Surge Of Activity Research Articles

Pesticide Exposure and Effects on Non-Apis Bees.

Bees are essential pollinators of many crops and wild plants, and pesticide exposure is one of the key environmental stressors affecting their health in anthropogenically modified landscapes. Until recently, almost all information on routes and impacts of pesticide exposure came from honey bees, at least partially because they were the only model species required for environmental risk assessments (ERAs) for insect pollinators. Recently, there has been a surge in research activity focusing on pesticide exposure and effects for non-Apis bees, including other social bees (bumble bees and stingless bees) and solitary bees. These taxa vary substantially from honey bees and one another in several important ecological traits, including spatial and temporal activity patterns, foraging and nesting requirements, and degree of sociality. In this article, we review the current evidence base about pesticide exposure pathways and the consequences of exposure for non-Apis bees. We find that the insights into non-Apis bee pesticide exposure and resulting impacts across biological organizations, landscapes, mixtures, and multiple stressors are still in their infancy. The good news is that there are many promising approaches that could be used to advance our understanding, with priority given to informing exposure pathways, extrapolating effects, and determining how well our current insights (limited to very few species and mostly neonicotinoid insecticides under unrealistic conditions) can be generalized to the diversity of species and lifestyles in the global bee community. We conclude that future research to expand our knowledge would also be beneficial for ERAs and wider policy decisions concerning pollinator conservation and pesticide regulation.

A Proposal for an Open-Source Bitcoin Forensics Tool

Over recent decades, the global financial paradigm has experienced significant transformations, notably the emergence and adoption of cryptocurrencies. The escalating prominence of assets like Bitcoin has inadvertently catalysed a surge in illicit activities associated with the currency. Consequently, the forensic examination of transactions within blockchains becomes imperative for the detection and surveillance of malevolent undertakings. This research delineates a preliminary pipeline for a Bitcoin forensic analysis tool. Moving forward, the ambition is to conceptualize and empirically validate this tool utilizing data procured from blockchain and ancillary sources. The methodology will harness Open-Source Intelligence (OSINT), clustering of Bitcoin addresses, and an exhaustive financial analysis. Upon finalizing the pipeline, the implementation of an open-source instrument is envisioned, poised to confer substantial advantages to the broader cryptocurrency milieu.

Analysing the viral conversation: sentiment analysis of hop latent viroid’s presence in cannabis on twitter

The medical cannabis industry has witnessed remarkable growth and is considered pivotal in modern healthcare. However, the emergence of the "duds" disease, linked to the hop latent viroid (HLVd), threatens this growth, potentially affecting both the yield and the potency of cannabis strains. Viroids, specifically HLVd, pose a significant threat to the agricultural sector, especially in cannabis cultivation. Recent studies highlight a staggering potential contamination rate of up to 90% in certain cultivation sites, translating to potential annual losses of around $4 billion. Addressing HLVd's impact is imperative not just for economic reasons but also to ensure the quality and safety of cannabis products. This study harnesses sentiment analysis to discern public opinion on this issue, utilizing the Streamlit Python library to analyse tweets related to the Hop Latent Viroid and its impact on cannabis. Spanning April 2021 to September 2023, 347 tweets from 190 contributors were examined, revealing a near-equal distribution of positive, negative, and neutral sentiments. Interestingly, periodic surges in related Twitter activity were identified over the years studied. The significance of sentiment analysis is amplified when contextualized for the cannabis industry: it can offer insights into grower and consumer sentiments, product perception, competitive landscapes, and targeted demographic profiles. Furthermore, by staying attuned to such real-time sentiments, stakeholders can adapt dynamically, ensuring product quality and sustaining trust. Ultimately, this analysis underscores the potential of sentiment analytics in guiding the cannabis industry through challenges such as HLVd, emphasizing the need for businesses to remain proactive and responsive to evolving consumer needs and perceptions.

Interplay of magnetism and band topology in Eu[formula omitted]Ca[formula omitted]Mg[formula omitted]Bi[formula omitted] (x=0, 0.5, 0.67) from first principles study

Recent discovery of the time reversal symmetry breaking magnetic Weyl semimetals (WSMs) has created a huge surge of activities in the field of quantum topological materials. In this work, we have studied systematically various magnetic orders, electronic structure and the interplay between the magnetic order and band topology in one such material, EuMg2Bi2 (EMB) and its Ca doped variant using first principle methods within the framework of density functional theory (DFT). Our thorough investigation reveals the existence of various magnetic order driven topological phases (for example, topological insulator in the A-type antiferromagnetic (A-AFM) phase with Eu moments aligned along the crystallographic a or b direction, Dirac semimetal in the A-AFM phase when Eu moments are parallel to c direction and Weyl semimetal in the ferromagnetic (FM) phase with Eu moments pointing along the c direction) in this material. These phases are found to be energetically very close and hence are expected to be tunable from one to the other by an external handle such as magnetic field or chemical substitution. Most importantly, we observe the existence of a single pair of Weyl points (WPs) connecting valence and conduction band very close to the Fermi level (FL) along Γ-A direction in the FM state of EuMg2Bi2 with Eu moments aligned along the c direction making it an ideal Weyl semimetal in its FM state exactly similar to EuCd2As2. On doping 50% and 67% Ca at Eu sites, we observe the single pair of WPs to move even closer to the FL which is highly desirable for application purposes. Further, we observe that the separation between the WPs in the pair decreases in doped compounds compared to that in the parent compound which has direct consequence on anomalous Hall conductivity (AHC). Our first principles calculation of AHC shows high peak values exactly at these WPs and the peak height decreases when we dope the system with Ca. Therefore, Ca doping can be a good external handle to tune AHC in this system.

The surge of activity in relations between North Korea and Russia

North Korea’s leader attended a summit with Russian President Vladimir Putin as part of a week-long trip to Russia’s Far East that apparently focused on weapons exchanges and other cooperation initiatives, although the countries did not announce any firm agreements that resulted from the visit.

Impact of interbasin transfer hydroelectric project on downstream aquatic habitat

AbstractThe recent surge of anthropogenic activities like damming is causing a severe threat to the aquatic ecosystem. A scope of minimizing the degradation of biota exists by modifying the dam operation policy. This can be achieved by releasing a minimum environmental flow (EF), which further requires more scientific investigations. The present study investigated the impact of an interbasin transfer hydropower dam on the downstream habitat of an endangered fish species, Tor putitora, using an adaptive modelling framework. The framework consists of coupling a reservoir operation and a hydrodynamic model. This study analyses the scope of releasing different EFs from a hydropower dam to meet the environmental flow requirement (EFR) at downstream with satisfactory power production efficiency. The EFR was estimated using flow duration curve and Tennant's method. A lean period inflow hydrograph was incorporated into the model with a series of alternate EFs. Two scenarios were explored, with 4 and 6 h of peaking hours, along with the three cases of different initial reservoir storage. The total usable area over the river reach of the mentioned fish species was evaluated from the flow parameters simulated by the hydrodynamic component of the model. The power production efficiencies and area utilization rates were assessed in all these cases to prepare an easy‐to‐use decision support tool in a tabular form depicting the possible states of EF. The generalized methodology adopted in this analysis will help the decision‐makers quantify EF to be released from the dam within an acceptable power generation efficiency.

Formation and Politicisation of Russian Crime at the Turn of the XIX–XX Centuries

The article is devoted to the problem of formation of organised crime in pre-revolutionary Russia in the context of political, socio-economic, moral and ideological transformations of the turn of the XIX — early XX centuries. The author analyses the reasons for the surge of criminal activity in this period, the specifics of the thieves’ world and its priorities, the process of organisational formation of criminal communities, their “rules of the game” and hierarchy. The question of the interaction between “criminal” and “political”, their tactical and strategic alliances, the influence of the atmosphere of ideocracy on the politicisation of the criminal element in the last decades of the Russian Empire is studied separately.

Лингвистическая экспертиза текстов, публично оправдывающих терроризм и/или экстремизм

The current political situation has given rise to a surge of terrorist activity, which is often embodied in the information space in texts with explicitly and implicitly expressed information of an extremist and terrorist nature. That is why the paper is devoted to the problems arising in the linguistic study of texts with signs of extremist or terrorist orientation distributed publicly. In addition, the paper raises the issue of information expansion as an integral part of modern terrorism- and extremism-related texts, since such texts have a manipulative impact on the addressee. The research aims to identify the main problems in conducting linguistic examinations of texts with signs of public justification of terrorism and/or extremism. The authors of the work clarify the role of information expansion in terrorism- and/or extremism-related texts. The research is novel in that it is the first to consider information expansion as a part of terrorism- and/or extremism-related texts. As a result, it has been proved that information expansion is a part of modern terrorism- and extremism-related texts. The identification of implicit information and strict compliance with scientific and methodological recommendations when conducting examinations are of particular importance for linguistic examinations.

Breast cancer classification using deep learned features boosted with handcrafted features

Breast cancer is one of the leading causes of death among women across the globe. It is difficult to treat if detected at advanced stages. However, early detection can significantly increase chances of survival and improves the lives of millions of women. Given the widespread prevalence of breast cancer, it is of utmost importance for the research community to provide comprehensive framework encompassing early detection, classification, and diagnosis. The artificial intelligence research community, in coordination with medical practitioners, is developing such frameworks to automate the task of detection. With the surge in research activities coupled with the availability of large datasets and enhanced computational powers, it is expected that AI framework results will help even more clinicians in making correct predictions. In this article, a novel framework for the classification of breast cancer using mammograms is proposed. The proposed framework not only uses features extracted from Convolutional Neural Network (CNN) but also combines these features with handcrafted features (Histogram of Oriented Gradients (HOG) and Local Binary Pattern (LBP)) which helps in embedding domain expert knowledge, a step towards making proposed framework transparent. Experimental results conducted on the CBIS-DDSM dataset demonstrate that the proposed framework outperforms the current state-of-the-art methods in breast cancer classification.

Neighbourhood deprivation, changes in walking attitudes during the COVID-19 pandemic and walking demand in the post-pandemic period

Although the Coronavirus has had substantial negative impacts on our lives, the surge in walking activities during the pandemic provides an unprecedented opportunity for planners to transform cities to be healthier and more sustainable. The changes in walking behaviour could affect attitudes towards walking, which would influence walking demand in the post-pandemic period. This impact, however, might vary across neighbourhoods with different levels of deprivation due to their own characteristics. This potential inequity issue should be examined to make more resilient cities in the future where the risk of outbreaks is high. We conducted a survey (GlaMAS) and utilised statistical methods to examine the extent to which the attitudes towards walking changed due to the COVID-19 pandemic and how its effect varies across neighbourhoods with different levels of deprivation. In addition, we investigated how the changes in attitudes are related to future walking demand. Our results show that residents in less deprived areas tend to have affirmative attitudes towards walking due to the COVID-19 pandemic, potentially leading to an inequity issue. In addition, attitudinal changes are significantly associated with the future walking demand, and it could result in health inequity between neighbourhoods in the future.

Effect and Safety of Pressure Sensor-equipped Handpiece in Phacoemulsification System.

To evaluate the effect of the Active Sentry handpiece of the Centurion Vision System compared to the Centurion Ozil handpiece for phacoemulsification in cataract surgery. A retrospective study was conducted on 281 patients (449 eyes) who underwent cataract surgery between August 2020 and June 2021. Preoperative measurements, intraoperative parameters, complication rate, and postoperative outcomes were compared between the Active Sentry handpiece and the Centurion Ozil handpiece groups. Additionally, the parameters were compared in different cataract severity groups and multiple predictive factors for the number of active surge mitigation (ASM) actuations were assessed with the Active Sentry handpiece. There were 198 eyes in the Active Sentry group and 251 eyes in the Centurion Ozil group. There were no statistically significant differences between the two groups, as the cumulative dissipated energy in the Active Sentry and Centurion Ozil groups were 8.32 ± 7.74 and 7.87 ± 9.25 μJ, respectively (p = 0.576). Total surgery time, ultrasound usage time, aspiration time, amount of fluid aspirated, postoperative corrected distant visual acuity, and postoperative decrease in corneal endothelial cell density were comparable between the two groups. The significant contributors to the number of ASM actuations were age, preoperative corrected distant visual acuity, axial length, and total ultrasound time. There was no clear advantage of the Active Sentry handpiece compared to the Centurion Ozil handpiece. ASM actuation increases with age, poor visual acuity before surgery, short axial length, and prolonged ultrasound usage time. It is expected that in more severe and high-risk cataract surgery, the Active Sentry handpiece functions more effectively, possibly affecting the safety and prognosis.

MT2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^2$$\\end{document}AD: multi-layer temporal transaction anomaly detection in ethereum networks with GNN

In recent years, a surge of criminal activities with cross-cryptocurrency trades have emerged in Ethereum, the second-largest public blockchain platform. Most of the existing anomaly detection methods utilize the traditional machine learning with feature engineering or graph representation learning technique to capture the information in transaction network. However, these methods either ignore the timestamp information and the transaction flow direction information in transaction network or only consider single transaction network, the cross-cryptocurrency trading patterns in Ethereum are usually ignored. In this paper, we introduce a Multi-layer Temporal Transaction Anomaly Detection (MT2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^2$$\\end{document}AD) model in Ethereum network with graph neural network. Specifically, for a given Ethereum token transaction network, we first extract its initial features including the structure subgraph and edge’s feature. Then, we model the temporal information in subgraph as a series of network snapshots according to the timestamp on each edge and time window. To capture the cross-cryptocurrency trading patterns, we combine the snapshots from multiple token transactions at a given timestamp, and we consider it as a new combined graph. We further use the graph convolution encoder with attention mechanism and pooling operation on this new graph to obtain the graph-level embedding, and we transform the anomaly detection on dynamic multi-layer Ethereum transaction networks as a graph classification task with these graph-level embeddings. MT2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^2$$\\end{document}AD can integrate the transaction structure feature, edge’s feature and cross-cryptocurrency trading patterns into a framework to perform the anomaly detection with graph neural networks. Experiments on three real-world multi-layer transaction networks show that the proposed MT2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^2$$\\end{document}AD (0.8789 Precision, 0.9375 Recall, 0.4987 FbMacro and 0.9351 FbWeighted) can achieve the best performance on most evaluation metrics in comparison with some competing approaches, and the effectiveness in consideration of multiple tokens is also demonstrated.

Towards the diversification of lactococcal starter and non-starter species in mesophilic dairy culture systems.

Lactococcus is one of the earliest identified fermentative bacterial genera and among its member species, the dairy-associated Lactococcus lactis and Lactococcus cremoris are undoubtedly the best studied. These two species are believed to have evolved from plant-associated lactococci and through genome decay and acquisition of plasmids, have adapted to the dairy niche. The past decade has witnessed a surge of activity in novel lactococcal species identification from insect, plant and animal sources. Currently, 22 Lactococcus species are described and in this review, we summarise the genome characteristics of and phylogenetic relationships among these species. Furthermore, we explore the role of mobile elements including plasmids and bacteriophages in the diversification of lactococcal species. The pace of identification of novel lactococcal species suggests that the number of lactococcal species is likely to continue to grow. With additional sequence data for the emerging species, it will be possible to perform pathogenicity/virulence risk evaluations and generate extensive insights into the niche adaptation strategies through which they have evolved.

Unveiling the interconnectedness between energy-related GHGs and pro-environmental energy technology: Lessons from G-7 economies with MMQR approach

Contrary to most existing studies in the literature, we adopt the energy-related GHG emission (EGHG), an aggregate measure of environmental pollution, to present how GDP, energy efficiency, environmental technology, and globalization affect the environmental changes in the G-7 region from 1990 to 2020. We leaned on the novel Method of Moments Quantile Regression (MMQR) approach to obtain the coefficients in heterogenous quantiles. The findings show that all identified parameters, except for GDP, meaningfully reduce energy-related GHG emissions in the G-7 forum, and the findings are homogenous in all quantiles. More specifically, a surge in economic activities boosts EGHG in G-7 territories. Since energy efficiency is negatively related to EGHG, we claim that the G-7 forum has been able to curb the rebound effect in energy consumption. Furthermore, environmental innovations are a boon for reducing EGHG in the forum, indicating that these nations are on the right track to attain carbon neutrality and green growth directories as prescribed by the last climate conference (COP27). Besides, globalization reduces EGHG through the technology-driven spillover effects, indicating that the G-7 forum has successfully harnessed globalization to exchange R&D, technology, and innovation skills, which have escalated this forum's capacity to fight against climate change.

Surges in volcanic activity on the Moon about two billion years ago

The history of mare volcanism critically informs the thermal evolution of the Moon. However, young volcanic eruptions are poorly constrained by remote observations and limited samples, hindering an understanding of mare eruption flux over time. The Chang’e-5 mission returned the youngest lunar basalts thus far, offering a window into the Moon’s late-stage evolution. Here, we investigate the mineralogy and geochemistry of 42 olivine and pyroxene crystals from the Chang’e-5 basalts. We find that almost all of them are normally zoned, suggesting limited magma recharge or shallow-level assimilation. Most olivine grains record a short timescale of cooling. Thermal modeling used to estimate the thickness and volume of the volcanism sampled by Chang’e-5 reveals enhanced magmatic flux ~2 billion years ago, suggesting that while overall lunar volcanic activity may decrease over time, episodic eruptions at the final stage could exhibit above average eruptive fluxes, thus revising models of lunar thermal evolution.

Reddit in the Time of COVID

When the COVID-19 pandemic hit, much of life moved online. Platforms of all types reported surges of activity, and people remarked on the various important functions that online platforms suddenly fulfilled. However, researchers lack a rigorous understanding of the pandemic's impacts on social platforms---and whether they were temporary or long-lasting. We present a conceptual framework for studying the large-scale evolution of social platforms and apply it to the study of Reddit's history, with a particular focus on the COVID-19 pandemic. We study platform evolution through two key dimensions: structure vs. content and macro- vs. micro-level analysis. Structural signals help us quantify how much behavior changed, while content analysis clarifies exactly how it changed. Applying these at the macro-level illuminates platform-wide changes, while at the micro-level we study impacts on individual users. We illustrate the value of this approach by showing the extraordinary and ordinary changes Reddit went through during the pandemic. First, we show that typically when rapid growth occurs, it is driven by a few concentrated communities and within a narrow slice of language use. However, Reddit's growth throughout COVID-19 was spread across disparate communities and languages. Second, all groups were equally affected in their change of interest, but veteran users tended to invoke COVID-related language more than newer users. Third, the new wave of users that arrived following COVID-19 was fundamentally different from previous cohorts of new users in terms of interests, activity, and likelihood of staying active on the platform. These findings provide a more rigorous understanding of how an online platform changed during the global pandemic.

Cyberbullying Detection using Machine Learning Algorithms

Abstract: In today's digital age, the internet has become an integral part of our lives, resulting in an exponential increase in the volume of data generated. However, this surge in online activity has also led to the emergence of cyberbullying as a major concern in web 4.0. Cyberbullying refers to the use of technology to intimidate, harass or threaten an individual, and is considered a form of cybercrime. Given the lack of available datasets, anonymous identities of perpetrators and the privacy of victims, previous research in cyberbullying detection has been limited. To address this issue, a new approach based on text mining and machine learning algorithms is proposed to proactively detect bullying text. Unlike previous research, which only considered textual features, the current study extracts three types of features: textual, behavioural and demographic features. Textual features include specific words commonly used in cyberbullying, which may indicate the presence of bullying behaviour. Behavioural features are based on personality traits and are extracted to determine the likelihood of a user engaging in bullying behaviour in the future. Demographic features, such as age, gender and location, are also extracted from the dataset. Overall, this text mining approach using machine learning algorithms can effectively detect cyberbullying, providing a valuable tool to combat this growing concern in the cyber world.

0982 Atrial fibrillation diagnosed from a home-based sleep study in a patient with REM behavior disorder

Abstract Introduction Atrial fibrillation (A-Fib) is the most common form of cardiac arrhythmia and is a major cause of stroke. Obstructive sleep apnea (OSA) is an established risk factor of A-Fib, but sleep disruption with frequent awakenings was recently identified as another risk factor, independent from OSA. Home sleep apnea test (HSAT) is widely used to diagnose OSA with low cost and short wait time, and HSAT that uses peripheral arterial tonometry (PAT) can reveal cardiac arrhythmia. Here we report a case of A-Fib detected by PAT-based HSAT in a patient with REM sleep disorder. Report of case(s) A 60-year-old man with hypertension, depression, anxiety, tobacco and alcohol use disorders presented to sleep clinic for frightening vivid dreams and violent dream enactments that gradually developed over the last two years. A diagnostic polysomnography study showed mild OSA, normal heart rhythm, and no epoch of REM sleep without atonia (RSWA). In the following four years, he used CPAP therapy, imagery rehearsal therapy, high-dose melatonin, doxepin and hydroxyzine. However, the frightening dreams and dream enactments persisted despite full adherence to CPAP. To reassess OSA, the patient took a PAT-based HSAT, which showed not only mild OSA, but one episode of irregularly irregular PAT-derived pulsation variations that lasted three hours and started and stopped abruptly. We therefore ordered longitudinal cardiac monitoring, which showed multiple asymptomatic episodes of A-Fib during nighttime. After treatments for A-Fib were started, a polysomnography study with effective CPAP titration showed one episode of A-Fib with rapid ventricular response that lasted 2.5 hours during non-REM sleep, and multiple epochs of RSWA. The diagnosis of REM sleep behavior disorder (RBD) was thus established. Conclusion We reported concurrent developments of RBD and asymptomatic paroxysmal A-Fib in a man in his early sixties. Our case suggests a potential association between RBD and A-Fib, which has not been studied but is conceivable, given that RBD causes frequent awakenings with surges of sympathetic activity. Our case also highlights the advantage of PAT in capturing not only OSA but cardiac arrhythmia. Clinical interpreters of PAT-based HSAT can seize the opportunity to diagnose A-Fib and prevent stroke. Support (if any)

Identifying the impact of bank competition on corporate shadow banking: Evidence from China

The surge of corporate shadow banking activities in China has drawn much attention from academia and policymakers; yet very little is known about whether bank competition matters for corporate shadow banking activities. Using a large sample of Chinese listed non-financial enterprises (NFEs) over the period of 2003–2019, we identify the impact of bank competition on corporate shadow banking activities. We find a negative relation of bank competition with corporate shadow banking activities. Greater bank competition reduces corporate shadow banking activities by relaxing the firms’ financing constraints, alleviating information asymmetries, and improving their investment efficiency. Our results are robust to alternative metrics of core variables, controlling for regional characteristics, instrumental variable analysis, and the difference-in-differences estimation. Our findings suggest that a deregulation of bank entry, which increases bank competition, is an effective way to restrain corporate shadow banking activities without sacrificing economic growth.

The Link Between Aid-for-Trade and Contingent Protection

Foreign aid, in theory, is expected to mitigate constraints that impede the economic development of recipient countries. At the same time that help is committed, donors are seemingly taking actions that are harmful to developing economies in obvious ways. An example is the tacit circumvention of the putative rules-based global trading system through contingent protection activities. In this article, it is postulated that, on one hand aid-for-trade (AfT) is expected to have positive impact on the exports of aid recipients by better integration into the global trading order, on the other hand, aid provider (donor) curtails access to its own markets by actuating contingent protection against the recipient (exporter). Using contingent protection cases data from 2003 to 2018 (a 15-year period) against 106 recipient countries of the United States of America’s AfT, this study finds a significant and positive impact of AfT on the surge in contingent protection activities. This effect is entirely driven by the aid for economic infrastructure and services, while the other main category of AfT- production sector, has no discernible effect on the rise in protection against the recipient. To examine the heterogeneity in donor decisions, this study is expanded to other traditional donors like Australia, Canada, the European Union (EU) and New Zealand. This article finds that Australia behaves similar to the USA; however, for Canada and the EU, the relationship between aid and market access is not statistically significant. JEL Codes: F1, F35, O19