Transformation Capabilities Research Articles

HERALD: A domain-specific query language for longitudinal health data analytics

BackgroundLarge-scale health data has significant potential for research and innovation, especially with longitudinal data offering insights into prevention, disease progression, and treatment effects. Yet, analyzing this data type is complex, as data points are repeatedly documented along the timeline. As a consequence, extracting cross-sectional tabular data suitable for statistical analysis and machine learning can be challenging for medical researchers and data scientists alike, with existing tools lacking balance between ease-of-use and comprehensiveness. ObjectiveThis paper introduces HERALD, a novel domain-specific query language designed to support the transformation of longitudinal health data into cross-sectional tables. We describe the basic concepts, the query syntax, a graphical user interface for constructing and executing HERALD queries, as well as an integration into Informatics for Integrating Biology and the Bedside (i2b2). MethodsThe syntax of HERALD mimics natural language and supports different query types for selection, aggregation, analysis of relationships, and searching for data points based on filter expressions and temporal constraints. Using a hierarchical concept model, queries are executed individually for the data of each patient, while constructing tabular output. HERALD is closed, meaning that queries process data points and generate data points. Queries can refer to data points that have been produced by previous queries, providing a simple, but powerful nesting mechanism. ResultsThe open-source implementation consists of a HERALD query parser, an execution engine, as well as a web-based user interface for query construction and statistical analysis. The implementation can be deployed as a standalone component and integrated into self-service data analytics environments like i2b2 as a plugin. HERALD can be valuable tool for data scientists and machine learning experts, as it simplifies the process of transforming longitudinal health data into tables and data matrices. ConclusionThe construction of cross-sectional tables from longitudinal data can be supported through dedicated query languages that strike a reasonable balance between language complexity and transformation capabilities.

Readiness of hospitality and tourism curricula for digital transformation

The digital transformation imperative requires hospitality and tourism firms to rapidly build and continuously upgrade the competitiveness of specific digital transformation and digital business capabilities. Using thematic and quantitative content analysis of online curricula artifacts from top-ranked universities globally, we investigate the extent to which these digital capabilities are integrated into hospitality and tourism management curricula, as well as the approaches to their integration. We add to the literature by providing empirical evidence of integration levels, by identifying various curricula integration approaches, and by discussing their integration strengths and weaknesses. Finally, we contribute to the industry's readiness for digital transformation.

Tuning Nanochannel Microenvironments of a Thermoresponsive MXene Membrane for Mixed Molecule Gradient Separation.

Drawing inspiration from dynamic biological ion channels, researchers have developed various artificial membranes featuring responsive nanochannels. Typically, these membranes modify mass transport behaviors by manipulating the responsive layer on the inner surfaces of the intrinsic layer. In this study, we build two-dimensional lamellar membranes composed of titanium carbide MXene and poly(N-isopropylacrylamide), endowed with dual-level regulatable nanochannels, achieved through adjustments of nanochannel microenvironments. The size of these two-dimensional nanochannels can be altered by both the thermoresponsive polymer layer and the intrinsic MXene layer that could undergo spontaneous oxidation. The multilevel regulation strategy substantially enhances the molecular selectivity of MXene separation membranes, which is further applied for precise gradient separation toward multiple molecules. This advancement showcases the versatility and transformative capabilities of responsive nanochannel technology, setting the stage for innovative developments in diverse fields.

Eutectogels: The Multifaceted Soft Ionic Materials of Tomorrow.

Eutectogels, a rising category of soft materials, have recently garnered significant attention owing to their remarkable potential in various domains. This innovative class of materials consists of a eutectic solvent immobilized in a three-dimensional network structure. The use of eco-friendly and cost-effective eutectic solvents further emphasizes the appeal of these materials in multiple applications. Eutectogels exhibit key characteristics of most eutectic solvents, including environmental friendliness, facile preparation, low vapor pressure, and good ionic conductivity. Moreover, they can be tailored to display functionalities such as self-healing capability, adhesiveness, and antibacterial properties, which are facilitated by incorporating specific combinations of the eutectic mixture constituents. This perspective article delves into the current landscape and challenges associated with eutectogels, particularly focusing on their potential applications in CO2 separation, drug delivery systems, battery technologies, biocatalysis, and food packaging. By exploring these diverse realms, we aim to shed light on the transformative capabilities of eutectogels and the opportunities they present to address pressing industrial, academic, and environmental challenges.

Transformative Institutions and Capabilities to Aspire in the Context of Radical Uncertainty: Lessons from the Covid-19 Pandemic

ABSTRACT The article addresses the issue of transformative institutions and capabilities to aspire by framing it in the context of the radical uncertainty arising from today's structural crises, which strongly challenge the very ways and possibility of prefiguring a desirable future. Following the reflections of Amartya Sen and Arjun Appadurai, capabilities to aspire are not just an individual matter, as the conditions that enable their development are deeply embedded in social and institutional contexts. The article explains why and in what sense current contexts of uncertainty require transformative institutional dynamics that strengthen the ways in which alternative futures can be imagined and pursued, and what this implies with respect to capabilities to aspire. The analysis refers to the empirical field of the Covid-19 pandemic and in particular to a research study on the community health programme called “Micro-areas” implemented in the north-east of Italy. The case is a paradigmatic example of the relationship between transformative institutional dynamics and capabilities to aspire in contexts of crisis. In the conclusions, the article highlights some institutional factors that emerge as important with respect to the development of capabilities to aspire.

Configurational Paths of Preconditions to Transformational Leadership Among Core Hospital Leaders: A Fuzzy-Set Qualitative Comparative Analysis.

Transformational leadership among core hospital leaders boosts medical organizations' competitiveness, adaptability, and sustainability, which is jointly affected by individual, organizational and environmental factors. This study aims to unpack its configurational framework and propose strategies to strengthen core hospital leaders' transformational leadership. Data were collected from an online questionnaire among 31 core hospital leaders. The fuzzy-set qualitative comparative analysis (fsQCA) was used to explore the causal mechanism of high-level transformational leadership. We enrich this mechanism by professional background, critical thinking, initiative spirit, family-work conflict, job satisfaction, subordinates' followership, and work pressure. Result shows initiative spirit is the only necessary condition (consistency=0.911) for the formation of high-level transformational leadership among core hospital leaders. Three configurations are the sufficient conditions that lead to high-level transformational leadership among core hospital leaders with two different professional backgrounds (overall solution consistency= 0.952). Core hospital leaders' initiative spirit is an indispensable condition for improving high-level transformational leadership, emphasizing the necessity for core leaders to be proactive in order to develop such leadership. Besides, the study also uncovered three configurations are the sufficient conditions for core hospital leaders with diverse professional backgrounds to achieve high-level transformational leadership. This finding offers significant insights into hospital management practices, suggesting that core hospital leaders' work should be managed in a personalized manner based on their professional backgrounds, thereby fostering favorable conditions conducive to the development of their high-level transformational leadership capabilities. Furthermore, the central insight of this study is that the formation of high-level transformational leadership contingent upon the collaboration of professional background, critical thinking, initiative spirit, family-work conflict, job satisfaction, subordinates' followership, and work pressure, contributing to a holistic and more rigorous view for the development of transformational leadership.

5G-Cloud-based real-time robotic part repairing for advanced manufacturing via computer vision

In this paper, we delve into the transformative capabilities of 5G wireless communication technology within the realm of robotics, particularly in the context of advanced manufacturing. Leveraging the power of computer vision and cloud/edge computing, we unveil the boundless possibilities that this technology heralds. Leveraging the ultra-reliable low latency communication (uRLLC) of 5G, we establish a distributed system showcasing its potential to revolutionize industrial processes. Additionally, to validate our framework, we developed a testbed tailored for real-world applications, focusing on milling operations using an industrial robot. Through high-level lidar data processing, we identify defects and create toolpaths, seamlessly to execute milling tasks. This research highlights 5G’s potential to redefine industrial processes while elucidating operational requirements for achieving uRLLC. Empirical testing underscores the benefits of 5G-robotics integration, enhancing efficiency and innovation in advanced manufacturing.

A quasi-zero-stiffness vibration isolator inspired by Kresling origami

Vibration isolators are widely used to mitigate undesirable vibrations in engineering systems and structures, but low-frequency vibration isolation remains a challenge. Origami-inspired structures, with their tunable stiffness and transformative capabilities, offer potential for vibration isolation. Inspired by conical Kresling origami, this study proposes a quasi-zero-stiffness vibration isolation system. Static analysis of the origami structure reveals that the system exhibits near-zero dynamic stiffness at the static equilibrium position, while maintaining static stiffness during the load-bearing process. The amplitude-frequency characteristic formula and vibration transmissibility formula are derived using the harmonic balance method. The effects of varying damping ratios and excitation amplitudes on the amplitude-frequency and transmissibility curves are examined. It is shown that the vibration isolation range increases with the number of layers. A comparison of the system's response to harmonic vibrations under different compressive deformations highlights the effectiveness of the origami quasi-zero-stiffness system as a vibration attenuator. Compared to existing quasi-zero-stiffness dampers, the origami-inspired system features a wider isolation frequency band, a lower initial isolation frequency, and reduced vibration transmissibility, demonstrating superior isolation performance, particularly at low frequencies.

Data Lakehouse: A survey and experimental study

Efficient big data management is a dire necessity to manage the exponential growth in data generated by digital information systems to produce usable knowledge. Structured databases, data lakes, and warehouses have each provided a solution with varying degrees of success. However, a new and superior solution, the data Lakehouse, has emerged to extract actionable insights from unstructured data ingested from distributed sources. By combining the strengths of data warehouses and data lakes, the data Lakehouse can process and merge data quickly while ingesting and storing high-speed unstructured data with post-storage transformation and analytics capabilities. The Lakehouse architecture offers the necessary features for optimal functionality and has gained significant attention in the big data management research community. In this paper, we compare data lake, warehouse, and lakehouse systems, highlight their strengths and shortcomings, identify the desired features to handle the evolving challenges in big data management and analysis and propose an advanced data Lakehouse architecture. We also demonstrate the performance of three state-of-the-art data management systems namely HDFS data lake, Hive data warehouse, and Delta lakehouse in managing data for analytical query responses through an experimental study.

Virtual multi-staining in a single-section view for renal pathology using generative adversarial networks

Sections stained in periodic acid-Schiff (PAS), periodic acid-methenamine silver (PAM), hematoxylin and eosin (H&E), and Masson's trichrome (MT) stain with minimal morphological discordance are helpful for pathological diagnosis in renal biopsy. Here, we propose an artificial intelligence-based re-stainer called PPHM-GAN (PAS, PAM, H&E, and MT-generative adversarial networks) with multi-stain to multi-stain transformation capability. We trained three GAN models on 512 × 512-pixel patches from 26 training cases. The model with the best transformation quality was selected for each pair of stain transformations by human evaluation. Frechet inception distances, peak signal-to-noise ratio, structural similarity index measure, contrast structural similarity, and newly introduced domain shift inception score were calculated as auxiliary quality metrics. We validated the diagnostic utility using 5120 × 5120 patches of ten validation cases for major glomerular and interstitial abnormalities. Transformed stains were sometimes superior to original stains for the recognition of crescent formation, mesangial hypercellularity, glomerular sclerosis, interstitial lesions, or arteriosclerosis. 23 of 24 glomeruli (95.83 %) from 9 additional validation cases transformed to PAM, PAS, or MT facilitated recognition of crescent formation. Stain transformations to PAM (p = 4.0E-11) and transformations from H&E (p = 4.8E-9) most improved crescent formation recognition. PPHM-GAN maximizes information from a given section by providing several stains in a virtual single-section view, and may change the staining and diagnostic strategy.

Digital transformation and digital dynamic capabilities improvement in low-medium technology sector: evidence from Thai family firms

Digital transformation and digital dynamic capabilities improvement in low-medium technology sector: evidence from Thai family firms

Exploring uncharted wilderness: a new conceptualization of transformative leadership education

In this explorative essay, we ask how to integrate current development in entrepreneurship education with an education for societal transformation towards sustainability, more explicitly phrased as what and how educators should teach to make students develop transformative capabilities and build action competence for leading societal transformation. We conclude that entrepreneurship education has much to offer and that educators and students ought to transgress institutional borders and explore wilderness together to learn how to create transformative leadership education. Current knowledge indicates that a purposeful education for this end should address seven complementary competencies where students learn to take action, collaborate, engage with society, manage own growth, ground themselves, scout the future, and reframe the system. Due to its inherent qualities, entrepreneurship education, built on engagement in real-world transformative problems, can be considered the most potent foundation for such aspirations. However, as current educational institutions are formed by the malfunctioning and unsustainable system they serve, today’s institutions might not be the best forerunners of change in any pedagogical practice. This is why we also conclude that progressive educators need to leave their ivory towers, classrooms, and lecture halls behind and engage in real transformative problems head-on with their students by their sides.

Supply chain transformative capabilities and their microfoundations for circular economy transition: A qualitative study in Made in Italy sectors

AbstractIn today's rapidly evolving landscape, supply chains must develop specific dynamic capabilities for initiating and successfully governing their transformations toward new configurations to withstand pressures arising from competitions and global changes. Differently from dynamic capabilities that enable adaptation, supply chain transformative capabilities include four core dimensions: triggering, envisioning, navigating, and stabilizing capabilities. By using a multiple‐case study approach involving six Italian companies related to fashion and furniture sectors, we investigate these concepts and identify the microfoundations of supply chain transformative capabilities for circularity. They are classified with reference to individuals, processes, and structures. Our findings illustrate how supply chains can facilitate and effectively govern their circular transformation by developing the transformative capabilities and their microfoundations. This paper provides valuable theoretical insights and managerial perspectives, advancing the concept of transformative supply chain management.

Study on Selenium Assimilation and Transformation in Radish Sprouts Cultivated Using Maillard Reaction Products.

The organic selenium (Se), particularly in the form of selenoamino acids, in non-edible sections or by-products of Se-enriched plants, has the potential to generate Maillard reaction products (MRPs) during thermal treatment or fermentation. To elucidate the recycling process of organic selenium in foods and improve the utilization rate of Se, the biotransformation of organic selenium was studied by the cultivation of edible radish sprouts with Se-MPRs. Maillard reactions were simulated using selenoamino acids (SeAAs; selenomethionine and methylselenocysteine) and reducing sugars (glucose and fructose) for preparing Se-MRPs. The structures of the possible dehydrated Se-MRPs were analyzed using a HPLC-ESI-MS/MS system based on their fragmentation patterns and Se isotopic characteristics. Se absorption by the radish sprouts cultivated using Se-MRPs was estimated by the corresponding Se in the SeAAs and the total Se contents. The capabilities of SeAA transformation and total Se assimilation by the sprouts were related to the substrate composition during the Se-Maillard reaction. A particular Se-MRP (selenomethionine + fructose) increased SeAAs transformation by 33.8% compared to selenomethionine. However, glucose and fructose seemed to inhibit the transformation of the Se-MRPs to SeAAs by 10.0 to 59.1% compared to purified Se-MRPs. These results provide key references for the efficient utilization of organic Se in the cultivation of Se-enriched sprouts.

The necessity of artificial intelligence in fintech for SupTech and RegTech supervisory in banks and financial organizations

In the rapidly evolving financial landscape, the integration of Artificial Intelligence (AI) into Supervisory Technology (SupTech) and Regulatory Technology (RegTech) has become increasingly vital. As banks and financial organizations grapple with the complexities of compliance, risk management, and regulatory oversight, AI offers transformative capabilities that enhance efficiency, accuracy, and resilience. This paper explores the critical need for AI in Fintech for SupTech and RegTech, focusing on its role in supervisory functions within the banking sector. A case study on AI-driven anti-money laundering systems is presented, along with a discussion of authentic laboratory research and survey results. The paper concludes by illustrating the potential impact of AI on financial supervision with graphs and data, underscoring its necessity in modern financial governance.

Accelerated structural transformation of metal-organic frameworks via interfacial engineering for efficient oxygen evolution reaction

Most metal-organic frameworks (MOFs) undergo structural transformations during the oxygen evolution reaction (OER), prompting the development of more catalytically active metal oxyhydroxides. However, one of the biggest challenges still is managing the MOF reconstruction process. Here, we present an interface engineering strategy to encourage MOFs to structurally change into metal oxyhydroxides, which are more active in the OER process. By meticulous design, the NiSe/CoFc′ (CoFc-MOF, Fc′ = 1,1′-ferrocenecarboxylic acid) heterojunction was successfully constructed, exhibiting remarkable interface coupling effects. This characteristic not only facilitates efficient electron transfer from CoFc′ to NiSe, and accelerates the structural transformation process of CoFc′, but also activates more catalytic active sites. Compared to pure CoFc′, NiSe/CoFc′ exhibits enhanced structural transformation capability, occurring in a shorter time and at a lower voltage, with more catalytic sites being activated. Furthermore, the transformed NiSe/CoFc′ displays suprior catalytic activity with an overpotential of 227 mV at a current density of 100 mA cm−2, surpassing the single-component CoFc′. And the Faradaic efficiency of NiSe/CoFc′ is near to 99.6%. This approach offers a noval avenue for the levelheaded plan of non-precious metal electrocatalysts for water electrolysis, enabling the control of structural conversion through interface engineering.

GCN-Transformer-Based Spatio-Temporal Load Forecasting for EV Battery Swapping Stations under Differential Couplings

To address the challenge of power absorption in grids with high renewable energy integration, electric vehicle battery swapping stations (EVBSSs) serve as critically important flexible resources. Current research on load forecasting for EVBSSs primarily employs Transformer models, which have increasingly shown a lack of adaptability to the rapid growth in scale and complexity. This paper proposes a novel data-driven forecasting model that combines the geographical feature extraction capability of graph convolutional networks (GCNs) with the multitask learning capability of Transformers. The GCN-Transformer model first leverages Spearman’s rank correlation to create a multinode feature set encompassing date, weather, and historical load data. It then employs data-adaptive graph generation for dynamic spatio-temporal graph construction and graph convolutional layers for spatial aggregation tailored to each node. Unique swapping patterns are identified through node-adaptive parameter learning, while the temporal dynamics of multidimensional features are managed by the Transformer’s components. Numerical results demonstrate enhanced accuracy and efficiency in load forecasting for multiple and widely distributed EVBSSs.

Enhanced degradation of crystal violet using PANI-ZnO nanocomposites: Electro-oxidation and photocatalysis studies

This work compares the transformation capabilities of crystal violet (CV), a toxic cationic dye of the triphenylmethane family, by advanced oxidation processes, especially heterogeneous photocatalysis and electrochemical oxidation, on new nanocomposite materials with a composition of zinc oxide (ZnO) and polyaniline (PANI) by chemical polymerization in solution. Concerning the synthesis of PANI, we have exploited the radical oxidative polymerization method; this latter is functional and uncomplex, as it does not require expensive equipment, using ammonium persulfate as an oxidant with a part of monomer (Aniline) to remove pollutants present in the environment. After physicochemical analyses including FTIR, SEM, and electrochemical characterizations by cyclic voltammetry and conductimetry, the results show that the photocatalytic activity of PANI-ZnO 5 % resulted in a better yield (85 %) compared to PANI alone and other composites (PANI-ZnO 10 % and PANI-ZnO 20 %). However, the electro-oxidation discoloration rates on all PANI alone and PANI-ZnO composite electrodes (5 %, 10 %, and 20 %) reached almost 99 %. To deepen our understanding, computational DFT calculations were employed, complementing experimental findings, and together, they contribute to elucidating the intricate mechanisms underlying the enhanced performance of these nanocomposite materials in both photocatalysis and electrochemical oxidation processes.

The Impact of Digital Transformation on the Innovation Capacity of Chinese-Listed Firms: The Role of Government Subsidies

As digital transformation progresses, listed Chinese firms are undergoing significant changes in their practices. These changes are crucial for establishing and maintaining a competitive advantage. This research investigates the process by which digital transformation affects firms' capacity to innovate, specifically focusing on the influence of government subsidies. The data was obtained from 1,063 publicly traded in China from 2018 to 2022 with a total of 4,027 data points. A fixed-effects model, stepwise regression analysis, and bootstrapping techniques were employed to construct the models. To address the conventional quantitative constraints and provide a nuanced comprehension of digital transformation's influence, this study uses textual analysis. Research has shown that digital transformation has a substantial positive impact on the ability of companies to innovate. Additionally, government subsidies are proven to have a role in facilitating this process. This paper offers a fresh viewpoint on comprehending the mechanism of government subsidies for digital transformation and corporate innovation capability. It also provides evidence supporting the idea that government subsidies may enhance innovation incentives more effectively

Semantic-aware Message Broadcasting for Efficient Unsupervised Domain Adaptation.

Vision transformer has demonstrated great potential in abundant vision tasks. However, it also inevitably suffers from poor generalization capability when the distribution shift occurs in testing (i.e., out-of-distribution data). To mitigate this issue, we propose a novel method, Semantic-aware Message Broadcasting (SAMB), which enables more informative and flexible feature alignment for unsupervised domain adaptation (UDA). Particularly, we study the attention module in the vision transformer and notice that the alignment space using one global class token lacks enough flexibility, where it interacts information with all image tokens in the same manner but ignores the rich semantics of different regions. In this paper, we aim to improve the richness of the alignment features by enabling semantic-aware adaptive message broadcasting. Particularly, we introduce a group of learned group tokens as nodes to aggregate the global information from all image tokens, but encourage different group tokens to adaptively focus on the message broadcasting to different semantic regions. In this way, our message broadcasting encourages the group tokens to learn more informative and diverse information for effective domain alignment. Moreover, we systematically study the effects of adversarial-based feature alignment (ADA) and pseudo-label based self-training (PST) on UDA. We find that one simple two-stage training strategy with the cooperation of ADA and PST can further improve the adaptation capability of the vision transformer. Extensive experiments on DomainNet, OfficeHome, and VisDA-2017 demonstrate the effectiveness of our methods for UDA.