Adjustment Policies Research Articles

Enhanced dynamic fine‐grained popularity‐based caching algorithm for ICN‐based edge computing networks

AbstractIn this letter, we propose an Enhanced Dynamic Fine‐Grained Popularity‐based Caching (ED‐FGPC) algorithm to efficiently cache static contents in ICN‐based edge computing networks. The content popularity changes with time. Thus, the ED‐FGPC algorithm considers the number of incoming interests, the practical file size, and the available cache size to adjust the content popularity threshold. This allows dynamic adjustment of the content popularity threshold. Additionally, ED‐FGPC incorporates a single bit called cached bit (CB) in the header field of the content. This eliminates caching redundancy in the on‐path routers. The cache eviction policy jointly considers the content popularity and the average content access timestamp for content replacement. Based on the content access pattern, this allows finer adjustment of the content eviction policy. We use analytical and mathematical modeling to derive the effectiveness and efficiency of our contribution. Results present a better hit ratio due to dynamic adjustment of the content popularity threshold and the elimination of caching redundancy from the on‐path routers.

Trends in Pregabalin Use and Prescribing Patterns in the Adult Population: A 10-Year Pharmacoepidemiologic Study.

Pregabalin is steadily gaining popularity worldwide, with epidemiological studies indicating an increase in labeled, off-labeled, and recreational uses. In Israel, pregabalin prescriptions are not regulated by the controlled substances legislations, prompting a need to examine its usage trends for potential policy adjustments. The objective of this study was to assess trends in pregabalin prescribing during a 10-year period, to characterize demographic and clinical characteristics of individuals prescribed pregabalin, and to identify risk factors associated with high-intensity pregabalin use. This retrospective, longitudinal study examined trends in pregabalin prescribing from 2010 to 2019 based on data extracted from the Clalit Health Services (CHS) electronic database. Annual pregabalin prescribing rate was calculated individually for each reporting year. A univariable analysis was conducted to compare the demographic and clinical characteristics of pregabalin users in 2019 with those in 2010. Multivariable regression analysis was performed to assess dose-related patterns by specific demographic and clinical characteristics. Pregabalin prescription rate more than doubled over 10 years [odds ratio (OR) 2.3, p = 0.001], reaching 7.2 [95% confidence interval (CI) 7.18-7.28] prescriptions per 100 CHS members in 2019. The highest prescription rates were observed among the elderly population (13.2 and 24.1 prescriptions per 100 CHS members for those aged 55-74 and over 75 years old, respectively). Same-year administration of pregabalin with opioids, benzodiazepines, and Z-drugs was common; however, the percentage of patients using these drugs together declined in 2019 compared with 2010 (p < 0.001). Males, patients with low socioeconomic status, patients aged 35-54 years, and those who consumed opioids, benzodiazepines, and Z-drugs received higher pregabalin doses. Pregabalin use has increased significantly in the Israeli adult-based CHS population, consistent with worldwide data. A growing use over time may indicate overprescription. More studies are needed on misuse patterns to identify populations most susceptible to high-dose and high-intensity pregabalin use.

An improved deep Q-learning algorithm for a trade-off between energy consumption and productivity in batch scheduling

The single-batch machine, commonly found in industrial manufacturing, can concurrently process a group of jobs in variable-speed batches, leading to fluctuating levels of both energy consumption and processing time. Identifying an optimal balance between total energy consumption and makespan presents a challenge due to the intricate nature of their relationship in real-world scenarios. This study introduces a mixed-integer programming model designed to determine optimal machine operating states for diverse workloads, meeting customer requirements while concurrently achieving energy savings for sustainability. An energy-aware batch scheduling deep Q-learning network (EBSDQN) framework has been created, encompassing sequencing policies, batching rules, and speed adjustment policies. This framework is explicitly crafted to tackle the NP-hard problem, a challenge that commonly perplexes commercial solvers like Gurobi when seeking optimal solutions within a 360-second time frame for small-scale instances. The EBSDQN design is fortified with efficient action decoding policies, refined reward assessments, exploitative neighborhood rules, and a streamlined buffer training process. This approach not only conserves training time but also adapts dynamically in accordance with the principles of the Markov Decision Process (MDP) and deep neural network. The developed algorithm exhibits impressive robustness, reaching convergence after 600 episodes of training. In separate comparisons with a commercial solver, two single-objective algorithms, and two multi-objective algorithms, our algorithm consistently demonstrates superior overall performance across the same instances. In the worst-case analysis, further examination delves into the influences of job features. In summary, this study underscores the high sensitivity of both TEC and Cmax to job processing time distribution, suggesting the prioritization of TEC over Cmax in optimization for sustainable planning and operations. Furthermore, this research has the potential to accelerate the integration of artificial intelligence into the manufacturing sector.

Unveiling COVID-19’s impact on Financial Stability: A Comprehensive Study of Price Dynamics and Investor Behavior in G7 Markets

The paper delves into the impact of the COVID-19 pandemic on foreign stock markets across several developed nations. It seeks to empirically validate the presence of contagion by employing an adjusted correlation test spanning 7 developing stock markets from February 1, 1992, to April 31, 2021. Employing the FIEGARCH (1.1), DCC-MGARCH(1,1), and Switching-Markov analysis models, the research uncovers compelling evidence of the pandemic's influence on most developed countries during the COVID-19 period. Notably, these markets appear significantly susceptible to the adverse effects brought about by the pandemic. Recognizing the substantial ramifications of financial downturns on monetary policy, risk assessment, asset valuation, and portfolio distribution, the findings hold paramount significance for policymakers, investors, and portfolio managers. This empirical investigation offers insights that could profoundly impact decision-making strategies in these domains, shedding light on crucial aspects for informed policy adjustments, investment decisions, and portfolio allocations amidst such critical market fluctuations.

Analyzing the impact of COVID-19 on consumption behaviors through recession and recovery patterns

The COVID-19 outbreak has dramatically impacted the economy, particularly consumption behaviors. Studies on how consumption responses to COVID-19 can be a powerful aid for urban consumption recovery. In this paper, based on a high-frequency consumption dataset from January 6, 2020, to April 28, 2020 covering 18 sectors and dataset from the corresponding lunar period in 2021, we look at how COVID-19 changed how people spent their money by looking at patterns of recession and recovery during the pandemic. Specifically, we first explore the recession-recovery pattern of national consumption and the effects of various policies and quantify it using regression methods. Then, recession-recovery patterns across cities are widely studied. We also reveal how consumption structures change during a pandemic and the relationship between patterns of change in citizens’ consumption and the socioeconomic characteristics of cities. And the specific empirical analysis is provided through panel regression models. In general, national consumption represented a Vshaped pattern during the pandemic, experiencing a dramatic decline and a rapid rebound. Consumption is significantly inhibited by lockdown, while it is stimulated positively but gradually by easing policies. Consumption patterns at the city level are associated with socioeconomic characteristics. Cities with high-income groups experience a more significant decline, and cities with a high share of the secondary sector have a higher recovery rate in consumption. The consumption structure redistributes but does not fundamentally change. During the recession and early recovery phase, consumption related to basic living saw a significant rise, whereas leisure-related consumption dropped dramatically and recovered slowly. Our study can assist policymakers in implementing diversified market provisions and targeted lockdown policy adjustments for consumption recovery in cities with different socioeconomic backgrounds.

Real-World Dispensing of Buprenorphine in California during Prepandemic and Pandemic Periods.

The opioid overdose crisis in the United States has become a significant national emergency. Buprenorphine, a primary medication for individuals coping with opioid use disorder (OUD), presents promising pharmacokinetic properties for use in primary care settings, and is often delivered as a take-home therapy. The COVID-19 pandemic exacerbated the scarcity of access to buprenorphine, leading to dire consequences for those with OUD. Most existing studies, primarily focused on the immediate aftermath of the COVID-19 outbreak, highlight the challenges in accessing medications for opioid use disorder (MOUDs), particularly buprenorphine. However, these studies only cover a relatively short timeframe. To bridge this research gap, in our study, we utilized 33 months of California's prescription drug monitoring program (PDMP) data to provide insights into real-world buprenorphine dispensing trends since the onset of the pandemic from 2018 to 2021, focusing on outcomes such as patient counts, prescription volumes, prescriber involvement, days' supply, and dosage. Statistical analysis employed interrupted time series analysis to measure changes in trends before and during the pandemic. We found no significant impact on patient counts or prescription volumes during the pandemic, although it impeded the upward trajectory of prescriber numbers that was evident prior to the onset of the pandemic. An immediate increase in days' supply per prescription was observed post-pandemic. Our findings differ in comparison to previous data regarding the raw monthly count of patients and prescriptions. The analysis encompassed uninsured patients, offering a comprehensive perspective on buprenorphine prescribing in California. Our study's insights contribute to understanding the impact of COVID-19 on buprenorphine access, emphasizing the need for policy adjustments.

Mapping long‐term terrace change in the Chinese Loess Plateau: A new approach by decision tree model with digital elevation model (DEM) and land use

AbstractHuman‐created terraces are distributed extensively in the Chinese Loess Plateau, which play key roles in soil conservation, agricultural production and sustainable development. However, large‐scale and long‐term terrace mapping remains a big challenge due to the complexity of topography, land cover and the deficiency of high‐quality historical spatial data. Facing this task, our study aims to develop a new approach for capturing 30 years (from 1990 to 2020) of terrace patterns at macroscales (the whole Loess Plateau, with an area of 6.4 × 105 km2). The decision tree model (DTM) was integrated with digital elevation model (DEM) and land use data to detect terrace change, and terraced samples were extracted from existing findings for spatial validation. Our study confirmed that this new approach can work successfully on identifying cultivated and grassy terraces, as evidenced by receiver operating characteristic (ROC) curves and area under curve (AUC) values. More notably, a decreasing trend was detected in cultivated terraces with continued uneven distribution from 1990 to 2020, while the areas of grassy terraces increased markedly with more‐concentrated larger patches. This finding indicated that huge areas of terrace abandonment may have already occurred in this region. More attention thus should be paid to the rising risks of cropland utilization and food security. Since it is the first time to get long‐term reliable terrace maps on the Loess Plateau, our efforts can help to better take stock of terrace resources for wiser land use managements and agricultural policy adjustments, finally benefiting socio‐ecological sustainability.

Comparing the Performance of Three Computational Methods for Estimating the Effective Reproduction Number.

The effective reproduction number is one of the most important epidemiological parameters, providing suggestions for monitoring the development trend of diseases and also for adjusting the prevention and control policies. However, a few studies have focused on the performance of some common computational methods for Rt. The purpose of this article is to compare the performance of three computational methods for Rt: the time-dependent (TD) method, the new time-varying (NT) method, and the sequential Bayesian (SB) method. Four evaluation methods-accuracy, correlation coefficient, similarity based on trend, and dynamic time warping distance-were used to compare the effectiveness of three computational methods for Rt under different time lags and time windows. The results showed that the NT method was a better choice for real-time monitoring and analysis of the epidemic in the middle and late stages of the infectious disease. The TD method could reflect the change of the number of cases stably and accurately, and was more suitable for monitoring the change of Rt during the whole process of the epidemic outbreak. When the data were relatively stable, the SB method could also provide a reliable estimate for Rt, while the error would increase when the fluctuation in the number of cases increased. The results would provide suggestions for selecting appropriate Rt estimation methods and making policy adjustments more timely and effectively according to the change of Rt.

Cultural-Land Bond: A Multifaceted Exploration of First Nations Historical Trajectory, Present Realities, and Sustainable Futures

This article explores the intricate and enduring connection between First Nations communities in Canada and their ancestral lands. The central research question guiding this investigation is: How do these multifaceted factors intersect to shape the lasting cultural-land bond? Utilizing a comprehensive literature review methodology, encompassing historical records, scholarly works, and indigenous perspectives, this study synthesizes information to provide a nuanced understanding of the complex dynamics at play. The key argument put forth is that the historical trajectory, colonial-era policies, Indigenous-colonial treaties, present-day social structures, economic considerations, environmental challenges, familial traditions, and core cultural values collectively contribute to the vitality of the cultural-land connection. The academic impact of this research lies in its revelation of patterns and common themes, offering crucial insights for policy adjustments. The article advocates for comprehensive legislation that respects Indigenous rights, encourages community-led initiatives, and promotes environmental sustainability. The implication of this work is a call to action, urging continued research, legislative support, and community engagement to ensure the preservation of this enduring connection for the benefit of current and future generations.

GLOBAL PERSPECTIVES ON MOTIVATION IN EFL LEARNING: A SYSTEMATIC LITERATURE REVIEW

This systematic literature review delves into the varied global motivations shaping English as a Foreign Language (EFL) learning within diverse university contexts. Examining Tay Do University in Vietnam, the study illuminates a blend of intrinsic and extrinsic factors contributing to moderate motivation levels. Lithuania's emphasis on instrumental orientation among university students showcases the multifaceted nature of motivation. In Sweden, the research brings attention to challenges faced by newly arrived students, suggesting a need for policy adjustments to address these issues effectively. Indonesian graduates opt for informal education to enhance English proficiency, underlining the importance of diverse methods and success stories in the learning process. Cambodia's exploration reveals nuanced motivational orientations among university students. French higher education students exhibit diverse attitudes toward EFL learning, Japan displays a positive outlook, Saudi students lean towards extrinsic motivations, and Taiwan emphasizes the creation of motivated learning experiences. These findings collectively offer valuable insights for refining teaching strategies, making informed policy adjustments, and inspiring further research initiatives within the realm of EFL learning. Ultimately, this comprehensive review contributes to a deeper understanding of the multifaceted nature of motivations influencing EFL learners globally.

Integrating marine functional zoning in coastal planning: Lessons from the Xiasha Beach Resort case study

Marine spatial planning (MSP) represents a critical tool for the worldwide design of coastal development and protection strategies. China, as one of the pioneers in establishing comprehensive MSP policies, has created a well-developed marine planning structure—known as the “multi-rule-in-one"—with Marine Functional Zoning (MFZ) serving as a cornerstone for the country's coastal economic and environmental development. Despite its significance, limited studies have presented the practical application of MSP/MFZ policies upon tourism exploitation in coastal regions. This paper fills this research gap by thoroughly examining China's marine planning structure, the implementation of provincial MFZ policy, and the operational mechanisms of these strategies. The analysis focuses on the Xiasha Beach Resort, a case study exemplifying the coordinated efforts required among governments, scientists, and the public for the successful implementation of MFZ policy. Findings from the Xiasha Beach Resort highlight the importance of robust tourist market management, a shift from hard seawall structures towards more eco-friendly alternatives, and a high degree of collaboration among key stakeholders. Furthermore, the study emphasizes the importance of ecosystem-based restoration technology that maintains the integrity of the beach-dune system, fosters diverse vegetation, and encourages adaptive measures such as environmental capacity control. The sustainability of the MSP approach hinges on wide and diverse public participation, as national MSP policies foster trust, credit, and feedback from local citizens, promoting necessary policy adjustments. In conclusion, this study offers valuable insights for domestic and international regions seeking to harmonize MSP operations to achieve high-quality coastal economic development and environmental protection targets.

Predicting China’s elderly population by using a fractional grey prediction model with time power term

Accurate prediction of the aging population can provide valuable reference and corresponding theoretical support for the adjustment of national population development policy and economic development strategy. To explore the future development trend of China’s aging population, this paper establishes a novel fractional grey prediction model with the time power term (abbreviated as FGM (1, 1, t α) model) to study China’s aging population. FGM (1, 1, t α) has the properties of fractional order accumulation operation and GM (1, 1, t α) model, which makes it good at capturing nonlinear features in time series. Furthermore, the quantum genetic algorithm is used to search for unknown parameters in the model to facilitate the solving task of the model. Data on China’s aging population from 2000 to 2009 are used to train the prediction models, and data from 2010 to 2019 are used to evaluate the models’ prediction performance. The results show that the FGM (1, 1, t α) model outperforms the other competing models, which means that it has good generalization. Finally, the FGM (1, 1, t α) model is used to forecast China’s aging population from 2020 to 2029.

Study on the logic and effectiveness of crisis learning in the promotion policy adjustment: an observation based on the adjustment of COVID-19 prevention policy in China.

As the impact of COVID-19 on normal production and living conditions diminishes, this serious emergency is come to an end. China's policy framework has facilitated positive adjustment over the past 3 years by timely modifying its emergency response to changes in viruses and epidemics. This paper aims to explore the logic of China's policy framework that promoted policy adjustment through crisis learning during COVID-19. By gathering and classifying China's epidemic prevention policies throughout the past 3 years, integrating policy texts, and analyzing key events, this article examines the process of supporting policy adjustment through crisis learning in the policy system during COVID-19. The Chinese government's COVID-19 policy adjustment process can be divided into four stages, namely 'The period of stress response', 'The period of COVID-19 prevention and control', 'The period of regular prevention and control', and 'The period of overall adjustment'. The policy adjustments in each stage demonstrate the logic and effectiveness of crisis learning in the promotion policy adjustment. The study has determined that the motivational logic comprises three crucial elements: security requirements, accountability pressure, and reputation management. The institutional logic encompasses both the organizational and resourceful environments, and the institutional and cultural environment. Additionally, the behavioral logic of policy adaptation aligns with the strategy of crisis learning. Meanwhile, the logical framework of 'crisis learning-policy adjustment' can be verified using the Chinese government's policy adjustment in COVID-19 as an example. Establishing an effective post-crisis learning system is crucial to improving the effectiveness of crisis response. There is a logical link between crisis learning and policy adjustment. The implementation of policy adjustment needs to be based on the results of crisis learning. Government departments are essential for crisis learning and policy adjustment.

Research on the Impact of the Adjustment of Fertility Policy on China's National Economy and Society

Since the family planning policy was put into effect, the aging of the Chinas population has become more and more serious, which has hindered Chinas economic and social development. In order to alleviate or even change this trend, China has decided to encourage childbirth and has successively issued the universal two-child policy and Three-child policy. This article adopts literature research and case analysis methods to study the effect of the adjustment of fertility policies on Chinas national economy and society from six aspects. At the same time, the influence of Japans fertility policy on Chinas fertility policy in response to its Sub-replacement fertility problem, which has similar population issues to China, was analyzed. Finally, based on the analysis of the changes brought about by the adjustment of the fertility policy in China, several suggestions were put forward for the adjustment of Chinas fertility policy based on future population trends, providing a reference for the formulation of Chinas fertility policy.

Analysis and Forecast of China's Unemployment Rate

The unemployment rate is an important economic indicator that measures the proportion of the unemployed labor force. The natural unemployment rate is the normal unemployment rate based on economic fluctuations. Analyze the unemployment rate to determine its root causes. By forecasting the unemployment rate, people can obtain an estimate of the future conditions of the labor market. Not only can the government make policy adjustments based on this, people can use this prediction to make wise career choices and planning, or to learn the necessary skills. The risk of unemployment is closely related to everyone, especially now during the economic downturn due to the impact of global catastrophic events such as COVID-19. Understanding the unemployment rate is an important part of analyzing the characteristics of the current labor market. If we can make more connections about the labor market and even make rough predictions about future trends, college students will be able to make future career choices that are more suitable for them. This study predicts the unemployment rate in the next nine years based on China's unemployment rate from 2012 to 2022, which will fluctuate within a fixed range. The government can increase spending (such as on education) and use monetary and finance polices to ensure that unemployment does not exceed forecasts.

Shaping the Future of Social Security Management Beyond Retirement: A Review of Literature

This research delves into the advancements, in managing Social Security with the goal of navigating the complexities of today’s socio landscape. By conducting a literature review and examining studies this paper explores significant transformations taking place within social security systems. Notable innovations encompass the integration of technologies, contribution models, personalized retirement planning, inclusive coverage for nontraditional workers flexible policy adjustments, international coordination efforts, applications of behavioural economics and strategies for managing longevity risks. The analysis synthesizes insights from perspectives to inform policymakers researchers and stakeholders about how social security management's evolving in response to demographic changes, technological advancements and shifts, in the workforce.

The Impact of the Corona Virus on Supply Chains: Opportunities and Challenges

&lt;p class="MsoNormal" style="margin-top: 12pt; text-align: justify;"&gt;&lt;span lang="EN-US" style="font-family: 'times new roman', times, serif; font-size: 14pt;"&gt;The COVID-19 pandemic wreaked havoc on economies everywhere, affecting everything from supply chain networks to international trade as a direct result of the increased vulnerability of more nations and the disruption of the activities of key traders. This research study examines how COVID-19 affected the supply chain on a worldwide and regional scale, as well as the systemic policy and economic steps governments and businesses took to rebound and maintain resilience. COVID-19 has had a significant influence, prompting governments and businesses to take steps to fortify their operations and businesses against disruptions, highlight the necessity for resilience in supply chains and emphasize the importance of managing risk. The supply value chain is at risk from the prolonged implementation of COVID-19 trade measures like border closure, export restrictions, and import sanctions. Many businesses are transitioning from a "recovery mode" to a resilient and sustainable mode" and are beginning to plan for the future. The study suggests that corporate and government leaders should prioritize proactive and adaptable policy, economic, and structural adjustment. This research will be useful to policymakers and industry leaders in their efforts to lessen the effect of COVID-19 on the global supply chain by enforcing governmental, economic, and business reforms built on resilient supply chains and mitigating economic risks during tough times.&lt;/span&gt;&lt;/p&gt;

Optimizing credit limit adjustments under adversarial goals using reinforcement learning

Reinforcement learning has been explored for many problems, from video games with deterministic environments to portfolio and operations management in which scenarios are stochastic; however, there have been few attempts to test these methods in banking problems. In this study, we sought to find and automatize an optimal credit card limit adjustment policy by employing reinforcement learning techniques. In particular, because of the historical data available, we considered two possible actions per customer, namely increasing or maintaining an individual’s current credit limit. To find this policy, we first formulated this decision-making question as an optimization problem in which the expected profit was maximized; therefore, we balanced two adversarial goals: maximizing the portfolio’s revenue and minimizing the portfolio’s provisions. Second, given the particularities of our problem, we used an offline learning strategy to simulate the impact of the action based on historical data from a super-app (i.e., a mobile application that offers various services from goods deliveries to financial products) in Latin America to train our reinforcement learning agent. Our results, based on the proposed methodology involving synthetic experimentation, show that a Double Q-learning agent with optimized hyperparameters can outperform other strategies and generate a non-trivial optimal policy not only reflecting the complex nature of this decision but offering an incentive to explore reinforcement learning in real-world banking scenarios. Our research establishes a conceptual structure for applying reinforcement learning framework to credit limit adjustment, presenting an objective technique to make these decisions primarily based on data-driven methods rather than relying only on expert-driven systems. We also study the use of alternative data for the problem of balance prediction, as the latter is a requirement of our proposed model. We find the use of such data does not always bring prediction gains.

Change and Development of News Communication in China

This paper discusses the change and development of the field of news dissemination in China, focusing on the far-reaching impact of technological progress, policy adjustments and changes in social demand on news dissemination. With the rapid development of the Internet and mobile communication technology, the form of news dissemination has undergone a fundamental change, gradually shifting from traditional media to new media. This transformation has not only changed the way of information production and dissemination, but also reshaped the audience’s habits of acquiring and consuming information. By systematically analysing these changes and developments, this paper reveals the important impacts of technological advancement drives, policy adjustments and changes in social demands on the development of news dissemination in China, which have promoted the speed and breadth of information dissemination, changed the way of news production, and allowed for increased audience participation, but also intensified media competition. It aims to provide reference for related research and useful insights for news communicators in practice.

COUNTING THE COSTS: THE UNINTENDED CONSEQUENCES OF GHANA’S FREE SENIOR HIGH SCHOOL POLICY ON HOUSEHOLD POVERTY IN GHANA

This study investigates the unexpected repercussions of Ghana's Free Senior High School (FSHS) program on household poverty, with a particular emphasis on household savings. Structured interviews were used to obtain primary data from a purposive sample of 130 parents and 8 schools, including both private and public institutions. The study unveils that the FSHS policy has led to several unintended negative outcomes, including higher financial expenses, longer periods spent at home, lower academic achievement, inadequate infrastructure, overcrowded classrooms, insufficient meal provision, lack of teaching materials, and an increase in teenage pregnancies. The results of multiple linear regression analyses show that compared to increased financial stress, high rates of teenage pregnancy, lack of educational resources, low academic performance, extended amounts of time spent at home, and inadequate nutrition, have a significant negative effect on monthly household savings. The perceived unnecessary nature of the school list also significantly contributes to the adverse effect on household savings. The study suggests that the criteria for giving schools prospectus should be reviewed to verify that they are essential and cost-effective, which would help to reduce excessive expenses for parents. Furthermore, it promotes ongoing engagement with parents, educators, and community stakeholders to collect feedback and improve the FSHS program in accordance with their experiences and requirements. An effective monitoring and evaluation structure is crucial for routinely assessing the effects of the FSHS program, utilizing data to inform adjustments in policy and practice.