Empirical Assessment Research Articles

An empirical assessment of seaports as facilitators of FOC-flagged transshipment landings

Transshipment is one of the most common activities occurring between carriers and fishing vessels to exchange fish, as well as fuel, crew, and gear at sea or at port. While transshipment reduces the need for the fishing vessels to visit ports to offload their catches, thus increasing their efficiency, research has shown that this activity is also one of the major facilitators of IUU fishing. Research on transshipments is limited, and the few studies that are available on the subject focus on identifying hotspots of transshipment, and networks of actors involved. No study to date has examined the role ports play in facilitating transshipment activities, nor are there any studies that examine whether ports that are affiliated with China (the country with the highest IUU Fishing Index Score and 38% of the global share of distant-water fleets) are more likely to experience disproportionately higher volumes of FOC-flagged carrier vessel visits. Therefore, using the carrier vessel portal database from Global Fishing Watch, which contains information on the origin and destination ports of the carrier vessels involved in transshipment activities between 2015 and 2022, this research aims to (a) understand hot spots of FOC-flagged carrier vessel activity in the high seas and where such vessels offload their catches around the world; and (b) empirically test the characteristics of the ports (and the countries where these ports are located) used by these vessels to offload their catches. Findings suggest that ports that experience higher volumes of fishing vessel traffic, are in close proximity to high-seas transshipment activities involving FOC-flagged carriers, are designated ports of entry for foreign vessels, are Chinese-affiliated, as well as have low monitoring, control, and surveillance capacity are significantly more likely to be visited by FOC-flagged high-risk carrier vessels. This research proposes policy recommendations deriving from the findings of this research.

Anthropomorphised or not? Natural-organic logo’s impact on product value perceptions and consumers’ willingness to pay

Purpose Building on associative priming, anthropomorphism and biophilia theories, this study aims to explain that a natural–organic (that shows a natural object) and an anthropomorphised natural–organic logo (that shows an anthropomorphised natural object) both act as primes and imbue specific product value perceptions, which subsequently influence willingness to pay a premium price when products have not been used by or are unfamiliar to consumers. Design/methodology/approach Two between-subjects experiments were conducted with different products (one with real, but unfamiliar to consumers brand and another with a fictitious brand). Structural equation modelling was used to test the hypotheses. Findings Experimental studies showed that natural–organic logos evoked stronger utilitarian (functional and economic) value perceptions, which triggered greater willingness to pay a premium price compared to anthropomorphised natural–organic logos. The effect of hedonic (emotional and novelty) values on willingness to pay a premium price was stronger when an anthropomorphised natural–organic logo was used. Research limitations/implications This research offers novel theoretical contributions highlighting the importance of careful logo design to imbue desired value perceptions when products have not been consumed or trialled. Practical implications Anthropomorphised natural–organic and natural–organic logos can provide different benefits to brand managers and can be used strategically to form desired value perceptions before products are consumed. Brands that wish to enhance premium pricing via hedonic values should consider using an anthropomorphised natural–organic logo. Natural–organic logos may be more suitable for brands that want to emphasise superior utilitarian values. Originality/value To the best of the authors’ knowledge, this research provides the first empirical assessment of the differential effects of the two forms of natural–organic logos on value perceptions and willingness to pay premium price.

Non-Linear Association of Exercise with Animal Protein Consumption among U.S. Adults

Supply chain disruptions, human and animal health concerns, and environmental impacts of livestock production have spurred renewed attention to animal protein consumption in the U.S. Prior research has sought to better understand animal protein consumption by assessing demographic and socioeconomic determinants. However, physical exercise represents a behavioral determinant of consumption that is societally important and, thus far, has not been considered in empirical assessments. Our objective was to quantify the association of exercise with the consumption of total protein, beef, pork, poultry, seafood, eggs, and dairy among U.S. adults. We analyzed 24 h dietary recall and physical activity data from adults in the 2007–2018 National Health and Nutrition Examination Survey (NHANES). The association of exercise with protein consumption (total and disaggregated animal protein) was assessed using ordinary least squares and multivariate Tobit regression. Non-linear associations of exercise with total protein consumption were found, with the magnitude of association highest at 121–180 min per day of exercise. Non-linear associations were also found with animal protein consumption, which differed in sign and magnitude across protein sources. The magnitudes of association, paired with a sizable share of the study sample engaged in exercise, suggest a substantial influence of exercise on protein consumption habits in the U.S.

Automatic classification and segmentation of blast cells using deep transfer learning and active contours.

Acute lymphoblastic leukemia (ALL) presents a formidable challenge in hematological malignancies, necessitating swift and precise diagnostic techniques for effective intervention. The conventional manual microscopy of blood smears, although widely practiced, suffers from significant limitations including labor-intensity and susceptibility to human error, particularly in distinguishing the subtle differences between normal and leukemic cells. To overcome these limitations, our research introduces the ALLDet classifier, an innovative tool employing deep transfer learning for the automated analysis and categorization of ALL from White Blood Cell (WBC) nuclei images. Our investigation encompassed the evaluation of nine state-of-the-art pre-trained convolutional neural network (CNN) models, namely VGG16, VGG19, ResNet50, ResNet101, DenseNet121, DenseNet201, Xception, MobileNet, and EfficientNetB3. We augmented this approach by incorporating a sophisticated contour-based segmentation technique, derived from the Chan-Vese model, aimed at the meticulous segmentation of blast cell nuclei in blood smear images, thereby enhancing the accuracy of our analysis. The empirical assessment of these methodologies underscored the superior performance of the EfficientNetB3 model, which demonstrated exceptional metrics: a recall specificity of 98.5%, precision of 95.86%, F1-score of 97.16%, and an overall accuracy rate of 97.13%. The Chan-Vese model's adaptability to the irregular shapes of blast cells and its noise-resistant segmentation capability were key to capturing the complex morphological changes essential for accurate segmentation. The combined application of the ALLDet classifier, powered by EfficientNetB3, with our advanced segmentation approach, emerges as a formidable advancement in the early detection and accurate diagnosis of ALL. This breakthrough not only signifies a pivotal leap in leukemia diagnostic methodologies but also holds the promise of significantly elevating the standards of patient care through the provision of timely and precise diagnoses. The implications of this study extend beyond immediate clinical utility, paving the way for future research to further refine and enhance the capabilities of artificial intelligence in medical diagnostics.

Diabetes, life course and childhood socioeconomic conditions: an empirical assessment for Mexico

BackgroundDemographic and epidemiological dynamics characterized by lower fertility rates and longer life expectancy, as well as higher prevalence of non-communicable diseases such as diabetes, represent important challenges for policy makers around the World. We investigate the risk factors that influence the diagnosis of diabetes in the Mexican population aged 50 years and over, including childhood poverty.ResultsThis work employs a probabilistic regression model with information from the Mexican Health and Aging Study (MHAS) of 2012 and 2018. Our results are consistent with the existing literature and should raise strong concerns. The findings suggest that risk factors that favor the diagnosis of diabetes in adulthood are: age, family antecedents of diabetes, obesity, and socioeconomic conditions during both adulthood and childhood.ConclusionsPoverty conditions before the age 10, with inter-temporal poverty implications, are associated with a higher probability of being diagnosed with diabetes when older and pose extraordinary policy challenges.

An empirical assessment of ML models for 5G network intrusion detection: A data leakage-free approach

This paper thoroughly compares thirteen unique Machine Learning (ML) models utilized for Intrusion detection systems (IDS) in a meticulously controlled environment. Unlike previous studies, we introduce a novel approach that meticulously avoids data leakage, enhancing the reliability of our findings. The study draws upon a comprehensively labeled 5G-NIDD dataset covering a broad spectrum of network behaviors, from benign real-user traffic to various attack scenarios. Our data preprocessing and experimental design have been carefully structured to eradicate any data leakage, a standout feature of our methodology that significantly improves the robustness and dependability of our results compared to prior studies. The ML models are evaluated using various performance metrics, including accuracy, precision, recall, F1-score, ROC AUC, and execution time. Our results reveal that the K-Nearest Neighbors model is superior in accuracy and ROC AUC, while the Voting Classifier stands out in precision and F1-score. Decision Tree, Bagging, and Extra Trees models exhibit strong recall scores. In contrast, the AdaBoost model falls short across all assessed metrics. Despite displaying only modest performance on other metrics, the Naive Bayes model excels in computational efficiency, offering the quickest execution time. This paper emphasizes the importance of understanding various ML models' distinct strengths, drawbacks, and trade-offs for network intrusion detection. It highlights that no single model is universally superior, and the choice hinges on the nature of the dataset, specific application requirements, and the computational resources available.

A Deep Learning Approach for Accurate Path Loss Prediction in LoRaWAN Livestock Monitoring

The agricultural sector is amidst an industrial revolution driven by the integration of sensing, communication, and artificial intelligence (AI). Within this context, the internet of things (IoT) takes center stage, particularly in facilitating remote livestock monitoring. Challenges persist, particularly in effective field communication, adequate coverage, and long-range data transmission. This study focuses on employing LoRa communication for livestock monitoring in mountainous pastures in the north-western Alps in Italy. The empirical assessment tackles the complexity of predicting LoRa path loss attributed to diverse land-cover types, highlighting the subtle difficulty of gateway deployment to ensure reliable coverage in real-world scenarios. Moreover, the high expense of densely deploying end devices makes it difficult to fully analyze LoRa link behavior, hindering a complete understanding of networking coverage in mountainous environments. This study aims to elucidate the stability of LoRa link performance in spatial dimensions and ascertain the extent of reliable communication coverage achievable by gateways in mountainous environments. Additionally, an innovative deep learning approach was proposed to accurately estimate path loss across challenging terrains. Remote sensing contributes to land-cover recognition, while Bidirectional Long Short-Term Memory (Bi-LSTM) enhances the path loss model’s precision. Through rigorous implementation and comprehensive evaluation using collected experimental data, this deep learning approach significantly curtails estimation errors, outperforming established models. Our results demonstrate that our prediction model outperforms established models with a reduction in estimation error to less than 5 dB, marking a 2X improvement over state-of-the-art models. Overall, this study signifies a substantial advancement in IoT-driven livestock monitoring, presenting robust communication and precise path loss prediction in rugged landscapes.

A new conceptual model for consumer willingness to pay premium prices for sustainable products

Purpose The purpose of this study is to develop an integrated conceptual framework to better understand the psychological pathways connecting consumer perceptions to purchasing intentions for premium sustainable products. Design/methodology/approach The study develops a conceptual model that theorizes relationships between consumer perceptions of a firm’s innovation, competitive advantage, sustainable practices and stakeholder orientation. It proposes that stakeholder orientation mediates the effects of these perceptions on consumers’ willingness to purchase premium sustainable products. Additionally, lifestyle is hypothesized as a moderator. The model advances knowledge through eight testable propositions. Findings The conceptual framework specifies indirect, mediated and moderated relationships that have not been fully captured by past literature. It theorizes that perceptions of a firm’s innovation, competitive advantage from sustainable practices and stakeholder orientation indirectly influence purchase willingness through the mediating role of stakeholder orientation. Lifestyle is proposed to moderate these relationships. Originality/value This conceptual model offers insights for cultivating consumer perceptions that strengthen a firm’s stakeholder image and endorsement of premium sustainable products. Its validated theoretical lens and propositions can provide strategic guidance for addressing the challenges of higher price points for sustainable products through capturing psychological drivers of values-based decision-making. Future empirical assessment is recommended to validate the specified relationships in the model.

Synthesis of recovery patterns in microbial communities across environments

BackgroundDisturbances alter the diversity and composition of microbial communities. Yet a generalized empirical assessment of microbiome responses to disturbance across different environments is needed to understand the factors driving microbiome recovery, and the role of the environment in driving these patterns.ResultsTo this end, we combined null models with Bayesian generalized linear models to examine 86 time series of disturbed mammalian, aquatic, and soil microbiomes up to 50 days following disturbance. Overall, disturbances had the strongest effect on mammalian microbiomes, which lost taxa and later recovered their richness, but not their composition. In contrast, following disturbance, aquatic microbiomes tended away from their pre-disturbance composition over time. Surprisingly, across all environments, we found no evidence of increased compositional dispersion (i.e., variance) following disturbance, in contrast to the expectations of the Anna Karenina Principle.ConclusionsThis is the first study to systematically compare secondary successional dynamics across disturbed microbiomes, using a consistent temporal scale and modeling approach. Our findings show that the recovery of microbiomes is environment-specific, and helps to reconcile existing, environment-specific research into a unified perspective.FPVwQXB65-N4idcpgwJZ6_Video

Impact of renewable energy consumption on sustainable development in Central Asia

AbstractThe energy transition in Central Asia faces multiple risks while lacking empirical assessment of renewable energy use from a sustainability perspective. This paper examined the relationship of renewable energy consumption with energy intensity and carbon intensity in Central Asia during 1993—2019, using the seemingly unrelated regression (SUR). Weighing the heterogeneity of countries in the region, the results showed that downstream countries build an inverted U‐shaped relationship between renewable energy consumption and carbon intensity, while the whole region indicates an inverted U‐shaped relationship between renewable energy consumption and energy intensity. Compared with hydrocarbon, nonfossil energy is expected to serve as a greater driving force for economic growth in the future, alleviating socio‐economic dependence on fossil fuels in downstream countries and generating additional revenues to ensure energy security in upstream countries. The findings suggest policymakers to considerably mitigate the risks of decarbonization across the region.

What polarizes citizens? An explorative analysis of 817 attitudinal items from a non-random online panel in Germany.

Various studies point to the lack of evidence of distributive opinion polarization in Europe. As most studies analyse the same item batteries from international social surveys, this lack of polarization might be due to an item's issue (e.g., the nature or substance of an item) or item formulation characteristics used to measure polarization. Based on a unique sample of 817 political attitudinal items asked in 2022 by respondents of a non-random online panel in Germany, we empirically assess the item characteristics most likely to lead to distributive opinion polarization-measured with the Van der Eijk agreement index. Our results show that only 20% of the items in our sample have some-at most moderate-level of opinion polarization. Moreover, an item's salience in the news media before the survey data collection, whether an item measures attitudes toward individual financial and non-financial costs, and the implicit level of knowledge required to answer an item (level of technicality) are significantly associated with higher opinion polarization. By contrast, items measuring a cultural issue (such as issues on gender, LGTBQI+, and ethnic minorities) and items with a high level of abstraction are significantly associated with a lower level of polarization. Our study highlights the importance of reflecting on the potential influence of an item's issue and item formulation characteristics on the empirical assessment of distributive opinion polarization.

Impact of natural resource dependence on green technology development: Role of digital governance in mitigating resource-curse using big data

The pursuit of a green economy as a strategy to counteract the “resource curse' phenomenon represents a burgeoning paradigm shift. A sophisticated digital governance infrastructure is vital, serving as an integral mechanism for ensuring transparency, enhancing data efficacy, and fostering public involvement—all crucial components of sustainable environmental stewardship. In light of this context, our analysis examines the interplay between natural resource dependence and green technological innovation, as well as delineating the potential pathways interlinking the two, through an empirical assessment using provincial data from China spanning the years 2011–2021. Employing advanced techniques in big data text mining, we have devised a government digital transformation index calibrated to elucidate the impacts of digitalization on the twin variables of natural resource reliance and the propagation of green technologies. Our findings indicate a marked deleterious influence of natural resource dependence on the evolution of green technological initiatives at the regional level, a trend most pronounced in China's central and western provinces as well as in locales with comparatively low economic development metrics. Furthermore, our research ascertains that the impeding force of natural resource dependence on green technology ascension is primarily exercised through diminished regional research and development capabilities, stagnation of industrial structure progression, and limited openness to external economic integration. Finally, the study posits that the implementation of digital governance measures holds the capability to substantially mitigate the restrictive impact of natural resource dependence on green technology advancement. This underscores the imperative for a digital governance architecture that actively neutralizes and meticulously monitors the adverse outcomes associated with resource consumption.

Cholera outbreak in Malawi: empirical assessment and lessons learnt

Cholera outbreak in Malawi: empirical assessment and lessons learnt

Empirical Safety Assessment of Double-Sided Adhesive Tapes

Empirical Safety Assessment of Double-Sided Adhesive Tapes

Impact of Information and Communication Technologies on Energy Consumption and Economic Growth: An Empirical Assessment for China

Regarding the association between information and communication technology (ICT) and economic growth and energy consumption, particularly in developing economies, there is a great deal of controversy and a lack of empirical study. This study applies Quantile Autoregressive Distributed Lag Model (QARDL) approach to empirically probe into the effect of ICT on energy consumption and economic growth in China. Two corresponding models for energy consumption and economic growth are formulated and the data of all of the variables spanning over 1995-2021 period is taken from different secondary sources. The findings of the study reveal significant and positive impact of ICT on economic growth at all quantiles in the short and long run. Similarly ICT has significant and positive impact on energy consumption also at all quantiles. The Quantile Granger Causality test provides evidence for bidirectional causality between ICT and economic growth and ICT and energy consumption. Wald test proved the presence of and symmetric and linear association between ICT and economic growth and ICT and energy consumption at quantiles in the long and short run both. In the light of these findings, the study recommends the Chinese government and investors to have more investment in energy efficient ICT products that consume less energy and help increase in economic growth in China.

EFFECTIVENESS ASSESSMENT OF INTERNATIONAL INVESTMENT IN THE UKRAINIAN TERRITORIAL COMMUNITIES’ ACTIVITIES

The article titled systematically examines and evaluates the impact and efficiency of international investments within the context of Ukrainian territorial communities. The research employs a comprehensive approach, delineating key factors influencing investment attractiveness and proposing indicators categorized into financial, economic, social, environmental, and legal dimensions. The study goes beyond theoretical considerations by conducting a survey involving experts from diverse fields, including state and municipal administration, community representatives, business stakeholders, and the education sector. Through this empirical assessment, the article provides a nuanced understanding of the multifaceted effects of international investments on Ukrainian territorial communities. It concludes with practical recommendations geared towards optimizing the efficiency of international investments within the activities of these communities, contributing valuable insights for policymakers, investors, and community stakeholders alike. In general, the article explores the critical role of international investment in Ukrainian territorial communities as a key avenue for economic development and the enhancement of the population's quality of life. The study aims to assess the effectiveness of international investment in these communities, recognizing their potential to significantly impact economic and social spheres. By conducting an assessment and taking the necessary measures based on the received data, the territorial communities of Ukraine can attract more international investments and contribute to the sustainable development of the territorial communities, and the conclusions can be used to make decisions on investment support, correction of development strategies and improvement of the investment climate.

An Empirical Assessment of Microfinance and its Associated Socio-Economic Development

Abstract Microfinance institutions (MFIs) provide support for the sustainability of livelihood and financial inclusion through social development. Micro-enterprises helps in empowering the communities especially marginalized section worldwide. The MFIs attempt to transform the lives of poor people, through different interventions. It revolved around the social development of marginalized people. In recent decades, microfinance played a vital role in alleviating poverty and involved empowering low-income earners socially and economically. The present study intended to highlight the role of MFIs located in Jabalpur (Madhya Pradesh) in enhancing the social and economic status of beneficiaries. This study also acknowledges the significance of MFIs in eradicating poverty, through financial services. In this research, questionnaire responses were gathered and analyzed from 150 beneficiaries of three MFIs (Credit Access Grameen, ESAF, and Fusion Micro-finance) in Jabalpur. The exploratory data analysis reveals diverse findings, regarding the impact of microfinance on the socio-economic conditions of beneficiaries through improving their savings, supporting the employment generation, and as a whole improving life standards as well as eradicating poverty. Alternatively, MFI programs also assist beneficiaries in employment generation, to a certain extent. The conclusion of the research prioritizes that MFI programs ought to be accelerated more by the Government through different schemes and can be easily accessed to even remote areas for economic expansion and social development of low-privileged people.

Examining the Relationship between Vaccine Utilization and Educational Attainment in Southeast Los Angeles

The Southeast Los Angeles cities of Maywood and Bell have educational attainment (bachelor’s degree or higher) rates of 8.7% and 8.8%, respectively, relative to California’s state average of 37.0% (U.S. Census Bureau 2022). The goal of this study is to evaluate the relationship between educational attainment and vaccine utilization in Southeast Los Angeles to inform future policies aimed at improving long-term health outcomes. For this study, the International Collegiate Health Initiative (ICHI), a 501(c)-3 nonprofit focused on providing multicultural low-income, immigrant, and refugee populations with educational, nutritional, and medical health services, conducted extensive surveying of the patient population at its mobile community health clinic events at the Maywood YMCA and the Bell Community Health Center. The data reveals significant differences in vaccine use between college- and non-college-educated individuals. 68.75% of college-educated respondents reported receiving a COVID-19 booster shot, whereas 56.82% of non-college-educated respondents reported the same. Similarly, college-educated respondents had a 62.5% flu vaccination rate in the 2022 flu season, compared to 47.7% for non-college-educated respondents. The trend of college attendees being more likely to use vaccine services than non-college attendees is corroborated by a longitudinal study of Wisconsin high school graduates. This study determined that college attendance was correlated with higher probability of vaccine utilization in general, and increased flu shot usage in particular (Fletcher & Frisvold, 2009). The study also indicates that there is causation, rather than simply correlation, between increased college attendance and use of vaccines. Moreover, an empirical assessment of OECD countries for the period 1995–2015 found that well-educated adults have better health relative to adults who are less-educated (​​Raghupathi & ​​Raghupathi, 2020). This underscores the need to support increased educational attainment in underserved communities in order to increase vaccination rates in the United States.

Two-way relationship between inequality and growth within the fiscal policy channel: an empirical assessment for European countries

PurposeWe investigate the role of fiscal policy, through several measures of government revenues and expenditures and redistribution, on disposable and market income inequality and economic growth as well as the interaction between inequality and growth for 31 European countries from 1995 to 2019.Design/methodology/approachWe use a simultaneous equations model to assess the linkage between economic growth, inequalities and fiscal policy variables.Findings(1) While disposable income inequality has a negative effect on all fiscal policy variables, market income inequality has a mixed effects; (2) for Eastern European countries, public consumption and direct taxation positively influence economic growth; conversely, for Western European countries, the effects are negative; (3) disposable and market income inequality have a positive effect on growth for Eastern European countries, and a negative influence on growth for Western European countries; (4) growth contributes to the increase of disposable and market income inequality for Eastern European countries; for Western European countries, the effects are opposite; and (5) fiscal policy allows for the attenuation of disposable income inequality.Originality/valueThe different results between the role of market and disposable income inequality levels lead us to suggest tax progressivity as an important feature to consider when analyse the trivariate relationship between inequalities, fiscal policy and growth. Furthermore, there are different dynamics between inequality and growth, and the role of fiscal policy, on both Eastern and Western European countries.

Innovative music education: An empirical assessment of ChatGPT-4’s impact on student learning experiences

Innovative music education: An empirical assessment of ChatGPT-4’s impact on student learning experiences