Miracle-dz/UniFAR-FTUs huggingface dataset:
The Macroeconomic Effects of Housing Wealth, Housing Finance, and Limited Risk-Sharing in General Equilibrium∗

This paper studies the role of time-varying risk premia as a channel for generating and propagating fluctuations in housing markets, aggregate quantities, and consumption and wealth heterogeneity. We study a two-sector general equilibrium model of housing and non-housing production where heterogeneous households face limited opportunities to insure against aggregate and idiosyncratic risks. The model generates large variability in the national house price-rent ratio, both because it fluctuates endogenously with the state of the economy and because it rises in response to a relaxation of credit constraints and decline in housing transaction costs (financial market liberalization). These factors, together with a rise in foreign ownership of U.S. debt calibrated to match the actual increase over the period 2000-2006, generate fluctuations in the model price-rent ratio that explains a large fraction of the increase in the national price-rent ratio observed in U.S. data over this period. The model also predicts a sharp decline in home prices starting in 2007, driven by the economic contraction and by a presumed reversal of the financial market liberalization. Fluctuations in the model’s price-rent ratio are driven by changing risk premia, which fluctuate endogenously in response to cyclical shocks, the financial market liberalization, and its subsequent reversal. By contrast, we show that the inflow of foreign money into domestic bond markets plays a small role in driving home prices, despite its large depressing influence on interest rates. Finally, the model implies that procyclical increases in equilibrium price-rent ratios reflect rational expectations of lower future housing returns, not higher future rents. JEL: G11, G12, E44, E21 joint seminar, the American Economic Association annual meetings, January 2009 and January 2010, the ERID conference at Duke 2010, the London School of Economics Conference on Housing, Financial Markets, and the Macroeconomy May 18-19, 2009, the Minnesota Workshop in Macroeconomic Theory July 2009, the NBER Economic Fluctuations and Growth conference, February 2010, the European Finance Association meetings Frankfurt 2010, the NBER PERE Summer Institute meeting July 2010, the SED Montreal 2010, and the Utah Winter Finance Conference February 2010, the NBER Asset Pricing Meeting April 2011, the 2011 WFA meeting, the Baruch NYC Real Estate Meeting 2012, and the 2012 Philadephia Workshop on Macroeconomics for helpful comments. Any errors or omissions are the responsibility of the authors.
Effectivity of Palliative Care Bundle on Advanced Gallbladder Cancer: A Randomised Controlled Trial

Objectives: The main aim of this study was to develop, test, and compare palliative care bundles to improve functional recovery, resilience, and quality of life among advanced gallbladder cancer patient with their routine palliative care. Material and Methods: This study was to test a palliative care bundle, a single-center, and two-arm randomised controlled trial done on a total of 116 participants (58 in each arm) from July 2019 to December 2021 at All India Institute of Medical Sciences, Rishikesh. Results: By the end of 4th month, the recruitment rate was 96.7%, retention rate acceptance rate was 95%, and adherence rate was 85%. The palliative care bundle showed that a significant difference in trial outcome index score (P = 0.014*) indicates the effectiveness of the palliative care bundle related to improvement in physical mobility, resilience, and quality of life of patients and reduced caregiver burden. Reported barriers faced by participants were physical exhaustion (65%), psychological factors (25%), social factors (15%) and unfamiliar surroundings (5%). Caregivers reported barriers that their job (40%), physical fatigue related to the care of their patient (40%), their education (10%), and lack of support for their other family members (10%) were some reasons forcing them not to practice palliative care bundle. Conclusion: The palliative care bundle did not interfere with the palliative treatment plan of any patients and significantly improved physical mobility, resilience, quality of life of patients, and reduced caregiver burden. Hence, a palliative care bundle can be considered in the palliative care of advanced cancer patients during their palliative treatment to provide holistic care.
Crowdfunding

Crowdfunding is changing how, why, and which ideas are brought into existence. With the increasing number of crowdfunded projects, it is important to understand what drives people to either create or fund these projects. To shed light on this new social phenomenon, we present a grounded theory of motivation informed by the first cross-platform qualitative study of the crowdfunding community. By performing 83 semistructured interviews, we uncover creator motivations, which include the desire to raise funds, expand awareness of work, connect with others, gain approval, maintain control, and learn; and supporter motivations, which include the desire to collect rewards, help others, support causes, and be part of a community. We also explore deterrents to crowdfunding participation, including, among creators, fear of failure, and, for supporters, lack of trust. Based on these findings, we provide three emergent design principles to inform the design of effective crowdfunding platforms and support tools.
Destination Images of Korea: An Alaskan Perception

Image plays a significant influencing role in destination selection. This study was conducted with the specific goal of determining the destination images of Korea as perceived by both Alaskan travelers and travel agents as a proxy for the general American population. Components of the image construct were identified through the collection of structured and unstructured data. The results showed that both groups possess somewhat different images of the same region of which are, to a degree, contrary to the images promoted by the marketers of Korea. Recommendations are provided to offer valuable direction to Korean tourism officials and marketers to better promote the region to Alaskans and Americans overall.
Generació de mapes a partir d'imatges preses amb un UAV

En els darrers anys s'ha popularitzat l'us dels UAVs (drones) en una gran varietat d'ambits. Aquest treball se centra en el camp de generacio de mapes (vistes arees) optima basat en imatges enregistrades amb un UAV. Per millorar la qualitat del mapa resultant es vol treure profit de la instal•lacio de sensors en l'UAV que proporcionin informacio que pugui ser util en el proces, com la posicio i l'orientacio de l'UAV. Com a tal, es descriuran tots els passos emprats per tal d'aprofitar les dades del sensor, com sincronitzar el sensor amb les imatges enregistrades, l'avaluacio i analisi de diferents descriptors i detectors de caracteristiques, l'estimacio de la transformacio geometrica entre parells d'imatges i finalment la confeccio del mapa ajuntant totes les imatges en una de sola. Els resultats obtinguts han sigut dos metodes per generar el mapa: basat nomes en imatges i basat nomes en les dades del sensor, cada un amb els seus avantatges i inconvenients. Com a treball futur queda cercar un metode valid per poder aprofitar els avantatges d'ambdos metodes, ja que en aquest treball el metode provat per tal d'assolir aquest objectiu no ha millorat els resultats previament obtinguts.
Microfacies Analysis of Recent Carbonate Environments in the Southern Gulf of California, Mexico — A Model for Warm-Temperate to Subtropical Carbonate Formation

Abstract The La Paz area in the southwestern Gulf of California, Mexico, provides an ideal site for studying Recent warm-temperate to subtropical carbonate environments. Carbonate factories include small pocket bays, a rhodolith dominated carbonate shelf, and a mixed-carbonate siliciclastic high-energy beach. Underwater mapping and constituent analysis have revealed free-living coralline red algae in the form of rhodoliths to be the main carbonate producers, contributing 33% of the biogenic constituents to the sediment. Other significant contributions come from corals (20%), molluscs (18%), echinoderms (5%), and benthic foraminifera (4%). The benthic foraminiferal community includes mixtures of tropical and temperate species. This compositional pattern stands in marked contrast to (1) better-studied coral and green algae dominated tropical carbonate systems and (2) foraminifer, bryozoan, and mollusc dominated cool-water carbonates. Cluster analyses of biogenic constituents and benthic foraminifera revealed distinctive coral, coralline red algal, and molluscan microfacies. However, subdivisions characterized by benthic foraminifera more closely reflect the bottom facies observed during underwater mapping as opposed to patterns defined by cluster analysis of biogenic components. Diagnostic features for interpreting similar environments found in the fossil record include the (1) co-occurrence of coralline red algae and corals together with the absence of calcareous green algae, (2) presence of 5–10 genera of larger benthic foraminifera, (3) laterally and vertically not extensive character of environments, and (4) changes of microfacies over short distances.
Comparative Analysis of Machine Learning Algorithms to Forecast Indian Stock Market

Forecasting the stock market is a complex and challenging task, as it involves analyzing a vast amount of data and taking into account various economic, political, and social factors. This paper presents an overview of different approaches and techniques used for stock market forecasting, including fundamental analysis and machine learning. The study also highlights the different algorithms used and discusses their effectiveness in predicting the stock market. This research proposes to use five different algorithms as Decision Trees, Random forest, Generalized Linear model,Gradient boosted trees, and Support Vector Machines. This research identifies models that are close to real predictions. These algorithms are applied to BSE index data from November 2017 to February28, 2023.
Comparative Analysis of Machine Learning Algorithms for Alzheimer's Disease Detection

Alzheimer's disease (AD) is an irreparable neurodegenerative disease. It impairs memory and thinking, while the aggregate brain size shrinks and is seen to affect the human brain at the age of sixty and above. An early diagnosis of AD can result in high chances of reducing its occurrence amongst the elderly. The conventional approach to detecting Alzheimer's is costly and often error-prone. Human diagnosis reliability suffers from fatigue, cognitive biases, system faults, and destructions. However, Artificial Intelligence (AI) based diagnosis systems are less error-prone. This work proposes to explore four Machine Learning algorithms for diagnosing Alzheimer's disease and its possible early-stage i.e., Mild Cognitive Impairment (MCI), using Magnetic Resonance Imaging (MRI). ML algorithms are applied to the OASIS dataset of the Kaggle repository to find the highest output in terms of accuracy.
COMPARATIVE ANALYSIS OF MACHINE LEARNING TECHNIQUES ON GENETIC MUTATION BASED CANCER DIAGNOSIS DATA

There are still several research studies about how precision medicine and advanced genetic testing are highly disrupted by how cancer-like diseases are treated. The major disadvantage is that identifying cancer disease by checking gene mutations is a manual process which leads to lot of misclassifications. The paper tries to intend several machine learning techniques to make the manual process into an AI-assisted process that makes the work much easier and efficient. The cancer dataset has a certain kind of complicated format. One such is text category; thus, to address the issue, the paper successfully established a complete data analysis that highly gave a detailed view about the data which is performed in the previous. The article applies several machine learning techniques like Naïve Bayes, K-Nearest Neighbours, and Logistic Regression to classify the data with and without class balancing to analyse the cancer dataset.
Automated dynamic schema generation using knowledge graph

On the internet where the number of database developers is increasing with the availability of huge data to be stored and queried. Establishing relations between various schemas and helping the developers by filtering, prioritizing, and suggesting relevant schema is a requirement. Recommendation system plays an important role in searching through a large volume of dynamically generated schemas to provide database developers with personalized schemas and services. Although many methods are already available to solve problems using machine learning, they require more time and data to learn. These problems can be solved using knowledge graphs (KG). This paper investigates building knowledge graphs to recommend schemas.
IAES International Journal of Artificial Intelligence (IJ-AI)

Received Apr 4, 2021 Revised Nov 4, 2021 Accepted Nov 20, 2021 In this study, a particle swarm optimization (PSO) is proposed to optimize the cost of generating thermal plants in the South Sulawesi system. The study was con ducted by analyzing several methods using the lagrange and ant colony optimization (ACO). PSO algorithm converges on the 11 iteration algorithm with the lowest generation cost obtained, which is Rp129687962.17/hour. While the ACO algorithm converges on the 34 iteration with a generation cost of Rp131,473,269.39/hour. The results of optimization using PSO produce a total thermal power of 400.75 MW and losses of 26.15 MW. The PSO method is able to reduce the cost of generating the South Sulawesi system by Rp11,118,312.07/hour or 7.9%. While using the ACO method generates a generation cost of Rp131,473,269.39/hour to generate power of 400,812 MW with losses of 26,219 MW. The ACO method is able to reduce the cost of generating the South Sulawesi system by Rp9,333,004.9/hour or 6.62%. PSO algorithm provides the lowest cost calculation of generator compared with conventional methods and ACO smart methods. This is also shown in the calculation process, the PSO method completes calculations faster than the ACO method.
IAES International Journal of Artificial Intelligence (IJ-AI)

Received Sep 13, 2022 Revised Jan 5, 2023 Accepted Mar 10, 2023 Brain cancer affects many people around the world. It's not just limited to the elderly; it is also recognized in children. With the development of image processing, early detection of mental development is possible. Some designers suggest deformable models, histogram averaging, or manual division. Due to constant manual intervention, these cycles can be uncomfortable and tiring. This research introduces a high-level system for the removal of malignant tumors from attractive reverberation images, based on a programmed and rapid distribution strategy for surface extraction and recreation for clinicians. To test the proposed system, acquired tomography images from the Cancer Imaging Archive were used. The results of the study strongly demonstrate that the intended structure is viable in brain tumor detection.
IAES International Journal of Artificial Intelligence (IJ-AI)

Received Sep 20, 2021 Revised Dec 20, 2021 Accepted Dec 31, 2021 This paper presents in detail how to build up and implement a real-time web-based face recognition application. The system works so that images of people are recorded and compared with the references on the database. If they match, the information about their presence will be recorded. As for the system architecture, the multi-task cascaded neural network was deployed for face detection. Followingly, for the recognizing tasks, we conducted a study to compare the accuracy level of three different face recognizing methods on three different public datasets by means of both the literature review and our simulation. From the comparison, it can be drawn that the FaceNet algorithm in-used with the support vector machine (SVM) classifier performs the best among others and is the most suitable candidate for the practical deployment. Eventually, the proposed system can deliver a highly satisfactory result, proving its potentials not only for the research but also the commercial purposes.
Does a Plant Growth Promoting Rhizobacteria Enhance Agricultural Sustainability?

Rhizosphere soil has large diversity of microbial community, including microorganisms which caused plant growth promoting activity. The plant growth promoting rhizobacteria (PGPR) colonize roots, increased root branching, root number and enhanced growth through direct and indirect mechanisms. PGPR modified root architecture by production of phytohormones, siderophores, HCN, Nitrogen fixation and Phosphate solubilization mechanisms. PGPR also modify root functioning, improve plant nutrition and influence the physiology of the whole plant. N-fixers and P-solubilizers play key role in plant growth and yield of various crops. However the PGPR also play very crucial role to maintain the soil fertility and health. In this paper, we address the effect of PGPR on growth, yield and fertility status in rhizosphere soil. Synergetic interactions of combined inoculation of PGPR strains might be more effective for various crops growth and yield. PGPR along with integrated nutrient management may be more effective for growth, yield and fertility status under sustainable agriculture.
Assessing, demonstrating and capturing the economic value of marine & coastal ecosystem services in the Bay of Bengal Large Marine Ecosystem

The objective of the study was to assess the economic value of ecosystem services in the Bay of Bengal.The manin aim was to support the development of a Strategic Action Plan (SAP). Findings included: economic consequences of ecosystem change; potential economic instruments to strengthen sustainable management; and recommendations on next steps in using economic valuation.
Moisture sorption and gas emissions during the storage of high moisture woody biomass

ii PREFACE . iii TABLE OF CONTENTS . iv LIST OF TABLES . viii LIST OF FIGURES . ix LIST OF ABBREVIATIONS . xiii LIST OF SYMBOLS . xiv ACKNOWLEDGEMENTS . xvi DEDICATION . xvii Chapter
UR Scholarship Repository UR Scholarship Repository

What, exactly, is the relationship between socioeconomic status (SES) and emotional intelligence? Is it possible that emotional intelligence can explain the well-established positive relationship between SES and wellbeing? The purpose of this study was to investigate a) a potential mediational pathway between SES, emotional intelligence, and wellbeing and b) conflicting research in the relationship between SES and emotional intelligence. This study was conducted using a variety of measures of socioeconomic status and wellbeing, as well as a performance-based measure of empathic accuracy.
International Journal of Surgery Case Reports Management of the Open Abdomen Using Negative Pressure Wound Therapy with Instillation in Severe Abdominal Sepsis a Review of 48 Cases in Hospital Mexico, Costa Rica

Keywords: Intra-abdominal sepsis Negative pressure wound therapy Negative pressure wound therapy with instillation Open abdomen a b s t r a c t INTRODUCTION: Despite the numerous advances in recent years, severe abdominal sepsis (with associated organ failure associated with infection) remains a serious, life-threatening condition with a high mortality rate. OA is a viable alternative to the previously used scheduled repeat laparotomy or continuous peritoneal lavage. The use of Negative Pressure Wound Therapy (NPWT) has been described as a successful method of management of the open abdomen. Adding instillation of saline solution to NPWT in a programmed and controlled manner, could offer the clinician an additional tool for the management of complex septic abdomen. OBJECTIVES: To explore if the concept of active two-way therapy (Negative pressure wound therapy with instillation or NPWT-I) yields superior control of underlying, life-threatening abdominal infections and its effects on survival and morbidity in patients with severe abdominal sepsis when management with an open abdomen is required. METHODS: A retrospective review of 48 patients with severe abdominal sepsis, who were managed with and open abdomen and NPWT-I was performed. NPWT-I was initiated utilizing the same parameters on all patients, this consisted of cycles of instillation of saline solution, which was removed through negative pressure after a short dwell period. We observed the effects on primary fascia closure rate, mortality, hospital and SICU length of stay and associated complications. RESULTS: Our patient group consisted of 20 (42%) males and 28 (58%) females. Average age was 48 years. Mortality in these patients was attributed to pulmonary embolism (n = 1), acute renal failure (n = 2) and cardiopulmonary arrest (n = 1). Average total hospital stay was 24 days, and stay in the SICU (n = 26) averaged 7.5 days. No acute complications related to the NPWT-I. All patients presenting with abdominal compartment syndrome resolved after initiation of the NPWT-I. A total of 46 patients (96%) patients achieved fascia closure after NPWT-I therapy after an average of 6 days. Four patients (8%) died during the course of treatment of causes unrelated to NPWT-I. CONCLUSION: This therapy showed added benefits when compared to traditional methods such as¨Bogota bä g, Wittmann patch, or NPWT traditional in the management of the open abdomen pertaining to severe abdominal sepsis. NPWT-I in patients with severe abdominal sepsis had promising results, since we obtained higher fascia closure rates, lower mortality and reduced hospital …
Journal of Immunology Research and Therapy

Transplantation of donor-derived allogeneic hematopoietic cells causes increased survival in patients suffering from various blood cancers and other hematologic and immunologic diseases. However, this health benefit is limited to certain patients. One major complication is graft-versus-host disease (GVHD) that occurs when donor-derived immune cells recognize host cells/tissues as foreign and perpetrate subsequent destruction. Cytokines are a major class of effector molecules that are involved in GVHD pathogenesis. Proinflammatory cytokines released by activated immune cells including T cells lead to the onset of GVHD. T cell depletion (TCD) is an effective approach for GVHD prevention. Several immune suppressive drugs are also used to treat GVHD. However, these prophylactic and treatment strategies often lead to an immune compromised state that increases the risk for infection and cancer relapse. Considering the adverse effects of TCD and overall immune suppression, more selective managements such as approaches targeting proinflammatory cytokines have emerged as a promising strategy to control GVHD. Therefore, this work is dedicated to review recent development in the studies of cytokines and their future implication in GVHD therapy.