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Modulation of nociceptive ion channels by protease-activated receptor-2 in inflammatory pain : molecular mechanisms and therapeutic potential
Get PDFProtease-activated receptor 2 (PAR2) is a G protein-coupled receptor (GPCR) expressed in both the peripheral and central nervous systems. It plays a pivotal role in mediating neuroimmune interactions, particularly in the context of inflammation and pain. Upon activation by proteases, PAR2 modulates nociception through signaling cascades that influence key ion channels, including transient receptor potential (TRP) ion channels vanilloid 1 and 4 (TRPV1 and TRPV4), ankyrin 1 (TRPA1), acid-sensing ion channel 3 (ASIC3), P2X purinoceptor 3 (P2X3), Cav3.2 (T-type Ca2+ channel), and potassium Kv7 (M-current) channels, altering their expression and function. Through this crosstalk, PAR2 contributes to heightened neuronal excitability and pain hypersensitivity in various inflammatory conditions. In this narrative review, we highlight and discuss the mechanistic and functional interplay between PAR2 and nociceptive ion channels, which might be contributing to the pathogenesis of inflammatory pain. Targeting these specific molecular interactions between PAR2 and nociceptive ion channels may offer a promising therapeutic strategy for treating inflammatory pain
Standard photonic (quantum) thermometry in the UK
Get PDFTwo emerging developments in photon based (quantum) thermometry are described, namely, active ring-resonator thermometry (ARRT) and small-scale practical Doppler broadening thermometry (pDBT). These developments are sensing methodologies that are frequency based and directly linked to the physics of the measurement approach. Through relying on the physics instead of external calibration to provide reliable thermometry direct traceability to the kelvin is obtained. The medium-term objective of this work is to develop practical primary thermometers that need no external calibration, having modest uncertainties (0.1–1 K) but able to provide reliable thermodynamic temperatures, during the lifetime of the required measurements. The long-term objective is to develop these (and possibly other) photon-based practical primary temperature measurement methods that can be widely deployed, supplanting conventional techniques but providing reliable permanent traceability to the kelvin in the measurement setting
A new bond specific energy-based failure criterion for peridynamics
Get PDFOne of the most important characteristics of peridynamics is its capability of predicting how cracks initiate and propagate in materials and structures. Definition of failure in peridynamics is different than classical approaches due to non-local feature of peridynamics. In this study, a new failure criterion, named as bond specific energy-based failure criterion, is introduced. Moreover, an in-depth investigation of different failure criteria used in peridynamic framework is presented. Three different failure criteria are considered including the widely used critical stretch failure criterion, critical energy failure criterion, and the newly introduced bond specific energy-based failure criterion. Numerical results demonstrated that new bond specific energy-based failure criterion shows better performance in capturing expected bond breakage with respect to other approaches. In addition, it can also accurately predict failure load for different crack lengths during crack propagation
Topology change aware distributed state estimation based on unsupervised bipartite graph-enabled causality-inspired sparse learning
Get PDFTopology changes in a distribution network are common due to planned reconfigurations and unintentional switching events during practical operations. Topology changes make it challenging for existing optimization- and learning-based distributed system state estimation methods to maintain accuracy. This difficulty arises from the lack of accurate structural information for the new topology and the absence of labeled data (recorded state variables) for model retraining. To this end, this article proposes an unsupervised-on-target learning-based state estimation method for the distribution network after topology changes without relying on the topology information and labeled data. In particular, a bipartite graph learning (BGL) method with rank constraints is first designed to learn the representation of each topology with a restricted set of measurements. Then, the Euclidean distance is employed to select the best-matched source domain historical topology according to the representation learned by the BGL. To extract invariant causal structures across the two topologies, a causality-inspired sparse structure learning for domain adaptation network is further designed. It relaxes the correlations between the selected historical and new topologies into an associative structure, represented by attention scores derived from the proposed inter- and intravariable attention networks. This allows the leverage of the causality to enhance the state estimation performance of the distribution network after topology changes without relying on accurate topology information and recorded labels used for training. The comparison results on two standard IEEE test systems validate the efficacy of the proposed method
Designing, managing and sustaining coopetition : a review of past achievements and future directions
Get PDFThis study integrates insights from a large-scale literature review using Latent Dirichlet Allocation (LDA) topic modelling and bibliometric analysis to analyse 2,104 articles, identifying latent themes across three decades of coopetition research (1996-2024). The analysis reveals 16 distinct topics that cluster into three overarching themes: designing coopetition (context and motives), managing coopetition (strategy and governance), and sustaining coopetition (relationships, trust, and tension management). Our findings show a clear shift in the coopetition literature from dyadic, trust-centric perspectives toward multi-actor, ecosystem-level configurations shaped by digitalisation, institutional mechanisms, and adaptive governance. Importantly, we demonstrate that trust and tensions are neither monolithic nor oppositional but co-evolving mechanisms whose roles vary across organisational, supply-chain, and ecosystem contexts. Rather than viewing tensions as dysfunctions to be resolved, the review highlights their potential as productive inputs for learning, capability development, and adaptability. The study contributes by reframing sustainable coopetition as a dynamic capability grounded in continuous recalibration of cooperation–competition balances. Methodologically, it advances coopetition research by offering a replicable, longitudinal topic-modelling approach that disambiguates overlapping constructs and generates a focused agenda for future research. Collectively, the findings offer an integrated framework that provides a nuanced and multi-level understanding of how to design, manage, and sustain coopetition through context setting, strategic governance, and long-term capability building
Generating stable and metastable critical points in uncertain systems via flow‐based models
Get PDFThis work proposes the use of conditional flow‐based generative models to learn an approximation of the distribution of the critical points of a cost function. This approximation is used to incrementally identify all critical points, in the feasible domain of said function, by iteratively alternating the sampling of the distribution and the retraining of the model with the newly discovered points. This paper will focus, in particular, on the identification and conditional generation of all local minima in the case in which the value of the cost function is subject to some uncertain parameters. The target application is the study of complex dynamical systems. It will be shown that when the cost function represents the potential of a dynamical system, the proposed flow‐based model can be used to generate minima conditional to their degree of stability or metastability. In dynamical systems subject to uncertainty in the dynamics, the existence of the minima and their stability characteristics are a function of the uncertain parameters. Thus, the proposed model architecture incorporates a conditional variable that can be the value of the uncertain parameters or a label indicating a characteristic of the critical points. The proposed conditional flow‐model allows the generation of points with the desired characteristics. This is of extreme importance in the analysis of equilibrium states and possible transitions, controlled or uncontrolled, to other equilibrium states. Some illustrative examples of functions with hundreds of local minima are used to test the potentialities of the proposed approach. It will be shown that the use of a generative approach is advantageous to explore more complex landscapes compared to a basic random local search algorithm. When applied to the analysis of the uncertain five body problem, the proposed generative model is shown to successfully identify all dynamical equilibrium solutions under uncertainty. Finally when trained on the dynamical stability properties of the critical points, the model can successfully differentiate between stable and metastable solutions. These results show that, for certain types of system, the flow‐based model can be trained to find equilibrium points more efficiently than a simple random search. Moreover, we demonstrate that conditional flow‐based models are capable of one‐shot sampling for specific values of uncertain parameters or characteristics of the equilibrium points
Parental attitudes and digital parenting in the early years : development and validation of the PADTS scale
Get PDFBackground This paper reports on the development and validation of the 15-item Parental Attitudes to Digital Technology Scale (PADTS), a brief, psychometrically validated measure assessing parents' beliefs confidence, and concerns about their very young children's use of digital technologies. Method Developed as part of the UK-wide Toddlers, Tech and Talk (TTT) study, PADTS addresses a gap in existing research by focusing on children from birth to 3 years, a stage often overlooked in digital parenting literature. Co-developed with parents and early years experts, the scale was tested with a nationally balanced UK sample (N = 934). Results Exploratory and confirmatory factor analyses supported a four-factor structure: perceived risks, perceived learning benefits, parental confidence and technology-related anxiety. The PADTS showed strong model fit and measurement invariance across parent gender, ethnicity and region, with some variation by child age. Correlational analyses indicated that benefits, perceptions and confidence were associated with supportive digital parenting, while anxiety was more weakly linked. Conclusion PADTS shows potential as a practical tool for researchers, practitioners and policy-makers and may support a more nuanced understanding of how parental attitudes shape early digital experiences
Modifiers regulate crystal morphology by generating lattice defects
Get PDFCrystal morphology plays a pivotal role in all applications because it affects the performance and processing behaviors of crystals. Foreign compounds are often employed to control the crystal morphology; conversely, morphology variations are sometimes attributed to uncontrolled impurities present in the crystallizing solution. Here, we explore the impact of modifiers on the crystallization and morphology of mefenamic acid (MFA), a nonsteroidal anti-inflammatory drug. We focus on the roles of benzoic acid, 2-chlorobenzoic acid, and 2,3-dimethylaniline, compounds that may be retained as minor components after MFA synthesis. We explored the effects of these compounds at varying MFA supersaturation levels. Whereas the additives did not alter the polymorphic form of MFA, they significantly modified crystal morphology, leading to elongated and blade-like crystals. In contrast to numerous published cases, these compounds do not affect the growth rates of the dominant crystal faces. Instead, we show that the observed morphology changes are due to modifier-driven crystal twinning that may be initiated by disruptions of the crystal nucleation. These findings highlight the importance of considering nonclassical nucleation , wherein impurities and modifiers may influence crystallization through pathways beyond direct surface adsorption such as cluster formation and lattice disruption. This study provides critical insights into the role of foreign compounds in crystal engineering, emphasizing the need for an integrated outlook on their effects on crystal nucleation and growth to optimize crystallization strategies and enhance drug performance
Enhancing inclusion through a service infrastructure of kindness
No full textWe introduce the concept of a service infrastructure of kindness, defined as the underlying features of an organisation that embed kindness into service delivery. Our findings identify four features of a service infrastructure of kindness: culture, materialities, socialities, and imaginaries. Drawing on in-depth research with a non-profit organisation, our theorisation demonstrates the cumulative and exponential power of seemingly minor aspects of the service encounter that have significant value for supporting inclusion. Our contributions reveal kindness as an animating force underpinning service inclusion, the tensions that can emerge in implementing a service infrastructure of kindness, and the micropolitical consequences of kindness that can be re-humanising for excluded consumers
Human-robot collaboration in healthcare : a comprehensive review of key components, applications, and future research
Get PDFHuman-Robot Collaboration (HRC) is an emerging paradigm in healthcare that leverages robotic systems to improve patient care, assist medical professionals, and optimize clinical workflows. As healthcare demands increase due to aging populations and resource limitations, HRC offers a promising solution by combining robotic precision with caregivers’ adaptability. This review provides a comprehensive analysis of HRC in healthcare, categorizing its key influencing factors into three components: (1) Healthcare Professional-Oriented, focusing on task allocation, communication, teamwork, and trust; (2) Patient-Centric, emphasizing patient safety, acceptability, interaction, and feedback; and (3) System-Critical, addressing system autonomy, adaptability, integration, and safety in medical environments. The review explores recent advancements in enabling technologies, including sensor developments, immersive interfaces, digital twin modeling, and artificial intelligence (AI), which drive more efficient and adaptive HRC. Despite these innovations, challenges such as ethical concerns, interoperability, and cost-related barriers remain obstacles to widespread implementation. Future research should focus on developing robust ethical frameworks, enhancing safety and reliability, improving interoperability, and fostering patient and caregiver acceptance through interdisciplinary collaboration
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