandemic period and 43 in the post-pandemic period that announced share buyback under the tender offer method were analyzed. The event study methodology using a market model was employed to determine the presence of abnormal returns during the event period, which consisted of –21 days and +21 days. The findings of the study revealed the existence of abnormal returns in and around the announcement date. Besides, statistically significant cumulative abnormal average returns (CAAR) were also found on the event day, i.e., on Day 0. The study found that the impact of buyback announcements on stock returns significantly differed before and after COVID-19 for 10 and 21-day periods, with no significant differences for shorter periods. These insights can help traders and fund managers make informed portfolio adjustments during turbulent market periods surrounding buyback announcements. AcknowledgementThe authors express their sincere gratitude and special thanks to Dr. Krishna T.A., Assistant Professor, Department of Professional Studies, School of Commerce, Finance and Accountancy, CHRIST (Deemed to be University), Bangalore, India, for encouraging, motivating and providing all the required support throughout this empirical investigation and to accomplish this research task.

Ways to stimulate business towards socially responsible behavior and respect for human rights in the economic sphere

The article focuses on the growing role and importance of socially responsible business behavior. This is associated with a number of factors, such as the increasing ethical consciousness of society, the growing impact of business on the environment and people’s lives, and intensified competition in the market. It is noted that socially responsible business behavior involves respect for human rights, environmental protection, investment in human capital, and the fight against corruption. The purpose of the article is to explore and substantiate the ways to stimulate business towards socially responsible and respect human rights in the economic sphere. This goal is realized in the article by solving the following research tasks: the author analyzes the concept and essence of socially responsible business behavior and respect for human rights in international legal documents; defines the basic principles of socially responsible business behavior; studies the factors influencing the incentives for business towards socially responsible behavior; and develops and substantiates a complex of measures to stimulate business towards socially responsible behavior and respect for human rights. The author explores the role of business in society and the need to take into account social and ethical aspects in its activities. The author analyzes the impact of socially responsible behavior of business entities on economic development and positive impact on society as a whole. The expected results of the proposed measures are determined. The general conclusion of the article is that socially responsible business behavior and respect for human rights in the economic sphere are important components of sustainable development in modern society. Specific recommendations are proposed for enterprises to engage in these processes and maximize their social impact. The results of this study can be used to develop and implement state policy in the field of stimulating socially responsible business behavior, as well as for self-improvement of business entities.

'A Meaningful Difference, but Not Ultimately the Difference I Would Want': A Mixed-Methods Approach to Explore and Benchmark Clinically Meaningful Changes in Aphasia Recovery.

Outcome measurement instruments (OMIs) are used to gauge the effects of treatment. In post-stroke aphasia rehabilitation, benchmarks for meaningful change are needed to support the interpretation of patient outcomes. This study is part of a research programme to establish minimal important change (MIC) values (the smallest change above which patients perceive themselves as importantly changed) for core OMIs. As a first step in this process, the views of people with aphasia and clinicians were explored, and consensus was sought on a threshold for clinically meaningful change. Sequential mixed-methods design was employed. Participants included people with post-stroke aphasia and speech pathologists. People with aphasia were purposively sampled based on time post-stroke, age and gender, whereas speech pathologists were sampled according to their work setting (hospital or community). Each participant attended a focus group followed by a consensus workshop with a survey component. Within the focus groups, experiences and methods for measuring meaningful change during aphasia recovery were explored. Qualitative data were transcribed and analysed using reflexive thematic analysis. In the consensus workshop, participants voted on thresholds for meaningful change in core outcome constructs of language, communication, emotional well-being and quality of life, using a six-point rating scale (much worse, slightly worse, no change, slightly improved, much improved and completely recovered). Consensus was defined a priori as 70% agreement. Voting results were reported using descriptive statistics. Five people with aphasia (n = 4, > 6 months after stroke; n = 5, < 65 years; n = 3, males) and eight speech pathologists (n = 4, hospital setting; n = 4, community setting) participated in one of four focus groups (duration: 92-112 min). Four themes were identified describing meaningful change as follows: (1) different for every single person; (2) small continuous improvements; (3) measured by progress towards personally relevant goals; and (4) influenced by personal factors. 'Slightly improved' was agreed as the threshold of MIC on the anchor-rating scale (75%-92%) within 6 months of stroke, whereas after 6 months there was a trend towards supporting 'much improved' (36%-66%). Our mixed-methods research with people with aphasia and speech pathologists provides novel evidence to inform the definition of MIC in aphasia rehabilitation. Future research will aim to establish MIC values for core OMIs. This work is the result of engagement between people with lived experience of post-stroke aphasia, including people with aphasia, family members, clinicians and researchers. Engagement across the research cycle was sought to ensure that the research tasks were acceptable and easily understood by participants and that the outcomes of the study were relevant to the aphasia community. This engagement included the co-development of a plain English summary of the results. Advisors were remunerated in accordance with Health Consumers Queensland guidelines. Interview guides for clinicians were piloted by speech pathologists working in aphasia rehabilitation.

Nighttime wildlife object detection based on YOLOv8‐night

AbstractMonitoring nocturnal animals in the field is an important task in ecological research and wildlife conservation, but the complexity of nocturnal images and low light conditions make it difficult to cope with traditional image processing methods. To address this problem, researchers have introduced infrared cameras to improve the accuracy of nocturnal animal behaviour observations. Object detection in nighttime images captured by infrared cameras faces several challenges, including low image quality, animal scale variations, occlusion, and pose changes. This study proposes the YOLOv8‐night model, which effectively overcomes these challenges by introducing a channel attention mechanism in YOLOv8. The model is more focused on capturing animal‐related features by dynamically adjusting the channel weights, which improves the saliency of key features and increases the accuracy rate in complex backgrounds. The main contribution of this study is the introduction of the channel attention mechanism into the YOLOv8 framework to create a YOLOv8‐night model suitable for object detection in nighttime images. When tested on nighttime images, the model performs well with a significantly higher mAP (0.854) than YOLOv8 (0.831), and YOLOv8‐night scores 0.856 on mAP_l, which is obviously better than YOLOv8 (0.833) in terms of processing large objects. The study provides a reliable technical tool for ecological research, wildlife conservation and environmental monitoring, and offers new methods and insights for the study of nocturnal animal behaviour.

Quantitative characterization and application of deep learning carbonate reservoir

Abstract Quantitative reservoir characterization is an important research task in carbonate reservoir development. At present, the comprehensive evaluation of drilling, logging, seismic, and productivity testing data is usually used, which requires a large number of test data and tedious manual data collation. Deep learning technology can independently learn complex reservoir characteristics, has a strong ability for high-dimensional data structure mining, and has a strong advantage in solving complex nonlinear reservoir description problems closely related to various geological characteristics. Therefore, this paper applies deep learning technology to reservoir characterization. First, a deep neural network model is constructed, and then seven characteristic parameters such as fracture density, fracture opening, fracture porosity, matrix porosity, total porosity, effective thickness, and specific oil production index are taken as input variables. After model training and verification, the effective permeability distribution map of the reservoir in the target area can be automatically generated. The results show that the deep learning method has strong adaptability and practicability for the quantitative characterization of complex reservoirs, and provides a new way of thinking for the quantitative characterization of reservoirs.