Economic Constraints Research Articles

The 'Zero consumption' phenomenon of elderly people's sports and leisure in the Yangtze River delta region

This study investigates the current status of the "zero consumption" phenomenon in sports and leisure activities among elderly individuals in the Yangtze River Delta region, analyzing its characteristics and underlying causes. Data related to the consumption of sports and leisure activities among the elderly in this region were obtained through methods such as questionnaires and interviews. The results indicate that "zero consumption" in elderly sports and leisure activities is a common social phenomenon in the Yangtze River Delta region of China. It is not only a result of economic income constraints but also a consumption trend formed under the influence of various factors such as aging and declining physical fitness. While "zero consumption" does not significantly affect the elderly' s participation in sports and leisure activities, it may to some extent impact the diversification of these activities. Given the current participation status of elderly individuals in sports and leisure activities in China, it is still unable to play a crucial leading role in the development of the leisure industry as seen in developed countries.

AI tool for predicting MGMT methylation in glioblastoma for clinical decision support in resource limited settings.

Glioblastoma is an aggressive brain cancer with a poor prognosis. The O6-methylguanine-DNA methyltransferase (MGMT) gene methylation status is crucial for treatment stratification, yet economic constraints often limit access. This study aims to develop an artificial intelligence (AI) framework for predicting MGMT methylation. Diagnostic magnetic resonance (MR) images in public repositories were used for training. The algorithm created was validated in data from a single institution. All images were segmented according to widely used guidelines for radiotherapy planning and combined with clinical evaluations from neuroradiology experts. Radiomic features and clinical impressions were extracted, tabulated, and used for modeling. Feature selection methods were used to identify relevant phenotypes. A total of 100 patients were used for training and 46 for validation. A total of 343 features were extracted. Eight feature selection methods produced seven independent predictive frameworks. The top-performing ML model was a model post-Least Absolute Shrinkage and Selection Operator (LASSO) feature selection reaching accuracy (ACC) of 0.82, an area under the curve (AUC) of 0.81, a recall of 0.75, and a precision of 0.75. This study demonstrates that integrating clinical and radiotherapy-derived AI-driven phenotypes can predict MGMT methylation. The framework addresses constraints that limit molecular diagnosis access.

"Will I survive this?": A qualitative exploration of the wider impacts of the COVID-19 pandemic on the health and well-being of pregnant and parenting youth and their children in Canada.

Pregnant and parenting youth in Canada face multiple challenges in fulfilling their parenting responsibilities while meeting their own unique needs. The COVID-19 pandemic and its associated public health restrictions exacerbated their challenges by reducing access to support networks, limiting opportunities to promote growth and well-being, and destabilizing their financial and economic standing. The impacts of the pandemic on the mental, physical, and emotional health of individuals are well-documented in the literature. However, there is limited research on how the pandemic influenced the lives, health, and well-being of pregnant and parenting youth and their children in Canada. Therefore, we aimed to examine the wider impacts (social and structural determinants that directly or indirectly impact health and well-being) of the pandemic and its implications for this population. We used a constructivist grounded theory approach to explore the experiences of pregnant and parenting youth during the pandemic and how certain social determinants of health intersected to shape health outcomes and influence their ability to access services. We conducted qualitative interviews using a semi-structured interview guide over Zoom. Using grounded theory principles, we inductively analyzed the data and categorized our findings into themes. We recruited a total of 50 participants from different regions in Canada. The findings from the interviews were categorized into the following themes: (1) "They bear the burden alone": Struggles of pregnant and parenting youth, (2) "How will I live the next day?": Concerns and consequences arising from the pandemic, and (3) "Try to keep your mask on": Navigating healthcare services while maintaining public health guidelines. Our findings highlight the various challenges encountered by the youth during the pandemic, particularly those associated with financial and economic constraints, lack of accessible health and social services, and increased stress from parenting responsibilities. Pregnant and parenting youth in Canada encountered various barriers while accessing health and social services, maintaining their well-being, and connecting with reliable sources of support during the pandemic. Our study findings emphasize the urgent need for adaptable and comprehensive support systems explicitly catering to the needs of pregnant and parenting youth during crises, such as the pandemic.

Referrals and Black-White Coronary Heart Disease Treatment Disparities: A Qualitative Study of Primary Care Physician Perspectives.

Black-White coronary heart disease (CHD) treatment disparities are well documented, especially regarding the use of high-quality hospitals. Physician referral networks may play a role. To understand how primary care physicians (PCPs) make specialty referrals for CHD treatment and how referrals may contribute to treatment disparities. Qualitative study using semi-structured interviews and focus group discussions. We purposively recruited 45 PCPs (50 invited, 90% response rate) in three metro areas with high Black-White segregation of cardiac care networks (New York City; Chicago; Atlanta). We developed the focus group discussion guide from interviews and current literature. We conducted two focus groups per metro area via Zoom. Two expert team members independently coded the transcripts using inductive techniques and analyzed focus group content and themes using Dedoose. Most participants were male (62.2%), White (57.8%), and practiced for at least 23years. We identified several recurrent themes for factors influencing cardiology referrals. The most frequently mentioned themes were heavy reliance on professional networks, specialist availability, timeliness, communication style, patient geographic and economic constraints, and patient preferences. PCPs used anecdotal and not data-driven evidence to assess hospital quality and viewed Black-White differences in high-quality hospital use as due to patient economic status and preferences or differences in hospital access and provider referral bias. PCPs' referral decisions for CHD treatment are primarily driven by access to specific professional networks and the socioeconomic circumstances of their patients. Nevertheless, PCPs strive to make the best available decisions, leaning into their networks and honoring patient preferences. While PCPs acknowledged existing disparities, they attributed them to patient and system factors rather than provider referral bias. Mitigating disparities will require interventions to improve minority-serving providers' formal and informal connections with high-quality specialists and hospitals, address patient socioeconomic constraints, and train providers to recognize their potential biases and misconceptions.

Caregiving Intensity, Duration, and Subjective Financial Well-Being Among Rural Informal Caregivers of Older Adults with Chronic Illnesses or Disabilities

Introduction: Rural informal caregivers (IC) experience major financial and economic constraints in caring for their older family members. Rurality combined with increased caregiving demands and intensity, poor economic opportunities, and limited financial resources and policies create multiple financial stressors and can lead to poor financial well-being. A cross-sectional survey was conducted to understand how caregiving demands, intensity, and duration impact the subjective financial well-being of rural caregivers of older adults. Methods: Informal caregivers (N = 196) residing in 12 rural counties in the central North Region of the Midwestern US participated in the survey. Ordinary Least Squares and Linear Probability Model regressions were conducted to measure the association among the study variables. Results: Our findings showed a moderate level of subjective financial well-being among informal caregivers (average = 51.62; SD 14.52). Caregiving intensity negatively affected financial well-being (β = −1.470, p < 0.05). More than half of informal caregivers (58%) were not satisfied with their household income, and 30% found it difficult to meet their family’s needs with their current income status. Discussion and Conclusions: Longer hours of care are associated with financial burden and insecurity and can significantly influence the financial health and well-being of rural informal caregivers of older adults. Older caregivers were found to manage their financial constraints more effectively. Future comparative and longitudinal studies with a more diverse sample are required to infer long-term interactions among the different variables in this study.

Sizing large-scale industrial heat pump for heat recovery from treated municipal sewage in coal-fired district heating system

Electrification of district heating and deep integration of sectors of national economies are fundamental elements of the future smart energy systems. This paper discusses the problem of optimal sizing of large-scale high-temperature heat pumps using treated sewage water as a heat source in a coal-fired district heating system. The study presents an approach to modelling of heat pump system that enables techno-economic analysis for investment decision making. Such analysis is enabled by a black-box-type identification model of the selected industrial heat pump. The model was developed based on the data generated by physical modelling of the heat pump using Ebsilon Professional software. In addition, it is proposed that the heat pump system is integrated with a dedicated photovoltaic power plant. The case study takes into consideration site-specific technical, economic, ecological, and legal constraints, weather conditions, hydraulic performance of the heat-ing network, and variability of loads within the sewage and the district heating systems. The results revealed that the proposed modelling approach is effective regarding multiple simulations and system optimisation. In addition, it was found that large-scale heat pump projects can be technically feasible and profitable if the heat pump is appropriately sized and operated. In the given case, the optimum size of the heat pump for a city of around 180 000 inhabitants is around 12 MW under maximum winter load.

Wind Farm Layout Optimization/Expansion of Real Wind Turbines with a Parallel Collaborative Multi-Objective Optimization Algorithm

The objective of this paper is to study the Wind Farm Layout Optimization/expansion problem. This problem is formulated here as a Multi-Objective Optimization Problem considering the total power output and net efficiency of Wind Farms as objectives along with specific constraints. Once formulated, this problem needs to be solved efficiently. For that, a new approach based on a combination of five Multi-Objective Optimization algorithms, which is named the Parallel Collaborative Multi-Objective Optimization Algorithm, is developed and implemented. This technique is checked on seven test cases; for each case, the goal is to find a set of optimal solutions called the Pareto Front, which can be exploited later. The acquired solutions were compared with other approaches and the proposed approach was found to be the better one. Finally, this work concludes that the proposed approach gives, in a single run, a set of optimal solutions from which a designer/planner can select the best layout of a designed Wind Farm using expertise and applying technical and economic constraints.

Review of Heavy Metal Removal from Soil: Methods and Technologies

Soil health is vital for ecosystem functioning, agriculture, and human well-being, yet heavy metal contamination poses significant risks to environmental and public health. This review examines various methods for removing heavy metals from contaminated soils, focusing on physical, chemical, and biological remediation techniques. Sources of contamination, including industrial activities, mining, and improper waste disposal, are discussed, alongside the environmental and health impacts of heavy metals like lead, cadmium, and mercury. Physical techniques such as soil washing and excavation effectively reduce contamination but generate secondary waste and incur high costs. Chemical methods, including soil stabilization and chemical leaching, immobilize or extract metals but may risk recontamination. Biological approaches like phytoremediation and bioremediation leverage natural processes for eco-friendly remediation, though they often require longer timescales for significant results. Emerging technologies, such as nanotechnology and biochar application, show promise for enhancing remediation efficacy. However, challenges remain, including economic constraints, regulatory inconsistencies, and the need for sustainable, long-term solutions. Future directions include integrating various remediation techniques, developing eco-friendly technologies, and emphasizing long-term monitoring to ensure the effectiveness of remediation efforts. This comprehensive overview aims to inform future research and policy development to address heavy metal contamination sustainably.

Methodological proposal for the implementation of Emancipatory Education in Ecuador

This paper proposes a methodology to implement the principles of emancipatory education, based on the Pedagogy of the Oppressed by Paulo Freire and other theorists, in the Ecuadorian educational context. The main focus is to propose the strategy and teacher training to promote students' critical thinking and active participation, preparing them to contribute to the transformation of their environment. The results show that a methodological proposal must address economic constraints and limited access to digital resources, promoting critical analysis of social realities and transformative action through dialogue, reflection, and collaborative work.

The intersection of financial modeling and public health: A conceptual exploration of cost-effective healthcare delivery

This review paper explores the intersection of financial modeling and public health, highlighting the importance of cost-effective healthcare delivery in contemporary health systems. Financial modeling serves as a crucial tool for policymakers, providing evidence-based insights that inform decision-making and optimize resource allocation. Key financial models, including cost-benefit analysis and predictive modeling, are examined for their applications in public health initiatives, particularly in areas such as preventive care and chronic disease management. The paper discusses core public health priorities—prevention, access, and quality—and the economic constraints faced by healthcare systems. Recommendations are provided for policymakers and healthcare professionals to enhance the application of financial models, emphasizing the need for training, investment in data infrastructure, and fostering cross-sector collaborations. By leveraging financial modeling, public health systems can achieve improved health outcomes while maximizing resource efficiency, ultimately contributing to a more sustainable and equitable healthcare landscape.. Keywords: Financial Modeling, Public Health, Cost-Effectiveness, Healthcare Delivery, Resource Allocation, Preventive Care.

Acute stroke care in low and middle-income countries.

The purpose of this article is to discuss the global impact of stroke, the disparities and barriers to implement stroke care, and the global efforts to improve access to acute treatments in low and middle-income countries (LMICs). Disparities in access to stroke care are influenced by socioeconomic inequalities, geographic disparities, and limited healthcare infrastructure, particularly in LMICs. Effective stroke care requires a coordinated approach involving emergency services, rapid diagnosis, timely treatment, and early rehabilitation. However, there are significant delays in implementing evidence-based practices, particularly in areas where stroke care resources are scarce.Key barriers include geographic disparities, economic constraints, insufficient healthcare infrastructure, low public awareness, and weak policy frameworks. Addressing these challenges requires strengthening health systems, promoting universal health coverage, enhancing public and healthcare provider education, leveraging technology like telemedicine, and fostering international collaboration. Global efforts, including initiatives by the World Stroke Organization, focus on improving stroke care through infrastructure development, workforce training, and policy advocacy. These recommended strategies aim to make stroke care accessible and effective for everyone, regardless of location or socioeconomic status, ultimately helping to reduce the global burden of stroke.

Gravity Predictions in Data-Missing Areas Using Machine Learning Methods

Gravity data, comprising a key foundational dataset, are crucial for various research, including land subsidence monitoring, geological exploration, and navigational positioning. However, the collection of gravity data in specific regions is difficult because of environmental, technical, and economic constraints, resulting in a non-uniform distribution of the observational data. Traditionally, interpolation methods such as Kriging have been widely used to deal with data gaps; however, their predictive accuracy in regions with sparse data still needs improvement. In recent years, the rapid development of artificial intelligence has opened up a new opportunity for data prediction. In this study, utilizing the EGM2008 satellite gravity model, we conducted a comprehensive analysis of three machine learning algorithms—random forest, support vector machine, and recurrent neural network—and compared their performances against the traditional Kriging interpolation method. The results indicate that machine learning methods exhibit a marked advantage in gravity data prediction, significantly enhancing the predictive accuracy.

In‐Silico Optimization of a Bi‐Enzymatic Reactor for Mannitol Production Using Pareto‐Optimal Fronts

AbstractFor multi‐enzymatic cases, the determination of the batch reactor (BR) optimal operating policy often translates into a difficult multi‐objective problem. Exemplification is made here for the enzymatic reduction of D‐fructose to mannitol by using the mannitol dehydrogenase (MDH) enzyme and nicotinamide adenine dinucleotide (NADH) cofactor, with in situ regeneration of NADH at the expense of formate degradation by using the FDH enzyme. This paper presents an original rule to in silico generate the problem solution, by using the Pareto optimal‐front approach with accounting for pairs of competing economic goals and constraints. The optimal BR is then compared to an optimal fed‐BR (FBR), or a series of equal BRs (SeqBR). As proved, the Pareto‐optimal front alternative is an advantageous option, compared to the classical nonlinear programming technique, being simple to apply, by considering pairs of opposite objective functions. In the present case study, the Pareto‐optimal BR operating mode predicts an M‐productivity 1.5x better than those of an optimized FBR, with comparable enzymes consumption. The MDH consumption of this Pareto‐optimal BR is 10x smaller than an optimal SeqBR, and 130x smaller vs. heuristic (sub)optimal BR.

Developing a Cloud and IoT-Integrated Remote Laboratory to Enhance Education 4.0: An Approach for FPGA-Based Motor Control

Remote laboratories are essential in addressing access and quality challenges in technical education. They enable students from various locations to engage with real equipment, overcome geographic and economic constraints, and provide solutions during crises, such as pandemics, when in-person learning is limited. As a key element of Education 4.0, remote labs promote technical skill development, enhance engineering education, and support diverse learning approaches. This study presents a remote laboratory based on Field Programmable Gate Arrays (FPGAs), developed using a waterfall methodology integrating IoT and Cloud Computing technologies to facilitate close interaction between hardware and software. The lab focuses on controlling DC, servo, and stepper motors, allowing students to apply theoretical concepts such as digital signals, pulse-width modulation (PWM), and data representation in bits in a practical setting. The testing phase involved 50 robotics and mechatronics engineering students who participated in hands-on sessions for one month, followed by a structured survey evaluating their experience, interaction, and the educational relevance of the platform. The survey shows high student satisfaction, highlighting the platform’s strengths and identifying areas for improvement. The results also underscore the system’s potential to significantly enhance the educational experience in remote environments, aligning with the United Nations Sustainable Development Goals (SDGs).

Effect of substrate rotation on the growth behavior and topography of the [formula omitted] film deposited over a large area using DC magnetron sputtering with a rectangular target: Simulation approach and experiment

It is often challenging to achieve a uniform film with the necessary surface properties when using deposition techniques. If the film is not uniformly deposited or lacks suitable surface roughness, it may not meet performance standards. To understand and control the factors controlling the deposition process, this paper presents a simulation approach to predict the growth behavior of a Ti-film deposited on substrates during the bombardment of the target with argon ions at low pressure. The simulation of the deposition process consists of several Monte Carlo simulations, which include predicting the source of the atoms flow from the target surface and tracing its trajectories through the vacuum chamber until it falls or reaches the surface of the substrates, then predicting the thickness distribution and topography of film. After conducting a comparison between simulation predictions and experimental data, it was found that the measurements obtained using the Profilometer, Scanning Electron Microscope, and Atomic Force Microscopy were relatively close. The results indicate that substrate rotation during deposition leads to a thinner film that is more uniform and less rough, it was also found that the coefficient of variation in the film thickness distribution was improved from 3.96% in the case of a non-rotating substrate to 0.33% in the case of a rotating substrate. The proposed approach can be used to improve the design of magnetron sputtering systems and to provide theoretical guidance for optimizing the thickness distribution and topographical characteristics of the performance specifications of high-quality film without economic constraints.

Critical Perspectives on the Adoption of Circular Design Strategies in the Built Environment for Sustainable Development in Sub-Saharan Africa

Adopting circular design strategies (CDS) in building development presents a transformative approach toward sustainability by promoting resource efficiency and reducing environmental impacts. This paper examined the current landscape of CDS adoption, focusing on barriers, enablers, benefits, and critical success factors within the built environment (BE) in Sub-Saharan Africa. Drawing on a comprehensive review of global contexts literature, the study identifies and categorizes 185 factors, subsequently pruning them to 121 pertinent to the design stage. Barriers such as low awareness, regulatory gaps, and economic constraints are explored alongside enablers like leadership commitment, technological innovation, and supportive policies. The paper underscores the pivotal role of these factors in shaping sustainable building practices, emphasizing the need for tailored strategies to overcome local socio-cultural, economic, and technological challenges in the BE of Sub-Saharan Africa. This research contributes a nuanced understanding of how CDS can be effectively integrated into the BE, offering practical implications for policymakers, industry stakeholders, and researchers striving to embrace sustainable practices.

A Thematic Exploration of Gender Inequality in the Gujjar Community of Kotranka Tehsil, Rajouri District

The study looks into the gender disparities that exist among the Gujjar clan in Kotranka Tehsil, Rajouri District in Jammu and Kashmir. It investigates the socio-cultural and economic constraints that women confront in their communities, examining how traditional norms influence their autonomy, involvement, and access to resources. Data from 35 respondents were acquired using a descriptive study method, with structured interviews and questionnaires. The research indicates considerable limitations on women's daily lives, particularly in decision-making, mobility, and educational prospects. The findings show that male dominance prevails in financial and family decisions, but social conventions continue to limit women's roles. While some respondents acknowledge a shift toward more gender-equitable practices, traditional gender norms are nonetheless deeply established, restricting women's potential and increasing dependency. Despite government efforts and changing societal perceptions, Gujjar women in Kotranka Tehsil continue to encounter obstacles as a result of established socio-cultural expectations. The study emphasizes the crucial need for comprehensive policies and community-based programs to achieve gender equality and empower women in the Gujjar tribe.

The Importance of Optimal Nutritional Intake for The Health of Pregnant Women in Indonesia: Key Challenges and Strategic Solutions

Optimal nutrition during pregnancy plays a vital role in ensuring maternal health and healthy fetal development. Appropriate nutritional needs, including macronutrients and micronutrients, are essential to support fetal growth, prevent pregnancy complications, and reduce the risk of premature birth and low birth weight. In Indonesia, maternal nutrition is still a major challenge, with a high prevalence of anemia and chronic energy deficiency. Factors such as economic constraints, unequal access to health services, lack of education about the importance of nutrition, and socio-cultural influences on diet, exacerbate this condition. The challenge in meeting the nutritional needs of pregnant women is also related to the low awareness and knowledge of nutrition among the community, especially in remote areas. To overcome this problem, a multi-sectoral approach is needed involving government intervention, community empowerment, and increasing access to health services. Continuous education and targeted nutritional assistance programs can be effective solutions in improving the nutritional status of pregnant women in Indonesia. Thus, strong cooperation is needed between various stakeholders to ensure optimal maternal health, which will ultimately improve the quality of future generations.

Effectiveness of individualized counseling in reducing leave against medical advice among neurosurgical patients in the emergency department

Objective: Leave against medical advice (LAMA) in the emergency department (ED) is a significant challenge in low- and middle-income countries, often leading to higher rates of readmission, increased mortality, and disrupted continuity of care. This study aimed to evaluate the effectiveness of individualized counseling in reducing LAMA incidents among neurosurgical patients in the ED. Materials and Methods: This quality improvement study employed a prospective case-control design to compare LAMA rates between patients who received individualized counseling and those who did not. A “nursing-coordinator program” was introduced, focusing on patient communication and engagement. Trained staff followed a standardized checklist to address the concerns of patients and their caregivers, providing “individualized counseling.” Data collected after the intervention were compared with retrospective pre-intervention data, and the results were analyzed. Results: A total of 554 patients, ranging from 1 to 94 years of age, were included in the study, with 60.5% being male. Among the 353 patients who received the “individualized counseling” intervention, 60 opted for LAMA (study group), compared to 49 LAMA cases out of 201 patients who did not receive counseling (control group). The LAMA rate significantly decreased from 24.4% to 16.9% post-intervention (P = 0.035). The “individualized counseling” intervention effectively reduced LAMA due to factors such as misinformation about disease severity (P = 0.009), economic constraints (P < 0.001), and poor prognosis (P < 0.001). However, it was less effective for reasons like proximity to home (P = 0.007), inadvisable transfers for personal reasons (P = 0.023), and family problems (P = 0.293). Conclusions: This study demonstrates that individualized counseling is an effective intervention for reducing LAMA among neurosurgical patients in the ED. By addressing non-medical factors often overlooked due to inadequate counseling, this quality improvement strategy can help prevent vulnerable patients from prematurely terminating their treatment.

A novel method for rapidly simulating X-ray pulsar signals at a spacecraft

This paper addresses the challenges posed by the low photon flux and limited atmospheric penetration capabilities of X-ray pulsar signals, which are further exacerbated by technical, economic, and temporal constraints during in-orbit observations. These challenges result in a lack of continuous and high-fidelity data support for ground-based X-ray pulsar navigation (XPNAV) research and validation. To overcome these obstacles, we propose an innovative method for the efficient and rapid simulation of X-ray pulsar signals as received by spacecraft. Building on the physical process that, although the arrival times of the same pulsar signal differ between the spacecraft and the Solar System Barycenter (SSB), these times can be calibrated to the same phase at the SSB using a timing model, we leverage the relationship between the pulsar signal’s arrival rate at the SSB and its arrival phase. Using the Order Statistical Method (OSM), we directly and effectively generate the arrival phase series of pulsar signals received by the spacecraft, which follow a non-homogeneous Poisson process (NHPP). Subsequently, from these phase series, we derive the arrival time series of spacecraft-received pulsar signals, incorporating multiple physical effects based on a relationship model between arrival time series and phases. This method significantly reduces the required simulation time while maintaining high accuracy, thereby greatly enhancing simulation efficiency. Validation against data from the Rossi X-ray Timing Explorer (RXTE) and the Neutron Star Interior Composition Explorer (NICER) shows that the correlation coefficients between the accumulated profiles of simulated and measured data exceed 0.99. Furthermore, our method reduces the simulation time of high-flux pulsar signals by 25.7%, high spin frequency signals by 40.8%, and long observation duration signals by 25.38% compared to the latest fast simulation techniques, highlighting its advantages in accuracy, speed, and versatility for simulating pulsar signals across varying flux rates, spin frequencies, and observation durations. This approach provides continuous and effective data support for XPNAV research, significantly advancing theoretical and practical developments in the field.