Miracle-dz/UniFAR-FTUs huggingface dataset:
AUTONOMOUS ROBOT NAVIGATION USING THE UTILITY FUNCTION METHOD AND MICROSOFT KINECT

In this thesis, a system for autonomous navigation using the Microsoft Kinect sensor and the utility function (UF) method for decision-making, has been developed. In the UF method an artificial brain decides what control system procedures to activate or deactivate, based on their utility. The system uses the Kinect sensor for obstacle avoidance and localization. The Kinect sensor readings are matched to a predefined map, using a scan matching algorithm based on the Hough transform, in order to correct pose estimates acquired through odometry. The A-algorithm is used for path planning and the algorithm is applied on a grid representing the operating area accessible to the robot.The results show that the UF method can be applied in systems for autonomous navigation, using the Kinect sensor for localization and obstacle avoidance. The Kinect sensor has proven to be a useful alternative to more expensive range sensors, despite its limitations in terms of field of view, range, and accuracy. On the other hand, the results indicate that improvements are needed, in order to improve the robustness of the system. During testing, the robot completed on average 87.6 % of a 45.1 m long, predefined course and on average 121 m when choosing random targets. Tuning the parameters of the decision-making system and improving the maneuverability at low speeds are examples of future work that could increase the performance of the navigation system.
A standard computable general equilibrium (CGE) model in GAMS

Computable general equilibrium (CGE) models are used widely in policy analysis. The purpose of this paper is to contribute to and facilitate the use of CGE models. The paper includes a detailed presentation of a “standard” CGE model (an equation-by-equation description) and its required database. It incorporates features developed in recent years in research projects conducted at IFPRI. These features, which are of particular importance in developing countries, include household consumption of non-marketed (or “home”) commodities, explicit treatment of transaction costs for commodities that enter the market sphere, and a separation between producing activities and commodities that permits any activity to produce multiple commodities and any commodity to be produced by multiple activities. The paper discusses the implementation of the model in GAMS (the General Algebraic Modeling System) and is accompanied by a self-extracting zip file, which includes the GAMS files for the model, sample databases, simulations, solution reports, and a SAM aggregation program. Although the paper provides a standardized framework for analysis, the analyst is not forced to make “one-size-fits-all” assumptions. The GAMS code is written in a manner that gives the analyst considerable flexibility in model specification.
A Survey on Generalization in Deep Reinforcement Learning

Reinforcement learning research obtained signiﬁcant success and attention with the utilization of deep neural networks to solve problems in high dimensional state or action spaces. While deep reinforcement learning policies are currently being deployed in many different ﬁelds from medical applications to self driving vehicles, there are still ongoing questions the ﬁeld is trying to answer on the generalization capabilities of deep reinforcement learning policies. In this paper, we will go over the fundamental reasons why deep reinforcement learning policies encounter over-ﬁtting problems that limit their generalization capabilities. Furthermore, we will formalize and unify the diverse solution approaches to increase generalization, and overcome overﬁtting in deep reinforcement learning policies. We believe our study can provide a compact systematic uniﬁed analysis for the current advancements in deep reinforcement learning, and help to construct robust deep neural policies with improved generalization abilities.
Kaiser Permanente's experience of implementing an electronic medical record: a qualitative study

Abstract Objective To examine users' attitudes to implementation of an electronic medical record system in Kaiser Permanente Hawaii. Design Qualitative study based on semistructured interviews. Setting Four primary healthcare teams in four clinics, and four specialty departments in one hospital, on Oahu, Hawaii. Shortly before the interviews, Kaiser Permanente stopped implementation of the initial system in favour of a competing one. Participants Twenty six senior clinicians, managers, and project team members. Results Seven key findings emerged: users perceived the decision to adopt the electronic medical record system as flawed; software design problems increased resistance; the system reduced doctors' productivity, especially during initial implementation, which fuelled resistance; the system required clarification of clinical roles and responsibilities, which was traumatic for some individuals; a cooperative culture created trade-offs at varying points in the implementation; no single leadership style was optimal—a participatory, consensus-building style may lead to more effective adoption decisions, whereas decisive leadership could help resolve barriers and resistance during implementation; the process fostered a counter climate of conflict, which was resolved by withdrawal of the initial system. Conclusions Implementation involved several critical components, including perceptions of the system selection, early testing, adaptation of the system to the larger organisation, and adaptation of the organisation to the new electronic environment. Throughout, organisational factors such as leadership, culture, and professional ideals played complex roles, each facilitating and hindering implementation at various points. A transient climate of conflict was associated with adoption of the system.
Acute Stroke: An Evidence-Based Review Imaging and Treatment of Patients with

SUMMARY: Evidence-based medicine has emerged as a valuable tool to guide clinical decision-making, by summarizing the best possible evidence for both diagnostic and treatment strategies. Imaging plays a critical role in the evaluation and treatment of patients with acute ischemic stroke, especially those who are being considered for thrombolytic or endovascular therapy. Time from stroke-symptom onset to treatment is a strong predictor of long-term functional outcome after stroke. Therefore, imaging and treatment decisions must occur rapidly in this setting, while minimizing unnecessary delays in treatment. The aim of this review was to summarize the best available evidence for the diagnostic and therapeutic management of patients with acute ischemic stroke
Using hydrochemical tracers to assess impacts of unsewered urban catchments on hydrochemistry and nutrients in groundwater

We applied graphical methods and multivariate statistics to understand impacts of an unsewered slum catchment on nutrients and hydrochemistry of groundwater in Kampala, Uganda. Data were collected from 56 springs (groundwater), 22 surface water sites and 13 rain samples. Groundwater was acidic and dominated by Na, Cl and NO3. These ions were strongly correlated, indicating pollution originating from wastewater infiltration from on‐site sanitation systems. Results also showed that rain, which was acidic, impacted on groundwater chemistry. Using Q‐mode hierarchical cluster analysis, we identified three distinct water quality groups. The first group had springs dominated by Ca‐Cl‐NO3, low values of electrical conductivity (EC), pH and cations, and relatively high NO3 values. These springs were shown to have originated from the acidic rains because their chemistry closely corresponded to ion concentrations that would occur from rainfall recharge, which was around 3.3 times concentrated by evaporation. The second group had springs dominated by Na‐K‐Cl‐NO3 and Ca‐Cl‐NO3, low pH but with higher values of EC, NO3 and cations. We interpreted these as groundwater affected by both acid rain and infiltration of wastewater from urban areas. The third group had the highest EC values (average of 688 μS/cm), low pH and very high concentrations of NO3 (average of 2.15 mmol/l) and cations. Since these springs were all located in slum areas, we interpreted them as groundwater affected by infiltration of wastewater from poorly sanitized slums areas. Surface water was slightly reducing and eutrophic because of wastewater effluents, but the contribution of groundwater to nutrients in surface water was minimal because o‐PO4 was absent, whereas NO3 was lost by denitification. Our findings suggest that groundwater chemistry in the catchment is strongly influenced by anthropogenic inputs derived from nitrogen‐containing rains and domestic wastewater. Copyright © 2013 John Wiley & Sons, Ltd.
Summary of the validation of the second version of the ASTER GDEM

Abstract. On October 17, 2011, NASA and the Ministry of Economy, Trade and Industry (METI) of Japan released the second version of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Model (GDEM) to users worldwide at no charge as a contribution to the Global Earth Observing System of Systems (GEOSS). The first version of the ASTER GDEM, released on June 29, 2009, was compiled from over 1.2 million scene-based DEMs covering land surfaces between 83°N and 83°S latitudes. The second version (GDEM2) incorporates 260,000 additional scenes to improve coverage, a smaller correlation kernel to yield higher spatial resolution, and improved water masking. As with GDEM1, US and Japanese partners collaborated to validate GDEM2. Its absolute accuracy was within -0.20 meters on average when compared against 18,000 geodetic control points over the conterminous US (CONUS), with an accuracy of 17 meters at the 95% confidence level. The Japan study noted the GDEM2 differed from the 10-meter national elevation grid by -0.7 meters over bare areas, and by 7.4 meters over forested areas. The CONUS study noted a similar result, with the GDEM2 determined to be about 8 meters above the 1 arc-second US National Elevation Database (NED) over most forested areas, and more than a meter below NED over bare areas. A global ICESat study found the GDEM2 to be on average within 3 meters of altimeter-derived control. The Japan study noted a horizontal displacement of 0.23 pixels in GDEM2. A study from the US National Geospatial Intelligence Agency also determined horizontal displacement and vertical accuracy as compared to the 1 arc-second Shuttle Radar Topography Mission DEM. US and Japanese studies estimated the horizontal resolution of the GDEM2 to be between 71 and 82 meters. Finally, the number of voids and artifacts noted in GDEM1 were substantially reduced in GDEM2.
Snowmelt on the Greenland Ice Sheet as Derived from Passive Microwave Satellite Data

Abstract The melt extent of the snow on the Greenland ice sheet is of considerable importance to the ice sheet’s mass and energy balance, as well as Arctic and global climates. By comparing passive microwave satellite data to field observations, variations in melt extent have been detected by establishing melt thresholds in the cross-polarized gradient ratio (XPGR). The XPGR, defined as the normalized difference between the 19-GHz horizontal channel and the 37-GHz vertical channel of the Special Sensor Microwave/Imager (SSM/I), exploits the different effects of snow wetness on different frequencies and polarizations and establishes a distinct melt signal. Using this XPGR melt signal, seasonal and interannual variations in snowmelt extent of the ice sheet are studied. The melt is found to be most extensive on the western side of the ice sheet and peaks in late July. Moreover, there is a notable increasing trend in melt area between the years 1979 and 1991 of 4.4% per year, which came to an abrupt halt in 1.
RADIOACTIVE ABYSSAL GRANITIC PEGMATITES AND LEUCOGRANITES IN THE WOLLASTON DOMAIN, NORTHERN SASKATCHEWAN, CANADA: MINERAL COMPOSITIONS AND CONDITIONS OF EMPLACEMENT IN THE FRASER LAKES AREA

The Fraser Lakes area in the Wollaston Domain, in northern Saskatchewan, Canada, is located 25 km from the southeastern edge of the uranium-rich Athabasca Basin; it hosts a number of U- and Th–REE-bearing granitic pegmatites and leucogranites. At Fraser Lakes Zone B, the pegmatites and leucogranites intrude the deformed contact between Paleoproterozoic metasedimentary gneisses of the Wollaston Group and Archean orthogneisses, and have characteristics typical of the Abyssal pegmatite subclass. For two examples of Group-A U- and Th-enriched pegmatite and leucogranite, and two examples of Group-B Th- and LREE-enriched pegmatite and leucogranite samples that have minimal petrographic evidence of later alteration, we analyzed selected minerals by electron microprobe to provide constraints on the age and temperature of intrusion. The Group-A pegmatites contain uraninite with variable CaO and SiO 2 , indicative of later recrystallization, uranoan thorite spatially associated with complexly zoned zircon (with some intermediate solid-solution between the two minerals), rare coffinite, and titaniferous magnetite and ilmenite where they intrude the Archean orthogneisses. CHIME dating of the most pristine uraninite yielded ages between 1.85 and 1.80 Ga, consistent with crystallization from the pegmatite-forming melt. The Group-B pegmatites contain monazite-(Ce) with significant Th substitution, uranoan thorite, zircon (also showing extensive solid-solution toward thorite), and rare xenotime and pyrochlore. CHIME dating of monazite from the Group-B pegmatites gave older ages of ca. 2.1 to 2.2 Ga, suggesting that they are xenocrysts from the source region of the melt. Biotite dehydration reactions have led to significant development of leucosome in the host pelitic gneisses, and likely resulted in the formation of granitic melt at depth.
Eﬀicient Miss Ratio Curve Computation for Heterogeneous Content Popularity

The Miss Ratio Curve (MRC) represents a fundamental tool for cache performance proﬁling. Approximate methods based on sampling provide a low-complexity solution for MRC construction. Nevertheless, in this paper we show that, in case of content with a large variance in popularity, the approximate MRC may be highly sensitive to the set of sampled content. We study in detail the impact of content popularity heterogeneity on the accuracy of the approximate MRC. We observe that few, highly popular, items may cause large error at the head of the reconstructed MRC. From these observations, we design a new approach for building an approximate MRC, where we combine an exact portion of the MRC with an approximate one built from samples. Results for different real-world traces show that our algorithm computes MRC with an error up to 10 times smaller than state-of-the-art methods based on sampling, with similar computational and space overhead.
Ecosystem change and public health: a global perspective.

Contents: List of Contributors Foreword, by Carlos Morel Preface and Acknowledgments Introduction: How to Use This Book Purpose The Target Audience Multiple Ways to Use the Chapters Electronic References and Information Literacy The Format of Suggested Study Projects Conclusion PART I: APPROACHES 1 Information on Issues of Global Change, by Erika G. Feulner 2 Epidemiological Study Designs, by Alfred A. Buck and Joan L. Aron 3 Geographical Information Sysytems, by Joan L. Aron and Gregory E. Glass 4 The Science/Policy Interface, by Jonathan M. Samet 5 Integrated Assessment, Joan L. Aron, J. Hugh Ellis, and Benjamin F. Hobbs PART II: ENVIRONMENTAL CHANGES 6 Human Populations in the Shared Environment, by Dennis C. Pirages, Paul J. Runci, and Robert H. Sprinkle 7 The Changing Chemistry of Earth's Atmosphere, by Steven A. Lloyd 8 An Earth Science Perspective on Global Change, by George W. Fisher 9 Water Resources Management, by Nicolaas J. P. M. de Groot 10 Ecology and Infectious Disease, by Mark L. Wilson PART III: CASE STUDIES 11 Cholera and Global Ecosystems, by Anwar Huq, R. Bradley Sack, and Rita R. Colwell 12 Malaria and Global Ecosystem Change, by Joan L. Aron, Clive J. Shiff, and Alfred J. Buck 13 Global Climate Change and Air Pollution: Interactions and Their Effects on Human Health, by Jonathan A. Patz and John M. Balbus 14 Too Little, Too Much: How the Quantity of Water Affetcs Human Health, by Les Roberts, Ulisses E.C. Confalonieri, and Joan L. Aron A Stimulating Inquiry: Textbooks and Information Literacy in the Internet Environment, by Erika G. Feulner B The Resource Center, compiled by Erika G. Feulner List of Abbreviations Glossary, by Joan L. Aron and Mark L. Wilson Index
Sustainability performance in ﬁ rms located in a science and technology park: the in ﬂ uence of knowledge sources and absorptive capacity

Purpose – The purpose of this study is to identify how firms ’ sustainability performance is affected by external knowledge sources and absorptive capacity, accounting for the influence of being located in a scienceandtechnologypark(STP). Design/methodology/approach – Drawing on data from the Spanish Technological Innovation Panel, the authors estimate the determinants of sustainability performance using fixed effects multiple linear regression models with robust standard errors. The analysis covers the period 2009 – 2016, with a total panelof8,874companiesanda totalsample of47,870observations. Findings – This study highlights the heterogeneity in on-park firms’ sustainability performance, which can be explained by the different capacities of these firms when it comes to embedding themselves in STP networks and processes and effectively absorbing the knowledge from the many knowledge sourcesthat may be onofferinthepark. Originality/value – This paper contributes to the literature by examining the influence of external sources of knowledge and absorptive capacity, and the relationship between them, on sustainability performance. This study approaches sustainability performance as an aggregate measure of firms’ competitiveness and potential for long-term survival from the triple bottom line perspective. In addition, this study examines the effect that location in an STP can have on business sustainability performance and, more specifically, the mediating effect that knowledge sources and absorptive capacity can exert onthis relationship.
Prevention of postpartum haemorrhage in patients with severe Prevention of postpartum haemorrhage in patients with severe preeclampsia using Carbetocin versus Misoprostol preeclampsia using Carbetocin versus Misoprostol

Background: A significant reason of maternal deaths is postpartum hemorrhage (PPH) that accounted for one-quarter of all maternal mortality globally. Primary postpartum hemorrhage (PPH) is conventionally described as a loss of blood of ≥ 500 ml within the first 24 hours following childbirth. Aim of the work: Using carbetocin versus misoprostol in postpartum hemorrhage prevention in sufferers with intense preeclampsia. Patients and methods: A randomized prospective study is done to compare use carbetocin vs misoprostol in postpartum hemorrhage prevention with severe preeclampsia. In the present research a total of 100 patients who attended Sohag general Hospital included. First group was formed of 50 pregnant women candidate for cesarean section delivery with intense preeclampsia obtained carbetocin (IV injection (100 ug ,one ampoule pabal) following baby delivery. While the second group is formed of another 50 pregnant women that are also candidate for cesarean section delivery with intense preeclampsia but obtained Sublingual 600 ug misoprostol following baby delivery. Results: The findings revealed that there was a highly substantial statistical variation among the baseline & post-partum Hb & HCT in both Misoprostol group and carbitocin group. Conclusion: Carbetocin is more effective than Misoprostol in preventing incidence of atonic postpartum hemorrhage in patients performing elective caesarean delivery with severe preeclampsia.
Predicting Adherence to Behavior Change Support Systems Using Machine Learning: Systematic Review

Background: There is a dearth of knowledge on reliable adherence prediction measures in behavior change support systems (BCSSs). Existing reviews have predominately focused on self-reporting measures of adherence. These measures are susceptible to overestimation or underestimation of adherence behavior. Objective: This systematic review seeks to identify and summarize trends in the use of machine learning approaches to predict adherence to BCSSs. Methods: Systematic literature searches were conducted in the Scopus and PubMed electronic databases between January 2011 and August 2022. The initial search retrieved 2182 journal papers, but only 11 of these papers were eligible for this review. Results: A total of 4 categories of adherence problems in BCSSs were identified: adherence to digital cognitive and behavioral interventions, medication adherence, physical activity adherence, and diet adherence. The use of machine learning techniques for real-time adherence prediction in BCSSs is gaining research attention. A total of 13 unique supervised learning techniques were identified and the majority of them were traditional machine learning techniques (eg, support vector machine). Long short-term memory, multilayer perception, and ensemble learning are currently the only advanced learning techniques. Despite the heterogeneity in the feature selection approaches, most prediction models achieved good classification accuracies. This indicates that the features or predictors used were a good representation of the adherence problem. Conclusions: Using machine learning algorithms to predict the adherence behavior of a BCSS user can facilitate the reinforcement of adherence behavior. This can be achieved by developing intelligent BCSSs that can provide users with more personalized, tailored, and timely suggestions.
Aggregators to realize Scalable Querying across Decentralized Data Sources

The hyper-centralized nature of personal data has been getting some doubt due to recent data scandals. Decentralized ecosystems, like Solid, allow for users to take back control of their data by storing it in a data pod. This decentralization of data, together with the vision of Solid where everything has to be interoperable, causes the querying of this data to be slow. We propose to address this problem by introducing aggregators. Aggregators are a network of query and reasoning agents, each of which contribute (partial) results to a query by providing an up-to-date materialized view on the decentralized data.
Population Genetic Structure of a Viviparous Sand Lizard, the Phrynocephalus forsythii in the Tarim Basin, Xinjiang of China

The desert ecosystem occupies an important position in the composition of global biodiversity. The Tarim Basin is located in south Xinjiang of China and has the world’s second largest mobile desert, the Taklamakan Desert. As an endemic species in this region, Phrynocephalus forsythii has been demonstrated to have a potentially high extinction risk due to climate change. In order to understand the overall genetic status and provide accordant conservation strategies for the species, we investigated the genetic diversity and population structure of P. forsythii from 15 sites in the Tarim Basin using 21 highly polymorphic microsatellite markers. We found significant genetic structure across the study region. We also revealed generally low levels of gene flow between the 25 sites, suggesting individual dispersal and migration may be restricted within populations. In addition, geographical distance and ambient temperature might be important factors in explaining the observed genetic structure. Our results will provide a scientific basis for the future protection of P. forsythii in this area, as well as an important reference for the conservation and management of biodiversity in desert ecosystems.