Consumption Data Research Articles

Aging Retardation of Lead-Acid Batteries by Adjusting Charge Controller Threshold Values in Off-grid Photovoltaic System

Hybrid systems or coupled power plants with energy storage are becoming more and more popular due to the rising need for energy. Researchers have been conducting several experiments to assure longer-lasting battery use due to the increasing widespread use of energy storage and the depletion of resources such as silicon and precious metals. In this study, a simulation study is carried out in PVSyst software on lead-acid batteries, which have a low cycle and a very traditional electrochemical structure. The simulation is realized by scaling Spain’s consumption curve in 2023, taken from the European Network of Transmission System Operators for Electricity (ENTSO-E), together with lead acid batteries and off-grid photovoltaic (PV) modules. After the off-grid system design is created and consumption data is imported, it is aimed to extend battery life by changing the charge-discharge threshold values of the charge controllers. Threshold values are in two categories, high and low, and seventeen different cases are simulated. According to the results of the simulation, the state of charge (SOC) levels of the batteries decreases significantly due to radiation and cloudiness in the winter months and remain high in the spring and summer months. When charge controllers are set to high thresholds levels, they cause gassing currents that rise to an average of 20 amps, while battery life extends up to 5.3 years. When low threshold values are used, negligible levels of oxygen and hydrogen gasses are formed in the battery, but battery life decreases to 4.1 years.

SUSTAINABLE GROWTH THROUGH GREEN ECONOMY: AN ECONOMETRIC ANALYSIS

Objective: In recent years, the growing interest and awareness of environmental issues have once again brought the issue of population growth to the forefront, exerting significant pressure on production. The increased pressure on production and the resultant rise in production have led to a faster trend in consumption. As a natural consequence of both trends, detrimental effects on ecological balance and the environment have begun to emerge. The concept of "green economy" has emerged as an approach to restore the disrupted ecological balance and operationalize this order. This widely spreading concept has begun to generate significant policies at both national and international levels. Method: This study aims to examine the relationship between renewable energy production, consumption, wastewater, and solid waste data, covering the years 1980-2022, using econometric methods to explore the relationship between green economy and economic growth in the context of Turkey. The objective is to analyze the impact of the recent and rapidly increasing transformation towards "green economy" on the long-term trajectory of economic growth in Turkey. Result and Discussion: Based on the findings of the study, it has been determined that, at the 0.05 level of statistical significance, there is no long-term relationship between the variables and economic growth, as well as no long-term significant relationship between any of the variables. When the statistical significance is accepted at the 0.10 level, a significant long-term relationship is identified between the amount of wastewater and renewable energy, with a 1% increase in wastewater leading to a 0.82% increase in renewable energy. Implications: The findings of this study and the transformation towards a green economy, widely covered in the literature, have been recognized in Turkey and an unnamed green transformation movement has begun. As the results of the analysis of the study indicate, the policies introduced for the transition to a green economy and transformation efforts have a positive and significant impact on economic growth in the long term, but the same cannot be claimed for the short term, but in the future, advances in scientific and technological progress and over-specialization will inevitably have positive consequences on global and domestic sustainable development in the short term. Originality/Value: The distinctive feature of this study from similar studies in the literature is that the relationship between the green economy and sustainable growth was analyzed empirically and with five separate econometric models. Recommendations: Turkey needs serious reforms in education in its transition to a green economy. In addition, there is a need for political determination to use environmentally friendly sectors that are more open to innovation and competition, such as renewable energy and green building sectors.

A novel approach to predict buildings load based on deep learning and non-intrusive load monitoring technique, toward smart building

Short-term load forecasting is critical in managing energy production, domestic consumption, and grid integration. Short-term load forecasting is an essential tool for managing and controlling the processes required to make buildings smarter. Short-term load forecasting at this level of the network is extremely difficult due to the high uncertainty in load demand. This study presents a novel approach for predicting building loads using deep learning and non-intrusive load monitoring (NILM) techniques, aimed at enhancing smart building energy management. The methodology involves the integration of Recurrent Neural Networks (RNNs) to effectively capture temporal patterns in power consumption data, which are critical for accurate load forecasting. The model uses a feature matrix created from power signals to improve its forecast accuracy. The results show that the suggested model greatly outperforms standard models, with an overall accuracy of 97.2 %. The model's robustness is proven through comprehensive experiments, which demonstrate higher performance in short-term load forecasts. Furthermore, a comparison with existing models demonstrates the proposed approach's efficacy in recognizing and predicting load fluctuations, making it a useful tool for smart grid applications.

Advanced Therapy Medicinal Products: Availability, Access and Expenditure in Italy.

Advanced therapy medicinal products (ATMPs) are an innovative output of biomedical research, characterized by a high level of uncertainty on long-term efficacy and safety, elevated price tags and often complex administration. All these elements compounded make their European authorization, national price negotiation for reimbursement and subsequent dispensation and administration to the patient less straightforward and often less successful than for less innovative drugs. To assess if these hurdles have affected patient access and how are ATMPs used in Italy, we have analysed availability, access and expenditure of ATMPs in the period spanning from 2016 to 2023. We have analysed real world data on the duration of ATMP regulatory evaluations for authorisation and reimbursement, time to first patient access and expenditure for ATMPs through the Italian National Health System (INHS) expenditure data flow, as well as information on patient mobility and availability of health facilities specialized in administering ATMPs. Of the 18 ATMPs currently authorized in Europe, 9 are reimbursed by the INHS, but only 6 were actually used, generating a cumulative expenditure of roughly 300 Mln€ from 2016 to 2023, largely owing to CAR-T therapies. Time to patient access reaches an average of 340.6 days from the day publication in the official Gazette of the reimbursement decision to first patient treatment in one of the 107 health facilities authorized for ATMP administration, after an even longer evaluation time by regulatory agencies. Since the first reimbursement decision for an ATMP in Italy, back in 2016, these innovative drugs became progressively more and more available, both in terms of numbers and in terms of coverage across the country. Almost all Italian regions have at least one centre for ATMP administration and has performed a treatment in 2023. Notwithstanding their high per-treatment prices, ATMPs currently have a rather contained expenditure, however it is bound to keep growing in the next few years.

ADDITIVE HOLT-WINTERS METHOD FOR FORECASTING GROSS REGIONAL DOMESTIC PRODUCT AT CONSTANT PRICES OF EXPENDITURE OF WEST SUMATRA

Regional disparities often pose significant challenges, with some areas experiencing rapid economic growth while others lag behind. An essential macro benchmark to gauge the success of development initiatives is economic growth, as indicated by changes in a region's Gross Regional Domestic Product (GRDP). Using GRDP at constant prices (GRDP CP) helps eliminate the impact of price fluctuations, focusing on real increases in production activities. Expenditure GRDP reflects the value of goods and services produced within a region and consumed by the community. In the case of West Sumatra, the GRDP CP expenditure data reveals simultaneous seasonal and trend elements. The seasonal pattern, occurring quarterly each year, exhibits an additive seasonal effect. The Additive Holt-Winters method has been proven effective for data containing seasonal patterns with constant seasonal variation (additive) and linear trends, where the level, trend, and seasonal pattern can change. The data used is secondary data of GRDP CP of expenditure quarterly of West Sumatra in 2010 - 2022 obtained from the official website of the Indonesian Central Bureau of Statistics. According to research findings, the GRDP CP expenditure with a Mean Absolute Percentage Error (MAPE) value of 1.03% for quarter I to quarter IV in 2023 are Rp46,284,010.59, Rp46,472,223.99, Rp47,512,197.79, and Rp48,445,184.94, respectively. This suggests that the model equation performs exceptionally well predicting future economic trends.

Structural transformation without industrialization? Evidence from Tanzanian consumers

AbstractExport‐oriented industrialization was central to many Asian countries' structural transformation processes, but many African countries are bypassing industrialization, and the prospects for competing in global manufacturing markets are poor. Alternative structural transformation pathways all rely on domestic markets, and thus understanding their trajectories requires uncovering consumer preferences. Using detailed household expenditures data from a nationally representative panel survey in Tanzania, I estimate a flexible, stylized consumer demand system. I recover estimates of expenditure elasticities of demand for goods, services, and food in the aggregate, along with price elasticities of demand, identified using within‐household variation in prices and expenditures. I find that, across the expenditures distribution and in both rural and urban areas, consumer preferences are service facing. In particular, I show that (1) consumers sharply increase spending on services relative to goods and food as incomes increase; (2) demand for services in the aggregate is somewhat sensitive to changes in service prices; and (3) food, goods, and services are substitutes for each other. On one hand, a high propensity to consume the types of services that generate large local economic growth multipliers is consistent with driving growth in service sector employment. However, sustained growth in the long run may be limited because these services are low productivity and nontradable, and domestic markets are size constrained.

Assessment of antibiotic consumption patterns in hospital and primary healthcare using WHO Access, Watch and Reserve classification (AWaRe) in Sichuan Western China: 2020

BackgroundThe Access, Watch, and Reserve (AWaRe) classification of antibiotics was a useful tool to support antibiotic stewardship. However, the AWaRe patterns of antibiotic consumption in Western China were unclear. We aimed to assess the antibiotic consumption patterns using the AWaRe Classification of public hospitals in Sichuan Province Western China.MethodsAntibiotic consumption data of year 2020 were obtained from the Sichuan Province Drug Use Monitoring Platform. We measured the antibiotic consumption (DDDs per 1,000 inhabitants per day, DIDs), calculated the proportion of antibiotic use, the ratio of Access to Watch antibiotics and patterns of antibiotic use by using drug utilization 90%.ResultsThis analysis included 4452 public health institutions. The antibiotic consumption rate was 10.39 DIDs (Median 8.50, IQR 7.71–12.96). The proportions of Access antibiotic use and Watch antibiotic use were 46.83% (Median 47.49, IQR 44.16–52.02) and 51.20% (Median 51.43, IQR 45.42–54.61), respectively. The Access-to-Watch index was 0.91 (Median 0.92, IQR 0.81–1.15). Amoxicillin (16.85%), cefuroxime (9.21%), cefixime (8.60%%), levofloxacin (8.11%) and metronidazole (6.16%) were the most consumed antibiotics.ConclusionsThe proportion of Access antibiotic consumption in Sichuan Western China has not achieved the WHO target of 60%. Overuse of antibiotic is serious in Sichuan. National and regional antibiotics management systems, stewardship programs and surveillance of antibiotic consumption based on AWaRe classification are needed to improve antibiotic consumption patterns, curb antibiotic overuse and combat antimicrobial resistance in Western China.

Reducing uncertainty of building shape information in urban building energy modeling using Bayesian calibration

This study proposes urban building energy modeling that extends beyond single-building-level models to the urban level. However, most urban building energy models use representative buildings that may not accurately reflect the diversity of building shapes, systems, and envelope performance when conducting building energy evaluations at the urban scale. To address this issue, previous studies have utilized representative buildings and Bayesian calibration to estimate uncertain building information parameters without considering building shape information. Therefore, the primary objective of this study is to estimate building shape information using artificial neural networks and Bayesian calibration based on building energy consumption data to identify the shape information uncertainty of representative buildings. The results indicate that some shape information can be estimated by comparing the overall distribution of the building stock using the two-sample Kolmogorov–Smirnov test. Furthermore, we found that the proposed energy modeling methodology yields energy consumption patterns similar to those of the target building stock. This preliminary investigation addresses the uncertainty of representative buildings in urban-scale modeling, elucidates the relationship between building form and energy consumption, and introduces a method for inferring shape information from energy consumption data.

Study on the concentration, distribution, and persistence of health spending for the contributory scheme in Colombia

Colombia is among the countries with the most robust financial protection against personal health spending in the world, with out-of-pocket spending ranking lowest across OECD countries. We investigate the evolution, distribution, and persistence of health spending by age group, sex, health care setting, health condition and geographic region for over 19 million users of Colombia’s health system between 2013 and 2021 (contributory scheme). We use average patient-level expenditure data from the Health-Promoting Entities of the Ministry of Health and Social Protection. We applied multivariate statistical techniques such as multiple correspondence analysis, factor maps and correlations. For both sexes, average health expenditure increases gradually with age until 60 years, accelerating thereafter abruptly. Health conditions with the highest percentage of expenditure were those related to neoplasms, blood diseases, circulatory system, pregnancy, puerperium and perinatal period. We found that home-based care in Amazonía-Orinoquía is almost non-existent, and that outpatient care represents a high proportion in all age groups (over 65%) compared to the other regions. There is a strong persistence of expenditure from one year to the next (i.e. they can provide relevant information for prediction), especially in areas with a larger supply of health services such as Bogotá-Cundinamarca. To the authors’ knowledge, this is the most comprehensive and detailed micro-analysis of health spending that has been developed for a Latin American country to date.

Use of free preventive dental programme for children: 10 years of Lov'U'r Teeth, France.

Lov'U'r Teeth is a French oral health prevention programme targeting children. The programme offers an appointment with a dentist for oral examination and individual oral health advice. It covers any necessary primary dental care resulting from that examination. Despite being free, it was under-utilised at its previous evaluation 10 years ago. The study aims to identify the determinants of participation and their evolution since 2009. We worked on the General Beneficiary Sample, a medico-administrative database representative of the French population, including health-care consumption data. We selected individuals aged 6 to 15. We performed descriptive statistics and multivariate logistic regressions to analyse and compare the determinants of participation between 2009 and 2019. The final sample included 26 288 children. Lov'U'r Teeth participation rate increased by 4.2 percentage points from 2009 to 2019. Participation was highest at age 6 and declined as age increased. Children from households facing financial difficulties have a 72% lower probability of participating than other children. Over the study period, increasing poverty rates were consistently associated with lower participation. Children living in the areas with the highest poverty rates were up to 46% less likely to participate than those in areas with the lowest poverty rates. Participation in Lov'U'r Teeth has improved over the past 10 years, but the programme continues to reproduce social inequalities, as the determinants of using free preventive dental care mirror those of non-preventive care. Lov'U'r Teeth should enhance communication strategies to reach those in need and clarify the programme's benefits.

Analysis and prediction of energy consumption in office buildings with variable refrigerant flow systems: A case study

Accurately predicting building air-conditioning energy consumption is particularly important for energy management. However, for small datasets, there is a lack of simple and effective prediction methods applicable to multiple buildings. In order to fully explore the energy consumption mode of Variable Refrigerant Flow (VRF) air conditioning system and improve the accuracy of the model. This study uses the weather data of an outdoor meteorological data collection system and the air conditioning system and energy consumption data of an office building in Jinan. Initial analysis revealed that occupant behavior and daily variations in outdoor air temperatures significantly impact the energy consumption of air conditioning systems. Then, by applying the random forest algorithm and Pearson correlation analysis, the study identified daily average outdoor air temperature and VRF indoor unit running time as critical features for prediction. Subsequently, three machine learning models—Multiple Linear Regression (MLR), Back Propagation Neural Network (BPNN), and Long Short-Term Memory (LSTM) neural network—were developed and assessed using a 4:1 training-to-testing set ratio. Comparative analysis revealed that all models performed robustly, with the MLR model exhibited superior predictive accuracy (R2 (Coefficient of Determination) = 0.98, MAE (Mean Absolute Error) = 0.005 kWh/(m2·day), and RMSE (Root Mean Square Error) = 0.007 kWh/(m2·day) for the testing set) and simplicity. Furthermore, the effectiveness of the MLR model in handling small sample sizes was confirmed through cross-validation of data from two additional office buildings, highlighting its suitability for predicting energy consumption of VRF air conditioning systems in office buildings.

Quantitative risk assessment for aflatoxins in beans from northern Tanzania

Abstract The study focussed on quantitative risk assessment for population exposure to aflatoxins by consuming beans from bean farmers in three villages; Long, Sabilo and Seloto in Babati District, Northern Tanzania. Aflatoxin levels were determined in a total of 144 bean samples; a total of 38 samples were collected from harvesting beans, while 106 samples were collected from stored beans. This kind of assessment allows modelling bean consumption data (kg/kg body weight (bw)/day) using an estimated average weight of an adult of 60 kg with previously collected data for total aflatoxin (AF) contamination (μg/kg) beans samples from the 2013/14 survey. Consumption was estimated by using the minimum annual per capita consumption of beans in Tanzania of 13.02 kg (which is equivalent to 35.67 g/day) and the exposure assessment was performed with the @RISK analysis software. Results from the assessment showed exposure for lower bound (LB) having a mean value of 1.06, 0.79 and 0.95 ng/kg bw/day in Long, Sabilo and Seloto villages, respectively, while for the medium bound (MB) the mean values were 1.33, 1.07 and 1.26 ng/kg bw/day, respectively, and for the upper bound (UB) the values were 1.65, 1.38 and 1.58 ng/kg bw/day, respectively. The margins of exposure (MoE) from this study were below 10,000 for all scenarios and this is of public health concern. Even though all samples had aflatoxin contamination below the maximum tolerable limit (MTL) of 4, 10 and 20 μg/kg for aflatoxins in cereals and pulses in the EU, the East African Community (EAC) and the USA, there is still a serious long-term health implication. Meeting MTL will not by itself guarantee food safety, but using the data collected and available on various health effects from aflatoxin and other mycotoxins, it is important to incorporate them into a risk assessment and show how excessive consumption of foods meets MTLs can still carry significant public health risks.

Lifetime healthcare expenditures across socioeconomic groups

BackgroundA socioeconomic gradient affects healthcare expenditures and longevity in opposite directions as less affluent individuals have higher current healthcare expenditures but simultaneously enjoy shorter lives. Yet, it is unclear whether this cross-sectional healthcare expenditure gradient persists from a lifetime perspective. This paper analyzes lifetime healthcare expenditures across socioeconomic groups using detailed individual-level healthcare expenditure data for the entire Danish population.MethodUsing full population healthcare expenditures from Danish registries, we estimate lifetime healthcare expenditures as age-specific mean healthcare expenditures times the probability of being alive at each age. Our data enables the estimation of lifetime healthcare expenditures by sex, socioeconomic status, and by various types of healthcare expenditure.ResultsOnce we account for mortality differences and all types of healthcare expenditures, all socioeconomic groups spend an almost equal amount on healthcare throughout a lifetime. Lower socioeconomic groups incur the lowest lifetime hospital expenditures, whereas higher socioeconomic groups experience the highest lifetime expenditures on long-term care services. Our findings remain robust across various socioeconomic measures and alternative estimation methodologies.ConclusionImproving the health status of lower socioeconomic groups to align with that of higher socioeconomic groups is costly but may ultimately reduce current healthcare expenditures. Enhanced health outcomes likely increase lifespan, leading to extended periods of healthcare consumption. However, since all socioeconomic groups tend to have similar lifetime healthcare expenditures, this prolonged consumption has limited impact on overall lifetime healthcare costs. Additionally, a significant benefit is the deferment of healthcare expenditures into the future. Overall, our results diminish concerns about socially inequitable utilization of healthcare resources while socioeconomic differences in health and longevity persist, even in a universal healthcare system.

Natural Gas Consumption Forecasting Based on Homoheterogeneous Stacking Ensemble Learning

Natural gas consumption is an important indicator of energy utilization and demand, and its scientific and high-accuracy prediction plays a key role in energy policy formulation. With the development of deep neural networks and ensemble learning, a homoheterogeneous stacking ensemble learning method is proposed for natural gas consumption forecasting. Firstly, to obtain the potential data characteristics, a nonlinear concave and convex transformation-based data dimension enhancement method is designed. Then, with the aid of a stacking ensemble learning framework, the multiscale autoregressive integrated moving average (ARIMA) and high-order fuzzy cognitive map (HFCM) methods are chosen as the base learner models, while the meta learner model is constructed via a well-designed deep neural network with long short-term memory (LSTM) cells. Finally, with the natural gas energy consumption data of national and 30 provinces (where the data of Xizang are unavailable) of China from 2000 to 2019, the numerical results show the proposed algorithm has a better forecasting performance in accuracy, robustness to noise, and sensitivity to data variations than the seven compared traditional and ensemble methods, and the corresponding model applicability rate could achieve more than 90%.

A Bag-of-Words Approach for Information Extraction from Electricity Invoices

In the context of digitization and automation, extracting relevant information from business documents remains a significant challenge. It is typical to rely on machine-learning techniques to automate the process, reduce manual labor, and minimize errors. This work introduces a new model for extracting key values from electricity invoices, including customer data, bill breakdown, electricity consumption, or marketer data. We evaluate several machine learning techniques, such as Naive Bayes, Logistic Regression, Random Forests, or Support Vector Machines. Our approach relies on a bag-of-words strategy and custom-designed features tailored for electricity data. We validate our method on the IDSEM dataset, which includes 75,000 electricity invoices with eighty-six fields. The model converts PDF invoices into text and processes each word separately using a context of eleven words. The results of our experiments indicate that Support Vector Machines and Random Forests perform exceptionally well in capturing numerous values with high precision. The study also explores the advantages of our custom features and evaluates the performance of unseen documents. The precision obtained with Support Vector Machines is 91.86% on average, peaking at 98.47% for one document template. These results demonstrate the effectiveness of our method in accurately extracting key values from invoices.

Formal Security Analysis of the OpenID FAPI 2.0 Family of Protocols: Accompanying a Standardization Process

FAPI 2.0 is a suite of Web protocols developed by the OpenID Foundation’s FAPI Working Group (FAPI WG) for third-party data sharing and digital identity in high-risk environments. Even though the specifications are not completely finished, several important entities have started to adopt the FAPI 2.0 protocols, including Norway’s national HelseID, Australia’s Consumer Data Standards, as well as private companies like Authlete and Australia-based connectID; the predecessor FAPI 1.0 is in widespread use with millions of users. The FAPI WG asked us to accompany the standardization of the FAPI 2.0 protocols with a formal security analysis to proactively identify vulnerabilities before widespread deployment and to provide formal security guarantees for the standards. In this paper, we report on our analysis and findings. Our analysis is based on a detailed model of the Web infrastructure, the so-called Web Infrastructure Model (WIM), which we extend to be able to carry out our analysis of the FAPI 2.0 protocols including important extensions like FAPI-CIBA. Based on the (extended) WIM and formalizations of the security goals and attacker model laid out in the FAPI 2.0 specifications, we provide a formal model of the protocols and carry out a formal security analysis, revealing several attacks. We have worked with the FAPI WG to fix the protocols, resulting in several amendments to the specifications. With these changes in place, we have adjusted our protocol model and formally proved that the security properties hold true under the strong attacker model defined by the FAPI WG.

Preventive oral healthcare for children: towards appropriate care

In recent decades, oral healthcare has shifted from curation to prevention. Preventive oral healthcare for children consists mainly of oral health education to improve health behaviors and clinical prevention such as fissure sealants, application of fluoride varnish, removal of supragingival plaque and dental polishing. In addition, the periodic clinical examination is an important preventive instrument. These 5 preventive interventions for children up to 12 years of age can be discussed using the principles of 'appropriate care'. Appropriate care is an approach of the Dutch government to ensure the accessibility, quality and affordability of care in the Netherlands. Central to the approach are the effectiveness, efficiency and patient-centeredness of care. This article describes to what extent preventive oral healthcare meets these principles based on literature, care consumption data and recommendations from the clinical guidelines 'Oral care for children: prevention and treatment of dental caries' and the 'Advice on Caries Prevention' by the Ivory Cross, a Dutch scientific association for oral health. Appropriate oral healthcare is best served by care that is tailored to both the individual oral disease burden of the patient and their risk of disease as well as to the wishes of the patient.

Gender-Based Analysis of Online Shopping Patterns on Shopee in Malaysia: A J48 Decision Tree Approach

The purpose of this study is to investigates the gender differences of Shopee platform for online shopping behavior by using the J48 decision tree algorithm to classify and predict shopping frequency among male and female consumers for Malaysia context. WEKA software was used in this study to analyze the datasets. From the experiments, the majority of Shopee user were female consumers. The findings shows that female consumer behavior is more complicated and more varied regarding purchasing behavior. The study's findings demonstrate the potential of gender specific insights to enhance e-commerce strategies, particularly in product recommendations and targeted marketing. Although the J48 model performed well in predicting male shopping patterns, it was less effective for females, indicating the need for more advanced modeling techniques is used to better capture the complexities of female consumer behavior. This research also emphasizes the significance of using machine learning tools like the J48 decision tree to analyze consumer data, providing valuable insights for improving customer satisfaction and business performance. However, limitations such as sample size and the focus on a single platform suggest that further research is needed, including the exploration of alternative algorithms and broader demographic factors.

СУЧАСНИЙ ІНТЕРНЕТ-МАРКЕТИНГ З ВИКОРИСТАННЯМ ТЕХНОЛОГІЙ ШТУЧНОГО ІНТЕЛЕКТУ

The article discusses the essence and features of modern Internet marketing based on the use of artificial intelligence (AI) technologies. The article examines how AI can help online marketers collect and analyze consumer data, segment and personalize audiences, create and distribute effective content, optimize and automate advertising campaigns, and measure and improve marketing results. The article also analyzes the latest trends in the use of AI technologies by Ukrainian and foreign companies and identifies their advantages.The investigation identified effective AI tools in online marketing, such as chatbots, recommender systems, text generators, sentiment analysers, face and speech recognition systems, etc. The article presents a comparison of AI tools in online marketing of Ukrainian and foreign companies. Foreign companies use modern products that have no analogues in Ukraine. This is due to the popularity of the trend of using AI technologies in foreign companies. In Ukraine, this trend is developing and we can see it in the use of chatbots and CRM systems. They have been implemented by such companies as PrivatBank and Promodo. Despite this, the development of foreign companies is progressive. Previously, only large corporations could afford to use AI technologies, but now young startups are catching up with the trend. AI combined with human capabilities can actually turn business into a universal tool for profit. However, this path is not without obstacles, so the article highlights the challenges of AI that may affect the economic, legal, ethical, and social spheres of life. Scenarios for preventing possible problems allow for the effective use of AI's potential. The article suggests measures that should help society adapt to changes in the socio-economic environment associated with the displacement of humans by AI. It is important to understand the advantages and disadvantages of modern online marketing tools to ensure effective solutions to business problems.

Trends in Antimicrobial Consumption in Tertiary Care Hospitals in Costa Rica from 2017 to 2021: A Comparative Analysis of Defined Daily Doses per 100 Bed Days and per 100 Discharges.

Background: Antimicrobial consumption (AMC) data in Latin America are scarce and usually spread out across different sources used to make AMC calculations, making it difficult to both standardize and compare regions through similar time frames. The main objective was to analyze AMC trends in Social Security tertiary care hospitals in Costa Rica in the period spanning January 2017 to December 2021, using both the defined daily dose (DDD)/100 bed days and DDD/100 discharges. Methods: This is a retrospective observational study of antimicrobial consumption. Global consumption trends were calculated and expressed as DDD/100 bed days and DDD/100 discharges. Trends in antimicrobial consumption were analyzed using a simple linear regression model to determine potential differences in antimicrobial usage throughout the study's duration. Results: A statistically significant increase in the consumption expressed in DDD/100 discharges was observed in the following groups: carbapenems, 7.6% (trend: 64.68, p < 0.0001), trimethoprim-sulfamethoxazole: 12.6% (trend: 16.45, p < 0.0001), quinolones 9.4% (trend: 36.80, p = 0.02), vancomycin 2.0% (trend: 16.30, p = 0.03), echinocandins: 6.0% (trend: 15.17, p = 0.01) and azole antifungals: 12.10% (trend: 102.05, p < 0.0001). Additionally, a statistically significant increase of 10.30% in the consumption of azole antifungals expressed in DDD/100 bed days was observed (p = 0.0008). In contrast, a statistically significant decrease in consumption, expressed in DDD/100 discharges, was identified for cephalosporins -6.0% (p < 0.0001) and macrolides -16.5% (p < 0.0001). Macrolides also showed a downward trend in consumption, as expressed in DDD/100 bed days (-14.3%, p < 0.0001). According to World Health Organization (WHO) access, watch and reserve (AWaRe) classification trend analysis, only the reserve group showed a statistically significant upward change of 9.2% (p = 0.016). Conclusions: This five-year analysis demonstrated trends over time in overall antimicrobial consumption measured in DDD/100 bed days and DDD/100 discharge rates that correlate. In general, for all antimicrobials, after the implementation of antimicrobial stewardship programs (ASP), a downward trend is reported; in contrast, during the COVID-19 pandemic the AMC shows a general upward trend. The comparison between DDD/100 bed days and DDD/100 discharges allows for complementary comparisons to be made regarding antimicrobial exposure in a clinical setting.