Recent Growth Research Articles

Competition and coordination between traditional and internet hospitals

AbstractMotivated by the recent emergence and rapid growth of online healthcare platforms, this paper investigates its impacts on the healthcare ecosystem by taking into consideration of heterogeneous patients, homogeneous service providers and profit-driven hospitals. A game-theoretical model with a queueing framework is developed, and two healthcare systems are considered, including a monopolistic system with either an offline, traditional hospital, or an online, Internet hospital, and a duopolistic system with both kinds of hospitals. Our study finds that in both systems a large population of patients always increases the healthcare supply and demand and the hospital utilization, which leads to an improvement not only in the hospital profitability, but also in the welfare of both patients and service providers. When considering a monopolistic system only, our study suggests that the monopolistic Internet hospital outperforms its counterpart in hospital utilization only when the wait cost is not high (i.e., patients are less sensitive to wait time), especially when the wait cost is medium and the patient-provider ratio is small. When considering a duopolistic system, this study concludes that the decisions of patients, service providers, and hospital managers of both hospitals are interrelated, and that a large population of service providers increases its hospital’s supply, demand, and wage rate, while decreases its wait times. When comparing the two monopolistic with the duopolistic system, the latter outperforms and improves every entity’s utility (i.e., patients, hospital, and society) when there is abundance of service providers with more being allocated to the Internet hospital, whereas the monopolistic system dominates when the patient population is large, and more providers are allocated to the traditional hospital.

Medicare Advantage Plans With High Numbers Of Veterans: Enrollment, Utilization, And Potential Wasteful Spending.

Medicare Advantage (MA) plans are increasingly enrolling veterans. Because MA plans receive full capitated payments regardless of whether or not veterans use Medicare services, the federal government can incur substantial duplicative, wasteful spending if veterans in MA plans predominantly seek care through the Veterans Health Administration (VHA) system. The recent growth of MA plans that disproportionately enroll veterans could further exacerbate such wasteful spending. Using national data, we found that veterans increasingly enrolled in MA between 2016 and 2022, including in a growing number of MA plans in which 20percent or more of the enrollees were veterans. Notably, about one in five VHA enrollees in these high-veteran MA plans did not incur any Medicare services paid by MA within a given year-a rate 2.5 times that of VHA enrollees in other MA plans and 5.7 times that of the general MA population. Meanwhile, VHA enrollees in high-veteran MA plans were significantly more likely to receive VHA-funded care. In 2020, the Centers for Medicare and Medicaid Services paid more than $1.32billion to MA plans for VHA enrollees who did not use any Medicare services, with 19.1percent going to high-veteran MA plans.

Enhancing computational thinking in children through logical puzzles: A machine learning approach for early coding education

This recent growth in early education in coding evidences the rising need for the computing-thinking-skills development in children. Though most of the programming curricula are based upon syntax and other basic concepts of coding, computational thinking encompasses broader cognitive skills, such as problem-solving, abstraction, and logical reasoning. This research therefore intends to fill a gap in research and will investigate the ways pre-coding games employ logical puzzles as an enhancement in developing computational thinking skills in children aged 7-10 years and how these affect the acquisition of programming knowledge. In all, there will be two groups assigned: one will be exposed to logical-oriented puzzle training, Group A, and the other will be a control group, Group B, which receives only regular standard lessons in programming introduction. Pre- and post-measures of computational thinking and basic programming skills will be obtained from the participants. The machine learning algorithms, such as Random Forest for prediction in terms of improvement in subject performance in computational thinking and K-Means clustering to identify patterns in their development of skills, will be implemented in their pure or integrated forms to analyze data. Logistic Regression will be executed in order to model the odds of improved performance in programming given an intervention of exposure to puzzles. The research study will, therefore, apply these algorithms in comparing the performance of the two groups and reveal the aspects of computational thinking to which the greatest influence in a positive way through puzzle activities is exerted. It is expected that children exposed to logical puzzles may show significant improvements in both computational thinking and programming skills. These findings should be useful to educators in their effort to show how logical puzzles can be integrated into the curricula of early coding education to build foundational skills and thereby contribute to a refinement of best practices in STEM education.

Exploring Urbanization Strategies by Dissecting Aggregate Crowd Behaviors: A Case Study in China

Town development, a crucial stage of urbanization, has been increasingly prioritized in recent sustainable socio-economic growth strategies. Vitality, especially the one measured by aggregate crowd behaviors, is widely recognized as a crucial development element. Conducting comprehensive assessments of the drivers of town vitality, particularly crowd vitality, is thus essential for addressing challenges and monitoring progress. This study examines representative towns in China and employs multiple datasets along with XGBoost-SHAP to investigate the mechanisms of development environment factors on aggregate crowd vitality. Key findings highlight the study’s novelty and broader implications: (1) The degree of industrial agglomeration is the most significant factor impacting the dependent measures, providing new data-driven insights into the role of economic clustering in town development. (2) Other indicators, such as the minimum distance to the town center, the enclosure, and car and pedestrian friendliness, can effectively predict town vitality, offering practical considerations for town planning. (3) Industrial innovation and diversification, rational planning of living circles, and enhancement of town conditions emerge as three crucial strategies for promoting urbanization. This study enhances empirical insights with strategies for addressing urbanization challenges, emphasizing how crowd data can be used to inform urbanization policies and planning practices, aiding urban planners in building more sustainable systems.

Comparative Historical Analysis in Political Theory

AbstractTwo important methodological debates in political theory concern (i) the place of historical research and interpretation in normative inquiry; and (ii) the importance of comparing different cultural traditions of political thought. The first question animates a long-standing controversy over the relative importance of ‘history of political thought’ versus ‘philosophy’ in political theory, while the second has been central to the recent growth of ‘comparative political theory’. Despite both debates being concerned, in fundamental ways, with history and comparison, they have remained surprisingly disconnected from broader debates in the humanities and social sciences about the use of comparative history and, in particular, of the prominent methodological approach in political science known as ‘comparative historical analysis’. This may reflect the fact that the debates over history of political thought and comparative political theory have fundamentally revolved around thought—history as the history of thought and comparison as the comparison of thought—whereas comparative historical analysis is more centrally focused on the study of political behaviour. In this paper I suggest that this neglect of comparative historical analysis in political theory represents a missed opportunity. Like other forms of social scientific inquiry, comparative historical analysis can yield important empirical knowledge for political theorists. But I wish to more ambitiously suggest that comparative historical analysis can also be adapted for conducting normative political theory itself. I summarise the key features of comparative historical analysis for political theorists, and explain how its use of historical cases can supplement both the use of imaginary thought experiments and the study of contemporary politics. I then delineate three specific ways in which comparative historical analysis might be used to support normative inquiry: deductive testing, inductive construction, and casuistic elaboration. All three, I argue, can help meet recent calls to bring the study of real political behaviour more centrally into political theory research.

Autism Early Intervention - Progress, Steps Backward, and the Reconciliation of Conflicting Narratives.

We review recent research on interventions, services and supports for children on the autism spectrum, examining both advancements and setbacks. Progress has included an increase in quantity and rigor of intervention science, as well as a broadening of disciplines and perspectives engaged in the examination of early interventions, including their effectiveness, social validity and the contextual determinants of implementation outcomes. Setbacks have included the decrease in research involving children on the autism spectrum who have co-occurring profound intellectual disability, minimal or no spoken language, and who require constant assistance with daily living activities. This trend is alarming because it contributes to the marginalization and unmet needs of children who need intervention the most. Additionally, access to early intervention services is unequal and complicated by the misalignment of policy with the evolving evidence base in the field. The recent growth in the depth and breadth of knowledge related to autism early intervention means that policies, practices, advocacy efforts and research directions can be grounded on a more comprehensive evidence base and societal appraisal of autism. Nevertheless, these indisputable markers of success co-exist with conflicting narratives that hinder the establishment of a cohesive agenda to tackle inequities experienced by marginalized subgroups. Reconciliation of conflicting narratives requires a nuanced and compassionate appraisal of sources of tensions and heterogeneity of needs within the autism spectrum.

Veteran Affairs disability compensation: Likely the U.S.’s largest disability program, but what do we know about its impacts on service‐disabled veterans?

AbstractThe last few decades have seen rapid growth in the size of the Veterans Affairs Disability Compensation (VADC) program, which provides tax‐free cash benefits to veterans with disabilities connected to military service. Given this recent growth, VADC is on pace to eclipse Social Security Disability Insurance (SSDI) to become the largest U.S. disability program by expenditures. Although there are decades of causal research on the design and impacts of SSDI, there is no comparable body of evidence for VADC. In this Policy Insight, we discuss how causal evidence was produced for Social Security‐administered disability programs and why there is a lack of this evidence for VADC. Chief among the explanations is a lack of VADC data access available to the broader researcher community, access that Social Security facilitates for the programs it administers. However, even with this access, the proliferation of benefits and services targeted to service‐disabled veterans implies that existing earnings loss studies and causal estimates of the impact of VADC benefit receipt on any given outcome of interest likely are mismeasurements of true effects. We conclude with recommendations for restructuring approaches to research design to accurately estimate impacts of VADC benefits as well as the wide array of other programs supporting service‐disabled veterans.

Budgeting for defense in the United Kingdom

The recent growth in international tension has been associated with countries either increasing their military spending or discussing such increases—consequently, defense budgeting has attracted increased attention. This article reviews the U.K. defense budget in an historical context. It sets out the position in mid-2024 when the newly elected Labour Government commissioned a Strategic Defence Review. It summarizes the planning process and the government’s budget constraints; considers the issues involved in measuring the defense budget and making international comparisons; reviews trends in the defense budget; and the choices that need to be made between spending on personnel, conventional equipment, and nuclear weapons.

The evolution of transnational municipal knowledge networks

AbstractThe recent growth of transnational municipal networks requires a better understanding of their evolution. For some actors, this growth has resulted in arbitrary or top‐down decisions on network membership followed by low commitment and inefficient use of time and financial resources. By reinterpreting secondary data through an evolutionary approach, this paper argues that the overall systemic nature of the networks is shaped not only by their institutional settings and the composition of actors but also by a set of specific qualities (coherence, boundedness and unified function) that may change over time. Based on the network perspective and system approach, the paper advances our understanding of transnational knowledge networks and the maximising of their effectiveness. Using exemplars, it suggests a conceptual framework for the evolution of transnational municipal knowledge networks.

No longer just work and play? Exploring recent residential growth within America’s “edge cities”

No longer just work and play? Exploring recent residential growth within America’s “edge cities”

The Creation of a Free Outpatient Surgical Clinic: A Descriptive Report

Student run free clinics (SRFCs) are continuing to grow in scope and number given the significant benefits to both learners and disadvantaged patients. Despite recent growth, very few provide any form of surgical services. In order to address inequalities in patient access to care and clinical outcomes, it is imperative to include disadvantaged surgical patients, including in the settings of SRFCs. The Kansas City based chapter of Socially Responsible Surgery (SRS) opened a free, volunteer based, student and resident run outpatient surgical clinic. In this article we describe detailed steps to the establishment of the SRS Clinic to lay the framework for other SRFCs or chapters of SRS to continue to grow the number of free clinics able to provide surgical services.

Supporting energy policy research with large language models: A case study in wind energy siting ordinances

The recent growth in renewable energy development in the United States has been accompanied by a simultaneous surge in renewable energy siting ordinances. These zoning laws play a critical role in dictating the placement of wind and solar resources that are critical for achieving low-carbon energy futures. In this context, efficient access to and management of siting ordinance data becomes imperative. The National Renewable Energy Laboratory (NREL) recently introduced a public wind and solar siting database to fill this need. This paper presents a method for harnessing Large Language Models (LLMs) to automate the extraction of these siting ordinances from legal documents, enabling this database to maintain accurate up-to-date information in the rapidly changing energy policy landscape. A novel contribution of this research is the integration of a decision tree framework with LLMs. Our results show that this approach is 85 to 90 % accurate with outputs that can be used directly in downstream quantitative modeling. We discuss opportunities to use this work to support similar large-scale policy research in the energy sector. By unlocking new efficiencies in the extraction and analysis of legal documents using LLMs, this study enables a path forward for automated large-scale energy policy research.

DETECTION OF ORAL SQUAMOUS CELL CARCINOMA USING PRE-TRAINED DEEP LEARNING MODELS.

Oral squamous cell carcinoma (OSCC), the 13th most common type of cancer, claimed 364,339 lives in 2020. Researchers have established a strong correlation between early detection and better prognosis for this type of cancer. Tissue biopsy, the most common diagnostic method used by doctors, is both expensive and time-consuming. The recent growth in using transfer learning methodologies to aid in medical diagnosis, along with the improved 5-year survival rate from early diagnosis serve as motivation for this study. The aim of the study was to evaluate an innovative approach using transfer learning of pre-trained classification models and convolutional neural networks (CNN) for the binary classification of OSCC from histopathological images. The dataset used for the experiments consisted of 5192 histopathological images in total. The following pre-trained deep learning models were used for feature extraction: ResNet-50, VGG16, and InceptionV3 along with a tuned CNN for classification. The proposed methodologies were evaluated against the current state of the art. A high sensitivity and its importance in the medical field were highlighted. All three models were used in experiments with different hyperparameters and tested on a set of 126 histopathological images. The highest-performance developed model achieved an accuracy of 0.90, a sensitivity of 0.97, and an AUC of 0.94. The visualization of the results was done using ROC curves and confusion matrices. The study further interprets the results obtained and concludes with suggestions for future research. The study successfully demonstrated the potential of using transfer learning-based methodologies in the medical field. The interpretation of the results suggests their practical viability and offers directions for future research aimed at improving diagnostic precision and serving as a reliable tool to physicians in the early diagnosis of cancer.

Impact of Motivational Factors on Employee Performance on Banking Industry in Nepal

This research investigates the impact of motivational factors on employee performance within the Nepalese banking industry, a sector critical for the nation's financial stability and economic growth. Recent growth in Nepal's banking sector, driven by factors such as increased financial inclusion and technological advancements, underscores the need for a highly motivated and productive workforce. Employee performance, defined as the measurable contribution of an individual to organizational goals, is crucial for maintaining a competitive edge. This study explores the relationship between motivation and performance, drawing on need-based theories, goal-setting theory, and social cognitive theory, while considering Nepal's unique socio-cultural context. This study investigates the impact of intrinsic and extrinsic motivational factors on employee performance within the banking sector in Nepal. Data were collected from 350 respondents across various banks through online surveys, with a pilot test conducted to ensure clarity and reliability. The survey assessed both types of motivational factors and their influence on employee performance. Demographic analysis provided key insights into the participant pool, enhancing the understanding of the sample's characteristics. The findings underscore the importance of both intrinsic factors, such as job satisfaction and professional development, and extrinsic factors, including competitive salaries and job security, as key motivators for employee performance. Additionally, the study highlights the influence of cultural values, such as collectivism and respect for hierarchy, in shaping employee motivation in Nepal. By employing a descriptive research design and rigorous methodological steps, including statistical analysis using SPSS, this study provides a comprehensive understanding of the motivational landscape in Nepal's banking sector. The insights gained can guide banks in developing effective strategies to enhance employee motivation and performance, contributing to the overall success and growth of the industry.

Water quality assessment using national Water Quality Index (WQI): a case study of the Song Be River, Bing Duong Province, Vietnam

Abstract The Song Be River is a vital water source in Binh Duong Province, Vietnam, playing a crucial role in supplying water for domestic use and agriculture, as well as supporting local ecosystems. However, due to the recent rapid economic and industrial growth, the river’s water quality has encountered significant challenges, particularly from the improper discharge of untreated wastewater from various sources and activities. This study conducts a comprehensive analysis of the Song Be River’s water quality by employing the Water Quality Index (WQI) in conjunction with Geographic Information System (GIS) technology. Subsequently, several strategies are also proposed to effectively manage and enhance the water quality of the Song Be River. Over the period from March 2023 to March 2024, 52 samples were collected from four monitoring points (SB1, SB2, SB3, and SB4) along the Song Be River, with a frequency of once per month. All analytical parameters (such as NH4 +_N: 0.01–0.61 mg l−1, NO2 −_N: 0.014–0.03 mg l−1, Fe: 0.01–0.81 mg l−1, Chemical Oxygen Demand (COD): 4–26 mg l−1, Dessolved Oxygen (DO): 3.8–6.6 mg l−1, Suspendid Solids (SS): 5–69 mg l−1, Coliform: 1200–4300 MPN/100 ml, Total phosphorous: 0.01–0.08 mg l−1 and Total nitrogen: 1.16–4.5 mg l−1) fell within the permissible limits according to the Vietnamese National Standards. Furthermore, the WQI ranged from 73 to 100, indicating that the water quality of The Song Be River is good and suitable for domestic use.

Graph Neural Network-Based Node Deployment for Throughput Enhancement.

The recent rapid growth in mobile data traffic entails a pressing demand for improving the throughput of the underlying wireless communication networks. Network node deployment has been considered as an effective approach for throughput enhancement which, however, often leads to highly nontrivial nonconvex optimizations. Although convex-approximation-based solutions are considered in the literature, their approximation to the actual throughput may be loose and sometimes lead to unsatisfactory performance. With this consideration, in this article, we propose a novel graph neural network (GNN) method for the network node deployment problem. Specifically, we fit a GNN to the network throughput and use the gradients of this GNN to iteratively update the locations of the network nodes. Besides, we show that an expressive GNN has the capacity to approximate both the function value and the gradients of a multivariate permutation-invariant function, as a theoretic support to the proposed method. To further improve the throughput, we also study a hybrid node deployment method based on this approach. To train the desired GNN, we adopt a policy gradient algorithm to create datasets containing good training samples. Numerical experiments show that the proposed methods produce competitive results compared with the baselines.

Does structural change matter for sustainable development in newly industrialized countries? Fresh evidence from a new sustainability indicator

The economic gap between developed and developing economies has been diminishing due to the recent rapid economic growth performance of developing economies. While achieving this swift growth level, developing economies have structurally transformed their economies. However, the impact of this structural change on environmental sustainability remains unclear. Therefore, this study focuses on the impacts of structural change, energy structure, and economic growth on the load capacity factor, a comprehensive sustainability indicator, in newly industrialized countries during 2000–2020. To this end, second-generation panel data techniques, which account for cross-sectional dependence and heterogeneity, are used to provide more robust and reliable estimates. The results show that economic growth, structural change, and fossil energy utilization decrease the load capacity factor while renewable energy utilization increases it. These findings underscore the need for energy efficiency and resource-conscious policies that align with environmental sustainability while promoting economic growth, highlighting their crucial role in the future of sustainable development.

The Fertility Transition in Sub-Saharan Africa: The Role of Structural Change.

Despite recent economic growth and reductions in child mortality in many African countries, the region has experienced a slow fertility transition. In this study, we explore whether the slow structural economic change on the continent can explain this discrepancy. We construct a unique panel dataset combining Demographic and Health Surveys and nighttime light intensity data (an indicator of industrialization) from 57 countries at the subnational region level over three decades to analyze the drivers of fertility transitions across low- and middle-income countries. Our results confirm that household wealth, reduced child mortality, and female primary education are crucial for fertility declines. However, our analysis also highlights the importance of indicators of structural economic change, including the share of labor in nonagricultural occupations, industrialization, the share of women with higher education, and the formalization of the economy. Our simulations suggest that if high-fertility countries in sub-Saharan Africa underwent structural economic transformations comparable to those of other low- and middle-income countries with low fertility rates, their fertility levels could fall by 1 to 1.6 children.

The Impact of Social Class Solidification on the Educational Opportunities of the Next Generation in the Philippines and the Barriers to Social Mobility in Primary Education

This paper explores the impact of social class solidification on the educational opportunities of the next generation in the Philippines, focusing specifically on primary education and the barriers to social mobility faced by children from low-income families. Despite recent economic growth, the Philippines remains deeply stratified, with income inequality and social class divisions reinforcing each other. This entrenched disparity limits access to quality education for disadvantaged students, exacerbating the cycle of poverty. Key barriers include economic constraints, underfunded public schools, language mismatches, and limited access to early childhood education, all of which contribute to lower academic achievement and higher dropout rates among poor students. The long-term effects of these barriers perpetuate inequality, hinder the development of a skilled workforce, and contribute to social instability. The paper argues that systemic reforms, such as increased investment in public education, targeted support for disadvantaged students, language and curriculum reforms, and expanded access to early childhood education, are critical to breaking this cycle. By addressing these barriers, the Philippines can create a more equitable education system that enables upward social mobility and promotes long-term social and economic stability.

Unraveling the Genetic Legacy of Commercial Whaling and Population Dynamics in Arctic Bowhead Whales and Narwhals.

Assessing genetic structure and diversity in wildlife is particularly important in the context of climate change. The Arctic is rapidly warming, and endemic species must adapt quickly or face significant threats to persistence. Bowhead whales (Balaena mysticetus) and narwhals (Monodon monoceros) are two long-lived Arctic species with similar habitat requirements and are often seen together in the Canadian Arctic. Although their ranges overlap extensively, bowhead whales experienced significantly greater commercial whaling mortality than narwhals over several centuries. The similar habitat requirements but different harvest histories of these two species provide an opportunity to examine present-day genetic diversity and the demographic and genetic consequences of commercial whaling. We whole-genome resequenced contemporary Canadian Arctic bowhead whales and narwhals to delineate population structure and reconstruct demographic history. We found higher genetic diversity in bowhead whales compared to narwhals. However, bowhead whale effective population size sharply declined contemporaneously with the intense commercial whaling period. Narwhals, in contrast, exhibited recent growth in effective population size, likely reflecting exposure to limited opportunistic commercial harvest. Bowhead whales will likely continue to experience significant genetic drift in the future, leading to the erosion of genetic diversity. In contrast, narwhals do not seem to be at imminent risk of losing their current levels of genetic variation due to their long-term low effective population size and lack of evidence for a recent decline. This work highlights the importance of considering population trajectories in addition to genetic diversity when assessing the genetics of populations for conservation and management purposes.