Search Results (66,601)

The expansion of carbon pricing instruments, such as carbon taxes and emissions trading systems (ETS), has been rapid over the last three decades. However, the global quantitative evidence is often presented in descriptive reports rather than in a unified empirical framework. The present study documents the long-run co-evolution between three factors: firstly, the global diffusion of carbon pricing mechanisms, secondly, the share of global greenhouse gas emissions covered by an explicit carbon price, and thirdly, global carbon-pricing revenues. The present study utilises annual global time-series data spanning the period 1990–2024 (mechanisms) and overlapping samples for coverage and revenues (2005–2024; 2006–2023). Employing correlation analysis, trend modelling and robustness checks tailored to trending series, the study offers a transparent and replicable quantitative synthesis of the data. The findings suggest a robust positive long-term correlation between the number of mechanisms in operation and emissions coverage. Revenues manifest a pronounced non-linear scaling over time; nevertheless, given the aggregate nature of the dataset, the estimates are interpreted as co-movement patterns rather than causal effects of specific instruments. The paper makes a significant contribution to the field by offering a transparent and replicable quantitative synthesis of global carbon-pricing diffusion and fiscal scaling. It is important to note, however, that the paper also explicitly states the limits of causal inference and outlines panel-data extensions for future research. Full article

The Citrobacter freundii (C. freundii) complex represents an increasingly significant group of opportunistic pathogens within healthcare settings. This bacterial complex demonstrates remarkable genomic plasticity, characterized by extensive horizontal gene transfer capabilities that facilitate rapid acquisition of resistance determinants and virulence factors. Although originally considered environmental organisms with limited pathogenic potential, members of the C. freundii complex have emerged as important nosocomial pathogens responsible for urinary tract infections, bacteremia, wound infections, and neonatal meningitis. Importantly, their clinical significance lies less in unique disease manifestations and more in the moderate risk of resistance emergence during therapy with third-generation cephalosporins, driven by inducible chromosomal AmpC β-lactamase production. Beyond this intrinsic mechanism, the genomic adaptability of the C. freundii complex also enables acquisition of additional resistance determinants, including extended-spectrum β-lactamases (ESBLs) and carbapenemases, further limiting therapeutic options and complicating clinical management. Understanding the molecular mechanisms underlying genomic plasticity, virulence expression, and resistance development in the C. freundii complex is crucial for developing effective diagnostic strategies, infection control measures, and novel therapeutic approaches. This pathogen exemplifies the challenge of emerging multidrug-resistant bacteria in contemporary healthcare and underscores the need for continued surveillance and research. This narrative review provides current insights into the taxonomy, genomic plasticity, virulence, and mechanisms of antibiotic resistance. Full article

The rapid rise in cryptocurrencies has created an investment environment marked by unprecedented levels of information volume, fragmentation, and volatility. While prior research has examined drivers of trust and adoption in crypto markets, far less is known about the psychological consequences of information overload on investor decision-making. This study addresses this gap through nineteen semi-structured interviews with individual cryptocurrency investors, analyzed using an inductive, manually conducted thematic approach. Findings reveal four interconnected dynamics: decision fatigue and paralysis, heuristic reliance on influencers and peers, emotional strain characterized by anxiety and fear of missing out (FOMO), and diverse coping strategies ranging from selective filtering to withdrawal. These results demonstrate that crypto investing is not only a financial process but also a cognitively and emotionally taxing experience. By linking investor narratives to broader theories of decision fatigue, bounded rationality, and consumer vulnerability, the study contributes to interdisciplinary debates in marketing, behavioral finance, and consumer psychology. Practically, the findings highlight the need for clearer communication strategies, supportive platform design, and financial education initiatives that help investors manage cognitive strain and decision fatigue. In a market where credibility is fluid and decisions are often made under conditions of overload, understanding the psychological dimensions of investment behavior is essential. Full article

Background: The COVID-19 pandemic revealed persistent gaps in local health department (LHD) health informatics capacity. This study examines organizational characteristics of LHDs associated with the adoption of six health information systems: electronic case reporting (eCR), electronic disease reporting systems (EDRS), electronic health records (EHR), electronic lab reporting (ELR), health information exchange (HIE), and immunization registries (IR). Methods: We used a mixed-methods design, including multinomial or binary logistic regression analyses of quantitative data from the 2022 NACCHO National Profile of Local Health Departments (n = 441) and thematic analysis of semi-structured interviews with five LHD staff members. Results: About half (49.9%) of LHDs had implemented eCR, while higher proportions had implemented EDRS (78.0%), EHR (62.4%), ELR (57.2%), HIE (92.6%), and IR (92.6%). Workforce size was associated with the implementation of eCR, EHR, and IR. The number of vacant staff positions was associated with a lower odds of IR implementation; compared with medium-sized LHDs, both small and large LHDs had higher odds of IR implementation. Shared-governance LHDs had higher odds of adopting ELR and HIE than state-governed LHDs. Qualitative themes highlighted challenges, including staff burnout, high turnover, pay inequities, role ambiguity, political pressures, rapid changes in informatics, and interoperability problems. Conclusions: Findings underscore the need to improve LHD workforce capacity and governance structures to support a resilient public health informatics infrastructure. Full article

Background/Objectives: Virtual histological staining offers a rapid, cost-effective alternative to physical reprocessing but faces challenges related to spatial misalignment and staining heterogeneity between Hematoxylin and Eosin (H&E) and Masson’s Trichrome (MT) domains. This study develops a robust framework for H&E-to-MT virtual staining to enable accurate fibrosis assessment without additional tissue consumption. Methods: We propose a transformer-based generative adversarial network (TbGAN) supported by a multi-stage alignment pipeline (SIFT (scale-invariant feature transform) coarse alignment, ORB/homography patch registration, and B-spline free-form deformation) and a weighted fusion mechanism combining four configuration outputs (O/10/3, O/3/10, R/10/3, and R/3/10). The framework was validated on 27 whole-slide images (>100,000 aligned patches) through 24 independent experiments. Results: The fused approach achieved state-of-the-art performance: MI = 0.9815 ± 0.0934, SSIM = 0.7474 ± 0.0597, NCC = 0.9320 ± 0.0220, and CS = 0.9946 ± 0.0014. Statistical analysis confirmed enhanced stability through narrower interquartile ranges, fewer outliers, and tighter 95% confidence intervals compared to individual configurations. Qualitative assessment demonstrated preserved collagen morphology critical for fibrosis staging. Conclusions: Our framework provides a reliable, IRB-compliant solution for virtual MT staining that maintains high structural fidelity suitable for diagnostic support. It enables resource-efficient fibrosis quantification and supports integration into clinical digital pathology workflows without patient-specific recalibration. Full article

Precise control of humidity levels within semiconductor manufacturing environments is paramount to ensuring product quality and yield. Unsuitable conditions can induce various wafer-related defects, including corrosion, oxidation, and poor film adhesion, thereby increasing production costs and compromising equipment reliability. This paper presents an innovative artificial intelligence-based framework, Lifelong Boosting Learning ($L^2$ Boost), for rapid and accurate environmental detection within manufacturing facilities. By utilising datasets correlating sensor data with wafer defect labels, we establish links between environmental conditions and defects. Our approach employs an $L^2$ Boost strategy to analyse heterogeneous sensor data and identify patterns indicative of environment-induced anomalies. The proposed system enables near-real-time environmental monitoring by indirectly measuring process characteristics correlated with defects, providing an early warning mechanism for environmental control systems. Experimental results demonstrate that $L^2$ Boost accurately identifies environment-related defects from sensor data, achieving a macro-averaged Precision of 0.9912, Recall of 0.9804, F1 of 0.9858, and an ROC-AUC of 0.9945. This research contributes to the development of intelligent environmental monitoring systems for semiconductor manufacturing, offering a cost-effective solution for maintaining optimal production conditions. Ultimately, this framework provides actionable insights and automated diagnostic capabilities that are highly useful for process engineers, facility managers, and quality control teams striving to optimize yield in smart manufacturing environments. Full article

During ischemia, endothelial cell integrity is compromised, as a consequence, blood barrier homeostasis is disrupted. Therefore, the structural and functional preservation of endothelial cells is paramount when trying to improve outcomes after ischemic injury. Endoplasmic reticulum (ER) stress is increasingly recognized as a key player in ischemic injury through unfolded protein response (UPR) signalling, and its crosstalk with mitochondrial death pathways. This study provides a cost-effective and straightforward method to delve into the relationship between ER stress and ischemia in human microvascular endothelial cells-1 (HMEC-1). HMEC-1 was exposed to 8 h of oxygen–glucose deprivation (OGD) in glucose-free medium with rapidly induced hypoxia. Hypoxia, oxygen consumption, cell viability, apoptosis, and ER stress markers (BiP/GRP78, PERK, ATF6, IRE1/XBP1s, CHOP) were assessed by RT-qPCR and Western blot. Cell viability decreased by approximately 33% following OGD, while CHOP expression increased ~4-fold, indicating significant ER stress induction. The model enables quantification of metabolic stress (OCR), as well as evaluation of viability loss, membrane integrity, apoptotic commitment, and discrimination between ER stress resolution versus maladaptation. Overall, GasPak EZ Pouch Systems provide a reproducible and practical in vitro platform to study ischemic injury down to the mechanistic details of ER-mitochondria signalling. They give the opportunity to evaluate therapeutic approaches that target ER homeostasis to limit apoptosis and/or recovery of metabolic function after ischemia. This method could allow rapid screening of ER stress-modulating interventions aimed at preserving endothelial barrier function, in various ischemic contexts. Full article

The rapid expansion of the Internet of Things (IoT) has opened resource-limited devices to novel physical threats, such as Side-Channel Attacks (SCAs) and Hardware Trojans (HTs). Traditional security mechanisms are often not capable of standing against such hardware-based attacks, specifically on low-power System-on-Chip (SoC) where static defenses can incur 2× to 3× overhead in silicon area and power. Herein, the gap between hardware security and embedded AI is compositionally formulated for discussion. We present a comprehensive survey of the current hardware threat landscape and analyze the emergence of “Secure-by-Design” paradigms, specifically focusing on the integration of Edge AI and TinyML as active, on-chip intrusion detection mechanisms. This review presents a critical analysis of trade-offs for running lightweight ML models on hardware by comparing state-of-the-art approaches. Our analysis highlights that optimized architectures, such as Mamba-Enhanced Convolutional Neural Networks (CNNs) and Gated Recurrent Unit (GRU), can achieve detection accuracies exceeding 99% against SCA and >92% against stealthy Hardware Trojans, while offering up to 75% lower power consumption compared to standard deep learning baselines. Finally, open challenges such as adversarial attacks on defense models are briefly discussed, and the focus is put on future directions toward constructing secure chips based on robust, AI-driven technology. Full article

As network attack methods continue to evolve, flooding attacks remain a major threat that causes network paralysis and service disruption. Statically configured systems are particularly vulnerable, as attackers can exploit reconnaissance information to launch large-scale attacks, while conventional defense mechanisms often fail under high-intensity traffic. To address this problem, this paper introduces Moving Target Defense (MTD) within a decentralized framework and proposes a blockchain-based decentralized End Hopping system. The system employs the Practical Byzantine Fault Tolerance (PBFT) consensus protocol for dynamic controller election and incorporates a disaster recovery mechanism, which eliminates single points of failure while ensuring reliable controller transitions and rapid service restoration. Experimental results demonstrate that the proposed system achieves satisfactory performance in terms of availability, effectiveness, and security, providing a practical approach to constructing robust proactive defense networks. Full article

In the wake of rapid digital transformation, emerging technologies like FinTech, AI, and Blockchain are reimagining how countries pursue sustainable development. This study examines how FinTech adoption, Artificial Intelligence (AI) readiness, and Blockchain activity influence sustainable development performance across G20 economies over the period 2015–2023. Drawing on Innovation-Driven Growth Theory, the Technology–Organization–Environment framework, and Institutional Theory, the analysis evaluates both the direct and complementary effects of these digital technologies on Sustainable Development Goal (SDG) outcomes using cross-country panel data and key macroeconomic controls. The results show that FinTech, AI, and Blockchain each exert a positive and statistically significant impact on national sustainability performance, with AI exhibiting the strongest effect. Moreover, the findings reveal meaningful digital complementarities, indicating that coordinated adoption of these technologies amplifies sustainable development gains. Overall, the study provides robust macro-level evidence that digital transformation functions as a strategic driver of sustainability and offers policy-relevant insights for G20 governments seeking to accelerate inclusive, transparent, and environmentally responsible development. Full article

Herein, RATSGO (Real-time Automated Training and Sensing for Gas Odor), a fully automated live-animal olfactory training platform, for the detection of GBL as a sexual assault-facilitating drug is reported. The system integrates four distinct operant conditioning-based training paradigms, all executed without human intervention, to enhance learning speed, consistency, and scalability. Using this fully automated framework, four rats were trained to identify γ-butyrolactone (GBL). Three of the four animals successfully reached the predefined learning completion criterion, whereas one failed to meet the criterion. Across 320 automated trials, the GBL rats achieved a mean detection accuracy of 90%, with sensitivity and specificity values of 97% and 82%, respectively. The corresponding positive and negative predictive values (PPV and NPV) were 85% and 96%. When challenged with GBL diluted in drinking water (180 trials), performance remained high, yielding 88% accuracy, 89% sensitivity, 87% specificity, 85% PPV, and 90% NPV. Similarly, in experiments involving GBL mixed with whisky (200 trials), the rats demonstrated robust recognition capability, achieving 90% overall accuracy, perfect sensitivity (100%), 84% specificity, 79% PPV, and 100% NPV. Importantly, odor discrimination performance was preserved when reassessed four months after the completion of training, indicating strong long-term retention of the learned odor representations. Collectively, these findings confirm that the RATSGO system supports rapid, stable, and precise odor learning, underscoring its promise as a practical and extensible biological sensing platform for chemical detection applications. Full article

The detection of low humidity levels remains a great challenge in relative humidity (RH) sensing technologies. In this work, methacryloxyethyl trimethyl ammonium chloride (DMC) was coated around SiO₂ microspheres to form DMC/SiO₂ composite microspheres, which were self-assembled into a three-dimensional (3D) network structure for low humidity detection. The hydrophilic nature of the DMC component enhances the adsorption capacity for water molecules even at ultra-low humidity levels (1–18.6% RH), while the 3D network structure provides abundant channels for fast water molecule transport, facilitating rapid response and recovery processes. The optimized sensor shows high response (13.544%) in 1–18.6% RH, with short response/recovery time (6 s/10 s) and a small humidity hysteresis (1.4% RH). Such high performance shows that this type of sensor has great potential for application in widespread fields, such as electricity, semiconductor manufacturing, pure gas supply, aerospace, and pharmaceutical formulations. Full article

Condensation of water vapor into discrete droplets on the surface of transparent optical devices-commonly known as fogging-severely degrades their optical performance. To address this issue, a highly transparent, self-healing, and durable polymer-based anti-fogging coating was developed via a facile one-pot copolymerization of 2-acrylamido-2-methylpropanesulfonic acid (AMPS), acrylic acid (AA), and vinyltrimethoxysilane (VTMOS). The chemical structure and composition were thoroughly characterized. The introduction of VTMOS constructs a hydrophilic-hydrophobic microphase structure through in situ formation of a Si–O–Si network, which significantly enhances the mechanical stability and water resistance. The polymer coating can maintain high transparency (>90%) under extreme conditions (85 °C steam and −40 °C freezing), exhibits long-term anti-frosting performance for 180 days, and demonstrates rapid water-assisted self-healing within 30 s. Differential scanning calorimetry (DSC) analysis reveals that each polymer unit binds approximately seven water molecules, elucidating the mechanism behind its exceptional anti-frosting capability. This work presents a practical strategy for designing high-performance, long-lasting anti-fogging coatings suitable for extreme environment applications. Full article

With the rapid acceleration of technological revolutions and industrial upgrading, firms are increasingly exposed to environmental uncertainty, intensified competition, and continuous technological disruption. Under such conditions, sustainable corporate development depends not only on innovation performance, but on the ability to sustain innovation activities over time. Innovation resilience, defined as the capacity to withstand shocks, reconfigure resources, and maintain innovation momentum, therefore represents a critical foundation of corporate sustainability. Using panel data from Chinese A-share listed firms from 2009 to 2024, this study examines how CEO power shapes sustainable innovation resilience. Drawing on upper echelons theory and signaling theory, we investigate the direct effect of CEO power, the mediating role of corporate reputation, and the moderating role of artificial intelligence adoption. Fixed-effects regression results indicate that CEO power is positively associated with sustainable innovation resilience, and this relationship is partially mediated by corporate reputation. Furthermore, artificial intelligence adoption strengthens the positive association between CEO power and innovation resilience. By linking executive governance, reputational mechanisms, and digital transformation to sustained innovation capacity, this study advances understanding of the organizational foundations of corporate sustainability under uncertainty. The findings provide theoretical insights and managerial implications for designing governance structures that support long-term sustainable development. Full article

Background/Objectives: Deep neck space abscesses (DNSAs), representing severe suppurative infections, continue to pose a significant global health challenge due to their morbidity, mortality, and evolving epidemiology. This review synthesizes existing knowledge regarding DNSA definitions, anatomic basis, epidemiological trends, microbiology, clinical presentation, diagnostic strategies, treatment paradigms, outcomes, health system challenges, and disparities to guide global efforts in DNSA prevention, management, and research. Methods: A structured narrative review was performed following SANRA guidelines. PubMed/MEDLINE and the Cochrane Library were searched from January 2000 to May 2025, retrieving 1102 records. After screening, 49 studies met the inclusion criteria. Data were extracted using standardized templates and synthesized thematically. Results: During the period 2004–2015, annual case increases were reported in a Finnish population-based retrospective cohort (n = 277), going from 14 to 24 subjects, and for a UK tertiary center retrospective series, going from 1 to 15 cases annually (2006–2015) (Pearson’s correlation, r = 0.9; p = 0.00019). The microbiological environment is mostly polymicrobial, composed of group streptococci and staphylococcus strains and anaerobes. Factors associated with poor outcomes include diabetes mellitus (adjusted hazard ratio of 10.7 [95% CI 6.0–19.1] in a retrospective, population-based cohort of 12,738 diabetic patients compared to 50,952 individuals without diabetes), immunosuppressed state, elderly age, and multispace involvement. Diagnosis relies on contrast-enhanced CT imaging (sensitivity > 90%), and treatment consists of early multidisciplinary intervention combining empiric broad-spectrum antibiotics with surgical drainage in 60–97% of cases. Mortality ranges from 1.6% to 7.6%, with higher rates in cases complicated by mediastinitis (up to 40%). Conclusions: DNSAs demonstrate a clear upward incidence trend across high-income and resource-limited settings. Establishing standardized DNSA registries, validating risk-stratification tools, reinforcing antimicrobial stewardship to address rising resistance, and implementing early detection protocols in primary care remain critical priorities. While emerging technologies, including rapid molecular diagnostics and AI-based decision support, represent promising research directions, current DNSA management relies fundamentally on conventional clinical assessment, prompt imaging, and coordinated multidisciplinary care. Full article