United States Market Research Articles

E-Cigarette Brand Trends in the United States: An Investigation of Data From a Youth and Young Adult Sample and the E-Cigarette Retail Market (2022).

Electronic cigarettes (e-cigarettes) remain the most used tobacco product among young people in the United States (US). Given the need for current data on popular e-cigarette products, the current study leverages data from a rapid surveillance survey of young people and examines whether the top e-cigarette brands identified from this source align with US market data. Data were obtained from current e-cigarette users (N = 4145) participating in the Truth Continuous Tracker Online (CTO; a cross-sectional tracking survey of 15-24 year-olds sourced from the national Dynata panel) and NielsenIQ retail scanner data, collected in 2022 and aggregated by quarter (Q1, Q2, and Q3). The top 15 e-cigarette brands were determined from respondents' endorsement in the Truth CTO and ranked total sales in NielsenIQ in nominal dollars. Overall, 58% of e-cigarette brands overlapped across the Truth CTO and NielsenIQ data (60% for Q1, 47% for Q2 and 67% for Q3). Pod-based (JUUL; VUSE) and disposable (Hyde; Breeze Smoke) brands appeared as top brands in both datasets. Top brands were fairly consistent within and across quarters; though, more variability was found in the Truth CTO, relative to NielsenIQ. Many top brands were disposable. Results suggest that data from rapid surveillance and retail data can be used complementarily to characterize the popular e-cigarette brands currently on the US market. Many of these popular e-cigarette brands have yet to receive marketing granted orders under the US Food and Drug Administration, suggesting the need to continue monitoring e-cigarette brands among young people.

The Emergence of the Old Drug Captagon as a New Illicit Drug: A Narrative Review.

First developed in the 1960s in Europe and approved briefly for use in the United States, fenethylline (sold as Captagon, one of its early trade names) is now a prominent drug of abuse in the Eastern Mediterranean Region. The drug was withdrawn from the United States market because of side effects that included hallucinations, visual distortions, and psychosis; it has also been linked to rare cases of myocardial infarction, seizures, and delusions. The chemical synthesis of fenethylline is straightforward and inexpensive. Manufactured in clandestine labs in Southern Europe and the Middle East, these amphetamines had been used by affluent Middle Eastern young people for recreation or study aids. Captagon has periodically emerged as a drug used in combat and conflict, and it was implicated in the 2015 riots in Paris. It has been described as "chemical courage" for combatants giving them focus, energy, and endurance in battle situations. Captagon is addictive but no cases of direct captagon-associated mortality have been reported. The use of drugs in war is nothing new, but captagon is also used heavily in the civilian population in war-torn areas to help them cope with food insecurity and maintain courage in dangerous situations. Captagon production and distribution drives the Syrian economy, but the drug's use is limited to certain regions and is rarely seen in North America. The drug is available online, but product may be contaminated with the inclusion of procaine, caffeine, or other substances.

Analysis of Indonesia coffee exports competitiveness in the United States and Japan to promote sustainable market

Coffee is one of the main export commodity in Indonesia which has contributed to the foreign exchange. Indonesia is the fourth largest coffee producer in the world after Brazil, Vietnam and Colombia. The aim of this research was to analyze the level of competitiveness of Indonesian coffee exports to the United States and Japan markets. Data sources were obtained from the Indonesian Central Statistics Agency (BPS), UN Comtrade and Trade MAP. The data used is secondary time series data over a period of 5 years (2018-2022). The research method used Revealed Comparative Advantage (RCA) to calculate the level of competitiveness of Indonesian coffee exports to the United States and Japan markets. The research results showed that Indonesian coffee has a stronger competitiveness in the United States market compared to the Japanese market. However, when compared with Colombia, Brazil and Guantemala, the value of Indonesian RCA in the United States and Japanese markets is still far behind. Therefore, it is necessary to increase the production for sustainable coffee exports in the United States and Japan markets.

Export competitiveness of Indonesian coffee in the United States market

Indonesia’s economic growth has slowed down during the pandemic. Coffee is a foreign exchange-producing agricultural commodity for Indonesia and has become a pillar of the national economy because of its contribution to the gross domestic product. The purpose of this study was to analyse the competitiveness of Indonesian coffee exports in the United States market. The methods employed included the Revealed Comparative Advantage, which can measure export competitiveness comparatively, and the Export Product Dynamic method to analyse and map the position of export competitiveness. The analysis results were also measured to compare the export competitiveness of Vietnamese coffee. The findings showed that Indonesian coffee and Vietnamese coffee in the United States market have a value above 1, proving that both have export competitiveness advantages. The average Revealed Comparative Advantage value of Indonesian coffee is higher than that of Vietnamese coffee, meaning that Indonesian coffee exports are superior to Vietnamese coffee exports to the United States. Meanwhile, the results of the Export Product Dynamic analysis showed that the competitiveness of Indonesian coffee exports is in the position of Lost Opportunity, where the position has shifted from the pre-pandemic position, namely Retreat. Meanwhile, the competitiveness of Vietnamese coffee exports was in the Rising Star position, which shifted from the previous position of Lost Opportunity. Lost Opportunity of Indonesian coffee shows that the strength of Indonesia’s coffee commodity business is weak, but commodity exports are still competitive. The analysis results indicated that even though Indonesia’s coffee has export competitiveness, it is not competitive enough in the United States. This study provided an overview of the export competitiveness of Indonesian coffee in the United States, which can be used as a basis for determining strategic steps in an effort to dominate the market to obtain trade benefits

USPTO's Lax Policy Leads to Humira Formulation Thicket.

Biosimilar drugs enter the United States market well after they enter the European market. That is likely because pharmaceutical companies have many more patents in the United States than in Europe. But why is patent coverage of biological drugs so much more extensive in United States? This case study seeks to answer this question for drug formulation patents.

Analysts’ Advice on IPOs and Regulations: An Analysis of United States and European Markets

Using recommendations and target prices on initial public offerings (IPOs), we examine the impact of regulations in the United States (US) and European Union (EU) markets that were aimed at curbing conflicts of interest in sell-side research. Conflicted analysts, proxied by whether their brokerage houses (henceforth brokers) acted as lead or co-managers in the IPO process, issued more optimistic recommendations in the US and EU markets in the pre-regulatory period. However, this extra optimism is absent after the adoption of regulations. A similar pattern emerges when examining the returns implied from US IPO target prices. Investors seem to capture the pattern, as they discount optimistic recommendations from conflicted analysts before, but not after, the new regulations. Using the staggered implementation of the new regulations—whereby US regulations take place strictly before any changes in the EU markets—we show that US brokers take the new modus operandi to Europe. In the time between the passage of US and EU regulations, US brokers in the lead role acted similarly in Europe to how they acted in the US. The EU brokers continued with their optimistic projections until the implementation of the local regulations.

Quality Evaluation of Turmeric Dietary Supplements in the United States Market Using a Quantification and UHPLC-HRMS Metabolite Ratio Approach

Quality Evaluation of Turmeric Dietary Supplements in the United States Market Using a Quantification and UHPLC-HRMS Metabolite Ratio Approach

The takeover Tango: Unraveling the impact of state-owned enterprise acquisitions on American competitors

When foreign state-owned enterprises (SOE) enter the United States (US) market by acquiring domestic targets, they increase competition in the targeted industry and pressure domestic competitors. Competitors should respond to such acquisitions by changing their behavior. Using acquisition data from 2010 to 2018, we examine the impact of acquisitions by foreign SOEs on the US competitors of acquisition targets. We analyze the initial response to acquisition announcements, the attempts of industry incumbents to increase their political connections, and the change in profitability and efficiency of such incumbents. Investors respond most negatively to SOE acquisitions when the domestic competitor is politically connected, implying such firms have the most to lose from such acquisitions. The US target firms can access resources from SOEs to offer better and cheaper products and thus increase their market share. Our analysis shows that competitors’ profitability and lobbying activity decrease while efficiency increases after SOE acquisitions. These results indicate that SOE acquisitions force domestic competitors to adapt to a changing competitive environment.

Greenhouse gas emissions embodied in the U.S. solar photovoltaic supply chain

Solar photovoltaic (PV) electricity is considered to be an important source of electricity generation in the quest for net-zero carbon emissions. However, the growth of solar electricity is creating both increased material demands and increased greenhouse gas (GHG) emissions from silicon and PV manufacturing (also referred to as embodied GHG emissions of solar electricity). Here we analyze the silicon and solar PV supply chain for the United States (U.S.) market and find that the embodied GHG emissions of solar PV panel materials (such as silicon), manufacture, logistics, and installation in the U.S. given the current supply chain are 36 g CO2e kWh−1 of solar electricity generated. Eighty-five percent of the embodied GHG emissions are from PV panel production processes in China and other Asia–Pacific countries. Moving the silicon and PV manufacturing to the U.S. would reduce the embodied GHG emissions of solar electricity by 16% from its current level, primarily because of the lower GHG emission intensity of the U.S. electrical grid and the lower GHG emissions for aluminum electrolysis in North America. Future scenario analysis shows that by 2030, with the U.S. PV domestic supply chain and its decarbonized grid electricity and aluminum production, as well as improving PV conversion efficiency, the embodied GHG emissions of solar electricity in the U.S. will be reduced to 21 g CO2e kWh−1.

Analysis of Eight Types of Plant-based Milk Alternatives from the United States Market for Target Minerals and Trace Elements.

A wide variety of commercial plant-based foods that are marketed and sold as alternatives for milk (plant-based milk alternatives or PBMAs) are available to consumers. In this study, PBMAs from the United States (n=85) were subjected to analysis for target minerals (magnesium, phosphorus, selenium, and zinc) to compare their variability across PBMA types, brands, and production lots. Samples were also screened for the environmental contaminant elements arsenic, cadmium, and lead. The eight PBMA types sampled were produced from almond, cashew, coconut, hemp, oat, pea, rice, and soy. Elemental analysis was conducted using microwave-assisted acid digestion followed by inductively coupled plasma-mass spectrometry. The results showed that pea PBMAs contained the highest mean amounts of phosphorus, selenium, and zinc, while soy PBMAs were highest in magnesium. Mean amounts of minerals were lower than those found in milk for the majority of PBMA types. There was significant variation (P<0.05) in amounts of minerals across the majority of product brands. The amounts of phosphorus and magnesium varied across production lots (P<0.05), but the absolute value of these differences was low. Total arsenic was highest in rice PBMAs; amounts of cadmium and lead across PBMAs were generally found at low or non-quantifiable amounts. These results underscore the importance of generating analytical data on the elemental composition of products within the rapidly growing category of PBMA.

Nintedanib as the First Treatment for Group of Progressive Interstitial Lung Diseases: A Review of Patent Literature.

Nintedanib is a competitive inhibitor of non-receptor tyrosine kinase (nRTKs) and receptor tyrosine kinase (RTKs). Nintedanib is a substrate for the P-glycoprotein transporter, which returns ingested substances to the gastrointestinal lumen. At present, Nintedanib is being prescribed for individuals diagnosed with idiopathic pulmonary fibrosis (IPF). This assessment provides a concise overview of the latest patents pertaining to Nintedanib. The patents covered in this analysis are categorized into sections, including patents related to the active ingredient, polymorph, formulation, and treatment method. The purpose of this compilation is to offer researchers convenient access to all the relevant patents in a single resource. Information was collected from diverse web databases, including both paid and free sources. Paid databases, such as Orbit® and SciFinder® were utilized as examples. On the other hand, free databases, such as the European Patent Office's Espacenet®, WIPO Patentscope® the Indian patent database, and Google Patents, were also accessed for data gathering purposes. The orange-book listed patents for Nintedanib are set to expire in July 2029. These patents explore various excipients to address the solubility issue in the long-term storage of the formulation. However, despite these efforts, there is still a need for further research to enhance the properties of the Nintedanib formulation. Extensive research has been conducted on multiple methods for manufacturing Nintedanib and its formulations. This dynamic study has the potential to create opportunities for numerous generic companies to enter the United States market. This, in turn, will improve healthcare accessibility by lowering costs.

Reinforcement learning methods for network-based transfer parameter selection

A significant challenge in self-driving technology involves the domain-specific training of prediction models on intentions of other surrounding vehicles. Separately processing domain-specific models requires substantial human resources, time, and equipment for data collection and training. For instance, substantial difficulties arise when directly applying a prediction model developed with data from China to the United States market due to complex factors such as differing driving behaviors and traffic rules. The emergence of transfer learning seems to offer solutions, enabling the reuse of models and data to enhance prediction efficiency across international markets. However, many transfer learning methods require a comparison between source and target data domains to determine what can be transferred, a process that can often be legally restricted. A specialized area of transfer learning, known as network-based transfer, could potentially provide a solution. This approach involves pre-training and fine-tuning "student" models using selected parameters from a "teacher" model. However, as networks typically have a large number of parameters, it raises questions about the most efficient methods for parameter selection to optimize transfer learning. An automatic parameter selector through reinforcement learning has been developed in this paper, named "Automatic Transfer Selector via Reinforcement Learning". This technique enhances the efficiency of parameter selection for transfer prediction between international self-driving markets, in contrast to manual methods. With this innovative approach, technicians are relieved from the labor-intensive task of testing each parameter combination, or enduring lengthy training periods to evaluate the impact of prediction transfer. Experiments have been conducted using a temporal convolutional neural network fully trained with the data from the Chinese market and one month's US data, focusing on improving the training efficiency of specific driving scenarios in the US. Results show that the proposed approach significantly improves the prediction transfer process.

(Digital Presentation) Data-Driven Prognosis of Lithium-Ion Batteries Thermal Runaway Early Warning and Detection

Li-ion batteries (LIBs) are becoming ubiquitous in portable, stationary, and electrical energy storage applications that power various devices, from cell phones to the zero-emission plane. Market research suggests the $26.9 billion-a-year (2018) worldwide LIB market could reach $107 billion-a-year by 2025. Taking electric vehicles (EVs) for example, according to the Electric Drive Transportation Association (EDTA), the number of EV sales in the United States market has increased from 345 vehicles in 2010 to 331,617 in 2019, with a total cumulative number of 1.44 million vehicles over the ten-year sales period. This trend has also been observed in the global market as EVs' sales reached 2.1 million worldwide in 2019, boosting the stock to 7.2 million. Although the COVID-19 pandemic dramatically affected the global EV market and prompted the market to plummet over the year 2020 relative to 2019. The global EVs stock projections are expected to increase from 7.2 million to 14 million in 2025 and 25 million in 2030, accounting for 10% of global passenger vehicle sales in 2025 and 28% in 2030. Despite rapidly skidding into the EV era - a technical fait accompli- significant obstacles to vehicle electrification and adoption, especially the safety of their batteries, continue to threaten that future. The risk of anomaly generation and evolution (e.g., thermal runaway and exploration) is currently a major safety concern and challenge in applications powered by LIBs.The traditional first-principle simulations and comprehensive experiments are time-consuming or less effective in detecting and predicting the above problems induced by a synergistic effect of multiple degradation mechanisms. The above challenge eventually necessitates predictive or prognostic capability for Prognostics and Health Monitoring (PHM) under a complexly hostile working environment.This proposal will break away from existing practices, and instead, for the first time, the proposed data-driven prognosis (DDP) requires the assumption of the existence of a conservation principle but does not require a priori knowledge of the key mechanisms and boundary conditions. Alternatively, the exact form of the conservation functional is only specified locally, in the neighborhood of each observation point (and is assumed to be piecewise quadratic in the current work), whereas its global form remains unknown. The conservation principle is then defined as the minimization of system curvature at each observation point. The DDP methodology is based on the local curvature of the mathematical manifold representing the Euler-Lagrange governing equation of a system. The curvature represents a derivative of the total energy with respect to sensed variables, and instability is anticipated whenever the curvature exceeds a critical threshold, denoted by the inverse of the local length-scale of the manifold (or its extrinsic curvature). The length-scale, in turn, is calculated by solving the first moment of the state-space description (conservation of linear momentum) based on two consecutive time instants of measurements. In another sense, the DDP technique can estimate a mass normalized Energy Exchange Amplitude (EEA) that also represents a scaled “entropy rise” of the system. After acquiring the input data from the literatures, they were fed into DDP in a continuous order separated by each time step. At each time step, two consecutive time instances were considered that are sufficient to make the prognostication. The results of the simulation using the DDP are shown below. First the progression of PDI which shows the percentage of points that have reached different path dependence index (PDI) markers as shown in Figure 1 (a). It signifies the instants when the battery shows local instabilities. As shown in the figure, battery starts to show local instability from time instant 20 and beyond, highlighting the instability's progression. The chain length (CL) values and residual curvature information were analyzed as well and shown in Figure 1 (b) along with other criterias. As illustrated in the Figure, at time instant 10, the system crosses the critical chain length threshold and intensifies around time instance 20. Furthermore, the residual curvature (RC) value exceeds the critical threshold at time instance 19 and 21. The moment when all three criterias were met, the battery is expected to fail which is time instance 21 as shown in the Figure. Figure 1

Analisis Daya Saing Ekspor Biji Kopi Indonesia dan Vietnam di Pasar Amerika Serikat

Coffee is one of the products from Indonesia's leading agricultural sector. Coffee participates greatly in Indonesia's export activities, especially to the United States. But so is Vietnam. The purpose of this study was to analyze the competitiveness of Indonesian and Vietnamese coffee beans in the United States market as the largest importing country for Indonesian coffee beans. The research method used is panel data analysis taken from secondary data. The analysis uses two methods, namely Revealed Comparative Advantage (RCA) analysis and also Constant Market Share Analysis (CMSA). The results show that Indonesia and Vietnam have a comparative advantage in several types of coffee commodities. In general, the results from the CMSA also show that the competitiveness of Indonesian coffee is positive, indicating that Indonesia is able to take a share of the coffee bean market in the 2016-2018 period.

Asymmetric causality between Bitcoin and tech stocks in the US market using mixed frequency data

PurposeThis study is the first to investigate the causal relationship between Bitcoin and equity price returns by sectors. Previous studies have focused on aggregated indices such as S&P500, Nasdaq and Dow Jones, but this study uses mixed frequency and disaggregated data at the sectoral level. This allows the authors to examine the nature, direction and strength of causality between Bitcoin and equity prices in different sectors in more detail.Design/methodology/approachThis paper utilizes an Unrestricted Asymmetric Mixed Data Sampling (U-AMIDAS) model to investigate the effect of high-frequency Bitcoin returns on a low-frequency series equity returns. This study also examines causality running from equity to Bitcoin returns by sector. The sample period covers United States (US) data from 3 Jan 2011 to 14 April 2023 across nine sectors: materials, energy, financial, industrial, technology, consumer staples, utilities, health and consumer discretionary.FindingsThe study found that there is no causality running from Bitcoin to equity returns in any sector except for the technology sector. In the tech sector, lagged Bitcoin returns Granger cause changes in future equity prices asymmetrically. This means that falling Bitcoin prices significantly influence the tech sector during market pullbacks, but the opposite cannot be said during market rallies. The findings are consistent with those of other studies that have established that during market pullbacks, individual asset prices have a tendency to decline together, whereas during market rallies, they have a tendency to rise independently. In contrast, this study finds evidence of causality running from all sectors of the equity market to Bitcoin.Practical implicationsThe findings have significant implications for investors and fund managers, emphasizing the need to consider the asymmetric causality between Bitcoin and the tech sector. Investors should avoid excessive exposure to both Bitcoin and tech stocks in their portfolio, as this may lead to significant drawdowns during market corrections. Diversification across different asset classes and sectors may be a more prudent strategy to mitigate such risks.Originality/valueThe study's findings underscore the need for investors to pay close attention to the frequency and disaggregation of data by sector in order to fully understand the true extent of the relationship between Bitcoin and the equity market.

Herding in the Australian stock market during the era of COVID-19: the roles of liquidity, government interventions and mood contagion

PurposeThis study explores the economic impact of the COVID-19 crisis on herding behaviour in the Australian equity market by considering liquidity, government interventions and sentiment contagion.Design/methodology/approachThis study utilizes a daily dataset of the top 500 stocks in the Australian market from January 2009 to December 2021. Both predictive regression and portfolio approaches are employed to consider the impact of COVID-19 on herding intention.FindingsThis study confirms that herding propensity is more pronounced at the beginning of the crisis and becomes less significant towards later phases when reverse herding is more visible. Investors herd more toward sectors with less available information on financial support from the government during the financial meltdown. Conditioning the stock liquidity, herding is only detectable during highly liquid periods and high-liquid stocks, which is more observable during the initial phases of the crisis. Further, the mood contagion from the United States (US) market to Australian market and asymmetric herding intention are evident during the pandemic.Originality/valueThis is the first study to shed further light on the impact of a health crisis on the trading behaviour of Australian investors, which is driven by liquidity, public information and sentiment. Notwithstanding the theoretical contributions to the prior literature, several practical implications are proposed for businesses, policymakers and investors during uncertainty periods.

Climate and environmental data contribute to the prediction of grain commodity prices using deep learning

AbstractBackgroundGrain commodities are important to people's daily lives and their fluctuations can cause instability for households. Accurate prediction of grain prices can improve food and social security.Methods &amp; MaterialsThis study proposes a hybrid Long Short‐Term Memory (LSTM)‐Convolutional Neural Network (CNN) model to forecast weekly oat, corn, soybean and wheat prices in the United States market. The LSTM‐CNN is a multivariate model that uses weather data, macroeconomic data, commodities grain prices and snow factors, including Snow Water Equivalent (SWE), snowfall and snow depth, to make multistep ahead forecasts.ResultsOf all the features, the snow factor is used for the first time for commodity price forecasting. We used the LSTM‐CNN model to evaluate the 5, 10, 15 and 20 weeks ahead forecasting and this hybrid model had the lowest Mean Squared Error (MSE) at 5, 10 and 15 weeks ahead of prediction. In addition, Shapley values were calculated to analyse the feature contribution of the LSTM‐CNN model when forecasting the testing set. Based on the feature contribution, SWE ranked third, fifth and seventh in feature importance in the 5‐week ahead forecast for corn, oats and wheat, respectively, and 7–8 places higher than total precipitation, indicating the potential use of SWE in grain price forecasting.ConclusionThe hybrid multivariate LSTM‐CNN model outperformed other models and the newly involved climate data, SWE, showed the research potential of using snow as an input variable to predict grain prices over a multistep ahead time horizon.

Cost-effectiveness of ColoSTAT for primary colorectal cancer screening in the United States.

e15525 Background: Colorectal cancer (CRC) screening is at the cornerstone of strategies for reducing the mortality, healthcare and societal cost burden of CRC, the third most common cancer in the United States (US). ColoSTAT immunoassay is a simple, highly accurate, minimally invasive blood-based screening test for CRC that will be launched in the US market. Using a de novo decision analytic model, we compared the health benefits and cost-effectiveness of CRC screening with ColoSTAT with current screening offered by private insurers assuming a 5-yearly colonoscopy (COL) or biennial fecal immunochemical test (FIT) for the US population. Methods: For the three screening strategies, the expected number of CRC cases detected, and total screening program cost were estimated using a decision-analytic model based on: published CRC incidence in the US, tests performance characteristics, adherence rates to screening (61% for COL; 46% for FIT) and to diagnostic assessment via COL after a positive FIT or ColoSTAT test (80.4%). The gain in quality-adjusted life year (QALY) associated with every CRC case detected through screening, and confirmed by diagnostic assessment, was estimated by linking US-specific estimates of: CRC deaths by age-subgroups, life expectancies at the time of CRC death, quality of life projections for the general population and reduction in CRC mortality associated with screening. Healthcare cost savings associated with every CRC case detected through screening were estimated based on the distribution of CRC across disease stages by diagnosis method (screening versus symptomatically) and stage-specific CRC treatment costs. Results: Biennial screening with ColoSTAT, compared with 5-yearly COL screening offered by private insurers, is estimated to generate cost savings on screening programs of around $40 million per 100,000 people of screening age (PoSA) per one-off screening. When scaled up to the total US population within screening age covered by private insurance (66%), the estimated cost savings with ColoSTAT exceed $30 billion per one-off screening. The reduction in the number of CRC cases detected would generate a modest loss of 58 discounted QALYs per 100,000 people, that could be mitigated if ColoSTAT were to boost screening uptake. In the context of government-sponsored screening programs, replacing FIT with ColoSTAT is estimated to increase the number of CRC cases detected via screening by 9% (to 50 cases per 100,000 PoSA), assuming an unchanged screening uptake of 46%. At the commonly used value of $100,000 per QALY, this would equate to a discounted benefit of $834,820 per 100,000 PoSA per one-off screening. Conclusions: CRC screening with ColoSTAT as an alternative to COL could result in multi-billion-dollar cost savings to US private insurers while still detecting most CRC cases and generate population-health gains if used as an alternative to FIT in government-sponsored screening programs.

ANALYSIS OF INDONESIAN COFFEE EXPORT DEMAND IN THE UNITED STATES USING THE AIDS MODEL

Indonesia is the fourth largest coffee producing country in the world after Brazil, Vietnam and Colombia. Of the total production, around 67% of the coffee is exported while the rest (33%) is used to meet domestic demand. The level of domestic coffee consumption based on the results of the 1989 LPEM UI survey was 500 grams/capita/year. Coffee entrepreneurs estimate that coffee consumption in Indonesia has reached 800 grams/capita/year. Likewise, within 20 years the increase in coffee consumption has reached 300 grams/capita/year. (AEKI 2022). competition research on Indonesian coffee in the United States market by using the Almost Ideal Demand System (AIDS) method. The use of the AIDS model is used to analyze the position and level of competition between coffee exporting countries (Indonesia, Vietnam, and Brazil and Colombia) from each export destination country, namely the United States. the estimation results on the AIDS model of Indonesian coffee in the United States market get an R-Square value of 0.4136 percent. Vietnam in the United States market obtained an R-Square value of -0.216 . Brazil in the United States market obtained an R-Square value of 0.44%. Columbia in the United States market obtained an R-Square value of 0.4488%. Coffee from Indonesia competes with coffee from Vietnam, Brazil and Columbia. The cross-price elasticity obtained between the price elasticity of Indonesia-Vietnam coffee is positive at 0.9443, and the price elasticity value of Indonesia-Brazil coffee is 0.0330, the price elasticity value for Indonesia-Columbia coffee is 0.5854. The expenditure elasticity values obtained by Indonesian coffee were 0.4479, Vietnam coffee were 1.7403, the expenditure elasticity values for Brazil and Columbia coffee were 1.0155 and 0.9042. in the United States Market.

E-cigarettes and asthma in adolescents.

E-cigarettes have been long purported to be a mechanism of harm reduction in current smokers. However, market expansion to adolescents has been aggressive, despite government interventions. Research examining the adverse effects of e-cigarettes in teens with asthma has been limited. We discuss the most recent data on the pulmonary manifestations of e-cigarettes use and exposure in adolescents with asthma. Adolescents with asthma are more likely to be e-cigarette users than those without asthma and more likely to have asthma exacerbations. Increased pulmonary inflammatory cytokines have been seen in e-cigarette users and mouse models. Yet, providers are not confident in e-cigarette screening and counselling despite acknowledging adolescents are using e-cigarettes regularly. Since the introduction of e-cigarettes into the United States market in 2007, adolescents use of these products has risen, even after a brief decline during the height of the COVID-19 pandemic. This review will describe the most recent studies on e-cigarette use trends, cytotoxicity of e-cigarette aerosol and associations with the diagnosis and symptoms of asthma. Knowledge gaps, advocacy efforts, evidence on e-cigarette cessation will be highlighted.