Approach For Creation Research Articles

A generative AI-driven interactive listening assessment task

IntroductionAssessments of interactional competence have traditionally been limited in large-scale language assessments. The listening portion suffers from construct underrepresentation, whereas the speaking portion suffers from limited task formats such as in-person interviews or role plays. Human-delivered tasks are challenging to administer at large scales, while automated assessments are typically very narrow in their assessment of the construct because they have carried over the limitations of traditional paper-based tasks to digital formats. However, computer-based assessments do allow for more interactive, automatically administered tasks, but come with increased complexity in task creation. Large language models present new opportunities for enhanced automated item generation (AIG) processes that can create complex content types and tasks at scale that support richer assessments.MethodsThis paper describes the use of such methods to generate content at scale for an interactive listening measure of interactional competence for the Duolingo English Test (DET), a large-scale, high-stakes test of English proficiency. The Interactive Listening task assesses test takers’ ability to participate in a full conversation, resulting in a more authentic assessment of interactive listening ability than prior automated assessments by positing comprehension and interaction as purposes of listening.Results and discussionThe results of a pilot of 713 tasks with hundreds of responses per task, along with the results of human review, demonstrate the feasibility of a human-in-the-loop, generative AI-driven approach for automatic creation of complex educational assessments at scale.

SDG Innovations & Impact: Relational Approach in Enabling Innovative Solutions and Impact Creation at the Grassroots

SDG localisation is a primary goal of APPGM-SDG, and solution projects are the key component of this effort. Formed in 2019, mirroring the UK Parliament setup, the APPGM-SDG Malaysia has been the forefront organisation in localising SDGs in parliamentary constituencies. Despite Covid-19 and other practical challenges. The grounded research approach undertaken by APPGM-SDG paved the way for innovative grassroots solutions designed and undertaken by local organisations targeting vulnerable communities in their own localities. From 2020 to November 2023, APPGM-SDG has undertaken targeted solution projects at an unprecedented level, with 664 SDGs solution projects in 4 years and 364 solution providers around the country. These innovative solutions are developed through a unique relational approach between multiple stakeholders and engagements in multiple processes in the making. This approach has been the key framework in designing solution projects that have proven to have a much greater impact towards the community as a whole and personally on the livelihoods of the participants involved. This is also evident in our recent publication, SDG Solution Projects: Best Practices Impacting the Grassroots, which carries 35 best practices for solution projects and innovative stories from the ground that can be examples of model solutions for vulnerable communities. This paper will discuss the relational approach that brings out SDG innovations in finding solutions between the partners involved, the empowerment and participatory models in place, and the processes involved in curating suitable solutions. The lessons learnt from 2020 until now in crafting fine-tuned solutions will also be included in this paper, together with real-life case studies of SDG innovative solutions that have a greater relational approach between stakeholders.

Contextualizing Lean Startup and Alternative Approaches for New Venture Creation: Introducing the Special Issue

The Lean Startup movement fundamentally changed entrepreneurial education and the way new ventures evolve. While Steve Blank and other founders of the movement embraced academic ideas, the movement grew among practitioners largely disconnected from academic entrepreneurship research. The purposes of this special issue are (1) to better connect Lean Startup practice to academic entrepreneurship research and (2) to advance theory regarding Lean Startup practices and their outcomes. After a brief and personal story of Lean Startup’s beginnings by its founder, Steve Blank, the first set of papers in this special issue juxtapose Lean Startup with alternative approaches to new venture creation developed by scholars outside the influence of the Lean Startup movement. The second set of papers describe how Lean Startup might be contextualized for different unique situations. The third set dives into different Lean Startup practices to help researchers and practitioners think more deeply about decisions and trade-offs made during implementation of Lean Startup.

Software for Bioprinting.

The bioprinting of heterogeneous organs is a crucial issue. To reach the complexity of such organs, there is a need for highly specialized software that will meet all requirements such as accuracy, complexity, and others. The primary objective of this review is to consider various software tools that are used in bioprinting and to reveal their capabilities. The sub-objective was to consider different approaches for the model creation using these software tools. Related articles on this topic were analyzed. Software tools are classified based on control tools, general computer-aided design (CAD) tools, tools to convert medical data to CAD formats, and a few highly specialized research-project tools. Different geometry representations are considered, and their advantages and disadvantages are considered applicable to heterogeneous volume modeling and bioprinting. The primary factor for the analysis is suitability of the software for heterogeneous volume modeling and bioprinting or multimaterial three-dimensional printing due to the commonality of these technologies. A shortage of specialized suitable software tools is revealed. There is a need to develop a new application area such as computer science for bioprinting which can contribute significantly in future research work.

Design and synthesis of a new pyrazole-naphthalene Schiff base: Al3+recognition, DNA binding studies and theoretical calculations

Herin, a Schiff base (L) based on 2‑hydroxy-1-naphthaldehyde and 3-amino-4-pyrazolecarboxylic acid ethyl ester was successfully designed, synthesized and characterized. Spectroscopic experiments demonstrated that L can selectively recognize Al3+ with fluorescence, exhibiting a detection limit (DL) of 4.2 × 10−8 mol/L, significantly lower than that of previously reported Schiff base fluorescent sensors. The Job's plot analysis demonstrated the 1:1 stoichiometry between the Al3+ and L, with bright blue fluorescence observed under 365 nm ultraviolet light irradiation. The recognition mechanism involves the coordination of Al3+ ions with two oxygen atoms originating from the carboxylate and phenol groups, as well as the nitrogen atom of the imine moiety in Schiff bases. Additionally, the interaction nature between L and Zebra fish DNA (zf-DNA) was found to be intercalation and minor groove based on both spectroscopic method and theoretical docking study. Furthermore, the main interaction force between L and DNA was hydrophobic with a spontaneous process based on the fluorescence spectra obtained at different temperatures, which can also be attributed to static quenching. Above all, this Schiff base could be used to detect Al3+ ions in the environmental system with high accuracy. This study is also expected to contribute to the understanding of the binding mode of this type of complex to DNA, proposing a novel approach for development and creation of versatile sensors, and investigating new anticancer agents, modifying or detecting bio-molecules.

Light field imaging technology for virtual reality content creation: A review

AbstractThe light field (LF) imaging technique can capture 3D scene information in 4D by recording both 2D intensity and 2D direction of incoming light rays. Due to this capability, LF has shown a great interest in virtual reality (VR) and augmented reality (AR) for enhanced immersion, improved depth perception and reconstruction of realistic 3D environments. This paper presents a comprehensive review of LF imaging technology and other approaches used for VR content creation. The applications of LF technology beyond VR and AR are also discussed. The challenges and limitations of other approaches for VR content creation are examined. State‐of‐the‐art research has focused on how VR experiences benefit from LF technology and identified the challenges to creating comfortable, immersive and realistic VR content such as (1) image size and resolution, (2) processing speed, (3) precise calibration and (4) depth reconstruction. Recommendations that can be considered for creating immersive VR content are provided to enhance user experience. These recommendations aim to contribute to developing more comfortable and realistic VR content, extending the potential applications of LF imaging technology in diverse fields.

Effect of synthetic CT on dose-derived toxicity predictors for MR-only prostate radiotherapy.

Toxicity-driven adaptive radiotherapy (RT) is enhanced by the superior soft tissue contrast of magnetic resonance (MR) imaging compared with conventional computed tomography (CT). However, in an MR-only RT pathway synthetic CTs (sCT) are required for dose calculation. This study evaluates 3 sCT approaches for accurate rectal toxicity prediction in prostate RT. Thirty-six patients had MR (T2-weighted acquisition optimized for anatomical delineation, and T1-Dixon) with same day standard-of-care planning CT for prostate RT. Multiple sCT were created per patient using bulk density (BD), tissue stratification (TS, from T1-Dixon) and deep-learning (DL) artificial intelligence (AI) (from T2-weighted) approaches for dose distribution calculation and creation of rectal dose volume histograms (DVH) and dose surface maps (DSM) to assess grade-2 (G2) rectal bleeding risk. Maximum absolute errors using sCT for DVH-based G2 rectal bleeding risk (risk range 1.6% to 6.1%) were 0.6% (BD), 0.3% (TS) and 0.1% (DL). DSM-derived risk prediction errors followed a similar pattern. DL sCT has voxel-wise density generated from T2-weighted MR and improved accuracy for both risk-prediction methods. DL improves dosimetric and predicted risk calculation accuracy. Both TS and DL methods are clinically suitable for sCT generation in toxicity-guided RT, however, DL offers increased accuracy and offers efficiencies by removing the need for T1-Dixon MR. This study demonstrates novel insights regarding the effect of sCT on predictive toxicity metrics, demonstrating clear accuracy improvement with increased sCT resolution. Accuracy of toxicity calculation in MR-only RT should be assessed for all treatment sites where dose to critical structures will guide adaptive-RT strategies. Patient data were taken from an ethically approved (UK Health Research Authority) clinical trial run at Guy's and St Thomas' NHS Foundation Trust. Study Name: MR-simulation in Radiotherapy for Prostate Cancer. ClinicalTrials.gov Identifier: NCT03238170.

Traversing the social media landscape: Digital health communications in 2024

Amidst a post-pandemic landscape marked by dis- and misinformation, new technologies, and platform shifts, health communications will need to be rightly balanced between convenience, quality, and trust to cut through the digital noise online. Navigating the dynamic social media terrain will require adherence to regulatory standards, while the adoption of new trends like artificial intelligence offers new approaches for content creation and audience insights. Medical communicators will need to harness the power of new technologies, employ content strategies tailored to audiences and platforms, and continue to measure social media metrics to stay ahead of the times and deliver effective and impactful campaigns.

Concept Paper for a Digital Expert: Systematic Derivation of (Causal) Bayesian Networks Based on Ontologies for Knowledge-Based Production Steps

Despite increasing digitalization and automation, complex production processes often require human judgment/decision-making adaptability. Humans can abstract and transfer knowledge to new situations. People in production are an irreplaceable resource. This paper presents a new concept for digitizing human expertise and their ability to make knowledge-based decisions in the production area based on ontologies and causal Bayesian networks for further research. Dedicated approaches for the ontology-based creation of Bayesian networks exist in the literature. Therefore, we first comprehensively analyze previous studies and summarize the approaches. We then add the causal perspective, which has often not been an explicit subject of consideration. We see a research gap in the systematic and structured approach to ontology-based generation of causal graphs (CGs). At the current state of knowledge, the semantic understanding of a domain formalized in an ontology can contribute to developing a generic approach to derive a CG. The ontology functions as a knowledge base by formally representing knowledge and experience. Causal inference calculations can mathematically imitate the human decision-making process under uncertainty. Therefore, a systematic ontology-based approach to building a CG can allow digitizing the human ability to make decisions based on experience and knowledge.

Metaverse: Value creation and performance measurement across physical and digital world

This study provides a first conceptualization of the Metaverse from a strategic management perspective and advances performance measurement approaches for the Metaverse value creation. Specifically, the study aims to provide an in-depth analysis of Metaverse and the related implications on the value creation processes of the companies. In order to achieve this goal, we performed a detailed literature review on digital transformation, Metaverse and performance measure-ment topics. Thus, we have developed a theoretical framework useful to facilitate the implementation of successful strategies in the Metaverse and subsequent mon-itoring and evaluation of achieved results. The paper provides relevant contributions in theory and practice. From a theoreti-cal perspective, the results contribute to the strategic management and perfor-mance management literature streams, extending them to the dimension of the "digital world". From a practical perspective, the results identify new performance measurement and new indicators useful for companies and practitioners to under-stand the link between Metaverse and value creation paths. Future studies should integrate the theoretical analysis with explorative empirical investigations.

From digital to quantum epidemiology: The Quantum Data Lake concept for big data related to viral infectious diseases

The development of quantum epidemiology represents the next anticipated phase in epidemiology transformation, driven by the emergence of new quantum technologies. Epidemiology is currently transitioning into the digital era and undergoing a paradigm shift from a data-driven to a value-driven strategy. Epidemiology data are characterized by uncertainty, multidimensionality, and disconnection, thereby correlating with the preferential quantum approach for data exposition, value creation, and modeling. Examples of such complex epidemiology data include the data on DNA viruses with associated symptoms and diseases. The Quantum Data Lake concept is proposed and consists of several layers and quantum tools, including Robson semantic triples, Quantum Universal Exchange Language, Hyperbolic Dirac Net, “quantum ribosome” structure, quantum random access memory, teleportation, Quantum Query Language, non-Hermitian gates, and tensor networks (e.g., matrix product state, projected entangled pair state, and multiscale entanglement renormalization ansatz [MERA]), alongside PT-symmetry properties. PT-symmetry can serve as an intuitive modeling tool, and PT-symmetry breaking can detect the hidden shift in the information that is permanently updated in the Data Lake. The computational output is presented as PT-symmetry gain/loss equilibrium breaking in the form of a complex number, i.e., two possible variants of epidemic modeling. For MERA, non-Hermiticity with spontaneous PT-symmetry breaking can theoretically appear as a violation of the entanglement monotonicity and local entanglement gain, leading to a non-reversible character of the coarse-graining transformation. The duality of PT-symmetry equilibrium breaking can be compared to, for example, the estimation of the best and worst scenarios simultaneously, or the gain of entanglement can display a significant correlation between some studied parameters embedded into the data. The fundamental difference between digital and quantum epidemiology is the implementation of quantum logic and reliance on a quantum theory.

Elevating low-effort engagement to cultivate stronger community commitment: A study of social capital within Instagram's brand community

Purpose: This study aimed to analyze the role of social capital in enhancing the engagement of community members, specifically transitioning them from low-effort participation to higher levels of commitment toward the community.Design/methodology: This study presents the results derived from a quantitative analysis of 483 participants across diverse automotive brand communities found on Instagram.Findings: Based on the foundation of social capital theory, this research enquires into the effects of shared relational elements of social capital on the commitment of members to communities. The results indicate that when members perceive an adequate degree of social trust and adhere to a recognized norm of reciprocity, there is a notable influence on the potential for heightened commitment to the community.Research limitations/implications: This study solely focused on a specific type of social media, underscoring the prominent role of visual presentation style as the primary approach in content creation. As a result, this has a notable impact on the mechanism of social interaction among users.Originality/value: In visually oriented settings such as Instagram, the barriers to participating in social interactions are noticeably reduced. Consequently, activities that require only minimal engagement effort have a higher probability of evolving into more profound and intensified forms of social exchange among community members. This, in turn, greatly facilitates the cultivation of social trust and the establishment of a norm of reciprocity.

A Novel AI Approach for the Systematic Creation of Empathy Maps

The utilization of Empathy Maps in User Experience and Design Thinking is becoming commonplace. They can be seen as a valuable tool to help developers better understand the user’s requirements. During the interaction design, it is essential to have a thorough knowledge of what the user wants, which might be difficult to gather or portray to others. In this sense, Empathy Maps visually represent the user’s information, attitude, and behavior. Nevertheless, creating Empathy Maps can become tedious, as this usually requires the involvement of a group of people with expert knowledge, deeply analyzing the information elicited from the user. This is the reason why most Empathy Maps are created manually. A first literature analysis reports that there are still no specific tools to generate Empathy Maps automatically, although there are technologies to take on this challenge. In this paper, we present a tool for automatically generating an initial version of an Empathy Map that can help speed up the capture of explicit knowledge, and then it can be further worked on by the development team. The tool creates the Empathy Map from an audio interview, featuring audio transcription technology and natural language processing. More specifically, this paper includes the architecture, a proof of concept, and user testing, reporting numerical evidence and promising results to corroborate the proposal’s feasibility, reaching an overall accuracy of 86% at classification.

International survey of radiocephalic arteriovenous fistula: ISRAF survey.

The objective of this survey was to encompass the full scope of international practice, entailing all technical, non-technical, preoperative stratification and functional maturation (FM) of RCAVF. The survey contained n = 19 questionnaires with n = 46 variables completed by n = 85 providers from n = 21 nations across n = 5 continents. The numerical values were subjected to mean with standard error whereas the nominal data to a non-parametric (Kruskal-Wallis & Spearman correlation test) and analysis of variance (ANOVA). The test of homogeneity, & probability was reported 95% confidence intervals (CI) alongside error plots. Furthermore, a decision and higher attribute tree model was constructed based on current survey for higher FM in RCAVF. FM is independently associated with volume of surgeon per year (procedures performed) (p < 0.01) [High Volume: 73% (95% CI, 68-77%) versus Average volume: 63% (95% CI, 59-66%) vs Low volume: 56% (95% CI, 51-61%)]. FM increased by 8% with every 20 more procedures per group of surgeons on end point of FM. Amongst continents: Australia, America, Asia and South America demonstrated higher FM to Africa & Europe (p < 0.05). UK possessed a lower FM 58% (95% CI, 48-68%) in comparison to the world & Europe respectively [65% (95% CI, 61-70%) vs 61% (95% CI, 58--65%)]. There was a positive causal link between angle of anastomosis at 30-76° (p < 0.01), longitudinal & S-shaped incision & arteriotomy length of 3 & 4 mm to higher FM (p < 0.05). FM in RCAVF is independently & incrementally associated with the volume of surgeon per year. There is a diverse inclusion, exclusion and technical approach in RCAVF creation. This survey advocates the importance of international collaboration and/or registry in assimilation, consolidate and development of consensus.

Decentralized and collaborative machine learning framework for IoT

Decentralized machine learning has recently been proposed as a potential solution to the security issues of the canonical federated learning approach. In this paper, we propose a decentralized and collaborative machine learning framework specially oriented to resource-constrained devices, usual in IoT deployments. With this aim we propose the following construction blocks. First, an incremental learning algorithm based on prototypes that was specifically implemented to work in low-performance computing elements. Second, two random-based protocols to exchange the local models among the computing elements in the network. Finally, two algorithmics approaches for prediction and prototype creation. This proposal was compared to a typical centralized incremental learning approach in terms of accuracy, training time and robustness with very promising results.

디자인씽킹 수업에서 메타버스(제페토, 이프랜드)와 ChatGPT 활용 방안

This study aims to investigate the utilization of Meta-verse and ChatGPT in Design Thinking classes. The importance of creative problem-solving skills is increasing, and there is a growing interest in the educational application of Metaverse and ChatGPT. The significance of this research lies in maximizing creativity and expression through the practical implementation of the educational advantages of Metaverse and ChatGPT within the stages of the Design Thinking process, aiding in creative problem-solving, logical thinking, emotional judgment, and integrative thinking. Within the Design Thinking process (empathy, define, ideate, prototype, test), this research suggests specific actions: using ChatGPT for brainstorming team-building activities in the empathy stage and employing Metaverse avatar customization for persona creation; utilizing ChatGPT to illustrate problem statements in the problem definition stage; leveraging Metaverse spaces as meeting venues and using ChatGPT for scriptwriting for role-playing scenarios in the ideation stage; and presenting approaches for role-playing scenario creation using the Metaverse platforms ZEPETO and Ifland in the prototype stage. Based on these results, this research provides assistance in vividly expressing learners' ideas through the combined effect of Metaverse and ChatGPT within Design Thinking, offering value in proposing active learning activities and new educational methodologies.

On Finetuning Large Language Models

Abstract A recent paper by Häffner et al. (2023, Political Analysis 31, 481–499) introduces an interpretable deep learning approach for domain-specific dictionary creation, where it is claimed that the dictionary-based approach outperforms finetuned language models in predictive accuracy while retaining interpretability. We show that the dictionary-based approach’s reported superiority over large language models, BERT specifically, is due to the fact that most of the parameters in the language models are excluded from finetuning. In this letter, we first discuss the architecture of BERT models, then explain the limitations of finetuning only the top classification layer, and lastly we report results where finetuned language models outperform the newly proposed dictionary-based approach by 27% in terms of $R^2$ and 46% in terms of mean squared error once we allow these parameters to learn during finetuning. Researchers interested in large language models, text classification, and text regression should find our results useful. Our code and data are publicly available.

Size-matching compositing nanoprobe of AIE-type gold nanocluster supramolecular nanogels wrapped by hypergravity-tailored MnO2 nanosheets for cellular glutathione detection

Compositing has been the main approach for material creation via wisely combining material components with different properties. MnO2 nanosheets (MNSs) with thin 2 D morphology are usually applied to composite molecules or nanomaterials for biosensing and bioimaging applications. However, such composition is actually structurally unmatched, albeit performance matching. Here, a series of benefits merely on the basis of structural match have been unearthed via tailoring MNSs with four sizes by synthesis under controllable hypergravity field. The classical fluorophore-quencher couple was utilized as the subject model, where the soft supramolecular nanogels based on aggregation-induced emission (AIE)-active gold nanoclusters were wrapped by MNSs of strong absorption. By comparative study of one-on-one wrapping and one-to-many encapsulation with geometrical selection of different MNSs, we found that the one-on-one wrapping model protected weakly-bonded nanogels from combination-induced distortion and strengthened nanogel networks via endowing exoskeleton. Besides, wrapping pattern and size-match significantly enhanced the quenching efficiency of MNSs towards the emissive nanogels. More importantly, the well-wrapped nanocomposites had considerable enhanced biological compatibility with much lower cytotoxicity and higher transfection capacity than the untailored MNSs composite and could serve as cellular glutathione detection.

A Review of User Identification Methods Based on Digital Fingerprint

Methods of user identification based on digital fingerprints are considered. The main approaches for the browser fingerprints creation which is installed on the user's device and characterizes the device belonging to the user are presented. The methods used to identify a person (user) during the operation of the device are also described. Methods using both the dynamics of keystrokes and interactions with the touch screen, voice and geolocation data, as well as behavioral biometrics and behavioral profile are presented. The concept of continuous authentication is described as a development of the identification approach. A list of publicly available data sets mentioned in the studies reviewed in the review is provided, with links to download them.

Modified Approach for Extraperitoneal Colostomy Creation in Laparoscopic Abdominoperineal Resection.

Parastomal hernia is a major long-term complication after abdominoperineal resection. Extraperitoneal colostomy has been proposed as an effective step for parastomal hernia prevention, but it has not been widely used as it is technically demanding and time-consuming. We proposed a modified approach for extraperitoneal colostomy creation by entering the extraperitoneal space through the arcuate line of the posterior rectus sheath. To evaluate the safety, difficulty, and efficacy of long-term parastomal hernia prevention of the modified approach for extraperitoneal colostomy creation compared with the conventional transperitoneal colostomy approach. This was a retrospective evaluation of a surgical and video database. This was a single-institution retrospective study. Clinical data of 74 patients who underwent laparoscopic abdominoperineal resection surgery from January 2019 to January 2020 in the Department of General Surgery, Qilu Hospital of Shandong University, were retrospectively reviewed. Baseline characteristics, time required for colostomy creation (from skin incision to colostomy maturation), perioperative complications, and long-term colostomy-related complications were compared. Baseline characteristics did not differ between the 2 approaches. The BMI level ranged from 19.5 to 29.4 for patients undergoing extraperitoneal approach. Time required for colostomy creation median [interquartile range], (22 [21-25] minutes for extraperitoneal vs 23 [21-25] minutes for transperitoneal, p = 0.861) were comparable between the 2 approaches. The cumulative incidence of parastomal hernia was significantly greater with transperitoneal colostomy than extraperitoneal colostomy at 2 and 3 years postoperatively (16.2% vs 0%, p = 0.025, and 21.6% vs 0%, p = 0.005). The remaining perioperative complications and long-term colostomy-related complications did not differ between the 2 approaches. This study is limited by its retrospective design and small sample size. The modified approach for extraperitoneal colostomy creation is safe, technically simple, and effective for long-term parastomal hernia prevention in patients with a BMI of 19.5 to 29.4.