Distinct Challenges Research Articles

ADRD: A REVIEW OF THE PATIENT-CENTERED OUTCOMES RESEARCH INSTITUTE’S (PCORI) RESEARCH PORTFOLIO AND PRIORITIES

Abstract Given the aging of the U.S. population, the number of individuals living with Alzheimer’s Disease and Related Dementias (ADRD) is projected to reach 13 million by 2050. There is an urgent need for research efforts to advance prevention, diagnosis, and treatment strategies for ADRD. Furthermore, caregivers of individuals with ADRD often face distinct challenges and experience significant caregiving burden that has an impact on their well-being. To address unmet needs of individuals with ADRD and their caregivers, PCORI has invested nearly $85 million in patient-centered comparative clinical effectiveness research (CER) focused on ADRD and has initiated several stakeholder convenings to identify future research priorities. PCORI’s ADRD portfolio of 15 CER studies includes interventions focused on caregiving, polypharmacy, telehealth, care models, and palliative care. Additionally, PCORI has funded engagement awards, dissemination & implementation initiatives, and research infrastructure projects to help facilitate the conduct and awareness of research on persons living with ADRD and their caregivers. To support continued research in this area, PCORI plans to re-issue a $50 million funding initiative on Healthy Aging with a special area of emphasis on interventions for caregivers of older adults living with ADRD. By systematically synthesizing PCORI’s portfolio and themes from stakeholder convenings in ADRD, we can identify the strengths of current investments and understand evidence gaps to guide PCORI’s future investments. This will enable us to holistically address future real-world, decisional dilemmas most important to patients, caregivers, and other stakeholders.

Breaking the barriers: overcoming dementia-related stigma in minority communities.

Dementia is a global health concern that affects individuals irrespective of their cultural or linguistic backgrounds. However, research has long recognized the pronounced stigma associated with dementia, particularly within Culturally and Linguistically Diverse (CALD) communities. This article seeks to summarize the underlying factors contributing to the heightened levels of dementia stigma within CALD communities, through a review of the literature. Our examination shows that cultural beliefs, language barriers, limited awareness, and the impact of migration on perceptions of aging and cognitive decline are contributing factors. Consequently, our analysis highlights the need for tailored, culturally appropriate interventions aimed at mitigating stigma and enhancing dementia care within CALD populations. Our proposed solutions, built on a social-ecological approach, highlights the critical role of collaborative efforts involving policymakers, healthcare providers, community organizations, and CALD community members in fostering a more dementia-inclusive society. This perspective piece aims to shed light on the distinct challenges faced by CALD communities, while advocating for a holistic approach to redefine perceptions and care strategies tailored to these populations.

Multi‐scalar interactions between mismanaged plastic waste and urban flooding in an era of climate change and rapid urbanization

AbstractMismanaged plastic waste (MPW) and urban flooding are typically considered distinct environmental challenges. However, there are notable links between growing quantities of MPW and the rising incidence of floods in cities. A majority of people now live in cities, especially along coastlines and in estuaries where residents are both vulnerable to flooding and significant sources of plastic waste. Predicted increases in the frequency and severity of heavy rainfall associated with anthropogenic climate change coincide with rising global quantities of MPW, much of which is discharged into water bodies and the sea. Given the urgent and expanding nature of these issues, understanding impacts of urban floods on solid waste management and vice versa is crucial. Social‐ecological systems (SES) thinking calls for a holistic approach to the relationships and interactions between human actors and environmental systems which can result in dynamic and emergent outcomes. In this review paper, we make a first step towards better understanding of the interactions between urban flooding and MPW by synthesizing emerging quantitative and qualitative research on particular aspects of such interactions. More research is needed which explicitly focuses on and elaborates the nature of these interactions, and also to consider potential relations across scales (from global to local) and over both long and short timeframes. We conclude that an SES approach can make visible novel possibilities for interventions which are context specific and sensitive to the interactions between urban floods and MPW.This article is categorized under: Science of Water > Water and Environmental Change Human Water > Water Governance Engineering Water > Planning Water

Online COVID-19 diagnosis prediction using complete blood count: an innovative tool for public health

BackgroundCOVID-19, caused by SARS-CoV-2, presents distinct diagnostic challenges due to its wide range of clinical manifestations and the overlapping symptoms with other common respiratory diseases. This study focuses on addressing these difficulties by employing machine learning (ML) methodologies, particularly the XGBoost algorithm, to utilize Complete Blood Count (CBC) parameters for predictive analysis.MethodsWe performed a retrospective study involving 2114 COVID-19 patients treated between December 2022 and January 2023 at our healthcare facility. These patients were classified into fever (1057 patients) and pneumonia groups (1057 patients), based on their clinical symptoms. The CBC data were utilized to create predictive models, with model performance evaluated through metrics like Area Under the Receiver Operating Characteristics Curve (AUC), accuracy, sensitivity, specificity, and precision. We selected the top 10 predictive variables based on their significance in disease prediction. The data were then split into a training set (70% of patients) and a validation set (30% of patients) for model validation.ResultsWe identified 31 indicators with significant disparities. The XGBoost model outperformed others, with an AUC of 0.920 and high precision, sensitivity, specificity, and accuracy. The top 10 features (Age, Monocyte%, Mean Platelet Volume, Lymphocyte%, SIRI, Eosinophil count, Platelet count, Hemoglobin, Platelet Distribution Width, and Neutrophil count.) were crucial in constructing a more precise predictive model. The model demonstrated strong performance on both training (AUC = 0.977) and validation (AUC = 0.912) datasets, validated by decision curve analysis and calibration curve.ConclusionML models that incorporate CBC parameters offer an innovative and effective tool for data analysis in COVID-19. They potentially enhance diagnostic accuracy and the efficacy of therapeutic interventions, ultimately contributing to a reduction in the mortality rate of this infectious disease.

The Role of Gender, Age and Education in the Emotional Responses of Parents of Children with Developmental Challenges

This research endeavor sought to explore how demographic factors such as gender, age, and parental education influence the emotional experiences of parents’ of children with developmental challenges. Utilizing a cross-sectional research design and purposive sampling, data were gathered from various regions in KP (Peshawar) and Punjab (Wah Cantt, Islamabad, and Jhelum). The study comprised a total of 150 participants, with an equal gender distribution (75 fathers and 75 mothers). Data collection tools included a consent form booklet, a demographic information sheet, and the Scale of Positive and Negative Experience. The study's findings unveiled that fathers of children with disability reported more positive experiences as compared to mothers. Additionally, it was observed that older parents exhibited a broader spectrum of both positive and negative emotions as compared to their younger counterparts. Furthermore, significant differences in negative emotions were identified based on educational levels. The results suggest that mothers may be susceptible to heightened negative emotional experiences, possibly due to their extensive caregiving responsibilities, in contrast to fathers who often participate more in social and recreational activities. These findings underscore the importance of implementing targeted supportive services for mothers by mental health professionals. Addressing negative emotional states is crucial in mitigating potential psychological, physical, and emotional challenges associated with the caregiving role. This study enhances our comprehension of emotional dynamics concerning demographics, emphasizing the necessity for tailored interventions to assist parents in navigating the distinctive challenges they encounter.

Navigating challenges in telechaplaincy: A thematic analysis of an international conference

Telehealth-based care models are being widely adopted by primary care providers and large healthcare institutions. Drawing on data collected at an international conference on the theory and practice of telechaplaincy, this article identifies and discusses how chaplains navigate various telechaplaincy-related challenges. A thematic analysis identified 49 codes and 11 themes at the individual-, organizational- and population levels. Presenters reported facing novel and qualitatively distinct challenges spanning an array of telechaplains’ professional activities, including the structure of work routines, the types of interventions used, the ways provider-patient connections are established and experienced, the strategic positioning of chaplains, their role in the model of care, and ultimately, the populations served. It is argued that, though telechaplaincy has gained prominence since the Covid-19 pandemic, the maintenance of professional standards in digital care settings is a systemic challenge related to long-term trends towards outpatient care.

Fixing Fieldnotes: Developing and Testing a Digital Tool for the Collection, Processing, and Analysis of Ethnographic Data

Ethnographic fieldnotes can contain richer and more thorough descriptions of social phenomena compared to other data sources. Their open-ended and flexible character makes them especially useful in explorative research. However, fieldnotes are typically highly unstructured and personalized by individual researchers, which make them harder to use as a method for data collection in collaborative and mixed methods research. More precisely, the unstructured nature of ethnographic fieldnotes presents three distinct challenges: 1) Organizability—it can be difficult to search and sort fieldnotes and thus to get an overview of them, 2) Integrability—it is difficult to meaningfully integrate fieldnotes with other more quantitative data types such as more such as surveys or geospatial data, and 3) Computational Processability—it is hard to process and analyze fieldnotes with computational methods such as topic models and network analysis. To solve these three challenges, we present a new digital tool, for the systematic collection, processing, and analysis of ethnographic fieldnotes. The tool is developed and tested as part of an interdisciplinary mixed methods pilot study on attention dynamics at a political festival in Denmark. Through case examples from this study, we show how adopting this new digital tool allowed our team to overcome the three aforementioned challenges of fieldnotes, while retaining the flexible and explorative character of ethnographic research, which is a key strength of ethnographic fieldwork.

Optimizing time, cost, and carbon in construction: grasshopper algorithm empowered with tournament selection and opposition-based learning

The global construction industry plays a pivotal role, yet its unique characteristics pose distinctive challenges. Each construction project, marked by its individuality, substantial value, intricate scale, and constrained adaptability, confronts crucial limitations concerning time and cost. Despite contributing significantly to environmental concerns throughout construction activities and infrastructure operations, environmental considerations remain insufficiently addressed by project managers. This research introduces an improved rendition of the muti-objective grasshopper optimization algorithm (MOGOA), termed eMOGOA, as a novel methodology to tackle time, cost, and carbon dioxide emission trade-off problems (TCCP) in construction project management. To gauge its efficacy, a case study involving 29 activities is employed. eMOGOA amalgamates MOGOA, tournament selection (TS), and opposition-based learning (OBL) techniques to enhance the performance of the original MOGOA. The outcomes demonstrate that eMOGOA surpasses other optimization algorithms, such as MODA, MOSMA, MOALO and MOGOA when applied to TCCP. These findings underscore the efficiency and relevance of the eMOGOA algorithm within the realm of construction project management.

REVIEW OF AI APPLICATIONS IN HEALTHCARE: COMPARATIVE INSIGHTS FROM THE USA AND AFRICA

The integration of Artificial Intelligence (AI) into healthcare systems has emerged as a transformative force, revolutionizing patient care, diagnostics, and treatment strategies. This comprehensive review delves into the application of AI in healthcare, drawing comparative insights from the healthcare landscapes of the United States and Africa. The study navigates through the distinct approaches, challenges, and advancements in AI adoption, emphasizing the unique considerations each region faces. The analysis explores AI applications in medical imaging, disease diagnosis, patient management, and healthcare accessibility, shedding light on the disparities and opportunities for collaborative advancements. By juxtaposing the experiences of these two regions, the study aims to contribute valuable insights for the global integration of AI in healthcare, promoting inclusive, efficient, and equitable healthcare solution.
 Keywords: Artificial Intelligence, Healthcare, Africa, USA, Treatment Strategy

Nutrition support management of organ transplant recipients in the acute posttransplant phase.

Patients who undergo solid organ transplant can have an extensive and challenging postoperative course. The chronicity of the disease state prior to transplant in combination with transplant-specific complications and immunosuppressant medications can lead to distinct challenges that are not observed in other critically ill patients. Although the manifestation of posttransplant complications may be specific to the organ being transplanted, there are common transplant challenges that affect nutrition therapy in these patients. Effects of malnutrition, metabolic aberrations, and posttransplant organ dysfunction should be considered when developing a nutrition care plan for patients in the immediate posttransplant phase. This article addresses the various complications that can arise in the immediate posttransplant phase among patients undergoing solid organ transplant and the appropriate nutrition interventions or considerations for this specialized patient population.

Challenges and Inefficiencies of Online Education among Malaysian Students

The global landscape of education has experienced a seismic shift towards online learning, a transformation underscored by the imperative demands of the COVID-19 pandemic. Malaysia, mirroring global trends, swiftly embraced online education as a crucial response to the unprecedented challenges. However, the integration of virtual classrooms has brought to light inherent inefficiencies, posing distinctive challenges to Malaysian students. This article aims This article embarks on a comprehensive exploration of the challenges intrinsic to online education faced by Malaysian students, peeling back layers to reveal the nuanced inefficiencies entangled in the digital shift. A deeper understanding of these challenges is essential for policymakers, educators, and stakeholders striving to construct a more inclusive, effective, and equitable online learning environment tailored to the diverse needs of the Malaysian student population.

Facile preparation of Pickering emulsion-based coatings for hydrophobic, oil-resistant, biodegradable green paper-based packaging

The usage of bio-based polymers to construct paper-based functional materials is currently an important topic in the food packaging industry. Nevertheless, the facile and effective construction of packaging paper materials with excellent water and oil repellency, self-cleaning property, biodegradability and recyclability currently faces distinct challenges. In this aspect, we have developed a one-step coating method for preparing hydrophobic and grease-resistant paper basing on templating dichloromethane-in-water (O/W) cellulose nanocrystals-stabilized Pickering emulsions, which contain chitosan (CS) and glutinous rice starch (GRS) in water phase and polylactic acid (PLA) in oil phase. Herein, the greaseproof polysaccharides CS and GRS are served as the coating film matrix, where the hydrophobic PLA is embedded in the form of microspheres. The optimized coated paper with rough surface structure display highly hydrophobic and water barrier properties with the water contact angle of larger than 130° and water vapor transmission rate of 3.57 × 10−10 gm−1s−1Pa−1, outstanding oil resistance with the kit rating of 9.5/12 and self-cleaning performance. Most interestingly, the soil burial test shows the favourable biodegradability of the coated paper. In addition, the pulp can be recycled from the coated paper and then reused to make paper, validating the recyclability of the coated paper. This study provides a new sustainable and universal research strategy for the large-scale preparation of fully bio-based, recyclable, hydrophobic and oil-resistant, self-cleaning, biodegradable paper materials, which exhibit the highly promising applications in food packaging industry.

An Enhanced Quantitative Reverse Transcription-PCR Approach for Measuring Circulating MicroRNAs.

Short (22nt) RNAs called microRNAs (miRNAs) bind to and inhibit target messenger RNAs in gene regulatory networks. Recent study suggests that miRNAs circulate in a stable, cell-free form and that particular miRNAs in plasma or serum may be biomarkers for cancer and other diseases. Circulating miRNAs as biomarkers provide distinct challenges including pre-analytic variance and data standardization. We describe our qRT-PCR approach for measuring circulating miRNAs as biomarkers, as well as sample preparation, experimental design, and data processing issues.

The Effect of Financial Inclusion on Poverty in Nigeria

The study investigates the nexus between financial inclusion and poverty in Nigeria, a nation where about 60% of the population lacks access to formal financial products and services. Financial inclusion is viewed as a crucial tool for alleviating poverty, and the research delves into the intricate interplay between these two factors in both urban and rural settings. Employing a linear probability model, the findings reveal that while having an account with a financial institution is positively correlated with poverty reduction, it does not stand alone as a solution for eradicating poverty. In this context, financial literacy plays a pivotal role in empowering individuals to make informed financial decisions, ultimately facilitating their escape from poverty. The promotion of financial literacy initiatives is of paramount importance. Furthermore, the study highlights the role of household size in poverty reduction. Smaller households, typically composed of 1-4 individuals, exhibit a greater potential for poverty reduction, while larger household sizes are associated with an increased likelihood of poverty. This pattern is consistent in both urban and rural areas, underscoring the need for precise interventions and policies that address the distinct challenges faced by rural communities, and concurrently, promote financial inclusion and stimulate economic development. In conclusion, the study underscores the significance of adopting a comprehensive approach to poverty reduction in Nigeria. It underscores the roles of financial inclusion, financial literacy, and customized strategies that consider household sizes and geographic contexts. By addressing these factors, policymakers, financial institutions, and communities can collaborate to develop sustainable solutions that uplift households from poverty and contribute to a more inclusive and prosperous society. Keywords: Poverty; Household Finance; Household Size, Urban; Rural

Special Issue: Machine Learning and Representation Issues in CAD/CAM

Abstract Machine Learning (ML), a sub-field of Artificial Intelligence (AI), is profoundly reshaping various aspects of human life. Its application in engineering systems promises to address long-standing challenges, although it also introduces new questions. While the potential of ML is undeniable, integrating existing ML methods into Computer-Aided Design and Manufacturing (CAD/CAM) presents distinctive challenges. These challenges encompass representation, adaptation, and the development of novel ML techniques to enhance CAD/CAM systems for diverse design and manufacturing solutions. This special issue aims to explore and resolve issues related to effective engineering model representation for ML, the integration of neural networks and deep generative models into CAD/CAM, mathematical frameworks combining ML with CAD/CAM in geometry and topology, data interpretation, and physics-based learning. The issue features ten papers that delve into various topics, including material prediction for assemblies, robotics mechanism design, multi-modal ML in engineering design, data-driven component segmentation in engineering drawings, evaluating assembly-part semantic knowledge in language models, three-dimensional slice reconstruction from high-resolution 2D images, physics-informed neural networks to expedite thermal simulations in additive manufacturing, transfer learning for defect detection in steel strips, AI-aided hull form design for energy-efficient unmanned underwater vehicles, and real-time high-precision calibration of quadruped robots using machine vision and artificial neural networks. Below, we provide concise summaries of each of the ten papers published in this special issue.

Virtual Care and Social Support for Refugee Mothers during COVID-19: A Qualitative Analysis

Introduction: The intersection of gender, motherhood, and migration status creates distinct challenges for refugee mothers, but social support can facilitate their navigation of migration and motherhood. Taking a Salutogenic Theory approach (Antonovsky, 1979), we examined refugee mothers’ access to virtual social support during the COVID-19 pandemic. Our objective is to understand the provision of virtual social support for refugee mothers from the perspective of service providers and recently arrived refugee mothers to Canada. Methods: Virtual semi-structured interviews were conducted with three service providers and five refugee mothers in one settlement agency in Ontario, Canada. Data were subjected to thematic analysis. Six main themes emerged. Results: From interviews with service providers the themes include: virtual adaptation of services; unique barriers to virtual services emerging from the intersection of gender, culture, and migration status; and supporting women’s agency and independence. From interviews with mothers, we identified the following themes: gratitude for instrumental support; organization as a link between self and society; and usefulness of virtual support, but preference for in-person support. Discussion: Providers acknowledged clients’ diverse circumstances. They developed flexible strategies to identify client needs and help them build skills. Clients found virtual services essential to resettlement, if not ideal. Conclusion: With tailored programming, virtual services can be effective in providing support. Moreover, refugee mothers acquired digital skills to independently navigate virtual resources, despite limited digital literacy. This demonstrates the value of using of virtual services for vulnerable or hard to reach populations.

Protocol optimization and reducing dropout in online research.

Online research has advantages over in-person research; it's cost-efficient, scalable, and may increase diversity. Researchers collecting data online can assess protocol performance with classification models like a decision tree. However, challenges include attrition, lack of testing environment controls, technical limitations, and lack of face-to-face rapport and real time feedback. It is necessary to consider human factors of the teleresearch process from recruitment to data collection. Here we document the impact of protocol optimizations on social media engagement and retention between a pilot sample of Veterans (n = 26) and a post-optimization sample of both Veterans and civilians (n = 220) recruited from Facebook advertisements. Two-sided tests for equality of proportions were statistically significant: advertisement views leading to clicks increased by 23.8% [X2(1) = 130.3, p < 0.001] and completion of behavioral tasks increased by 31.2% [X2(1) = 20.74, p < 0.001]. However, a proportion of participants dropped out of the study before completion for both samples. To explore why, a C5.0 decision tree was used to find features that classify participant dropout. The features chosen by the algorithm were nicotine use (100%) and cannabis use (25.6%). However, for those completing the study, data quality of cognitive performance was similar for users and nonusers. Rather than determining eligibility, participants who endorse using nicotine, or both nicotine and cannabis, may have individual differences that require support in online protocols to reduce drop out, such as extra breaks. An introduction page that humanizes participants' lifestyle habits as a naturalistic benefit of remote research may also be helpful. Strategies are discussed to increase engagement and improve data quality. The findings have implications for the feasibility of conducting remote research, an increasingly popular approach that has distinct challenges compared to in-person studies.

Robust conservative scheme and nonlinear solver for phase transitions in heterogeneous permafrost

In this paper we study computational schemes to simulate freezing and thawing in permafrost modeled by a nonlinear heat equation with constitutive properties resembling those in the well known Stefan problem but featuring distinct challenges. The models are discretized with low order conservative discretization and fully implicit time stepping. We explore the challenges due to the nonsmooth nature of the temperature–enthalpy relationship and the domain heterogeneity, with focus on the solver and the use of enthalpy as a primary variable in contrast to the temperature variable used commonly in applications literature. We prove and demonstrate the convergence of our algorithms in realistic physical scenarios.

Characterizing the spatio-temporal distribution, detection, and prediction of aerosol atmospheric rivers on a global scale

Aerosol Atmospheric Rivers (AARs) are elongated and narrow regions that carry high concentrations of aerosols (tiny particles suspended in the atmosphere) across large distances, exerting effects on both air quality and human health (Chakraborty et al., 2021, 2022). Monitoring and modeling these aerosols present distinct challenges due to their dynamic nature and complex interactions within the atmosphere. In this context, the present study detects and predicts the AARs using MERRA-2 reanalysis datasets with their seasonal climatology of key aerosol species, including Black Carbon (BC), Dust (DU), Organic Carbon (OC), Sea Salt (SS), and Sulphates (SU). The study employs an innovative Integrated Aerosol Transport (IAT) based AAR algorithm from 2015 to 2022. A total count of 44,020 BC AARs, 13,280 DU AARs, 21,599 OC AARs, 17,925 SS AARs, and 31,437 SU AARs were detected globally. The seasonal climatology of BC and OC AARs intensifies in areas such as the Amazon rainforest and Congo during AMJJAS (April–September) due to forest fires. Similarly, DU AARs are more frequent in regions near the Saharan desert, primarily around the equator during AMJJAS. SS AARs tend to predominate over the oceans, while SU AARs are predominantly found in the northern hemisphere, primarily due to higher anthropogenic emissions. Furthermore, convolutional autoencoder-based models were developed for key aerosol species, strengthening predictive accuracy by effectively capturing complex data relationships and delivering precise predictions for the last 5-time frames. During validation, the model evaluation parameters for image prediction such as the Structural Similarity Index ranged from 0.86 to 0.94, Peak Signal-to-Noise Ratio fluctuated between 1.14 and 42.25 dB, Root Mean Square Error varied from 2.39 to 296.4 mg/(m-sec), and Mean Square Error fell within the range of 1.55–17.22 mg/(m-sec). These collectively reflect image closeness, quality, dissimilarity, and accuracy in AAR prediction. This study demonstrates the effectiveness of advanced machine and deep learning models in predicting AARs, offering the potential for advanced forecasting and enhancing resilience in high-aerosol concentration regions.

HPV testing as an effective triage strategy in the follow-up after fertility-sparing treatment for glandular lesions of the uterine cervix

ObjectiveThe management and surveillance of glandular pre-cancerous lesions of the uterine cervix present distinct challenges compared with squamous lesions, primarily attributed to the lower effectiveness of diagnostic methods such as...