32584 research outputs found
Sort by
A Comparative Multivariate Analysis of VAR and Deep Learning-based Models for Forecasting Volatile Time Series Data
Get PDFThe existing literature on forecasting time series data is primarily based on univariate analysis and techniques such as Univariate Autoregressive (UAR), Univariate Moving Average (UMA), Simple Exponential Smoothing (SES), deep learning models, and, most notably, univariate Long Short-Term Memory (LSTM) built based on univariate variable where the next lag of time series is leveraged for forecasting the next cycle of data. This paper takes this line of research to the next level by focusing on forecasting time series data based on “multivariate” modeling and analysis. To have a better insight of the performance of various deep learning-based models when multivariate analysis is performed, the paper builds and reports the forecasting accuracy for techniques such as the Transformer-based Multi-head Attention network, Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (BI-LSTM), Temporal Convolution Network (TCN), and conventional Vector Autoregressive (VAR) models. The findings revealed that the TCN model achieved the average lowest RMSE values of 0.0589 for stock data and 0.1554 for cryptocurrency data. Notably, the Multi-Head Attention model achieved average R2 values of 0.92 for stock data and −1.98 for cryptocurrency data with respect to five variables (i.e., open, high, low, close and volume). According to the empirical studies conducted and reported in this paper, the transformer-based Multi-head Attention network outperformed other models such as LSTM, BI-LSTM, and more importantly conventional Vector Auto-Regression Models (VAR) in stocks and cryptocurrencies time series data where several variables were leveraged in building these multivariate-based models
A Sleep Health Education Intervention Improves Sleep Knowledge in Social Work Students
Get PDFIntroduction: Social workers, the largest group of mental health clinicians in the United States, play a pivotal role in mental health promotion. Despite the importance of sleep for mental health, there is no empirical research on sleep education interventions for social workers. Method: We designed an online sleep health education intervention to equip social work students to promote healthy sleep practices among their clients. An interdisciplinary team of experts devised the 90 min intervention using an empirically supported behavioral change theoretical model (COM-B). The intervention discusses multi-level factors that impact sleep and emphasizes considerations for health disparities in populations commonly served by social workers (e.g., unhoused populations, clients with substance use disorders, etc.). We assessed sleep knowledge, sleep quality, and acceptability using survey and focus group data. Results: Ninety social work students (92.2% female, 38.8% non-Hispanic white) completed pre- and post-intervention assessments. Participants demonstrated significant improvements in sleep health knowledge and their personal sleep quality. Quantitative and qualitative data revealed perceived usefulness for social work practice. Conclusions: A short online sleep education intervention can improve sleep health knowledge, offering a practical method to expand social workers’ understanding of healthy sleep promotion that can be readily implemented in clinical training and practice
Advanced Thermogravimetric Analyses of Stem Wood and Straw Devolatilization: Torrefaction through Combustion
Get PDFProcess design critically depends on the characterization of fuels and their kinetics under process conditions. This study steps beyond the fundamental methods of thermogravimetry to modulated (MTGA) and Hi-Res™ (high resolution) techniques to (1) add characterization detail and (2) increase the utility of thermal analysis data. Modulated TGA methods overlay sinusoidal functions on the heating rates to determine activation energy as a function of temperature with time. Under devolatilization conditions, Hi-Res™ TGA maintains a constant mass loss with time and temperature. These two methods, run independently or overlaid, offer additional analysis in which multiple samples at different heating rates are run to different final temperatures. Advanced methods allow researchers to use fewer samples by conducting fewer runs, targeting practical experimental designs, and quantifying errors easier. The parameters of the studies included here vary the heating rate at 10, 30, and 50 °C/min; vary gas-phase oxygen for pyrolysis or combustion conditions; and particle size ranges of 100–125 µm, 400–425 µm, and 600–630 µm. The two biomass fuels used in the studies are pinewood from Northern Sweden and wheat straw. The influence of torrefaction is also included at temperatures of 220, 250, and 280 °C. Apparent activation energy results align with the previous MTGA data in that combustion conditions yield higher values than pyrolysis conditions—200–250 kJ/mol and 175–225 kJ/mol for pine and wheat combustion, respectively, depending on pre-treatment. Results show the dependence of these parameters upon one another from a traditional thermal analysis approach, e.g., the Ozawa-Flynn-Wall method, as well as MTGA and Hi-Res™ thermogravimetric investigations to show future directions for thermal analysis techniques
Analysis of galaxies at the extremes: a kinematic analysis of the Virgo cluster dwarfs VCC 9 and VCC 1448 using the Keck cosmic web imager
Get PDFWe present spatially resolved Keck Cosmic Web Imager stellar spectroscopy of the Virgo cluster dwarf galaxies VCC 9 and VCC 1448. These galaxies have similar stellar masses and large half-light radii but very different globular cluster (GC) system richness (∼25 versus ∼99 GCs). Using the KCWI data, we spectroscopically confirm 10 GCs associated with VCC 1448 and one GC associated with VCC 9. We make two measurements of dynamical mass for VCC 1448 based on the stellar and GC velocities, respectively. VCC 1448’s mass measurements suggest that it resides in a halo in better agreement with the expectation of the stellar mass–halo mass relationship than the expectation from its large GC counts. For VCC 9, the dynamical mass we measure agrees with the expected halo mass from both relationships. We compare VCC 1448 and VCC 9 to the GC-rich galaxy Dragonfly 44 (∼74 GCs), which is similar in size but has ∼1 dex less stellar mass than either Virgo galaxy. In dynamical mass – GC number space, Dragonfly 44 and VCC 1448 exhibit richer GC systems given their dynamical mass than that of VCC 9 and other ‘normal’ galaxies. We also place the galaxies in kinematics–ellipticity space finding evidence of an anticorrelation between rotational support and the fraction of a galaxy’s stellar mass in its GC system, that is, VCC 9 is more rotationally supported than VCC 1448, which is more rotationally supported than Dragonfly 44. This trend may be expected if a galaxy’s GC content depends on its natal gas properties at formation
Children’s behavior at metaverses: Interactions, digital identities, and parent’s perceptions
Get PDFChildren are considered one of the main user profiles of metaverses, since they get into those digital universes by playing sandbox video games as Fortnite, Minecraft, or Roblox. However, a lack of research focused on the how, why and outcomes of this vulnerable target in metaverses has been noted. This study aims to investigate the behavior of children in the metaverses through their use of video games and the perceptions of their parents, considering them an indirect target. A total of 31 semi-structured interviews were conducted with children aged between six and 12 years old and their parents. Also, a non-participatory observation of five children while they were playing video games in their domestic environments were carried out. Results show that children play sandbox video games to have both online and offline presence, interacting with familiar people and strangers to feel connected to the community. Also, they recognize brands in those universes and the importance of designing avatars. For its part, parents identify some negative outcomes of video games, but their perception is mainly positive, above all in terms of collaboration and participation. A dichotomy between observation findings and parents’ response has been noted, reflecting a need of digital literacy in terms of communication
Enhancing Botnet Detection in Network Security Using Profile Hidden Markov Models
Get PDFA botnet is a network of compromised computer systems, or bots, remotely controlled by an attacker through bot controllers. This covert network poses a threat through large-scale cyber attacks, including phishing, distributed denial of service (DDoS), data theft, and server crashes. Botnets often camouflage their activity by utilizing common internet protocols, such as HTTP and IRC, making their detection challenging. This paper addresses this threat by proposing a method to identify botnets based on distinctive communication patterns between command and control servers and bots. Recognizable traits in botnet behavior, such as coordinated attacks, heartbeat signals, and periodic command distribution, are analyzed. Probabilistic models, specifically Hidden Markov Models (HMMs) and Profile Hidden Markov Models (PHMMs), are employed to learn and identify these activity patterns in network traffic data. This work utilizes publicly available datasets containing a combination of botnet, normal, and background traffic to train and test these models. The comparative analysis reveals that both HMMs and PHMMs are effective in detecting botnets, with PHMMs exhibiting superior accuracy in botnet detection compared to HMMs
IS KNOWLEDGE MANAGEMENT (FINALLY) EXTRACTIVE? – FULLER’S ARGUMENT REVISITED IN THE AGE OF AI
Get PDFAim/Purpose The rise of modern artificial intelligence (AI), in particular, machine learning (ML), has provided new opportunities and directions for knowledge management (KM). A central question for the future of KM is whether it will be dominated by an automation strategy that replaces knowledge work or whether it will support a knowledge-enablement strategy that enhances knowledge work and uplifts knowledge workers. This paper addresses this question by re-examining and updating a critical argument against KM by the sociologist of science Steve Fuller (2002), who held that KM was extractive and exploitative from its origins. Background This paper re-examines Fuller’s argument in light of current developments in artificial intelligence and knowledge management technologies. It reviews Fuller’s arguments in its original context wherein expert systems and knowledge engineering were influential paradigms in KM, and it then considers how the arguments put forward are given new life in light of current developments in AI and efforts to incorporate AI in the KM technical stack. The paper shows that conceptions of tacit knowledge play a key role in answering the question of whether an automating or enabling strategy will dominate. It shows that a better understanding of tacit knowledge, as reflected in more recent literature, supports an enabling vision. Methodology The paper uses a conceptual analysis methodology grounded in epistemology and knowledge studies. It reviews a set of historically important works in the field of knowledge management and identifies and analyzes their core concepts and conceptual structure. Contribution The paper shows that KM has had a faulty conception of tacit knowledge from its origins and that this conception lends credibility to an extractive vision supportive of replacement automation strategies. The paper then shows that recent scholarship on tacit knowledge and related forms of reasoning, in particular, abduction, provide a more theoretically robust conception of tacit knowledge that supports the centrality of human knowledge and knowledge workers against replacement automation strategies. The paper provides new insights into tacit knowledge and human reasoning vis-à-vis knowledge work. It lays the foundation for KM as a field with an independent, ethically defensible approach to technology-based business strategies that can leverage AI without becoming a merely supporting field for AI. Findings Fuller’s argument is forceful when updated with examples from current AI technologies such as deep learning (DL) (e.g., image recognition algorithms) and large language models (LLMs) such as ChatGPT. Fuller’s view that KM presupposed a specific epistemology in which knowledge can be extracted into embodied (computerized) but disembedded (decontextualized) information applies to current forms of AI, such as machine learning, as much as it does to expert systems. Fuller’s concept of expertise is narrower than necessary for the context of KM but can be expanded to other forms of knowledge work. His account of the social dynamics of expertise as professionalism can be expanded as well and fits more plausibly in corporate contexts. The concept of tacit knowledge that has dominated the KM literature from its origins is overly simplistic and outdated. As such, it supports an extractive view of KM. More recent scholarship on tacit knowledge shows it is a complex and variegated concept. In particular, current work on tacit knowledge is developing a more theoretically robust and detailed conception of human knowledge that shows its centrality in organizations as a driver of innovation and higher-order thinking. These new understandings of tacit knowledge support a non-extractive, human enabling view of KM in relation to AI. Recommendations for Practitioners Practitioners can use the findings of the paper to consider ways to implement KM technologies in ways that do not neglect the importance of tacit knowledge in automation projects (which neglect often leads to failure). They should also consider how to enhance and fully leverage tacit knowledge through AI technologies and augment human knowledge. Recommendations for Researchers Researchers can use these findings as a conceptual framework in research concerning the impact of AI on knowledge work. In particular, the distinction between replacement and enabling technologies, and the analysis of tacit knowledge as a structural concept, can be used to categorize and analyze AI technologies relative to KM research objectives. Impact on Society The potential of AI on employment in the knowledge economy is a major issue in the ethics of AI literature and is widely recognized in the popular press as one of the pressing societal risks created by AI and specific types such as generative AI. This paper shows that KM, as a field of research and practice, does not need to and should not add to the risks created by automation-replacement strategies. Rather, KM has the conceptual resources to pursue a (human) knowledge enablement approach that can stand as a viable alternative to the automation-replacement vision. Future Research The findings of the paper suggest a number of research trajectories. They include: Further study of tacit knowledge and its underlying cognitive mechanisms and structures in relation to knowledge work and KM objectives. Research into different types of knowledge work and knowledge processes and the role that tacit and explicit knowledge play. Research into the relation between KM and automation in terms of KM’s history and current technical developments. Research into how AI arguments knowledge works and how KM can provide an enabling framework
Spectroscopic and thermodynamic characterization of a cobalt-verdazyl valence tautomeric system. influence of crystal structure, solvent and counterion
Get PDFCrystallization of the verdazyl-based valence tautomeric ion [Co(dipyvd)2]2+ (where dipyvd is the radical ligand 1-isopropyl-3,5-di(2′-pyridyl)-6-oxoverdazyl) with a variety of different counterions results in materials that show varying degrees of valence tautomeric (VT) transition in the solid state. The X-ray structure of the SbF6 salt at 150 K reveals a localized structure for the S = 1/2 tautomer, with a Co3+ cation and distinct anionic and radical ligands. Comparison with the structure of the same material at 300 K reveals large structural changes in the ligand as a result of the valence tautomeric equilibrium. Data for the S = 3/2 form is less conclusive; X-ray spectroscopy on the PF6 salt suggests a degree of low spin Co2+ character for the S = 3/2 tautomer at very low temperature though this is inconsistent with EPR data at similar temperatures and structural information at 150 K. Magnetic measurements on the [BArF4]− and triflate salts in organic solvents show that the VT equilibrium is dependent on solvent and ion pairing effects
Validation of Satellite-Based Cloud Phase Distributions Using Global-Scale In Situ Airborne Observations
Get PDFUnderstanding distributions of cloud thermodynamic phases is important for accurately representing cloud radiative effects and cloud feedback in a changing climate. Satellite-based cloud phase data have been frequently used to compare with climate models, yet few studies validated them against in situ observations at a near-global scale. This study aims to validate three satellite-based cloud phase products using a compositive in situ airborne data set developed from 11 flight campaigns. Latitudinal-altitudinal cross sections of cloud phase occurrence frequencies are examined. The Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) show the most similar vertical profiles of ice phase frequencies compared with in situ observations. The CloudSat data overestimate mixed-phase frequencies up to 15 km but provide better sampling through cloud layers than lidar data. The DARDAR (raDAR/liDAR) data show a sharp transition between ice and liquid phase and overestimate ice phase frequency at most altitudes and latitudes. The satellite data are further evaluated for various latitudes, longitudes, and seasons, which show higher ice phase frequency in the extratropics in their respective wintertime and smaller impacts from longitudinal variations. The Southern Ocean shows a thicker mixing region where liquid and ice phases have similar frequencies compared with tropics and Northern Hemisphere (NH) extratropics. Two comparison methods with different spatiotemporal windows show similar results, which demonstrates the statistical robustness of these comparisons. Overall, this study develops a near global-scale in situ observational data set to assess the accuracy of satellite-based cloud phase products and investigates the key factors affecting the distributions of cloud phases
