Digital innovations have become increasingly widespread in almost every industry, and Australia’s energy sector has not been immune to this. It therefore follows that the coordination of energy exports like liquified natural gas (LNG) across government, industry, and community stakeholders will be largely dependent on the ability of these constituents to share digital information between and among each other in useful timeframes. However, the success of these integrated data systems will hinge on the ability of the energy sector’s workforce to use these technologies effectively, but it is increasingly coming into focus that the industry’s workers have very uneven levels of proficiency with digital technologies. By synthesizing earlier digital skills frameworks from other industries and adding qualitative evidence distilled from 42 interviews with a broad range of employees throughout a midsize Australian LNG company, this paper puts forward a digital skills framework for the energy sector which identifies five skill areas that will be required in 2030: (1) data analytics, (2) digital communication and collaboration, (3) digital security, (4) digital ethical and social responsibility, and (5) digital innovation. The required level of proficiency that will be required in 2030 for each of these skill areas is then assessed for different discipline groups throughout the industry. We conclude by explaining how the presented framework can be used as an assessment tool for identifying skill gaps throughout the energy sector, and exploring the policy-related implications of the findings.
Signaling theory explains how organizations convey credible information under conditions of information asymmetry. Yet, while recent work highlights that signals often interact—reinforcing or substituting—most studies have treated signals as independent and additive, and empirical evidence of configurational interaction patterns remains limited. This study addresses this limitation by examining how multiple institutional signals jointly shape evaluators’ judgments in peer-reviewed university research funding. Using fuzzy-set qualitative comparative analysis (fsQCA), we identify distinct configurations of capability, reputation, and status signals that lead to both high and low funding success. The findings reveal that no single signal ensures success; rather, outcomes emerge from specific portfolios of signals whose effects depend on their combinations. We extend signaling theory by demonstrating that signals can also suppress or neutralize one another, and by introducing a configurational perspective that captures synergy, substitution, and suppression within signal portfolios.
Fungal nail infections (onychomycosis) remain challenging to treat due to prolonged therapy, poor cure rates, and safety concerns associated with oral antifungal agents. Although topical formulations are the preferred treatment option, their clinical effectiveness is constrained by the dense keratinized nail barrier. While formulation-driven strategies and device-based therapies have expanded the therapeutic approaches for onychomycosis, their clinical efficacy remains suboptimal, pointing towards the need for pre-clinical evaluation of their performance using more clinically relevant models during the development stage. Nail-integrated model systems provide a platform for evaluating the efficacy of novel antifungal topical treatments in a controlled disease-mimicking environment, thereby enhancing the likelihood of clinical success. This review critically evaluates in vitro and ex vivo models, including keratin-supplemented media, keratin biomembranes, animal hooves, and human nails, focusing on their physiological relevance, advantages and inherent limitations. Ex vivo experimental model designs reported in recent studies are discussed to spotlight their application in evaluating topical formulations and device-based interventions. Ex vivo human nail models, when rationally designed to reflect the structural and pathological architecture of onychomycosis, offer robust, clinically predictive platforms for formulation and device optimization, dose and treatment frequency and comparative efficacy assessment. Infected animal hooves serve as a surrogate for human nails and provide reproducible experimental models for screening the antifungal efficacy under standardized conditions. The available ex vivo data support the utility of these models in predicting clinical performance of antifungal formulations and devices during pre-clinical development; however, broader industrial adoption will require standardized and harmonized protocols, as well as further clinical validation.
We reported Ir(III) complexes featuring a dicarbene pincer chelate, a bidentate carbene cyclometalate (with either N-benzyl or N-mesityl appendage) and a halide (X− = Cl−, Br− and I−), for probing the structure-property relationship. They exhibit blue emission with the peakmax spanning 465–480 nm in toluene at RT. Particularly, [Ir(DP)(MC5H)Cl] and [Ir(DP)(MS1)Cl] showed the best performance among these Ir(III) emitters due to the high MLCT and LC components and relatively diminished XLCT contribution. This is evidenced by high photoluminescent quantum yield (PLQY) of 60
Visual artificial intelligence (AI) is beginning to enter entrepreneurship research but adoption remains scarce, even as recent advances in vision-language models and AI-assisted coding are substantially lowering the technical barriers to entry. Combined with the abundance of visual data now available, visual AI opens new opportunities to study questions previously out of reach, while also raising methodological and ethical challenges. This paper provides a structured roadmap for using visual AI in entrepreneurship research: We map visual data sources, outline visual AI approaches, identify research insights, and propose an ethics audit. Ultimately, we illustrate the application of this visual AI pipeline with an example study: a facial image classification model distinguished between entrepreneur and non-entrepreneur portrait images in a platform-specific dataset with 79.5
