Search Results (94)

Background: Cefiderocol (FDC) presents challenges in antimicrobial susceptibility testing (AST). The reference standard is the broth microdilution (BMD) method with iron-depleted cation-adjusted Mueller-Hinton broth (ID-CAMHB). Still, it is cumbersome for routine clinical laboratory use, while variable accuracy has been reported with available commercial systems. Variability in interpretive criteria and areas of technical uncertainty (ATUs) further complicate assessments. Methods: This review and expert opinion presents: (1) an overview of non-susceptibility to FDC and then delves into the performance of current FDC AST methods for Enterobacterales, Pseudomonas aeruginosa, and Acinetobacter baumannii complex; (2) a practical decision framework to guide clinical microbiologists in making informed choices. Results and Conclusions: For Enterobacterales, including carbapenem-resistant Enterobacterales (CRE), and Pseudomonas aeruginosa, we propose disk diffusion (DD) as a preliminary screening tool to classify isolates as susceptible (S) or resistant (R). Confirmatory testing using the UMIC^® FDC system or the ID-CAMHB BMD method is recommended for R isolates. In cases of discrepancy, repeating the test with ID-CAMHB BMD is advised. Additionally, isolates falling within the ATU during DD testing should be retested using the UMIC^® system or ID-CAMHB BMD. For A. baumannii complex, since EUCAST breakpoints have not been defined yet, we propose a stepwise framework based on the first DD result: isolates with inhibition zones < 17 mm are considered non-susceptible and should be confirmed with standard BMD. Those between 17 and 22 mm require retesting with a commercial BMD method, with further confirmation recommended if S isolates with zones ≥ 23 mm may be considered S without additional testing. Full article

This work proposes a hybrid model that combines the Galam model of opinion dynamics with the Bass diffusion model used in technology adoption on Barabasi–Albert complex networks. The main idea is to advance a version of the Bass model that can suitably describe an opinion formation context while introducing irreversible transitions from group B (opponents) to group A (supporters). Moreover, we extend the model to take into account the presence of a charismatic competitor, which fosters conversion back to the old technology. The approach is different from the introduction of a mean field due to the interactions driven by the network structure. Additionally, we introduce the Kolmogorov–Sinai entropy to quantify the system’s unpredictability and information loss over time. The results show an increase in the regularity of the trajectories as the preferential attachment parameter increases. Full article

Scholars and journalists typically trace the diffusion of breed-specific legislation (BSL) in the U.S. to the surge in negative media portraits of pit bull-type dogs (PBTDs) during the late twentieth century. Yet, while news coverage still portrays these dogs unfavorably, we document a sharp rise in countervailing sources of “pit bull positivity” over the past two decades. Drawing on insights from the respective social science research on changes in attitudes and public policy, we argue that this influx of positivity should powerfully impact opinions and policies towards PBTDs. Our data and analyses consistently support that argument. We analyze two different series of repeated cross-sectional surveys to show that public support for “pit bulls” grew considerably from 2014 to 2024. We also show that voters’ support for ballot measures overturning local “pit bull bans” increased substantially during that same ten-year period. Finally, our analysis of the frames and narratives deployed in recent state and local policy debates shows how this growing pit bull positivity has helped overturn over 300 discriminatory laws against these dogs since 2012. We conclude with a discussion of how shifts in portraits and opinions of PBTDs will likely continue eroding breed-specific legislation going forward. Full article

In recent years, deep learning-based end-to-end Text-To-Speech (TTS) models have made significant progress in enhancing speech naturalness and fluency. However, existing Variational Inference Text-to-Speech (VITS) models still face challenges such as insufficient pitch modeling, inadequate contextual dependency capture, and low inference efficiency in the decoder. To address these issues, this paper proposes an improved TTS framework named Q-VITS. Q-VITS incorporates Rotary Position Embedding (RoPE) into the text encoder to enhance long-sequence modeling, adopts a frame-level prior modeling strategy to optimize one-to-many mappings, and designs a style extractor based on a diffusion model for controllable style rendering. Additionally, the proposed decoder ConfoGAN integrates explicit F0 modeling, Pseudo-Quadrature Mirror Filter (PQMF) multi-band synthesis and Conformer structure. The experimental results demonstrate that Q-VITS outperforms the VITS in terms of speech quality, pitch accuracy, and inference efficiency in both subjective Mean Opinion Score (MOS) and objective Mel-Cepstral Distortion (MCD) and Root Mean Square Error (RMSE) evaluations on a single-speaker dataset, achieving performance close to ground-truth audio. These improvements provide an effective solution for efficient and controllable speech synthesis. Full article

Faced with mounting pressure to achieve high-quality green transformation, manufacturing enterprises are increasingly scrutinized for greenwashing behaviors. This study develops a novel hybrid modeling framework that combines evolutionary game theory with the SEIR epidemic model to investigate the dynamic interactions between environmental regulation, media pressure, and green innovation behavior. The model captures how strategic decisions among boundedly rational actors evolve over time under dual external pressures. Simulation results show that stronger environmental regulatory intensity accelerates the adoption of substantive green innovation and concurrently reduces the media pressure associated with greenwashing. Moreover, while social media disclosure has a limited impact during the early stages of greenwashing information diffusion, its influence becomes significantly amplified once a critical dissemination threshold is surpassed, rapidly transforming latent information into widespread public concern. This amplification triggers significant public opinion pressure, which, in turn, incentivizes local governments to enforce stricter environmental policies. The findings reveal a synergistic governance mechanism where environmental regulation and media scrutiny jointly curb greenwashing and foster genuine corporate sustainability. Full article

This paper develops a novel probabilistic theory of belief formation in social networks, departing from classical opinion dynamics models in both interpretation and structure. Rather than treating agent states as abstract scalar opinions, we model them as belief-adoption probabilities with clear decision-theoretic meaning. Our approach replaces iterative update rules with a fixed-point formulation that reflects rapid local convergence within social neighborhoods, followed by slower global diffusion. We derive a matrix logistic equation describing uncorrelated belief propagation and analyze its solutions in terms of mean learning time (MLT), enabling us to distinguish between fast local consensus and structurally delayed global agreement. In contrast to memory-driven models, where convergence is slow and unbounded, uncorrelated influence produces finite, quantifiable belief shifts. Our results yield closed-form theorems on propaganda efficiency, saturation depth in hierarchical trees, and structural limits of ideological manipulation. By combining probabilistic semantics, nonlinear dynamics, and network topology, this framework provides a rigorous and expressive model for understanding belief diffusion, opinion cascades, and the temporal structure of social conformity under modern influence regimes. Full article

Algorithmic recommendation mechanisms of social media platforms, viral diffusion of user-generated content (UGC), and real-time public opinion pressures are fundamentally deconstructing the traditional corporate crisis response paradigm that used to rely on one-way statements and delayed reactions. This compels enterprises to elevate their crisis response standards and construct new response frameworks. Based on an empirical analysis of 3,135,675 social media dissemination data points from 94 corporate crisis incidents, this study explores effective crisis response patterns for enterprises through three dimensions: response timing, methods, and content. The key findings indicate that traditional crisis response timelines prove inadequate for social media scenarios, whereas intervention during the ascending phase of dissemination significantly curtails crisis propagation cycles. Beyond formal statements, informal responses demonstrate equivalent mitigation effects, with combined formal–informal approaches yielding optimal outcomes. The comparative analysis of four content strategies (downplaying, supporting, denying, and reframing) reveals differentiated impacts on dissemination volume and duration, highlighting an inherent trade-off between these parameters. This research contributes to the crisis management theory in social media contexts while providing actionable guidance for enterprises to establish systematic crisis response methodologies. The results emphasize temporal sensitivity in response deployment, strategic content formulation, and multimodal communication integration. Full article

This paper addresses public acceptance of a proposed sub-regional, hydrogen–electric, aviation service reporting initial empirical evidence from the UK HEART project. The objective was to assess public acceptance of a wide range of service features, including hydrogen power, electric motors, and pilot assistance automation, in the context of an ongoing realisable commercial plan. Both qualitative and quantitative data collection instruments were leveraged, including focus groups and stakeholder interviews, as well as the questionnaire-based Scottish National survey, coupled with the advanced discrete-choice modelling of the data. The results from each method are presented, compared, and contrasted, focusing on the strength, reliability, and validity of the data to generate insights into public acceptance. The findings suggest that public concerns were tempered by an incomplete understanding of the technology but were interpretable in terms of key service elements. Respondents’ concerns and opinions centred around hydrogen as a fuel, single-pilot automation, safety and security, disability and inclusion, environmental impact, and the perceived usefulness of novel service features such as terminal design, automation, and sustainability. The latter findings were interpreted under a joint framework of technology acceptance theory and the diffusion of innovation. From this, we drew key insights, which were presented alongside a discussion of the results. Full article

In an era where social media shapes public opinion, understanding information spreading is key to grasping its broader impact. This paper explores the intricacies of information diffusion on Twitter, emphasizing the significant influence of content saturation on user engagement and retweet behaviors. We introduce a diffusion model that quantifies the likelihood of retweeting relative to the number of accounts a user follows. Our findings reveal a significant negative correlation where users following many accounts are less likely to retweet, suggesting a saturation effect in which exposure to information overload reduces engagement. We validate our model through simulations, demonstrating its ability to replicate real-world retweet network characteristics, including diffusion size and structural properties. Additionally, we explore this saturation effect on the temporal behavior of retweets, revealing that retweet intervals follow a stretched exponential distribution, which better captures the gradual decline in engagement over time. Our results underscore the competitive nature of information diffusion in social networks, where tweets have short lifespans and are quickly replaced by new information. This study contributes to a deeper understanding of content propagation mechanisms, offering a model with broad applicability across contexts, and highlights the importance of information overload in structural and temporal social media dynamics. Full article

Digital transformation (DT) has become an important engine for the development of the digital economy and an important means of reshaping corporate culture, business processes, management models, and so on. Different social communities at different levels have different needs and understandings of digital transformation. Therefore, this paper proposes to explore the communication themes of digital transformation on social media. This study’s main objective is to uncover underlying thematic structures and core ideas from large amounts of textual data in different social media communities to better understand the significance of the communication themes. This paper also aims to reveal the characteristics of diffusion patterns of DT themes by opinion-themed mining. This study uses text mining and social network analysis methods to mine DT themes, theme structure, and the statistical characteristics of hot words across various online communities. The main findings of this study are as follows. The Huawei forum discusses the technological drivers of the digital economy from a micro level. Sohu News explores business operation strategies at a macro level. The Zhihu forum discusses the elements of digital development at the micro level. Moreover, the hot words’ degree centrality and betweenness centrality across various online communities exhibited a power law distribution. In conclusion, this research paper studies and analyzes DT themes of different social media platforms to discover the opinions and attitudes of various social groups in the digital transformation era and deeply interprets social trends and public opinions in order to provide valuable decision-making theoretical support for managers, enterprises, and governments. Full article

This opinion piece postulates that quantitative environmental research and public health spatial analysts unknowingly tolerate certain spatial statistical model specification errors, whose remedies constitute some of the urgent emerging trends and issues in this subfield (e.g., forecasting disease spreading). Within this context, this paper addresses misspecifications affiliated with omitted variable bias complications arising from ignoring, and hence abandoning, negative spatial autocorrelation latent in georeferenced disease data, and/or being ill-informed about reigning teledependencies (i.e., long-distance spatial correlations). As imperative academic challenges, it advances elegant and convincing arguments to do otherwise. Its two particular themes are positive–negative spatial autocorrelation mixtures, and hierarchical autocorrelation generated by hegemonic urban systems. Comprehensive interpretations and implementations of these two conjectures constitute future research directions. Important conceptualizations for treatments reported in this paper include confounding variables and Moran eigenvector spatial filtering. This paper’s fundamental implication is an advocacy for a prodigious paradigm shift, a marked change in the collective mindsets and applications of spatial epidemiologists when specifying spatial regression equations to describe either environmental health data, or a publicly transparent geographic diffusion of diseases. Full article

The modern society generates vast amounts of digital content, whose credibility plays a pivotal role in shaping public opinion and decision-making processes. The rapid development of social networks and generative technologies, such as deepfakes, significantly increases the risk of disinformation through image manipulation. This article aims to review methods for verifying images’ integrity, particularly through deep learning techniques, addressing both passive and active approaches. Their effectiveness in various scenarios has been analyzed, highlighting their advantages and limitations. This study reviews the scientific literature and research findings, focusing on techniques that detect image manipulations and localize areas of tampering, utilizing both statistical properties of images and embedded hidden watermarks. Passive methods, based on analyzing the image itself, are versatile and can be applied across a broad range of cases; however, their effectiveness depends on the complexity of the modifications and the characteristics of the image. Active methods, which involve embedding additional information into the image, offer precise detection and localization of changes but require complete control over creating and distributing visual materials. Both approaches have their applications depending on the context and available resources. In the future, a key challenge remains the development of methods resistant to advanced manipulations generated by diffusion models and further leveraging innovations in deep learning to protect the integrity of visual content. Full article

The advent of social commerce platforms fueled by the growing commercialization of social media and networking sites represents a significant evolution in e-commerce dynamics. This study investigates the pivotal role of key opinion leaders (KOLs), particularly YouTubers, in shaping consumer purchasing behavior. Recognizing the powerful influence exerted by KOLs, we examined their ability to promote product diffusion through credibility, specialized knowledge, and strategic word-of-mouth campaigns. This study employs a robust theoretical framework that foregrounds the influence of KOLs while integrating critical constructs, such as perceived value and risk, into a comprehensive model. Our empirical analysis, based on data from 411 valid responses, yields the following insights: the expertise and renown of KOLs exert a profound effect on consumer purchase intentions; consumer perceptions of value positively correlate with trust, whereas perceived risk negatively affects it; and trust mediates the relationship between KOL characteristics (popularity and professionalism) and consumers’ relationship strength with purchase intentions. The findings advocate leveraging KOLs’ renown and expertise while mitigating perceived risks to amplify consumer purchase intentions, thus providing actionable strategies for marketers in the burgeoning social commerce landscape. Full article

Introduction: Dysarthria, a motor speech disorder caused by neurological damage, significantly hampers speech intelligibility, creating communication barriers for affected individuals. Voice conversion (VC) systems have been developed to address this, yet accurately predicting phonemes in dysarthric speech remains a challenge due to its variability. This study proposes a novel approach that integrates Fuzzy Expectation Maximization (FEM) with diffusion models for enhanced phoneme prediction, aiming to improve the quality of dysarthric voice conversion. Methods: The proposed method combines FEM clustering with Diffusion Probabilistic Models (DPM). Diffusion models simulate noise addition and removal to enhance the robustness of speech signals, while FEM iteratively optimizes phoneme boundaries, reducing uncertainty. The system was trained using the Saarland University Voice Disorder dataset, consisting of dysarthric and normal speech samples, with the conversion process represented in the Mel-spectrogram domain. The framework employs both subjective (Mean Opinion Score, MOS) and objective (Word Error Rate, WER) metrics for evaluation, complemented by ablation studies. Results: Experimental results showed that the proposed method significantly improved phoneme prediction accuracy and overall voice conversion quality. It achieved higher MOSs for naturalness, intelligibility, and speaker similarity compared to existing models like StarGAN-VC and CycleGAN-VC. Additionally, the proposed method demonstrated a lower WER for both mild and severe dysarthria cases, indicating better performance in producing intelligible speech. Discussion: The integration of FEM with diffusion models offers substantial improvements in handling the irregularities of dysarthric speech. The method’s robustness, as evidenced by the ablation studies, shows that it can maintain speech naturalness and intelligibility even without a speaker-encoder. These findings suggest that the proposed approach can contribute to the development of more reliable assistive communication technologies for individuals with dysarthria, providing a promising foundation for future advancements in personalized speech therapy. Full article

Background/Objectives: Camptothecin (CPT) is a well-known chemical compound recognized for its significant anticancer properties. However, its clinical application remains limited due to challenges related to CPT’s high hydrophobicity and the instability of its active form. To address these difficulties, our research focused on the development of four novel nanoparticulate systems intended for either oral or intravenous administration. Methods: These nanosystems were based on a poly(amidoamine) (PAMAM) dendrimer/CPT complex, which had been coated with biodegradable homo- and copolymers, designed with appropriate physicochemical properties and chain microstructures. Results: The resulting nanomaterials, with diameters ranging from 110 to 406 nm and dispersity values between 0.10 and 0.67, exhibited a positive surface charge and were synthesized using biodegradable poly(L-lactide) (PLLA), poly(L-lactide-co-ε-caprolactone) (PLACL), and poly(glycolide-co-ε-caprolactone) (PGACL). Biological assessments, including cell viability and hemolysis tests, indicated that all polymers demonstrated less than 5% hemolysis, confirming their hemocompatibility for potential intravenous use. Furthermore, fibroblasts exposed to these matrices showed concentration-dependent viability. The entrapment efficiency (EE) of CPT reached up to 27%, with drug loading (DL) values as high as 17%. The in vitro drug release studies lasted over 400 h with the use of phosphate buffer solutions at two different pH levels, demonstrating that time-dependent processes allowed for a gradual and controlled release of CPT from the developed nanosystems. The release kinetics of the active compound at pH 7.4 ± 0.05 and 6.5 ± 0.05 followed near-first-order or first-order models, with diffusion and Fickian/non-Fickian transport mechanisms. Importantly, the nanoparticulate systems enabled the stabilization of the pharmacologically active form of CPT, while providing protection against hydrolysis, even in physiological environments. Conclusions: In our opinion, these results underscore the promising future of biodegradable nanosystems as effective drug delivery systems (DDSs) for targeted cancer treatment, offering stability and efficacy over short, medium, and long-term applications. Full article