bluuebunny/crossref_metadata_2025 huggingface dataset:
Why do adults with intellectual disabilities work in sheltered	workshops?
Authors: Migliore Alberto , Grossi Teresa , Mank David , Rogan Patricia
Published: November 2024

The literature shows that many adults with disabilities and their families prefer integrated employment. Federal and state policies promote participation of individuals with disabilities in the general labor market, yet the majority of individuals with intellectual disabilities (ID) attend facility-based programs. The purpose of this paper is to determine what factors influence adults with intellectual disabilities and their families in choosing between sheltered workshops and integrated employment. To this end, adults with ID (210), families (185), and staff members in sheltered workshops (224) were surveyed. Findings showed that, when deciding about day services, some adults with ID and their families had concerns about safety, transportation, long-term placement, work hours, disability benefits, social environment, and work skills issues. Long-term placement, safety, and social environment emerged as the most important concerns. Additionally, professionals in disability services appeared to play a relatively minimal role in encouraging adults with ID and their families to pursue integrated employment and in some cases, professionals have even encouraged adults with ID and their families to choose sheltered workshops. Recommendations are provided for advancing the transition of adults with ID from sheltered workshops to integrated employment.
Customized Employment: Where we are; where we're headed
Authors: Griffin Cary , Hammis Dave , Geary Tammara , Sullivan Molly
Published: November 2024

Customized Employment (CE) represents the natural evolution of supported employment (SE). The techniques used challenge traditional methods and build on SE and relevant competitive employment strategies, but they do represent a departure from having the local job market needs dictate the employment sought. Instead, CE starts with the person and engages employers through an interest-based negotiation revealing the benefits hiring a specific job seeker will have for both parties. The promise of this approach is that stereotypical jobs are reduced and employment better matching an individual's "personal genius" occurs.
Effective partnerships: Collaborative efforts that support	Customized Employment
Authors: Fesko Sheila , Varney Elena , DiBiase Cori , Hippenstiel Mandy
Published: November 2024

For individuals with significant disabilities, effective customized employment services often require the support and coordination of multiple entities and/or funding sources. This article describes a variety of partnership models that have been developed among the workforce development system and disability providers. Examples of partnerships demonstrated by Customized Employment and WorkFORCE Action grantees are provided, as well as practical suggestions for local implementation.
Building Social Capital: The communityworks, inc. experience
Authors: Williams Janet M.
Published: November 2024

This article explores the changing expectations, practices and policies supporting Social Capital for people with disabilities living in the community. It introduces relevant developments in how communityworks, inc. operates in two arenas to support community membership. First, in the arena of people who buy services from the agency, philosophical principles that support community membership are delineated, including a personal story that illuminates these principles. Second, in the arena of people paid to work through the agency, a discussion of standards developed by staff that give them ownership in the process and create consistency in developing Social Capital across the agency. In conclusion, the article addresses ongoing challenges we face as an agency that is always pushing the edge to further enhance Social Capital.
Instruction-Driven Game Engine: A Poker Case Study
Authors: Wu Hongqiu , Liu Xingyuan , Wang Yan , Zhao Hai
Published: November 2024

The _Instruction-Driven Game Engine (IDGE)_ project aims to democratize game development by enabling a large language model (LLM) to follow free-form game descriptions and generate game-play processes. The IDGE allows users to create games simply by natural language instructions, which significantly lowers the barrier for game development. We approach the learning process for IDGEs as a _Next State Prediction_ task, wherein the model autoregressively predicts the game states given player actions. The computation of game states must be precise; otherwise, slight errors could corrupt the game-play experience. This is challenging because of the gap between stability and diversity. To address this, we train the IDGE in a curriculum manner that progressively increases its exposure to complex scenarios. Our initial progress lies in developing an IDGE for Poker, which not only supports a wide range of poker variants but also allows for highly individualized new poker games through natural language inputs. This work lays the groundwork for future advancements in transforming how games are created and played.
Sample Compression Hypernetworks: From Generalization Bounds to Meta-Learning
Authors: Leblanc Benjamin , Bazinet Mathieu , D’Amours Nathaniel , Drouin Alexandre , Germain Pascal
Published: November 2024

Reconstruction functions are pivotal in sample compression theory, a framework for deriving tight generalization bounds. From a small sample of the training set (the compression set) and an optional stream of information (the message), they recover a predictor previously learned from the whole training set. While usually fixed, we propose to learn reconstruction functions. To facilitate the optimization and increase the expressiveness of the message, we derive a new sample compression generalization bound for real-valued messages. From this theoretical analysis, we then present a new hypernetwork architecture that outputs predictors with tight generalization guarantees when trained using an original meta-learning framework. The results of promising preliminary experiments are then reported.
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Authors: Zakaria Rinny
Published: November 2024

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Theoretical and computational modeling of the interactions of peptides and proteins with lipid bilayer surfaces
Authors:   , Smith Ryan
Published: November 2024

This dissertation combines computational and theoretical modeling with experimental data to advance our understanding of peptide-lipid interactions, leveraging coarse-grained (CG) molecular dynamics (MD) simulations and atomic force microscopy (AFM)-based force spectroscopy to elucidate these essential biological processes. We investigated the impact of polypeptide length on membrane interactions using a series of short, homologous peptides, revealing a linear dependence of free energy barriers and intrinsic dissociation rates on peptide length. Another study examined the effect of primary structure by varying guest residue species and positions within host-guest pentapeptides, finding significant effects of terminal residues on interactions with zwitterionic and anionic lipid bilayers and identifying multiple dissociation pathways. Additionally, we applied and compared several computational techniques for analyzing AFM force spectroscopy data, proposing machine learning methods for improved analysis. These findings suggest that peptide-membrane interactions might be more complex than previously understood, often requiring multiple dissociation pathways. Finally, we extended CG MD simulations to study a large peripheral membrane protein, providing insights into interactions at the lipid membrane surface from its dominant binding regions. This study highlights limitations in current methods and suggests alternative approaches for studying complete proteins.
Objective assessment of computed tomographic attenuation of inspiratory and expiratory series in dogs with and without bronchomalacia
Authors:   , Gerhard Charlotte
Published: November 2024

Paired inspiratory:expiratory breath-hold computed tomographic scans (I:E-BH CT) capture thoracic images with control of respiratory phase, important with dynamic airway collapse. Radiomics allows assessment of objective metrics of lung attenuation. Hypothesis/Objectives: Dogs with bronchomalacia (BM) would have significantly different objective metrics of attenuation on I:E-BH CT reflecting changes in airway caliber and impaired downstream parenchymal aeration versus dogs without BM (NoBM). The objectives of this study were to document objective metrics of attenuation of the pulmonary parenchyma on I:E-BH CT in dogs with BM and NoBM using automated software analysis. Metics included mean lung attenuation (MLA), percent low attenuation area -856 Hounsfield units (HU; LAA percent-856), percentage high attenuating areas -700 HU (HAA percent-700), and percent attenuation area between -600 and -250 HU (HAA percent-600--250). Participants included client-owned dogs with BM (n=123) and NoBM (n=20). This retrospective study utilized 3D Slicer software to assess CT attenuation. Analysis used Spearman correlation and 2-way ANOVA with beta regression. When comparing the difference between inspiratory/expiratory phases, there was a significantly greater increase in MLA (p=0.001), a significant reduction in LAA percent-856 (p=0.016), and a significant increase in HAA percent-700 and HAA percent-600-250 (p [less than] 0.001 and p [less than] 0.0001, respectively) in BM versus NoBM dogs. In conclusion, a higher lung attenuation in BM dogs supports presence of impaired downstream parenchymal aeration to segmental and subsegmental airway collapse. Quantitative imaging holds promise for objectively evaluating changes with BM.
A study of public relations strategies employed by healthcare organizations in Missouri to combat COVID-19 vaccine misinformation
Authors:   , Li Zhengyang
Published: November 2024

The development of the COVID-19 vaccine marked a significant milestone in the global effort to combat the COVID-19 pandemic. However, along with the invention of the COVID-19 vaccine came the challenge of misinformation, which threatened vaccination efforts and public health. Misinformation surrounding the safety and efficacy of the COVID-19 vaccines spread rapidly across various platforms, particularly on social media. Misinformation about the COVID-19 vaccine posed a significant challenge to vaccine acceptance, affecting public health efforts to achieve herd immunity and put the COVID-19 pandemic under control. The importance of effective public relations strategies to address COVID-19 misinformation cannot be overstated. This study explored strategies implemented by healthcare professionals working in Missouri to address COVID-19-related vaccine misinformation. I performed a mixed approach by using a survey with 153 responses from healthcare professionals working in Missouri and having semi-structured in-depth interviews with 4 local health department administrators. The survey data was examined in SPSS to find correlations between variables such as professional role, years of experience, and preferred sources of COVID-19 vaccine information. Additionally, thematic analysis was conducted on the qualitative data gathered from the in-depth interviews to identify key themes and insights regarding public relations strategies employed by healthcare organizations. The results show that peer-reviewed research, healthcare experts, and government agencies emerged as primary sources of reliable information about the COVID-19 vaccine among healthcare professionals. In-depth interview participants emphasized the importance of building trust with communities to provide accurate information and combat misinformation effectively. People's awareness of the echo chamber effect was positively correlated with confidence in distinguishing between accurate information and misinformation about COVID-19 vaccines. This suggests that awareness of how information circulates within social media platforms may enhance individuals' ability to distinguish credible information from misinformation. Healthcare professionals and organizations need to continue prioritizing communication strategies that build trust and engage communities. Additionally, raising awareness of how social media algorithms amplify sensational or controversial content within like-minded communities is crucial for empowering individuals to evaluate information and navigate digital environments effectively and critically. Through implementing proper public relations strategies, healthcare professionals can build trust with communities, empower individuals to make informed decisions about their health, and effectively combat the spread of misinformation.
Advancing space-borne computational vision for rare object mapping
Authors:   , Cheng Keli
Published: November 2024

[EMBARGOED UNTIL 08/01/2025] This work investigates and proposes solutions to common challenges in remote sensing for mapping rare targets. The first part focuses on the classification and precise localization of rare targets like Maasai Boma homesteads across broad geographic areas. A mapping workflow is developed using a Proxyless-NAS deep convolutional neural network (DCNN) for broad-area scanning and a two-phase weighted mean-shift algorithm for efficient clustering. This workflow achieves remarkably high precision in pinpointing the precise locations of Boma targets and identifies numerous human false-negatives, substantiating DCNN's feasibility for large-scale scanning. The second part explores using multi-temporal medium-resolution Sentinel-2 data instead of costly very-high-resolution imagery. A Deep Seasonal Network with an EfficientNet backbone is designed to effectively leverage multi-temporal data, significantly improving the F1 score for Boma classification compared to the plain EfficientNet architecture, demonstrating the viability of leveraging medium-resolution multi-temporal data. The third part tackles few-shot scene classification, critical when data annotation is limited. A Multi-head Hierarchical ProtoNet architecture is proposed, utilizing the hierarchical relationships among labels for few-shot classification. Multiple output integration methods are evaluated, with a hybrid approach combining weighted summation and joint probability leading to the best performance, significantly boosting few-shot classification across backbone architectures. The research addresses key rare target mapping challenges through novel architectures validated on real-world case studies and benchmark datasets, providing valuable insights for precise mapping, spatial-temporal data fusion, few-shot learning with label ontology, and analogous problems for rare objects.
Problems in high-dimensional probability
Authors:   , Sharma Shivam
Published: November 2024

[EMBARGOED UNTIL 08/01/2025] In Chapter 1, we focus on lower estimates for suprema of stochastic processes. We investigate the role of super-gaussianity and the Sudakov Minoration Principle for this concept. We derive an extension of celebrated Majorizing Measures Theorem (MMT) for Gaussian mixtures which in turn yields a comparison inequality between the expected suprema of sub-gaussian random vectors and Gaussian mixtures. In Chapter 2, we focus on an alternative method for deriving Sudakov Minoration type estimates via "discrepancy" bounds. More precisely, we obtain such bounds between the expected suprema of canonical processes generated by random vectors with independent coordinates and the expected suprema of Gaussian processes. In particular, we obtain a refined proximity estimate for Rademacher and Gaussian complexities. Our estimates are dimension-free and depend only on the geometric parameters and the numerical complexity of the underlying index set. In Chapter 3, we focus on concentration of measure phenomenon and study variancesensitive deviation inequalities. In particular, we derive generalizations of variancesensitive concentration inequalities in [PV18] and in [PV19] to include functions that are "nearly" convex. We also derive an important application in the form of Hanson- Wright type inequality.
We don't belong... but I deserve to be here! : Black women collegian's definitional understandings of belonging
Authors:   , Udoh Ekaete
Published: November 2024

[EMBARGOED UNTIL 08/01/2025] This dissertation delves into the complex experiences and definitions of belonging among Black women collegians at a predominantly white institution (PWI). Using the theories of Black feminism and Sense of Belonging as the theoretical framework, the study utilizes the Sista Circle Methodology (SCM), Sista Circles and Women of Color Reflections (WoCFR) to collect data, emphasizing the significance of communal and individual reflections. The research reveals that traditional ideas of belonging do not fully capture these women's experiences. Instead, the participants expressed an "Ethos of Deserving," which acknowledges their worth and right to be in academic spaces despite historical and systemic exclusion. This redefinition challenges existing literature and provides crucial insights for higher education institutions to better support Black women collegians. The study's findings are intended to guide policies and practices that promote inclusive and equitable educational environments in higher education by garnering the experiences of Black women students. By centering the voices of Black women, this research contributes to a deeper understanding of their unique challenges and aspirations, advocating for systemic change to enhance their academic and social experiences.
Effects of experimental autoimmune encephalomyelitis on properties and function of the mouse major pelvic ganglion neurons
Authors:   , Henderson Sherryl LaVon
Published: November 2024

[EMBARGOED UNTIL 08/01/2025] Experimental autoimmune encephalomyelitis (EAE) serves as a valuable mouse model for studying the pathophysiology of multiple sclerosis (MS), a chronic autoimmune disease characterized by inflammation and demyelination of the central nervous system (CNS). While EAE mimics many aspects of MS, its effects on the lower urinary tract (LUT) remain poorly understood. This study investigated alterations in lower urinary tract output, excitability of major pelvic ganglion (MPG) neurons, and expression profiles of MPG neurons in EAE. Adult female SJL/J mice were induced with EAE, and clinical scores were monitored daily. Real-time quantitative polymerase chain reaction (qPCR) was employed to analyze gene expression in MPGs, while functional bladder output and electrophysiological measurements of MPG neurons were conducted to evaluate lower urinary tract output and MPG neuron excitability. The results revealed significant changes in lower urinary tract output, with decreased urine output observed in EAE mice compared to controls. Furthermore, MPG neuron excitability was altered, with changes in passive membrane properties and action potential characteristics observed in EAE-induced animals. Analysis of gene expression profiles in MPG neurons revealed significant alterations in the mRNA levels of ion channels and receptors implicated in bladder control mechanisms. These findings provide valuable insights into the pathophysiological mechanisms underlying lower urinary tract dysfunction in EAE, suggesting potential targets for therapeutic intervention in MS-related bladder dysfunction. Further research is warranted to elucidate the complex interplay between immune-mediated CNS damage and LUT dysfunction in MS and related conditions.
Impacts of nitrogen fertilizers and nitrification inhibitors on greenhouse gas emissions from soil
Authors:   , Frossard Dean Michael
Published: November 2024

[EMBARGOED UNTIL 08/01/2025] This thesis investigates the impact of nitrification inhibitors and varying fertilizer rates on greenhouse gas emissions and crop yield in agricultural systems. Fertilized agricultural soils can experience notable losses of nitrogen gases, such as nitrous oxide (N2O) and ammonia (NH3) after the application of nitrogen (N) fertilizers. In the Midwest U.S., anhydrous ammonia and urea are common nitrogen sources used in corn cropping systems. Nitrification inhibitors pronitridine and dicyandiamide (DCD) have demonstrated potential to enhance corn grain yield when applied with N fertilizer. The first chapter explores the efficacy of pronitridine in reducing N2O and NH3 emissions from corn fields fertilized with anhydrous ammonia. Results from field trials conducted in Northeast Missouri over two growing seasons indicated that while pronitridine did not significantly reduce cumulative NH3 emissions in both seasons, it lowered N2O emissions from higher fertilizer rates in the 2023 growing season. Grain yield increased with fertilizer rate and the addition of pronitridine. Overall, we found that pronitridine did not affect cumulative N2O and NH3 emissions (outside of 202N fertilizer rate for N2O in 2023) but has the potential to increase grain yield. The second chapter examines the effects of urea fertilizer and DCD on N2O, NH3, carbon dioxide (CO2), and methane (CH4) in bare soil. Findings revealed that increased fertilizer rates led to higher NH3 emissions, while DCD application had a marginal effect on reducing N2O emissions, with greater reductions observed at higher DCD rates. Our data supports other work concluding DCD has the potential to reduce N2O emissions but highlights the potential for greater reductions through increased DCD rates.
The Impact of Modern Technology on Learning and Retention Outcomes in Elementary School Mathematics
Authors:   Department of Educational Sciences, Faculty of Education, Taybad Branch, Islamic Azad University, Taybad, Iran., Fadaei Moghadam Khadije , Razavi Majid Reza ,   Department of Educational Sciences, Faculty of Education, Taybad Branch, Islamic Azad University, Taybad, Iran.
Published: November 2024

Objective: This study aims to investigate the application of modern educational software (MES) in the mathematics classroom environment to the learning and retention process. Methods: The present study was applied in terms of purpose and quasi-experimental in pre-test-post-test design. The statistical population included 18794 fourth-grade female students in the elementary school in 2020-2021 in Mashhad City, Iran. Then the test group was taught using the creative technology program (CTP) in six sessions, and the control group was taught the traditional teaching method in six sessions. At the end of the training, math tests were performed in the test and control groups and the scores of the two groups were compared to measure learning. Then, two months later, to measure the amount of retention in the two groups, the math test was repeated and the results were compared with the pre-test results. Results: Based on the results, the CTP has a significant effect on learning and teaching mathematics to fourth-grade elementary students in Mashhad City. Conclusion: With the advancement of fundamentals of education, the implication of educational software has proved more favorable effects than traditional systems in education and has a higher success rate.
Self-compassion and Social Anxiety Symptoms: Fear of Negative Evaluation and Shame as Mediators
Authors:   Department of Clinical Psychology, Faculty of Psychology and Education, Allameh Tabataba’i University, Tehran, Iran., Sadrzadeh Fatemeh , Borjali Ahmad ,   Department of Clinical Psychology, Faculty of Psychology and Education, Allameh Tabataba’i University, Tehran, Iran., Rafezi Zohreh ,   Department of Clinical Psychology, Faculty of Psychology and Education, Allameh Tabataba’i University, Tehran, Iran.
Published: November 2024

Objective: Social anxiety disorder (SAD) can negatively impact various aspects of life, incurring significant personal and societal costs. Thus, it is necessary to identify protective factors that buffer against SAD symptoms and the mechanisms underlying their mitigation. This research aims to examine the relationship between self-compassion and social anxiety symptoms among university students, specifically exploring the potential mediating effects of fear of negative evaluation (FNE) and shame. Methods: This study examined the relationships between variables through structural equation modeling (SEM) using a correlational design. The sample included 242 undergraduate, master’s, and doctoral students from Tehran’s public universities, selected via the convenience sampling method. Social phobia inventory, the self-compassion scale, the brief form of FNE scale, and the shame subscale of self-conscious affect questionnaire were used to collect data. Data analysis involved descriptive statistics and Pearson correlation via SPSS software, version 22, and SEM using AMOS software, version 22. Results: The results from analyzing the structural relationships indicated a direct negative effect of self-compassion on social anxiety symptoms, FNE, and shame. Additionally, FNE and shame had a positive effect on social anxiety symptoms directly (P<0.01). Investigating the significance of mediating variables indicated an indirect negative effect of self-compassion on social anxiety symptoms, passing through FNE and shame as mediators (P<0.01). The research model had a good fit and accounted for 59% of the variance in social anxiety symptoms. Conclusion: Based on the research results, increasing self-compassion can reduce social anxiety symptoms through both direct and indirect paths, by reducing the effect of FNE and shame.
Investigating the Diagnostic Value of Vision Triage for Screening Patients With COVID-19
Authors:   Department of Infectious Diseases, Kamkar-Arabnia Medical Education Center, Faculty of Medicine, Qom University of Medical Sciences, Qom, Iran., Khodadadi Javad , Vahidian Mostafa ,   Department of Family and Community Medicine, Spiritual Health Research Center, School of Medicine, Qom University of Medical Sciences, Qom, Iran., Safai Moghadam Amir Hossein ,   School of Medicine, Groningen University of Medical Sciences, Groningen, Netherlands., Sadeghi Sepehr ,   Student Research Committee, Tehran University of Medical Sciences, Tehran, Iran., Moftakharzadeh Seyed Ahmad ,   Department of Infectious Diseases, Kamkar-Arabnia Medical Education Center, Faculty of Medicine, Qom University of Medical Sciences, Qom, Iran., Nourabadi Shabnam ,   Department of Infectious Diseases, Kamkar-Arabnia Medical Education Center, Faculty of Medicine, Qom University of Medical Sciences, Qom, Iran., Khoshgoftar Morteza ,   Department of Pediatric Diseases, Faculty of Medicine, Hazrat Masoumeh (S) Medical Education Center, Qom University of Medical Sciences, Qom, Iran.
Published: November 2024

Background and Aim: This study aims to design and evaluate vision triage for screening patients with coronavirus according to the patient’s conditions for hospitalization or discharge of patients with COVID-19. Materials and Methods: This study measured the tool’s value for the clinical triage of COVID-19 patients. In this form, the patient’s symptoms, underlying disease, contact with the COVID-19 patient in the last two weeks, and contact with healthcare personnel were evaluated. The diagnosis of COVID-19 was based on a positive real-time transcriptase polymerase chain reaction (PCR) test. We calculated the triage score for each patient based on the clinical form that uses signs and symptoms. In the next step, the sensitivity and specificity of the scored scale were analyzed. Results: Out of 57 patients who had a negative scoring system, 15 patients (7.5%) had a positive test, and out of 143 patients for whom the scoring system reported a high number, 116 patients (58%) had a positive test. This scoring system’s sensitivity, specificity, and positive and negative predictive values were calculated as 88.5%, 60.8%, 81.1% and 73.6%, respectively. Conclusion: This triage tool will be helpful in better management of patients with COVID-19. This system included clinical findings with high diagnostic accuracy.
The Relationship Between Diabetic Peripheral Neuropathy and Pulmonary Function Tests
Authors:   Department of Internal Medicine, Spiritual Health Research Center, School of Medicine, Qom University of Medical Sciences, Qom, Iran., Adeli Seyed Hasan , Javadi Saeedeh ,   Student Research Committee, Qom University of Medical Sciences, Qom, Iran., Aghaali Mohammad ,   Department of Family and Community Medicine, Neuroscience Research Center, School of Medicine, Qom University of Medical Sciences, Qom, Iran., Parham Mahmoud ,   Department of Internal Medicine, Gastroenterology and Hepatology Diseases Research Center, School of Medicine, Qom University of Medical Sciences, Qom, Iran.
Published: November 2024

Background and Aim: Diabetes mellitus (DM) is a systemic disease with complications in the cardiovascular system, nerves, eyes, and kidneys. The potential targeting of the lung as an organ in diabetes is suggested by the rich pulmonary microvascular circulation and ample connective tissue present. This study aims to assess the relationship between pulmonary function tests and diabetic peripheral neuropathy in patients with type 2 DM. Materials and Methods: One hundred patients were included in the study. Michigan neuropathy screening instrument (MNSI) was used to evaluate diabetic peripheral neuropathy in all patients. Based on the results of the MNSI, patients were divided into two groups, 50 diabetic subjects without neuropathy and 50 subjects with neuropathy. Also, a flow-sensitive spirometer was used to evaluate the pulmonary function test (PFT) for all subjects. All the pulmonary function parameters were measured as a percentage of predicted value. In addition, according to hemoglobin A1c (HbA1c) level, patients were divided to three groups (group 1≤7%, group 2=7%-9%, group 3≥9%). Results: No significant difference was observed between the two groups with and without diabetic neuropathy in terms of PFTs, but in subjects with diabetic peripheral neuropathy, a significant reduction of forced vital capacity (FVC) and forced expiratory volume in one second (FEV1) was observed in group 3 (HbA1c >9) compared to groups 1 (HbA1c <7). Conclusion: This study does not show a significant reduction in pulmonary function in patients with type-2 DM with neuropathy compared to those without neuropathy. However, a significant difference was observed in FVC, FEV1, and forced expiratory flow (FEF)25-75 based on HBA1C level in subjects with neuropathy.
Epidemiologic Evaluation of the Blunted Heart Rate Response in Chronic Kidney Disease and Its Associated Factors
Authors:   Department of Nuclear Medicine, Rajaie Cardiovascular, Medical and Research Center, Rajaie Cardiovascular, Medical and Research Institute, Tehran, Iran., Yaghoobi Nahid , Hekmat Sepideh ,   Department of Nuclear Medicine, School of Medicine, Iran University of Medical Sciences, Tehran, Iran., Malek Hadi ,   Department of Nuclear Medicine, Rajaie Cardiovascular, Medical and Research Center, Rajaie Cardiovascular, Medical and Research Institute, Tehran, Iran., Hedayati Raheleh ,   Department of Nuclear Medicine, School of Medicine, Iran University of Medical Sciences, Tehran, Iran., Salimian Saba ,   Department of Nuclear Medicine, School of Medicine, Iran University of Medical Sciences, Tehran, Iran.
Published: November 2024

Background and Aim: Previous studies on heart response rate (HRR) were conducted in diabetic patients. This study aims to evaluate HRR in end-stage renal disease (ESRD) patients and its correlation with myocardial perfusion imaging (MPI) findings and patients’ prognosis. Materials and Methods: The present study was conducted on patients with chronic kidney disease (CKD) who visited Shahid Hasheminejad Hospital between October 1, 2011, and September 30, 2019. Before the stress test, the patient was asked to rest in a supine position for 3 minutes while an electrocardiogram, blood pressure, and heart rate were recorded. Dipyridamole was then administered as an infusion with a dose of approximately 0.56 mg/kg of the patient’s monitoring of the electrocardiogram. The patient’s heart rate was recorded at 2-minute intervals after the start of the dipyridamole injection. Three minutes after the completion of the dipyridamole injection, radiopharmaceutical was injected. Finally, the findings of the myocardial perfusion scan were analyzed. Results: The summed stress score (SSS), and summed rest score (SRS) had a significant relationship with the female gender and ejection fraction (EF) with the male gender (P<0.05). These results were not significantly related to patients’ age and disease history (P>0.05). No significant difference was observed in the incidence of blunted HRR in patients with cardiac events by age, gender, and renal disease condition (P>0.05). Conclusion: The frequency of blunted HRR in patients with CKD based on various variables was determined for the first time in the country in the present study, which can be an introduction for future research.
Serological Findings of Children With COVID-19 and Clinical and Paraclinical Results
Authors:   Department of Pediatrics, School of Medicine, Hazrat-e Fateme Masoume Hospital, Qom University of Medical Sciences, Qom, Iran., Movahedi Zahra , Heydari Hosein ,   Department of Pediatrics, School of Medicine, Hazrat-e Fateme Masoume Hospital, Qom University of Medical Sciences, Qom, Iran., Vahedian Mostafa ,   Department of Family and Community Medicine, Spiritual Health Research Center, School of Medicine, Qom University of Medical Sciences, Qom, Iran., Hajian Hanieh ,   Department of Pediatrics, School of Medicine, Hazrat-e Fateme Masoume Hospital, Qom University of Medical Sciences, Qom, Iran., Noori Azadeh ,   Department of Pediatrics, School of Medicine, Hazrat-e Fateme Masoume Hospital, Qom University of Medical Sciences, Qom, Iran., Hosseini Reihanehsadat ,   School of Medicine, University of Virginia, Charlottesville, United States., Noori Simin ,   Department of Pediatrics, School of Medicine, Arak University of Medical Sciences, Arak, Iran.
Published: November 2024

Background and Aim: This research aims to examine the serological outcomes of pediatric patients with COVID-19 and their correlation with the clinical and paraclinical outcomes of children hospitalized at Hazrat Masoumeh Hospital in the mid-year of 2021. Materials and Methods: The study collected data from a database of patients who were definitively diagnosed with COVID-19. The study also recorded the clinical outcomes of these patients (such as discharge, death, and length of hospitalization) for one month after their hospitalization, which was the last day of follow-up. Information on epidemiological, clinical, laboratory data, radiological indicators, treatment, and clinical consequences was extracted from the patient’s electronic medical records using data collection forms. All the gathered information was then entered into SPSS software, version 22 and analyzed using statistical tests. The significance level for all tests was set at 0.05. Results: No significant relationship was observed between the immunoglobulin (Ig) M response and any of the clinical symptoms (P>0.05). The IgG response was statistically significantly related only to diarrhea and abdominal pain among the clinical symptoms experienced by patients (P<0.05). Among laboratory symptoms, thrombocytopenia had a statistically significant relationship with IgM (P=0.009), and positive urine culture had a statistically significant relationship with IgG (P=0.00). Other laboratory symptoms had no statistically significant relationship with serological findings (P>0.05). Imaging results had a statistically significant relationship only with Ig G (P=0.00) and no significant statistical relationship with IgM (P=0.23). Conclusion: The study results indicate that the serological results of these patients are also significant for diagnosing this disease.
Mekân ve Bellek: Çocukluk Evi
Authors: Sungur Mine
Published: November 2024

Kişi doğduğu andan itibaren yaşadığı çevreyi deneyimlemesi, anlamlandırması, hafızasında yer edinmesi mekân ile kurmuş olduğu duygusal bağ ile gerçekleşmektedir. Anılar, deneyimler, algılar, tutum ve davranışlar ile ilişkilendirilmiş mekân bellekte kodlanmaktadır. Bu kodlar özellikle bireyin yaşam sürecinin başlangıç evresi olan çocukluk döneminde hem mekânsal hem de sosyal olarak anlamlandırılarak uzun süreli bellekte kalabilmektedir. Temel amacı mekân ve hafıza arasındaki ilişkiyi çocukluk döneminde yaşanılan konutlar üzerinden araştıran bu çalışmada; bireyin çocukluk döneminde yaşamış olduğu konutun, mekânsal ve sosyal açıdan nasıl ve ne kadar zihinde tanımlanmış ve kodlanmış olduğu üzerine yoğunlaşılmaktadır. Ülkemizin farklı coğrafyalarında, farklı zaman dilimlerinde yaşamış 50 yaş ve üzerine sahip kişiler (n=60) ile çocukluk dönemlerindeki konutlar üzerine yüz yüze görüşme gerçekleşmiştir. Çalışmada literatür araştırması, görüşme, not alma, fotoğraf ile tespit, görüşmeyi kaydetme ve doküman analizi gibi veri toplama yöntemleri ile katılımcıların söylemleri doğrultusunda betimsel analizin gerçekleştiği niteliksel bir süreç izlenmiştir. Elde edilen bulgular neticesinde; hafıza sadece bireysel bir olgu olmayıp bireyin zihnindeki toplumsal yaşamın bir yansıması olarak karşımıza çıkmaktadır. Oyunlar, misafirlikler, bayramlar gibi sosyal etkileşim mekânların hatırlanmasında önemli rol oynadığı tespit edilmektedir. Mekânlarda yaşanılan mutlu/sevinçli ya da hüzünlü olaylar, tutum ve davranışlar mekânın hafızada kalma süresini etkilemektedir. Yerel mimarinin hafızalarda yer edinmiş mekânsal zenginliğin kaybolma tehlikesine karşı, güncel konut tasarımlarına aktarılması gerekliliği çalışma sonucu ortaya çıkmaktadır.
ФІЗИКО-ХІМІЧНА ВЗАЄМОДІЯ В СИСТЕМАХ Ag2S – {As, Sb, Bi}2S3 – GeS2
Authors: БЕРЕЗНЮК Орися , СЕМЕНЮК Віталія , КНЬОВЕЦЬ Анастасія , КОГУТ Юрій , ПІСКАЧ Людмила
Published: November 2024

В системах Ag2S – As2S3 – GeS2, Ag2S – Sb2S3 – GeS2 та Ag2S – Bi2S3 – GeS2 при 500 К досліджена фізико-хімічна взаємодія. В квазіпотрійній системі Ag2S – As2S3 – GeS2 при 500 К утворюються незначні однофазні області на основі вихідних Ag2S, As2S3, GeS2 та тернарних сполук обмежуючих систем: Ag3AsS3, AgAsS2, Ag10Ge3S11, Ag2GeS3. Область гомогенності на основі сполуки Ag8GeS6 має протяжність до 20 мол. % по перерізу Ag3AsS3 – Ag8GeS6. Виявлено існування тринадцяти двофазних рівноваг, п’ять з яких знаходяться всередині квазіпотрійної системи, які поділяють концентраційний трикутник на десять трифазних полів. В системі Ag2S – Sb2S3 – GeS2 побудовано політермічний переріз Sb2S3 – GeS2, який є евтектичного типу взаємодії з координатами 35 мол. % GeS2 при 747 К (L↔α+GeS2). Вперше встановлено утворення тетрарних сполук складів Ag11Sb3GeS12 та ~Ag23Sb3Ge7S30. Перша тіосіль утворюється на перетині перерізів AgSbS2 – Ag8GeS6 та Ag3SbS3 – Ag2GeS3; методом скануючої електронної мікроскопії встановлено однофазність зразка цієї сполуки. Тетрарна фаза складу ~Ag23Sb3Ge7S30 утворюється на перетині перерізів Sb2S3 – Ag10Ge3S11 та Ag3SbS3 – Ag2GeS3. У системі утворюються граничні тверді розчини на основі вихідного стибій (ІІІ) сульфіду та Ag2GeS3, які при температурі відпалу не перевищують 5 мол. %. В квазіпотрійній системі існує десять однофазних областей: Ag2S, Sb2S3 (α-твердий розчин), GeS2, Ag3SbS3, AgSbS2, Ag8GeS6, Ag10Ge3S11, Ag2GeS3 (β-твердий розчин), Ag11Sb3GeS12, ~ Ag23Sb3Ge7S30 та дев’ятнадцять двофазних рівноваг, з яких одинадцять знаходяться в середині квазіпотрійної системи, поділяють концентраційний трикутник на десять трифазних областей. На ізотермічному перерізі квазіпотрійної системи Ag2S – Bi2S3 – GeS2 при 500 К, як і в аналогічній системі з Арсеном, нових фаз не утворюється. На основі сполук із Вісмутом: Bi2S3, AgBi3S5 AgBiS2 існують однофазні області до 5 мол. %. Побудовано діаграму стану системи Bi2S3 – GeS2, яка є евтектичного типу (V тип діаграм стану за Розебомом). Евтектика плавиться при температурі 860 К, її склад відповідає 52 мол. % GeS2. За наведених умов синтезу в системі існують вісім однофазних полів: Ag2S, Bi2S3 (α-твердий розчин), GeS2, AgBiS2 (β-твердий розчин), AgBi3S5 (γ-твердий розчин), Ag8GeS6, Ag10Ge3S11, Ag2GeS3. Тринадцять двофазних рівноваг, з яких п’ять знаходяться в середині квазіпотрійної системи, тріангулюють трикутник на шість трифазних полів.
Deep Reinforcement Learning for Dynamic Task Scheduling in Edge-Cloud Environments
Authors: Rani D. Mamatha , K P Supreethi , Bihari Jayasingh Bipin
Published: November 2024

With The advent of the Internet of Things (IoT) and its use cases there is a necessity for improved latency which has led to edgecomputing technologies. IoT applications need a cloud environment and appropriate scheduling based on the underlying requirements of a given workload. Due to the mobility nature of IoT devices and resource constraints and resource heterogeneity, IoT application tasks need more efficient scheduling which is a challenging problem. The existing conventional and deep learning scheduling techniques have limitations such as lack of adaptability, issues with synchronous nature and inability to deal with temporal patterns in the workloads. To address these issues, we proposed a learning-based framework known as the Deep Reinforcement Learning Framework (DRLF). This is designed in such a way that it exploits Deep Reinforcement Learning (DRL) with underlying mechanisms and enhanced deep network architecture based on Recurrent Neural Network (RNN). We also proposed an algorithm named Reinforcement Learning Dynamic Scheduling (RLbDS) which exploits different hyperparameters and DRL-based decision-making for efficient scheduling. Real-time traces of edge-cloud infrastructure are used for empirical study. We implemented our framework by defining new classes for CloudSim and iFogSim simulation frameworks. Our empirical study has revealed that RLbDS out performs many existing scheduling methods.
Comparison Between Different Source Localization and Connectivity Metrics of Spiky and Oscillatory MEG Activities
Authors: ELBehy Ichrak , Hadriche Abir , Maalej Rahma , Jmail Nawel
Published: November 2024

Epilepsy is considered the second neurological disease in a coma after stroke. Famous markers of epilepsy are repetitive seizures, their origin is stroma and cortical deformation. A neurologist would be assisted by identifying Epileptogenic Zones EZ when diagnosing epilepsy.. Source localization is utilized to identify regions known as EZ, which are of excessive discharges. It consists of both forward and inverse problems. The forward problem models the head through analytical and numerical methods. The inverse problem can be resolved using several techniques to locate the cerebral abnormal sources, via the electrophysiological recording biomarkers. In our study, we will investigate four distributed inverse problem methods: minimum norm estimation MNE, standardized low-resolution brain electromagnetic tomography sLORETA, maximum entropy on the mean MEM, Dynamic statistical parametric maps dSPM, to define epileptic networks connectivity of spiky and oscillatory events. We will examine the epileptic network connectivity using Phase Locking Value (PLV), Phase Transfert Entropy (PTE) for oscillatory events, cross-correlation (CC), and Granger Causality (GC) for spiky events applied on 5 pharmaco resistant subjects. We suggest rating the effectiveness of these networks in locating EZ through a phase of confrontation within iEEG transitory and oscillatory networks connectivity by exploring concordant nodes, their distance, propagation delays connection strength, and their cooperation in recognition of seizure onset zone. All studied techniques of the inverse problem, connection metrics, for both biomarkers of the 5 patients succeed in detecting at least one part of SOZ. sLORETA provides the highest concordant nodes and the closed one for spiky events using CC and GC. sLORETA also depicts the lowest propagation delay for oscillatory events using PTE. Through the 5 patients, MEM, dSPM, and MNE using CC, CG for spiky events, and PTE, PLV for oscillatory activities provide about 72 % of concordant nodes between MEG and iEEG.
Predictive Modeling of US Stock Market and Commodities: Impact of Economic Indicators and Geopolitical Events Using Machine
Authors: MD Tushar Khan  , Rabeya Akter  , Hossain Mohammad Dalim  , Abdullah AL Sayeed  , Farhana Rahman Anonna  , MD Rashed Mohaimin  , Mitu Karmakar
Published: November 2024

Comprehending the dynamics of the stock market is pivotal for investors, policymakers, and economists in the USA. The United States stock market being one of the most influential financial markets in the world, contributes significantly to shaping the world economy. This research project aimed to bridge the important gaps in understanding the interrelationship between economic indicators and geopolitical events concerning the performance of the US stock market and commodities. The prime objective was to assess how different economic indicators have an impact on stock market performance over a specific period. In this regard, the development of machine learning models facilitated the ability to forecast stock market and commodities trends. These models utilized economic indicators and geopolitical events from historical data to predict future movements in the market with higher accuracy than the traditional forecasting technique followed. The study considered several different datasets to comprehensively analyze the effects that economic indicators and geopolitical events have on the stock market and commodity performances. The key datasets used in this analysis involve historical stock market indices such as the S&P 500, Dow Jones Industrial Average, and NASDAQ, as well as commodity prices for gold, oil, and silver. These datasets were collected from reputed financial databases such as Bloomberg and Federal Reserve Economic Data-FRED, for metrics including GDP growth rates, unemployment rates, inflation figures, and interest rates. The researchers also gathered data on geopolitical events: elections, trade wars, and military conflicts, using usually reliable news archives like Reuters, Bloomberg News, and The New York Times. Linear Regression, Random Forest, and XG-Boost algorithms were selected to capture various facets of the data. The performance metrics used to evaluate the models in this study included Mean Absolute Error, Root Mean Squared Error, and R-squared.  Random Forest Regressor outperformed the other models with the lowest RMSE, showcasing its ability to model complex relationships. XG-Boost Regressor equally delivered a strong balance between performance and computational efficiency, achieving similar accuracy to Random Forest. Results from this study therefore can inform policy makers, especially in regards to stabilizing the financial markets during periods of heightened economic or geopolitical uncertainty. Key recommendations include developing proactive policy measures that could dampen the effect of adverse economic indicators and geopolitical events on market stability.