Availability Of Data Research Articles

Development of ecotourism in the Volga region: Case studies and strategies

Purpose: This study investigates the advancement of ecotourism in the Volga Region, drawing insights from international and Russian case studies. The aim is to identify key factors contributing to successful ecotourism development and to provide recommendations for enhancing sustainability in the region's tourism sector. Research Methodology: Employing qualitative and comparative methodologies, the study utilized interviews, surveys, and document analysis for data collection. Additionally, site visits and observations were conducted to gain firsthand insights into ecotourism practices. Results: The Volga Region demonstrates substantial potential for ecotourism development, leveraging its rich natural resources, cultural heritage, and a burgeoning interest in sustainable tourism practices. However, challenges such as infrastructure limitations and inadequate marketing strategies need to be addressed to fully capitalize on this potential. Limitations: The study is constrained by data availability, time limitations, and the scope of the case studies examined. Additionally, the subjective nature of qualitative research methods may introduce bias into the findings. Contribution: This research enhances our comprehension of ecotourism development strategies, particularly within the Volga Region context. It offers valuable insights for policymakers, researchers, and industry practitioners aiming to foster sustainable tourism practices and economic growth in the region.

Fractional social optimization-based migration and replica management algorithm for load balancing in distributed file system for cloud computing.

Effective management of data is a major issue in Distributed File System (DFS), like the cloud. This issue is handled by replicating files in an effective manner, which can minimize the time of data access and elevate the data availability. This paper devises a Fractional Social Optimization Algorithm (FSOA) for replica management along with balancing load in DFS in the cloud stage. Balancing the workload for DFS is the main objective. Here, the chunk creation is done by partitioning the file into a different number of chunks considering Deep Fuzzy Clustering (DFC) and then in the round-robin manner the Virtual machine (VM) is assigned. In that case for balancing the load considering certain objectives like resource use, energy consumption and migration cost thereby the load balancing is performed with the proposed FSOA. Here, the FSOA is formulated by uniting the Social optimization algorithm (SOA) and Fractional Calculus (FC). The replica management is done in DFS using the proposed FSOA by considering the various objectives. The FSOA has the smallest load of 0.299, smallest cost of 0.395, smallest energy consumption of 0.510, smallest overhead of 0.358, and smallest throughput of 0.537.

Social considerations for the removal of dams and other aquatic barriers

Abstract Stream connectivity restoration through the removal or mitigation of dams and other anthropogenic barriers is critical for aquatic species conservation. Historically, stream connectivity restoration planning has been focused on biophysical criteria; however, aquatic barriers are embedded in social contexts that can constrain restoration decisions. To support the integration of social considerations into stream connectivity conservation planning, we synthesized the available research involving social criteria linked to barrier removal. We found that the literature has been focused predominantly on the removal of dams and identify four major categories of social considerations for barrier removal: economics, nonmarket social values, safety, and procedural context. We reviewed contemporary barrier removal prioritization efforts and found that existing approaches typically only consider small subsets of social considerations and face issues with data availability. The findings synthesized in the present article establish potential criteria that could be integrated into effective stream connectivity restoration planning and inform future data capture efforts for barrier removal.

Salzburg Intensive Care database (SICdb): a detailed exploration and comparative analysis with MIMIC-IV.

The utilization of artificial intelligence (AI) in healthcare is on the rise, demanding increased accessibility to (public) medical data for benchmarking. The digitization of healthcare in recent years has facilitated medical data scientists' access to extensive hospital data, fostering AI-based research. A notable addition to this trend is the Salzburg Intensive Care database (SICdb), made publicly available in early 2023. Covering over 27 thousand intensive care admissions at the University Hospital Salzburg from 2013 to 2021, this dataset presents a valuable resource for AI-driven investigations. This article explores the SICdb and conducts a comparative analysis with the widely recognized Medical Information Mart for Intensive Care - version IV (MIMIC-IV) database. The comparison focuses on key aspects, emphasizing the availability and granularity of data provided by the SICdb, particularly vital signs and laboratory measurements. The analysis demonstrates that the SICdb offers more detailed information with higher data availability and temporal resolution for signal data, especially for vital signs, compared to the MIMIC-IV. This is advantageous for longitudinal studies of patients' health conditions in the intensive care unit. The SICdb provides a valuable resource for medical data scientists and researchers. The database offers comprehensive and diverse healthcare data in a European country, making it well suited for benchmarking and enhancing AI-based healthcare research. The importance of ongoing efforts to expand and make public datasets available for advancing AI applications in the healthcare domain is emphasized by the findings.

ALTERATIONS IN RAT ILEAL WALL METRICS FOLLOWING ADMINISTRATION OF A COMPLEX OF FOOD CHEMICAL ADDITIVES DURIGN THE EARLY STAGES OF THE EXPERIMENT

The influence of exogenous factors is a common cause of digestive disorders. Food chemical additives are important in the list of harmful exogenous factors. Despite the availability of literature data on their adverse effects on various systems and organs, the information on their complex impact on the ileum is limited and requires further study. The present study aimed to determine the metric changes in the ileal wall of rats during the early stages of administration of a complex of food chemical additives including monosodium glutamate, sodium nitrite, and ponceau 4R. Materials and methods. The study was conducted on mature white rats, divided into control and experimental groups. The control group received saline, and the experimental group received a complex of food chemical additives prepared following the standard methodology for 1 and 4 weeks orally. Material sampling and morphometric studies were performed according to generally accepted methods. The following parameters of the ileum wall were determined: total average wall thickness and average thickness of the serous membrane, muscle layer, submucosal and mucosal layers lseparately. Results. Changes in the indicators were found after the first week of administration of the food additives complex. In particular, the total intestinal wall thickness increased by 1.51 times compared to the control; after the fourth week, this indicator slightly decreased, although it was 1.22 times higher than the control group (p<0.05). The average mucosal thickness was 1.67 and 1.35 higher than in the control group after the first and fourth week, respectively. The average thickness of the submucosal layer also changed; after the first week, this indicator was 1.12 times lower than the control one; after the fourth week, the indicator increased by 1.21 times and 1.34 times compared to the control group and the previous observation period (p<0.05), respectively. As for the muscular layer, compared to the control group, after the first week, this indicator was 1.58 times higher (p<0.05). After the fourth week, it was 1.19 times higher than the control group and 1.33 times lower than the previous period. The average thickness of the serous layer was 1.44 times higher after the first week and 1.58 times higher after the fourth week compared to the control group. Conclusion. Thus, the introduction of a complex of food chemical additives leads to metric changes in the ileal wall of the ileum at the early stages of the experiment due to hyperhydration of connective tissue, impaired blood circulation, and pericellular oedema of the structural components of all membranes.

Risk identification of public opinion on social media: a new approach based on cross-spatial network analysis

Purpose This study aims to use geographical information on social media for public opinion risk identification during a crisis. Design/methodology/approach This study constructs a double-layer network that associates the online public opinion with geographical information. In the double-layer network, Gaussian process regression is used to train the prediction model for geographical locations. Second, cross-space information flow is described using local government data availability and regional internet development indicators. Finally, the structural characteristics and information flow of the double-layer network are explored to capture public opinion risks in a fine-grained manner. This study used the early stages of the COVID-19 outbreak for validation analyses, and it collected more than 90,000 pieces of public opinion data from microblogs. Findings In the early stages of the COVID-19 outbreak, the double-layer network exhibited a radiating state, and the information dissemination was more dependent on the nodes with higher in-degree. Moreover, the double-layer network structure showed geographical differences. The risk contagion was more significant in areas where information flow was prominent, but the influence of nodes was reduced. Originality/value Public opinion risk identification that incorporates geographical scenarios contributes to enhanced situational awareness. This study not only effectively extends geographical information on social media, but also provides valuable insights for accurately responding to public opinion.

PrivShieldROS: An Extended Robot Operating System Integrating Ethereum and Interplanetary File System for Enhanced Sensor Data Privacy

With the application of robotics in security monitoring, medical care, image analysis, and other high-privacy fields, vision sensor data in robotic operating systems (ROS) faces the challenge of enhancing secure storage and transmission. Recently, it has been proposed that the distributed advantages of blockchain be taken advantage of to improve the security of data in ROS. Still, it has limitations such as high latency and large resource consumption. To address these issues, this paper introduces PrivShieldROS, an extended robotic operating system developed by InterPlanetary File System (IPFS), blockchain, and HybridABEnc to enhance the confidentiality and security of vision sensor data in ROS. The system takes advantage of the decentralized nature of IPFS to enhance data availability and robustness while combining HybridABEnc for fine-grained access control. In addition, it ensures the security and confidentiality of the data distribution mechanism by using blockchain technology to store data content identifiers (CID) persistently. Finally, the effectiveness of this system is verified by three experiments. Compared with the state-of-the-art blockchain-extended ROS, PrivShieldROS shows improvements in key metrics. This paper has been partly submitted to IROS 2024.

A Study on Capital Budgeting Techniques and preparation of DCF Model of NVIDIA Corporation

This study delves into the application of capital budgeting techniques in Nvidia Corporation, a global leader in graphics processing units (GPUs) and artificial intelligence (AI) technologies. Through secondary data analysis sourced from various reputable sources including webpages, case studies, and annual reports, the study aims to evaluate the impact of capital budgeting techniques on investment decision-making, assess the profitability of Nvidia, calculate its Net Present Value (NPV), and provide actionable suggestions for enhancing profitability and diversifying investment decisions. Additionally, a Discounted Cash Flow (DCF) model is prepared to forecast future performance and guide strategic planning. Limitations such as incomplete information, biased sources, and limited data availability are acknowledged, yet the study offers valuable insights into the strategic financial management of a technology giant like Nvidia.

Granular satellite data to assess the potential for nature based solutions at a national scale: a proof of concept with data from Rwanda and Lesotho

ABSTRACT Nature-based Solutions (NbS) form a substantial part of cost-efficient climate change mitigation options. However, public financial flows towards NbS have been limited. Among the factors impeding investments in NbS are challenges in reliable remote project identification and comparison. In this article, we demonstrate a technological solution to these challenges. Using cloud-based satellite data processing and the proliferation of open geospatial data, we developed a methodology to map NbS suitability based on a set of biophysical, pedological, hydrological and climatological criteria. To provide a proof of concept, we identify potential project areas for eight types of NbS in Rwanda and Lesotho. Building on these spatially-explicit NbS suitability data layers (accessible at https://wb-nbs.users.earthengine.app/view/ncs-potential), we develop marginal abatement cost curves for projects in Rwanda and Lesotho. We thus provide a proof of concept for remote identification and project comparison. These technological developments can help investors, such as multilateral donors or governments, alleviate additionality concerns that are implied by local project selection. Key Policy Insights Investment in climate change mitigation through Nature-based Solutions (NbS) is limited, in spite of the large, cost-efficient potential for achieving national and global goals. Remote project identification and comparison can help alleviate some of the impediments to international investment in NbS. Such remote identification and comparison of NbS projects is now possible because of breakthroughs in cloud-based satellite data processing technologies and increased availability of geospatial data. We present a proof of concept, establishing marginal abatement cost curves for climate change mitigation through NbS in Rwanda and Lesotho. Coupled with the institutional improvements necessary to create a reliable intergovernmental carbon credit market, these technological developments have the potential to boost international investment in NbS.

Enhancing drug and cell line representations via contrastive learning for improved anti-cancer drug prioritization

Due to cancer’s complex nature and variable response to therapy, precision oncology informed by omics sequence analysis has become the current standard of care. However, the amount of data produced for each patient makes it difficult to quickly identify the best treatment regimen. Moreover, limited data availability has hindered computational methods’ abilities to learn patterns associated with effective drug-cell line pairs. In this work, we propose the use of contrastive learning to improve learned drug and cell line representations by preserving relationship structures associated with drug mechanisms of action and cell line cancer types. In addition to achieving enhanced performance relative to a state-of-the-art method, we find that classifiers using our learned representations exhibit a more balanced reliance on drug- and cell line-derived features when making predictions. This facilitates more personalized drug prioritizations that are informed by signals related to drug resistance.

Machine Learning Models for Parkinson Disease: Systematic Review.

With the increasing availability of data, computing resources, and easier-to-use software libraries, machine learning (ML) is increasingly used in disease detection and prediction, including for Parkinson disease (PD). Despite the large number of studies published every year, very few ML systems have been adopted for real-world use. In particular, a lack of external validity may result in poor performance of these systems in clinical practice. Additional methodological issues in ML design and reporting can also hinder clinical adoption, even for applications that would benefit from such data-driven systems. To sample the current ML practices in PD applications, we conducted a systematic review of studies published in 2020 and 2021 that used ML models to diagnose PD or track PD progression. We conducted a systematic literature review in accordance with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines in PubMed between January 2020 and April 2021, using the following exact string: "Parkinson's" AND ("ML" OR "prediction" OR "classification" OR "detection" or "artificial intelligence" OR "AI"). The search resulted in 1085 publications. After a search query and review, we found 113 publications that used ML for the classification or regression-based prediction of PD or PD-related symptoms. Only 65.5% (74/113) of studies used a holdout test set to avoid potentially inflated accuracies, and approximately half (25/46, 54%) of the studies without a holdout test set did not state this as a potential concern. Surprisingly, 38.9% (44/113) of studies did not report on how or if models were tuned, and an additional 27.4% (31/113) used ad hoc model tuning, which is generally frowned upon in ML model optimization. Only 15% (17/113) of studies performed direct comparisons of results with other models, severely limiting the interpretation of results. This review highlights the notable limitations of current ML systems and techniques that may contribute to a gap between reported performance in research and the real-life applicability of ML models aiming to detect and predict diseases such as PD.

The role of atmospheric CO2 in controlling sea surface temperature change during the Pliocene

Abstract. We present the role of CO2 forcing in controlling Late Pliocene sea surface temperature (SST) change using six models from Phase 2 of the Pliocene Model Intercomparison Project (PlioMIP2) and palaeoclimate proxy data from the PlioVAR working group. At a global scale, SST change in the Late Pliocene relative to the pre-industrial is predominantly driven by CO2 forcing in the low and mid-latitudes and non-CO2 forcing in the high latitudes. We find that CO2 is the dominant driver of SST change at the vast majority of proxy data sites assessed (17 out of 19), but the relative dominance of this forcing varies between all proxy sites, with CO2 forcing accounting for between 27 % and 82 % of the total change seen. The dearth of proxy data sites in the high latitudes means that only two sites assessed here are predominantly forced by non-CO2 forcing (such as changes to ice sheets and orography), both of which are in the North Atlantic Ocean. We extend the analysis to show the seasonal patterns of SST change and its drivers at a global scale and at a site-specific level for three chosen proxy data sites. We also present a new estimate of Late Pliocene climate sensitivity using site-specific proxy data values. This is the first assessment of site-specific drivers of SST change in the Late Pliocene and highlights the strengths of using palaeoclimate proxy data alongside model outputs to further develop our understanding of the Late Pliocene. We use the best available proxy and model data, but the sample sizes remain limited, and the confidence in our results would be improved with greater data availability.

Oh, the Places They’ll Go: A Geographic Analysis of Gubernatorial Campaigns

Geography has long played a significant role in American electoral politics. Candidates engage in “retail politics” by visiting fairs, festivals, schools, churches, and businesses, to meet and greet potential voters. However, systematically understanding where candidates are actually going has plagued prior work due to small sample sizes and lack of data availability. Presidential elections have been used with success in previous scholarship, but there are few viable candidates to compare within a single cycle. Utilizing competitive gubernatorial elections, this work attempts to address these deficiencies by utilizing social media posts to track multiple candidates in real-time across the 2018 campaign cycle at the state level. The paper tests competing theories of candidate engagement regarding travel decisions: do candidates (1) focus their attention on their partisan base or (2) try to attract independent or “swing” voters? Following an original and intensive data collection effort, we identified location-specific information for over 4700 campaign stops made by major-party candidates across seventeen states. Our results lend support to candidates spending time with their respective bases of partisan support, with Republicans going to rural areas with non-college educated individuals while Democrats travel to urban counties with a more diverse electorate.

District Energy Management Systems: Key Data Points for System Integration and Related Challenges: Lessons Learned from Experts in Germany

District energy management systems (DEMS) offer the capability to harness the energy potential of districts. Currently, there is a lack of research describing the necessary underlying information systems. An overview of lessons learned is presented from a series of workshops with experts from industry, research, and further roles, predominately from Germany. Seven workshops discussing challenges within digitalization, data requirements, district energy system planning, common elements in district energy systems are conducted, ensuring long‐term operation and transferability, and validation of prior findings. Based on these discussions, insights into topics such as the key data and its requirements, typical distinctions in DEMS, their infrastructure, considerations in the information modeling, as well as challenges in data availability, are offered. These findings are mapped to a data value pipeline, illustrating key resources and tools needed to plan and operate DEMS. As demonstrated in this article, the requirements for DEMS highlight their complexity. Distinct categorizations, such as the ownership or heterogeneity in building and technologies, form the landscape of DEMS. The need for standardized, interoperable systems is often a contrast to the uniqueness of individual systems and their needs. Further adoption of the described concepts necessitates sustainable business models.

Personalized Dosing of Medicines for Children: A Primer on Pediatric Pharmacometrics for Clinicians.

The widespread use of drugs for unapproved purposes remains common in children, primarily attributable to practical, ethical, and financial constraints associated with pediatric drug research. Pharmacometrics, the scientific discipline that involves the application of mathematical models to understand and quantify drug effects, holds promise in advancing pediatric pharmacotherapy by expediting drug development, extending applications, and personalizing dosing. In this review, we delineate the principles of pharmacometrics, and explore its clinical applications and prospects. The fundamental aspect of any pharmacometric analysis lies in the selection of appropriate methods for quantifying pharmacokinetics and pharmacodynamics. Population pharmacokinetic modeling is a data-driven method ('top-down' approach) to approximate population-level pharmacokinetic parameters, while identifying factors contributing to inter-individual variability. Model-informed precision dosing is increasingly used to leverage population pharmacokinetic models and patient data, to formulate individualized dosing recommendations. Physiologically based pharmacokinetic models integrate physicochemical drug properties with biological parameters ('bottom-up approach'), and is particularly valuable in situations with limited clinical data, such as early drug development, assessing drug-drug interactions, or adapting dosing for patients with specific comorbidities. The effective implementation of these complex models hinges on strong collaboration between clinicians and pharmacometricians, given the pivotal role of data availability. Promising advancements aimed at improving data availability encompass innovative techniques such as opportunistic sampling, minimally invasive sampling approaches, microdialysis, and in vitro investigations. Additionally, ongoing research efforts to enhance measurement instruments for evaluating pharmacodynamics responses, including biomarkers and clinical scoring systems, are expected to significantly bolster our capacity to understand drug effects in children.

Defining the geomechanical operating limits for subsurface CO2 storage

Carbon capture and storage (CCS) is a critical component of proposed pathways to limit global warming, though considerable upscaling is required to meet emissions reduction targets. Quantifying and managing the risks of fault reactivation is a leading barrier to scaling global CCS projects from current levels of ~40 million tonnes of carbon dioxide(CO2) per year (to target levels of several gigatonnes of CO2 per year), because CO2 injection into reservoirs can result in increased pore-fluid pressure and temperature changes, which can reduce the strength of rocks and faults and induce brittle failure. This can result in induced seismicity, whilst hydraulic fracturing of seals could provide pathways for CO2 leakage. Consequently, identifying favourable geomechanical conditions (typically determined through data on pre-injection rock stress, mechanical and elastic properties, and pore-fluid pressures) to minimise deformation of reservoirs and seals represents a key challenge in the selection of safe and effective sites for CCS projects. Critically, however, such geomechanical data are typically spatially limited (i.e. restricted to wells) and mainly consist of pre-injection crustal stress orientation measurements, rather than a full 3D description of the stress tensor and related geomechanical properties. This paper reviews some key geomechanical issues and knowledge gaps (particularly those associated with data availability and limitations) that need to be understood to enable successful reservoir and seal management for CCS projects. We also highlight recent advances in multi-scale and dimensional geomechanical modelling approaches that can be used to assess sites for the secure storage of CO2 as well as other gases, including hydrogen.

A scaffolded learning approach to increasing student comfort with Microsoft Excel

AbstractAs the availability and importance of data to support marketing decision-making continue to grow, universities need to prepare students for working in data-rich environments. Part of this preparation involves equipping students with skills necessary to access, manipulate, organize, explore, summarize, and visualize data. Microsoft Excel is a widely used tool for these tasks. We present results obtained across four years and seven sections of an undergraduate Marketing Metrics and Analytics courses that demonstrate the effectiveness of a scaffolded learning approach for enhancing student comfort with Excel. To support instructors who teach Marketing Metrics and Analytics courses, we provide the framework of assignments that we have found to effectively grow student confidence in using Excel.

Does derivative usage boost firm value in an economy with controls? Evidence from India

AbstractThe role of derivatives on firm valuation has recently become a subject of intense debate for emerging markets where there is greater asset price volatility, illiquidity, and the absence of common regulations and accounting standards. Given the lack of data availability, disclosure inconsistency and distinctive regulatory requirements for Indian firms, we assess the impact of corporate hedging via derivative use on the performance of BSE-100 non-financial firms over the period 2010–2017. Our results from keywords-based hand-collected derivative usage data show that all types of derivatives, except rate derivatives, have a significant and positive impact on firm value through channels of reduced earnings volatility and increased leverage with tax benefits. Firms with greater leverage tend to benefit from the use of rate derivatives. Still, among those highly leveraged firms, the ones who experience high earnings volatility are unable to benefit from rate derivatives. Besides, hedge accounting benefits firms in dealing with earnings volatility when they have higher derivatives exposure. As capital controls on international flows can limit the use of derivatives, we show that firms with currency risk exposure in terms of long-term foreign currency debt do benefit from rate derivatives. Our results are also robust with a matched sample for derivative users and non-users. Overall, we conclude that easing regulatory controls on derivatives dealings can be beneficial for firms in growing emerging economies.

Identifying proteomic risk factors for cancer using prospective and exome analyses of 1463 circulating proteins and risk of 19 cancers in the UK Biobank

The availability of protein measurements and whole exome sequence data in the UK Biobank enables investigation of potential observational and genetic protein-cancer risk associations. We investigated associations of 1463 plasma proteins with incidence of 19 cancers and 9 cancer subsites in UK Biobank participants (average 12 years follow-up). Emerging protein-cancer associations were further explored using two genetic approaches, cis-pQTL and exome-wide protein genetic scores (exGS). We identify 618 protein-cancer associations, of which 107 persist for cases diagnosed more than seven years after blood draw, 29 of 618 were associated in genetic analyses, and four had support from long time-to-diagnosis ( > 7 years) and both cis-pQTL and exGS analyses: CD74 and TNFRSF1B with NHL, ADAM8 with leukemia, and SFTPA2 with lung cancer. We present multiple blood protein-cancer risk associations, including many detectable more than seven years before cancer diagnosis and that had concordant evidence from genetic analyses, suggesting a possible role in cancer development.

Evaluation of Near Real-Time Global Precipitation Measurement (GPM) Precipitation Products for Hydrological Modelling and Flood Inundation Mapping of Sparsely Gauged Large Transboundary Basins—A Case Study of the Brahmaputra Basin

Limited availability of hydrometeorological data and lack of data sharing practices have added to the challenge of hydrological modelling of large and transboundary catchments. This research evaluates the suitability of latest near real-time global precipitation measurement (GPM)-era satellite precipitation products (SPPs), IMERG-Early, IMERG-Late and GSMaP-NRT, for hydrological and hydrodynamic modelling of the Brahmaputra Basin. The HEC-HMS modelling system was used for the hydrological modelling of the Brahmaputra Basin, using IMERG-Early, IMERG-Late, and GSMaP-NRT. The findings showed good results using GPM SPPs for hydrological modelling of large basins like Brahmaputra, with Nash–Sutcliffe efficiency (NSE) and R2 values in the range of 0.75–0.85, and root mean square error (RMSE) between 7000 and 9000 m3 s−1, and the average discharge was 20611 m3 s−1. Output of the GPM-based hydrological models was then used as input to a 1D hydrodynamic model to assess suitability for flood inundation mapping of the Brahmaputra River. Simulated flood extents were compared with Landsat satellite-captured images of flood extents. In critical areas along the river, the probability of detection (POD) and critical success index (CSI) values were above 0.70 with all the SPPs used in this study. The accuracy of the models was found to increase when simulated using SPPs corrected with ground-based precipitation datasets. It was also found that IMERG-Late performed better than the other two precipitation products as far as hydrological modelling was concerned. However, for flood inundation mapping, all of the three selected products showed equally good results. The conclusion is reached that for sparsely gauged large basins, particularly for trans-boundary ones, GPM-era SPPs can be used for discharge simulation and flood inundation mapping.