Immigrant Strategies Research Articles

Migration policy as a factor influencing the USA presidential election

The article presents the transformation of US migration policy and analyzes its impact on the balance of power in the 2024 presidential election campaign. The 2024 election campaign in the US is characterized by significant differences between the major parties and their voters, as well as an emphasis on key economic and immigration issues that have divided American society. Mexico is the main labor donor to the US, and it is also a gateway for illegal migration from Latin America and Southeast Asia. Almost all the time of the existence of the United States, the immigration policy of the country shows that migration plays an important role in the development of American society. The problem of illegal migration has always existed and continues to have a significant impact on the socio-economic development of the country. The current U.S. immigration strategy combines continuity with restrictive measures, while seeking to adapt migration flows to the economic needs of the country. The article analyzes the impact of migration policy on the U.S. political landscape during the 2024 election campaign. Migration policy in the United States today is a tool for conducting political struggle and "settling scores". Illegal migrants from Latin American countries will have a particular impact on the upcoming elections, so a characteristic feature of the 2024 election campaign is a cross-party struggle for the Latin American electorate, coupled with tougher migration policies. The authors draw attention to the reasons for the transformation of the US migration system and give their forecast regarding the outcome of elections in the country under certain conditions.

Comparison of Mediterranean and Ukrainian Migration Impacts on V4 Countries

Today’s Europe still resonates with the migration crisis connected with the arrival of migrants from Africa and Asia. Despite its massive impact on Central Europe, the significant migratory flow from Ukraine avoids considerable media attention. This article compares the effect of the Mediterranean migration and Ukrainian migration on V4 countries before the Covid-19 pandemic. There is an significant disproportion in the volumes of the compared migratory flows favouring the Ukrainian migration. However, Mediterranean migration became a substantial issue in elections in particular V4 countries. Our analysis focuses on the causes of different perceptions of both migratory flows and their impacts on recipient countries at the social and foreign policy levels. According to our research, different perceptions of the flows by the people and politicians are connected to the level of immigrants’ cultural and linguistic proximity and their potential integration. As the current situation suggests, the stabilisation of the political situation in Ukraine will take a more extended period, and the subsequent social and economic renewal will take another period. We assume the migratory flows from Ukraine will continue in the following years. Similarly, the Mediterranean migratory flows are likely to continue, probably with the shifts in their intensity, depending on their home countries’ situation and the Schengen regulatory measures. All of the above-mentioned issues put pressure on recipient countries to create a long-term immigration strategy that would enable them to manage the migration flow-related problems without societal destabilisation. The submitted interdisciplinary article uses the theoretical basis of migration studies (immigration policies, push and pull factors) and international relations (neorealism). The methodology of the text is anchored in the analysis of the relevant literature and statistical sources. The title of the article suggests the use of the comparison method.

Directorate General of Immigration's Strategy in the Role of Border Security: An Index Study of Immigration Security

Directorate General of Immigration's Strategy in the Role of Border Security: An Index Study of Immigration Security

Intelligent optimization of inspection route based on multi-population integer coding particle swarm optimization algorithm

Aiming at the problems of premature convergence and slow convergence speed in the optimization process of single population particle swarm optimization algorithm, in order to improve the global convergence performance of particle swarm optimization algorithm, a hierarchical three-population integer coded particle swarm optimization algorithm based on grade evaluation with parallel structure is proposed and used to solve the traveling salesman problem (TSP). The algorithm imitates the form of biological aggregation in nature. In the initialization stage, three independent populations are generated according to different particle fitness, including an elite population composed of small-scale individuals with high fitness and two large-scale civilian populations composed of remaining individuals. The three populations apply the immigration strategy based on grade evaluation to exchange particles after a certain number of evolutionary generations. By applying the grade evaluation strategy, the natural law of the biological cluster is integrated into the particle swarm optimization algorithm, which effectively improves the optimization efficiency of the particle swarm optimization algorithm. The above algorithm is applied to the inspection route optimization of one of the traveling salesman problems. The results show that the improved algorithm is superior to the single population particle swarm optimization algorithm in terms of convergence speed, global optimization ability and stability.

The shaping of migrant society – Australia’s changing immigration strategy and policies

The shaping of migrant society – Australia’s changing immigration strategy and policies

Bringing the Doctor to the Patients: Cardiology Outreach to Rural Areas

Clinical outreach is a crucial but understudied health care service delivery model. Physicians staffing rural outreach clinics must allocate a limited resource (i.e., their time) between caring for patients at their main sites and outreach locations. Using a unique 30-year data set of decisions made by cardiologists, the authors estimate a constrained utility maximization model of time allocations across home and outreach locations. The results show that travel distance, potential competition, and patient demand for cardiology services significantly influence allocation decisions. This structural model is used to simulate the impact of a predicted reduction in cardiologist supply. The expected impacts are unevenly distributed, with some rural locations experiencing large decreases in access. The authors evaluate two policies to restore rural access: targeted immigration and a subsidy program. A subsidy program with an estimated annual cost of $406,000 can restore outreach after a 10% reduction in cardiologist supply. This option should be preferred to recruiting and supporting five additional cardiologists under a targeted immigration strategy. This research demonstrates the value of marketing modeling in addressing limited access to health care services and evaluating alternative policies for maintaining access in the face of coming physician shortages.

Dynamic electric vehicle routing problem considering mid-route recharging and new demand arrival using an improved memetic algorithm

With the widespread popularity of electric vehicles (EVs) in recent years, electric vehicle routing problem (EVRP) has received considerable attention from logistics management field. However, the real-world logistics and transportation system is always time-varying, such as new customer demand arrives dynamically. This dynamic transportation environment poses a great deal of challenges upon the decision-making of EVRP since the mid-route recharging of EVs has to be considered to meet the dynamic arrival of new customer demand due to the limited battery capacity. Therefore, a dynamic EVRP (DEVRP) with mid-route recharging and new demand arrival is investigated in this study. A mathematical model considering both recharging and routing decision-makings is developed, and then an improved memetic algorithm (IMA) with adaptive local search and economical random immigrant strategy is proposed. Four peer algorithms are used to test the performance of the proposed IMA on numerous extended benchmarks. It is observed that IMA can be treated as a more effective solution tool to achieve a better routing plan as well as the improved energy efficiency of EVs.

Genetic algorithm with immigration strategy to solve the fixed charge transportation problem

This paper is about improving the performance of genetic algorithm (GA) to solve the fixed-charge transportation problem (FCTP). Several approaches have been developed, based on adaptation and improvement of genetic operators. We propose a new genetic algorithm adopting an immigration strategy to maintain the diversity in the population and then overcome the stagnation of the values of the objective function. Thereby, we applied two types of immigration, random immigration and memory-based immigration. The numerical results obtained with several standard instances of the FCTP problem demonstrate the effectiveness of these strategies in improving the performance of the GA. Especilly, for the second strategy.

Comparison of active tuberculosis case finding strategies for immigrants in South Korea: Epidemiology and cost-effectiveness analysis.

Tuberculosis (TB) is one of the serious infectious diseases in South Korea, with 49 new cases per 100,000 people and 629 multi-drug resistant (MDR) cases reported in 2020. TB is increasing among immigrants in S. Korea, and various TB case finding strategies are being performed for screening. We compared active case finding (ACF) with passive case finding (semi-PCF) across epidemiological characteristics and investigated a cost-effective strategy for screening immigrants for TB. ACF driven by non-governmental organizations and semi-PCF as part of the government's visa renewal process using CXR with additional acid-fast bacilli (AFB) smear and cultures were performed. Epidemiological parameters were compared between the two TB screening projects, and costs were collected. Cost-effectiveness was evaluated using a decision analysis model from the health system perspective. The primary outcome was incremental cost-effectiveness ratio (ICER) per averted TB case. Additional probabilistic sensitivity analysis was conducted. ACF (2.02%) showed a higher TB prevalence rate than semi-PCF (0.67%) on CXR. For subjects older than 60 years, the suspected TB rate on CXR was significantly higher in ACF (36.6%) than in semi-PCF (12.2%) (P<0.01). TB incidence among the family visa type was significantly higher in ACF (1.96%) than in semi-PCF (0.88%) (P < 0.0012). Costs for ACF ($666.92) were $20.784 higher than for semi-PCF ($646.13), but TB progression decreased by 0.02, resulting in an ICER of $948.18 per averted TB case. In sensitivity analysis, the indirect costs of ACF and semi-PCF had the highest impact on ICER. ACF found more TB cases than semi-PCF through CXR screening, and suspect cases with old age and family visa type were more common in ACF than in semi-PCF. ACF is cost-effective as a TB screening strategy for immigrants.

A Trajectory-Based Immigration Strategy Genetic Algorithm to Solve a Single-Machine Scheduling Problem with Job Release Times and Flexible Preventive Maintenance

This paper considers the single-machine problem with job release times and flexible preventive maintenance activities to minimize total weighted tardiness, a complicated scheduling problem for which many algorithms have been proposed in the literature. However, the considered problems are rarely solved by genetic algorithms (GAs), even though it has successfully solved various complicated combinatorial optimization problems. For the problem, we propose a trajectory-based immigration strategy, where immigrant generation is based on the given information of solution extraction knowledge matrices. We embed the immigration strategy into the GA method to improve the population’s diversification process. To examine the performance of the proposed GA method, two versions of GA methods (the GA without immigration and the GA method with random immigration) and a mixed integer programming (MIP) model are also developed. Comprehensive experiments demonstrate the effectiveness of the proposed GA method by comparing the MIP model with two versions of GA methods. Overall, the proposed GA method significantly outperforms the other GA methods regarding solution quality due to the trajectory-based immigration strategy.

Integration of evolutionary algorithm in an agent-oriented approach for an adaptive e-learning

This paper describes an agent-oriented approach that aims to create learning situations by solving problems. The proposed system is designed as a multi-agent that organizes interfaces, coordinators, sources of information, and mobiles. The objective of this approach is to get learners to solve a problem that leads them to get engaged in several learning activities, chosen according to their level of knowledge and preferences in order to ensure adaptive learning and reduce the rate of learner abundance in an e-learning system. The search for learning activities procedure is based on evolutionary algorithms typically a genetic algorithm, to offer learners the optimal solution adapted to their profiles and ensure a resolution of the proposed learning problem. In terms of results, we have adopted “immigration strategies” to improve the performance of the genetic algorithm. To show the effectiveness of the proposed approach we have made a comparative study with other artificial intelligence optimization methods. We conducted a real experiment with primary school learners in order to test the effectiveness of the proposed approach and to set up its functioning. The experiment results showed a high rate of success and engagement among the learners who followed the proposed adaptive learning scenario.

Fake marriages, asylum, and gas station robberies: institutional determinants of migrants' strategies

I study institutional and political determinants of migrants’ immigration strategies using the United States’ immigration system as a case study. Drawing from work that theoretically connects decisions to immigrate legally vs. illegally as well as theoretical insights from literature on the economics of crime, I show how relative probabilities of successful migration using different strategies and relative utility gained using different strategies weigh heavily in immigrants’ calculus. To do so, I use qualitative evidence of migrant strategies in the face of migration policy constraints. These various policy constraints lead to a variety of different tactics revolving around decisions regarding whether to immigrate legally, to commit visa fraud, or to choose one type of visa over another. In the process, I also build on a rich body of Public Choice literature to demonstrate how complicated regulatory systems, in this case regulatory systems that quantitatively restrict migration, encourage rent-seeking by both migrants and nonmigrants.

Genetic Algorithm with Random and Memory Immigrant Strategies for Solving Dynamic Load Balanced Clustering Problem in Wireless Sensor Networks

In Wireless Sensor Networks (WSNs), clustering is an effective method to distribute the load equally among all the nodes as compared to flat network architecture. Due to the dynamic nature of the network, the clustering process can be viewed as a dynamic optimization problem and the conventional computational intelligence techniques are not enough to solve these problems. The Dynamic Genetic Algorithm (DGA) addresses these problems with the help of searching optimal solutions in new environments. Therefore the dynamic load-balanced clustering process is modeled using the basic components of standard genetic algorithm and then the model is enhanced is using immigrants and memory-based schemes to elect suitable cluster heads. The metrics nodes’ residual energy level, node centrality, and mobility speed of the nodes are considered to elect the load-balanced cluster heads and the optimal number of cluster members are assigned to each cluster head using the proposed DGA schemes such as Random Immigrants Genetic Approach (RIGA), Memory Immigrants Genetic Approach (MIGA), and Memory and Random Immigrants Genetic Approach (MRIGA). The simulation results show that the proposed DGA scheme MRIGA outperforms well as compared with RIGA and MIGA in terms of various performance metrics such as the number of nodes alive, residual energy level, packet delivery ratio, end-to-end delay, and overhead for the formation of clusters.

Increasing the Supply of Direct Support Professionals through Targeted Immigration

Agencies that provide supports and services to people who have intellectual and/or developmental disabilities are unable to hire sufficient number of adequately trained staff. Vacancy rates for those positions that provide direct support and services to people who have disabilities approach 20%. Insufficient compensation was noted as a primary cause of the workforce crisis, and the history of funding social does not suggest that increased funding is imminent. The crisis will continue without either a significant increase in the supply of workers or a decreased demand for workers. Targeted immigration strategies are proposed as a shortterm solution to increasing the supply of Direct Support Professionals currently existing in the intellectual disability field.

The construction, composition and rationale of immigrants’ network: The support strategies of Ghanaian immigrants in Toronto, Canada

AbstractIn this article, we investigate the construction, composition and rationale behind the personal networks of recent immigrants to Canada. Drawing on egocentric‐network analysis and interviews with 172 Ghanaian immigrants in Toronto, we reveal their networking strategies during their integration. First, we identify social locations that help create ties with different groups: workplaces and schools offer access to ties with non‐immigrants and other immigrants alike, while religious and ethnic organizations facilitate ties to co‐nationals (i.e., Ghanaians). Second, most individuals within immigrants’ network are co‐nationals whose relationship began in Canada, followed by sustained transnational ties in the origin and diaspora and few ties with the non‐immigrant. The nature of this network is explained by examining the migration project of immigrants together with the context of reception in Canada, which suggests a desire by immigrants to stay in Canada and make Canada their second home. The implications of these findings are discussed.

Migration, Marriage, and Cohabitation among Hispanic Immigrant Women in the United States.

Prior research shows links between the timing of migration and family formation, particularly childbearing, among Hispanic immigrants in the United States, with implications for women's socioeconomic well-being. However, temporal connections between migration and union formation, particularly non-marital cohabiting unions, remain underexplored. As cohabiting unions have long coexisted with marriage in parts of Latin America, this omission may be particularly misrepresentative of the family formation strategies of Hispanic immigrants. Drawing on data from the National Survey of Family Growth (2011-2017), I examined the association between the timing of migration and entry into first marital or non-marital (cohabiting) union, treating marriage and cohabitation as competing events for first union type. Among women whose first union was non-marital, I also examined the relationship between migration and the likelihood of transitioning out of the non-marital union, either through marriage or union dissolution. Results showed that marriage formation was high the year of migration, and increased again only after 6 years post-migration, whereas cohabitation was high the year of migration and continued to increase with each period following migration. Furthermore, non-marital unions formed prior to migration were likely to transition to marriage or dissolve, while those formed after migration were likely to remain non-marital. These findings point to distinctions in the types of partnerships formed before and after migration and to the salience of non-marital unions for women who migrate unpartnered, demonstrating the need for further research on the socioeconomic integration and well-being of unmarried or cohabiting immigrant women, and the dynamic connections between migration, gender, and family.

A Window Into Mental Health: Developing and Pilot-Testing a Mental Health Promotion Intervention for Mexican Immigrants Through the Ventanilla de Salud Program.

BackgroundMexican immigrants in the United States face mental health challenges, disparities, and limited access to healthcare; however, mental health promotion efforts specifically targeting this population have been insufficient. The objective of this study was to develop and test a mental health promotion intervention based on protective mental health factors and coping strategies for Mexican immigrants recruited through a free, consulate-based program in Atlanta.Material and MethodsWorking with the Ventanilla de Salud program, we conducted a longitudinal study in three phases: formative research and design, pre-intervention assessment and post-implementation evaluation. The intervention was designed based on the health promotion model and interviews with stakeholders. Qualitative information was collected by semi-structured interviews with participants before and after the intervention. Quantitative outcomes were knowledge about protective factors and coping mechanisms, and psychosocial distress. Differences were assessed using the Wilcoxon non-parametrical test. Intent-to-treat analysis was conducted with all participants who signed the informed consent (carrying last observation forward), and a complete case analysis was conducted with those who attended at least 70% of the sessions and completed the post- implementation evaluation.ResultsTwenty-five participants were enrolled in the intervention. Mean age was 38 years, and the majority were women. Only nine participants attended at least 70% of the sessions and completed the final evaluation. Men, those who did not complete high school, and workers in service or construction jobs were more likely to drop out. Knowledge about protective factors [pre- vs. post-intervention median (inter-quartile range) = 111 (100, 120) vs. 115 (100, 124)] and coping mechanisms [96 (85, 104) vs. 99 (90, 110)], as well as psychosocial distress [3 (2, 3) vs. 2 (2, 3)] improved after the intervention in both intent-to treat and complete case analyses (p < 0.05). Qualitative results also support improvements in targeted protective factors.DiscussionThe intervention was successful in improving psychological distress among Mexican immigrants. These results support the implementation of evidence-based mental health promotion interventions among Mexican immigrants via free and familiar programs. A limitation was the high attrition; future studies should explore approaches to improve retention in this population.

Research on Optimizing Multimodal Transport Path under the Schedule Limitation Based on Genetic Algorithm

Abstract Multimodal transportation is getting more and more attention due to its economical and efficient characteristics. Choosing a reasonable transportation plan can effectively control transportation costs. Considering that there are fixed schedules for railway and waterway transportation in real life, this article will establish a multimodal transportation route optimization model with the goal of minimizing the total transportation cost consisting of transportation cost, transit cost, waiting cost and carbon emission cost. The genetic algorithm based on preservation strategy and immigration strategy is used to solve the model. The effectiveness of the model is proved by the case study, and it can provide decision support for the choice of multimodal transportation scheme.

Biogeography-based optimization with adaptive migration and adaptive mutation with its application in sidelobe reduction of antenna arrays

Biogeography-based optimization (BBO) is a swarm intelligence optimization algorithm based on migration and mutation operations, which is usually used to solve the complex optimization problems. However, it is also a challenging task for conventional BBO to solve some complex and diversified optimization problems with a perfect balance between the performance of exploration and exploitation. In this paper, we propose a variant of BBO approach called BBO with improved migration and adaptive mutation (BBOIMAM) to improve the performance of conventional BBO for dealing with different optimization problems. First, BBOIMAM introduces an improved migration strategy which includes the generalized sinusoidal migration model and immigration strategy based on elite-learning mechanism for the improvement of the local search ability. Second, we propose an adaptive mutation strategy based on the spring vibration to further enhance the population diversity, so that improving the global search ability of the algorithm. By the combination of the proposed improved migration and adaptive mutation strategies, the exploration and exploitation performance of the algorithm can be balanced. We make a large number of experiments on a set of various kinds of benchmark functions, and the experimental results demonstrate that the proposed BBOIMAM approach achieves better performance than several state-of-the-art peer algorithms on CEC 2017 and CEC 2020 test function sets and three cases of the antenna array beam pattern optimization problems.

Social Support Effectiveness of the Mental Health of Immigrants Living in Belgium

Abstract The increase in immigration has required social workers to focus more on this field and understand the social support systems of immigrants and the effects of their relationships with these systems to improve their effectiveness. Subsequently, this study examined the perceived social support, psychological resilience and coping strategies of immigrants living in Belgium, which has a significant immigrant population compared with its population. Using a cross-sectional design, data were collected from 289 participants living in Belgium. According to our findings, immigrants with a high perceived level of social support also have high psychological resilience. However, immigrants’ high levels of perceived social support lead to more intense use of problem-focused and emotion-focused coping styles. These findings emphasise the functionality of the social support system for immigrants and shed light on the issues that social workers should focus on when working with immigrants.