Intensive Resources Research Articles

Regulators for ensuring the sustainability of reproductive processes in industrial fruit growing

The necessity of developing a system of regulators to ensure the sustainability of reproductive processes in industrial fruit growing is substantiated. An assessment of the efficiency of fruit production in the Russian Federation is given. The factors influencing the decrease in the stability of reproductive processes in industrial fruit growing are identified: an increase in the cost of purchased resources; high cost of finished products; a reduction in the share of subsidies in the created cost of planting; the insufficiency of agricultural producers’ own financial resources for carrying out planned plantings renovations, updating production infrastructure funds and carrying out current production activities. Functional imbalances in the organization of reproductive processes in industrial fruit growing have been identified: internal structural imbalances, high resource intensity of production and technological processes, non-compliance with optimal comparability of production and economic indicators. Generalizing criteria for the sustainability of reproductive processes are systematized: the ability of the system to withstand negative impacts of an economic and natural nature; increasing the possibilities of expanded reproduction of all used resources; ensuring qualitative changes in the production, socio-economic, environmental parameters of the system; increasing the conditions for subsequent improvements, preventing production declines. The article characterizes and evaluates the effectiveness of existing regulators to ensure the sustainability of reproductive processes in industrial fruit growing (macroeconomic, technical, technological and economic). A system of regulators has been developed to ensure the stability of reproductive processes in industrial fruit growing in functional areas of influence. An algorithm is proposed to substantiate the necessary dimensionality of regulators to ensure the stability of reproductive processes in industrial fruit growing.

A sustainable joint economic lot size model for supplier selection under carbon emissions: A case study

Selecting suppliers strategically is a critical challenge in supply chain design, particularly in a dynamic global marketplace where maintaining competitiveness and sustainability is essential. Supplier location is a key factor that significantly impacts supply chain sustainability. Hence, this study proposes a new Sustainable Joint Economic Lot Size (S-JELS) model that integrates average annual cost and total amount of emissions associated with annual activities related to both buyer and supplier. The developed model accounts for costs and emissions from the buyer’s purchasing, inventory management, and transportation activities, as well as from the supplier’s production, packaging, and delivery processes. Additionally, the model incorporates various transportation modes, including shipping, trucking, and rail, to more accurately assess the environmental impact of different supply chain strategies. The proposed JELS model can support managers performing sustainable purchasing decisions and assess a sustainable supplier selection. The developed model is applied in a leather case study characterized by high resource intensity, significant environmental impacts, and complex global logistics to accomplish supplier selection between one domestic and one international supplier, aiming to determine the most sustainable strategy in terms of cost and emission

Student-led Classroom: Review on the advantages and disadvantages

This paper examines the benefits and drawbacks of student-led classrooms within the context of constructivist learning theory. The principles of constructivism align with this educational approach, which emphasizes active participation, social interaction, and contextual learning. The study investigates the influence of student-led classrooms on factors such as engagement, critical thinking, and knowledge retention. Although constructivist approaches, such as problem-based and inquiry-based learning, have notable advantages, they also pose difficulties, such as the requirement for explicit instruction and resource intensity. The study examines existing literature on the efficacy of student-led classrooms and asynchronous online discussion forums (AODFs), providing valuable insights into techniques that improve student engagement and learning outcomes.

Economic cost of a case of diarrhoea in Uvira, Democratic Republic of the Congo: A cost of illness study.

Diarrhoea is one of the leading causes of disability-adjusted life years (DALYs) among children below five years, though the proportion of the burden occurring amongst those aged over 70 is increasing. The cost of treating and managing diarrhoea can place a burden on individuals, their households, and society in general. The cost can be high but is often undocumented, and many studies focus on children or hospitalised patients only. This study aimed to estimate the economic cost per case of diarrhoea amongst individuals of any age in Uvira, Democratic Republic of the Congo. The study was cross-sectional and retrospective, and based on a household survey approximately representative of the city undertaken in September 2021. Data on quantities and prices of resources were collected in the survey, as well as from interviews with staff at the Cholera Treatment Centre in the Uvira general hospital and their records on resource use and patient numbers. Direct and indirect costs were measured from the societal perspective, and generalised linear regression used to identify factors associated with higher costs. Of 2,820 members of the 528 households surveyed, 175 people (6.2%) were reported to have had diarrhoea in the previous seven days. The majority sought care (91%) of which most (64%) visited a pharmacy. The average economic cost of illness (COI) for an episode of diarrhoea was 33,816 Congolese Francs (CDF) (US$ 17.0) in 2021 prices. The median was CDF 14,000 (US$ 7.0). The average out of pocket COI to patients was CDF 15,579 (US$ 7.8), representing 9% of the estimated average monthly income of households. On average caregivers and patients lost 4 days per episode. A concentration index suggested a lower economic COI among poorer households (p=0.099). A regression analysis identified that being older than 5 years (p=0.001) or being water insecure (p=0.032) were associated with higher COI. Households in Uvira experience many diarrhoeal episodes per year, and the COI is an important burden for them and society. These costs could be avoided if diarrhoea were prevented through public health interventions to reduce prevalence and care-seeking was better managed to avert this public health burden.

Resources Price Adjustment in Cloud Computing for Load Balancing and Fairness Between Users and Suppliers

Resource price adjustment is a challenging issue when building a cloud computing system. For this purpose, we present an approach that adopts a market-driven price adjustment model as an economic incentive model and ensures the balance between supply and demand. We show how this model may be adapted and applied to an infrastructure as a service (IaaS) in cloud computing. We place ourselves in the context of the virtual machines market. To achieve competitive equilibrium, prices are adjusted according to a tâtonnement-like process. The prices are computed by a third party, the auctioneer. However, this process requires several assumptions to ensure the existence and stability of competitive equilibrium, not all of which are verified by cloud computing systems. The atomicity and convexity of preferences are not guaranteed by cloud computing systems. These questions will be the subject of our empirical study. We find empirically that the change in the sign of excess demand, indicating the existence of equilibrium, occurs between two similar prices. The experimental results show that the proposed dynamic price adjustment approach is more profitable than fixed-price pricing, allowing dynamic suppliers to become important market agents.

Reception of health data using the Australian Burden of Disease study as an example

Abstract Background Health data, especially on Burden of Disease (BoD), are vital for evidence-based health promotion and effective dissemination is crucial due to resource intensiveness. Germany’s national BoD study is currently transitioning to permanence. Analysing the Australian Institute of Health and Welfare’s (AIHW) well-established collaboration with stakeholders and their reception of health data at national and regional level offers insights for improved knowledge translation. Methods To follow the path of stakeholder engagement, this 3-month project involved scientific exchanges with the AIHW and Department of Health, Western Australia. It compiled communication strategy examples and documents stating examples of BoD data use. A quantitative online survey accompanied by a series of 18 qualitative 45-minute interviews was conducted. The survey implemented via Swift Digital has been running for 20+ days. Interview partners originated from the fields of health research, politics, planning and advocacy for patients or population groups. Results were analysed, illustrated digitally as a booklet and refined in a participatory workshop. Results Communication strategies such as embargoed press and stakeholder releases as well as regular online formats were identified. The survey and interviews displayed the required technical and content-detail level from key facts to raw data and visualisation needs. Noteworthy are the demand to link data to economic aspects like disease expenditure, trends and future scenarios. It elucidated how diverse target groups utilize data to place disease significance and preventive public health measures, aiding dissemination. Alongside, it shows examples of knowledge translation by emphasizing impact over indicators when targeting communities. Conclusions Prioritizing target group identification and employing tailored communication strategies regarding data format and detail level are essential when utilizing new data. Key messages • Analyzing a well-established collaboration with stakeholders offers insights for improved knowledge translation and reveals leveraging engagement strategies for others. • Understanding stakeholder needs is essential for promoting broader health data use in policy and practice, fostering evidence-based decision-making, and enhancing public health outcomes.

Techno-economic and sustainability assessment of the power to MeOH processes: The present and future perspective

This study presents a comprehensive analysis of a Power-to-Methanol (PtM) process, which converts CO2 and H2 derived from electrolysis of water into methanol (MeOH) using renewable energy sources. The process is analyzed for its technical, economic, and environmental aspects, focusing on production costs and the minimum selling price of MeOH. The simulation results show an energy efficiency of 56 % in the optimal state, with a CO2 consumption of 1.4 kg-CO2/kg-MeOH. Economic evaluations for 2023 indicate production costs of US$ 631 and 643 for PtM processes equipped with membrane and amine absorption technologies, respectively. The study also predicts the cost of production and minimum selling price of MeOH from 2023 to 2030 based on the trends of electrolyzer capital investment and renewable resource prices. The results provide valuable insights for the development and evaluation of sustainable MeOH production processes.

Assessing the initial impact of the Russian invasion on Ukrainian agriculture: Challenges, policy responses, and future prospects

The war in Ukraine has caused significant losses to the Ukrainian agricultural sector and threatened global food security. This study aims to comprehensively analyse the impact of the Russian invasion on Ukrainian agriculture in its initial phase, examine the responses of Ukrainian agricultural policy and outline key elements for the post-war development of the Ukrainian agricultural sector. Using a systematic approach that includes thorough data collection and analysis from official sources, the Ukrainian press, relevant legislation and statistical data, our study focuses on the first five months of the conflict, from February 24, 2022 to July 24, 2022, to identify the main challenges during this period. The agricultural policy analysis shows that the Ukrainian government has adopted a reactive approach, including tax simplification, affordable credit, deregulation, financial support for the agricultural sector, reduced prices for inputs and resources to support farmers, and improvements in logistics. Critical factors identified for post-war development include repatriation and agricultural education, support for small and medium-sized farms, integration into global markets, emphasis on organic practices and sustainable development, and digitalization in agriculture. The integration of a systematic overview of the Ukrainian agricultural sector and an analysis of key elements of post-war development provides essential insights for policy-makers and researchers concerned with the impact of war on the agricultural sector and food security.

Small-Sample Data Pricing Based on Data Augmentation and Meta-Learning

Data trading platforms play a crucial role in facilitating data circulation and promoting the sustainable allocation of data resources. Establishing a transparent, fair, and efficient pricing mechanism is key to ensuring the long-term stability and development of such platforms. However, these platforms face challenges in pricing due to the small sample problem, as traditional machine learning methods typically rely on large amounts of data. To address this issue, this paper proposes a data resource pricing model that combines WGAN-GP data augmentation and the Reptile algorithm. Data augmentation generates related datasets to increase sample size, enhancing the renewability of data resources, while meta-learning transfers knowledge across tasks, improving the model’s ability to quickly adapt to new tasks and efficiently utilize resources. Validation using actual trading data from the data trading platform shows that the proposed model accurately predicts data resource prices under small-sample conditions, outperforming other models. This study addresses the limitations of existing pricing methods in small-sample scenarios, providing a sustainable pricing solution for small-sample data resources and improving the accuracy and long-term stability of data pricing in the market.

AN OPTIMIZED BIDIRECTIONAL CONVOLUTIONAL RECURRENT NEURAL NETWORK ARCHITECTURE WITH GROUP-WISE ENHANCEMENT MECHANISM OF SENTIMENTS FOR THE PERSPECTIVE OF CUSTOMER REVIEW SUMMARIZATION

Customer reviews play pivotal roles in consumers’ purchase decisions, but the sheer volume of text data can be overwhelming. In existing system, while ensemble methods can enhance performance, the associated computational complexity and resource intensiveness should be carefully considered, and appropriate measures should be taken to address these challenges in the context of Customer Review Summarization. This study introduces a Particle Swarm Optimization (PSO) based optimized architecture for a Bidirectional Convolutional Recurrent Neural Network (BiCRNN) with a group-wise enhancement mechanism tailored for Customer Review Summarization and named as OBiCRNN. This model is designed for the perspective of customer review summarization, aiming to effectively capture sentiments and generate concise summaries. The integration of PSO optimizes the network parameters, enhancing the learning process. Feature extraction is done by Modified Principal Component Analysis (MPCA) which uses correlated feature sets and extracts most informative features for given datasets. BiCRNN utilizes bidirectional LSTM and GRU layers for comprehensive context understanding, while the group-wise enhancement mechanism categorizes sentiment-related features, amplifying essential sentiments and attenuating less relevant ones. With this novel approach, the architecture leverages both PSO and BiCRNN for an advanced framework in customer review summarization where outcomes demonstrate the effectiveness of the deep learning (DL) model in producing coherent and informative summaries, enhancing the accessibility of customer feedback for both consumers and businesses. The study contributes to the field of natural language processing (NLP) and customer sentiment analysis, offering a scalable solution for managing the wealth of information present in online customer reviews.

Development of in silico methodologies to predict the toxicity of novel proteins in the context of food and feed risk assessment

Abstract This report is the outcome of an EFSA procurement (OC/EFSA/GMO/2021/02 – LOT1) aiming at developing an in silico strategy to predict the toxicity of (novel) proteins. Up‐to‐date, commercially available tools predicting protein toxicity based on primary structures were evaluated for their accuracy and usability, using a curated dataset of annotated toxins and non‐toxins from UniProt. ToxinPred2 and Toxify emerged as the top performers, showing both high accuracy and suitability for integration into an automated pipeline. Additional bioinformatics methods were explored, which provide sequence similarity‐based information rather than direct predictions (BLAST, InterPro HMM profiles). By converting their outputs into features for machine learning models, a high prediction accuracy was achieved, though there is potential for improvement to reduce overfitting risks. An Artificial Intelligence (AI)‐based consensus pipeline, integrating results from ToxinPred2, Toxify, and our machine learning models was developed. This consensus model reached a 95% accuracy rate in distinguishing toxins from non‐toxins. Noteworthy, our BLAST‐based machine learning model ‐ although performance‐wise comparable to BLAST ‐ offers higher sensitivity and specificity across diverse queries than BLAST; it relies on database‐based evolutionary relationships, which may significantly limit its applicability to novel or mutated toxins. Structure‐based prediction methods are deemed impractical due to their resource intensity and reliance on accurate structural data; AI‐driven structure prediction methods ‐ like Rosetta and AlphaFold ‐ are promising, however they are still under development and may not be suitable for the regulatory context yet. Recommendations are provided, including enhancement of the proposed consensus pipeline to create an independent open‐source, user‐friendly tool for evaluating the safety of (novel) proteins in food and feed; regular updates of the proposed databases and models; incorporation of 3D structures and in general validation of AI and machine learning models for regulatory uses.

GLOBAL PRACTICE OF DIVERSIFICATION OF THE OIL AND GAS SECTOR OF THE ECONOMY

The article considers global trends in the development of the oil and gas sector of the economy. World economic systems, developing due to the availability of large amounts of natural resources, are increasingly specialised in the extraction and export of energy products, which are mainly exported to industry markets in raw form. Countries that supply hydrocarbon commodities to world markets are subject to price volatility and pricing policies, with the result that the economies of oil and gas exporting countries are not always sustainable. Historically, such countries have historically been less able to develop manufacturing sectors that produce value-added goods, while supplementing government budgets with quick revenues from oil and gas exports. These countries have a chance to develop their economies by selling raw materials for a certain period of time, but in the long term they switch to economic diversification to ensure sustainability in the face of uncertainties (lower demand for raw materials, volatility of resource prices, environmental challenges, scientific and technological development, increased use of unconventional energy sources, the impact of the COVID-19 pandemic, etc.). Oil and gas producing and exporting countries have been carrying out reforms for the last two decades in order to ensure strong economic growth through the expansion of industrial sectors of the economy by producing deep processing products with high added value. The purpose of the research is to study the world experience of oil and gas industry diversification development in order to adapt it to the realities of Kazakhstan Methodology - The work uses statistical, monographic, abstract-logical and other methods of research. Originality/value of the study is to consider the experience of foreign countries in the development of diversification of the oil and gas sector of the economy. Results of the study - The main directions of development of diversification of the oil and gas sector of the country's economy are determined.

Enhancing requirements-to-code traceability with GA-XWCoDe: Integrating XGBoost, Node2Vec, and genetic algorithms for improving model performance and stability

This study addresses the challenge of requirements-to-code traceability by proposing a novel model, Genetic Algorithm-XGBoost With Code Dependency (GA-XWCoDe), which integrates eXtreme Gradient Boosting (XGBoost) with a Node2Vec model-weighted code dependency strategy and genetic algorithms for parameter optimisation. XGBoost mitigates overfitting and enhances model stability, while Node2Vec improves prediction accuracy for low-confidence links. Genetic algorithms are employed to optimise model parameters efficiently, reducing the resource intensity of traditional methods. Experimental results show that GA-XWCoDe outperforms the state-of-the-art method TRAceability lInk cLassifier (TRAIL) by 17.44% and Deep Forest for Requirement traceability (DF4RT) by 33.36% in terms of average F1 performance across four datasets. It is significantly superior to all baseline methods at a confidence level of α¡0.01 and demonstrates exceptional performance and stability across various training data scales.

Enhancing IT Project Portfolio Management Through Dynamic Dashboard Visualization and Integrated Chatbot and Mathematical ML Model for Strategic Decision Making

This project paper presents an innovative approach to enhance IT Project Portfolio Management (IT PPM) using dynamic dashboard visualization and machine learning (ML) techniques. Effective IT PPM is essential for organizations to align IT projects with business objectives and optimize resource allocation. However, conventional methods often lack innovation and real-time visibility, leading to suboptimal decision-making and costly software investments. The objective of this project is to develop a dynamic dashboard visualization system integrated with a machine learning model derived from the Meta-Portfolio Method (MPM). The MPM leverages advanced analytics and historical project data to dynamically allocate resources and prioritize projects based on risk-return profiles. By incorporating the MPM into the dashboard, organizations gain real-time insights into project status, progress, and resource allocation, enabling informed decision-making and proactive portfolio optimization. The ML model derived from the MPM analyzes historical project data, including success rates, resource intensity, criticality, regulatory compliance, and portfolio diversification, to predict optimal project selection and resource allocation. By dynamically adapting to changing market conditions and project dynamics, the ML model facilitates strategic decision-making and enhances portfolio performance. Through the integration of dynamic dashboard visualization and ML-driven decision support, organizations can streamline IT PPM processes, improve project outcomes, and achieve strategic objectives effectively and efficiently. This innovative approach empowers organizations to navigate the complexities of IT project portfolios with agility, foresight, and confidence.

REVENUE FORECASTING SCENARIOS FOR INTERNATIONAL HOTEL CHAINS

The purpose of the study is to develop a system of balanced scorecards for forecasting the income of hotels belonging to international chains. The subject of the study is the system processes of evaluating the effectiveness of the management analysis system, which are determined on the basis of calculating key performance indicators, and as a result of the integral indicator through the analysis and aggregation of individual functional criteria based on comparison with the reference values of the revenue management model for pricing, load planning and its redistribution between revenue centres. Methodology. The study uses the methods of theoretical and logical generalisation. The article describes a set of strategies and tactics used by international hotel chains to manage the demand for hotel services. The results of the article are to form a model for conducting a detailed operational and financial analysis of the hotel enterprise by revenue centres, which contributes to the development of a strategy. The authors have selected a set of indicators that are used to ensure a balanced approach to measuring performance through the indicator method and visual representation in a graphical representation. The paper analyses the performance of international hotel chains over twelve years in order to assess the impact of key factors on their revenues and develop forecasts; the estimated indicators were classified into seven groups: assessment of the average daily revenue per room, room cost, occupancy rate, market share, staff productivity, resource intensity and digitalisation costs. The publication uses the example of international hotel chains to clarify the content and importance of revenue forecasting in the revenue management system.

MAPPING THE NEED FOR A CIRCULAR ECONOMY FOR FUTURE ECONOMIC BENEFITS: POTENTIALS AND PROJECTIONS FOR SMALL AND MEDIUM-SIZED ENTERPRISES IN NORTH MACEDONIA

The circular economy, a concept based on the principles of designing waste and pollution, keeping products and materials in use for a longer time, and regenerating natural systems is still in its infancy in the Western Balkans, especially in many analyses related to the development and future projections of small and medium enterprises. Over the years, there has been a steady increase in per capita waste generation in the region, with the municipal waste recycling rate among economies being very low, while resource intensity as a measure of resource efficiency has remained relatively high. In addition, enterprises in the region are rarely aware of the opportunities of a circular economy, such as reduced environmental impact, more sustainable use, and conservation of natural resources, lower production costs, increased innovation, increased competitiveness, greater resistance to external shocks through reduced dependence on resources or the creation of new markets and jobs. Of particular importance is the identification of key economic sectors that should be directly involved in the process of circular economic transformation. In addition, it is important in the European framework, to map of the good practices that could be transferred to the economy in the Republic of North Macedonia, when it comes to the circular economy. In the framework of this paper, through the application of a methodological framework of content analysis, comparison, synthesis, and generalization, a diagnosis of the current situation regarding a circular economy in North Macedonia will be presented, as well as the identification of priority sectors/areas as the main key for the future perspectives of the circular economy within these society frameworks. It requires the involvement of all key stakeholders, including government institutions, the private sector, civil society, and academia, to apply a comprehensive approach in setting a future roadmap for a circular economy and encouraging a circular transition for the greater benefit of society of the Republic of North Macedonia, with special reference to improving the future of small and medium enterprises.

Configuration optimization and performance analysis of hybrid PV/wind systems in building groups

Distributed hybrid renewable energy is a promising technology for addressing environmental and energy issues, and its performance and system configuration play an essential role in the application. However, the contribution of energy sharing among buildings in building groups to the matching performance has yet to be fully explored. This study established a joint MATLAB-TRNSYS optimization model for the hybrid PV/wind building system to investigate the matching performance of energy consumption in building groups and energy production in PV/wind systems. The optimal system configuration for three objectives, technical, economic, and environmental, was also studied using the multi-objective particle swarm algorithm. Several factors influencing system performance and configuration were considered: renewable energy hybrid, a hybrid of different buildings, intensity of energy use and energy resource, and economic parameters. The results reveal that a hybrid system outperforms a single system, particularly a PV system with a smaller roof area and less installed capacity. Additionally, wind turbines contribute more to system performance than PV panels. Mixing buildings with different heights and functions could also improve system performance by renewable energy sharing in most instances, except multi-story office buildings. Increasing wind resources improves system performance more than solar resources, and increasing energy use intensity negatively impacts system performance for residential buildings but is beneficial for office buildings. Higher grid prices and feed-in tariffs can bring more economic benefits. This study revealed the contribution of energy sharing among different buildings, which could support better application of distributed hybrid renewable energy systems in complex urban environments.

Fertility and tillage intensity affect weed community diversity and functional structure in long-term organic systems.

Knowledge of how agricultural management interacts with weed seed banks and emergent weed communities is crucial for proactive weed management. Though studies have detailed how differences in disturbance and nutrient applications between organic and conventional herbicide-based systems affect weed communities, few have focused on these same factors in contrasting organic systems. This study assessed the seed banks and emergent weed communities from the most recent crop rotation cycle (2017-2022) of a long-term experiment, which compared four organic grain and forage cropping systems differing in nutrient inputs and soil disturbance. The high fertility (HF) system received high-rate nutrient applications, low fertility (LF) received low-rate applications, enhanced weed management (EWM) focused on weed control through frequent soil disturbance, and reduced tillage (RT) prioritized soil health with less intense or frequent soil disturbance. Soil samples for greenhouse germination assays were collected at the beginning (2017) and end (2022) of the rotation to explore how these four systems influenced seed bank dynamics over time. Weed community biomass was also sampled in each crop during this time. Treatment effects on weed abundance, taxonomic diversity, and community-weighted means and functional dispersion of weed traits were analyzed with generalized mixed-effect models. The RT system had the highest weed seed bank taxonomic diversity, and EWM had the lowest. RT and LF had higher functional dispersion of traits than HF in the seed bank. Weed seed bank communities in HF and RT were characterized by short, small-seeded, and early germinating weed species. However, seed banks were also labile: Differences between systems in seed density and all other mean trait values were dependent on the crop, which preceded seed bank sampling. Likewise, differences among emergent weed communities in the four systems depended on an interaction between crop species and their planting year. Results suggest that resource availability and intensity of disturbance act as weed community assembly filters in organic cropping systems. Organic growers seeking to design systems that balance weed management and production goals can use relatively low soil disturbance and nutrient application to increase weed community taxonomic or functional diversity without necessarily increasing weed biomass or seed bank density.

Regarding the influence of national factors on the biotic component of the enterprise's material backpack formation

Relevance of research. Sustainable Development Goal No. 12 refers to reducing the resource intensity of the economy by reducing the costs of material input in production chains. The famous German scientist Friedrich Schmidt-Blick proposed the concept of an ecological backpack as a characteristic of hidden material flows accompanying the entire life cycle of a product. A separate component of the backpack is the biotic part, which was heavy enough when, for example, horse traction was used in old mines. Food self-satisfaction of the staff based on the salary led to the fact that the consumption of food in the production process remained outside the analysis of the company's material flows. The purpose of the study is to prove/refute the validity of the hypothesis regarding the significant dependence of the biotic component of the material backpack on national traditions and the material income of workers of industrial enterprises. Research methods: mathematical statistics. The basis of research is long-term observation of food consumption in Ukraine and abroad, in particular, Poland and China. Main results. In the course of research, certain traditions characteristic of Ukrainian realities were revealed: consumption of food products by the average Ukrainian with a probability of 0.95 is 58.75±0.04 kg per month (698±0.5 kg per year); the structure of the consumer basket in Ukraine contains 33% – milk and dairy products, 15% – vegetables; 14% – bakery products and cereals, 11% – potatoes, 8% – meat and meat products; the average person in Ukraine consumes more food than recommended by the Ministry of Health of Ukraine (by approximately 5%); residents of rural areas consume more food than urban residents (by almost 6% – 57.9 kg per month versus 54.7 kg). It has been statistically proven that the annual volume of food consumption by residents of Ukraine (689 kg) is significantly greater than that of residents of Poland (456 kg) and, even more so, China (413 kg). National differences concerning food preferences have been revealed. Quantitative differences in the national consumption of food products by producers determine the different content of the material backpack of industrial products, which provides certain competitive advantages/disadvantages to the subjects of international market activity. In addition to the national traditions of food consumption, there are laws that determine the influence of the average per capita household equivalent total income on the weight of the food basket. On the basis of official statistical data, a mathematical dependence of the logarithmic form of the amount of food consumption on the average per capita income of a household member is proposed.

The Use of Contribution Analysis in Evaluating Health Interventions: A Scoping Review.

Contribution Analysis (CA) is a promising theory-based evaluation approach for complex interventions, yet its application in health interventions remains largely unexplored. To bridge this gap, we conducted a scoping review to examine the extent of such applications and the methodologies, strengths, and limitations of this approach in health programming. Our comprehensive search strategy was developed and used in 15 databases to identify peer-reviewed articles from 1999 to 2023 that focused on using CA to evaluate health interventions. We then implemented rigorous double- and triple-screening processes for abstracts and full-text papers, respectively. Data were extracted and narratively summarized. Our review found seven relevant studies, which showed that CA has been employed in health promotion programs, health policies, and targeted health issues such as nutrition, cardiovascular disease, substance misuse, and suicide prevention. The studies identified strengths of using CA, including its flexible impact evaluation approach, capacity to inform decision-making, and potential to enhance understanding of health programs and policies. However, challenges such as how to determine suitable evidence levels and how to best manage resource intensity were also identified. The limited number of studies indicates that CA is still a novel approach, whereas the variation in the reporting of the studies suggests that this approach could benefit from more standardized methods and detailed stakeholder engagement strategies.