Search Results (614)

Bacteremia is a life-threatening condition contributing significantly to sepsis-related mortality worldwide. With delayed appropriate antibiotic therapy, mortality increases by 20% regardless of antimicrobial resistance. This study evaluated the perceived clinical utility of Artificial Intelligence (AI)-powered Clinical Decision Support Systems (CDSSs) (OneChoice and OneChoice Fusion) among specialist physicians managing bacteremia cases. A cross-sectional survey was conducted with 65 unique specialist physicians from multiple medical specialties who were presented with clinical vignettes describing patients with bacteremia and 90 corresponding AI-CDSS recommendations. Participants assessed the perceived helpfulness of AI decision-making, the impact of AI recommendations on their own clinical judgment, and the concordance between AI recommendations and their own clinical judgment, as well as the validity of changing therapy based on CDSS recommendations. The study encompassed a diverse range of bacterial pathogens, with Escherichia coli representing 38.7% of the isolates and 30% being extended-spectrum β-lactamase (ESBL) producers. Findings show that 97.8% [(95% CI: 92.2–99.7%)] of physicians reported that AI facilitated decision-making and substantial concordance (87.8% [95% CI: 79.2–93.7%; Cohen’s κ = 0.76]) between AI recommendations and physicians’ therapeutic recommendations. Stratification by pathogen revealed the highest concordance for Escherichia coli bacteremia (96.6%, 28/29 cases). Implementation analysis revealed a meaningful clinical impact, with 68.9% [(95% CI: 58.3–78.2%)] of cases resulting in AI-guided treatment modifications. These findings indicate that AI-powered CDSSs effectively bridge critical gaps in infectious disease expertise and antimicrobial stewardship, providing clinicians with evidence-based therapeutic recommendations that can be integrated into routine practice to optimize antibiotic selection, particularly in settings with limited access to infectious disease specialists. For optimal clinical integration, we recommend that clinicians utilize AI-CDSS recommendations as an adjunct to clinical judgment rather than a replacement, particularly in complex cases involving immunocompromised hosts or polymicrobial infections. Future research should prioritize prospective clinical trials that evaluate direct patient outcomes to establish evidence of broader clinical effectiveness and applicability across diverse healthcare settings. Full article

Background/Objectives: Oman Vision 2040, the national blueprint for socio-economic transformation, aims to elevate the Sultanate to developed nation status, with the “Health” priority committed to building a “Leading Healthcare System with International Standards” via a Health in All Policies (HiAP) approach. This paper critically reviews Oman’s strategic health directions and implementation frameworks under Vision 2040, assessing their alignment with global Sustainable Development Goals (SDGs) and serving as a case model for health system transformation. Methods: This study employs a critical narrative synthesis based on a comprehensive literature search that included academic, official government reports, and international organization sources. The analysis is guided by the World Health Organization’s (WHO) Health Systems Framework, providing a structured interpretation of progress across its six building blocks. Results: Key interventions implemented include integrated governance (e.g., Committee for Managing and Regulating Healthcare), diversified health financing (e.g., public private partnership (PPPs), Health Endowment Foundation), and strategic digital transformation (e.g., Al-Shifa system, AI diagnostics). Performance metrics show progress, with a rise in the Legatum Prosperity Index ranking and an increase in the Community Satisfaction Rate. However, critical challenges persist, including resistance to change during governance restructuring, cybersecurity risks from digital adoption, and system fragmentation that complicates a unified Non-Communicable Disease (NCD) response. Conclusions: Oman’s integrated approach, emphasizing decentralization, quality improvement, and investment in preventive health and human capital, positions it for sustained progress. The transformation offers generalizable insights. Successfully realizing Vision 2040 demands rigorous, evidence-informed policymaking to effectively address equity implications and optimize resource allocation. Full article

Non-small cell lung cancer (NSCLC) remains a leading cause of cancer-related mortality globally, driven by marked molecular and cellular heterogeneity that complicates diagnosis and treatment. Despite advances in targeted therapies and immunotherapies, treatment resistance frequently emerges, and clinical benefits remain limited to specific molecular subtypes. To improve early detection and dynamic monitoring, novel diagnostic strategies—including liquid biopsy, low-dose computed tomography scans (CT) with radiomic analysis, and AI-integrated multi-modal platforms—are under active investigation. Non-invasive sampling of exhaled breath, saliva, and sputum, and high-throughput profiling of peripheral T-cell receptors and immune signatures offer promising, patient-friendly biomarker sources. In parallel, multi-omic technologies such as single-cell sequencing, spatial transcriptomics, and proteomics are providing granular insights into tumor evolution and immune interactions. The integration of these data with real-world clinical evidence and machine learning is refining predictive models and enabling more adaptive treatment strategies. Emerging therapeutic modalities—including antibody–drug conjugates, bispecific antibodies, and cancer vaccines—further expand the therapeutic landscape. This review synthesizes recent advances in NSCLC diagnostics and treatment, outlines key challenges, and highlights future directions to improve long-term outcomes. These advancements collectively improve personalized and effective management of NSCLC, offering hope for better-quality survival. Continued research and integration of cutting-edge technologies will be crucial to overcoming current challenges and achieving long-term clinical success. Full article

Text-based CAPTCHAs remain a widely deployed mechanism for mitigating automated attacks across web platforms. However, the increasing effectiveness of convolutional neural networks (CNNs) and advanced computer vision models poses significant challenges to their reliability as a security measure. This study presents a comprehensive forensic and security-oriented analysis of text-based CAPTCHA systems, focusing on how individual and combined visual distortion features affect human usability and machine solvability. A real-world dataset comprising 45,166 CAPTCHA samples was generated under controlled conditions, integrating diverse anti-recognition, anti-segmentation, and anti-classification features. Recognition performance was systematically evaluated using both a CNN-based solver and actual human interaction data collected through an online exam platform. Results reveal that while traditional features such as warping and distortion still degrade machine accuracy to some extent, newer features like the hollow scheme and multi-layer structures offer better resistance against CNN-based attacks while maintaining human readability. Correlation and SHAP-based analyses were employed to quantify feature influence and identify configurations that optimize human–machine separability. This work contributes a publicly available dataset and a feature-impact framework, enabling deeper investigations into adversarial robustness, CAPTCHA resistance modeling, and security-aware human interaction systems. The findings underscore the need for adaptive CAPTCHA mechanisms that are both human-centric and resilient against evolving AI-based attacks. Full article

Surface coatings have proven highly effective in addressing the critical challenges of friction, wear, and corrosion on steel substrates, which are responsible for over 80% of mechanical failures in industrial applications. Recent research highlights that advanced coatings—such as ceramic carbides/nitrides, high-entropy alloys, and metal-matrix composites—significantly enhance hardness, wear resistance, and environmental durability through mechanisms including protective oxide film formation, solid lubrication, and microstructural refinement. Moreover, these coatings exhibit robust performance under combined tribological-corrosive (tribocorrosion) conditions, where synergistic interactions often accelerate material degradation. Key developments include multilayer and composite architectures that balance hardness with toughness, self-lubricating coatings capable of in situ lubricant release, and active or self-healing systems for sustained corrosion inhibition. Despite these advances, challenges remain in predicting coating lifetime under multifield service conditions and optimizing interfacial adhesion to prevent delamination. Future efforts should prioritize multifunctional coating designs, improved tribocorrosion models, and the integration of sustainable materials and AI-driven process optimization. This review consolidates these insights to support the development of next-generation coatings for extending the service life of steel components across demanding sectors such as marine, aerospace, and energy systems. Full article

The rapid increase in retired lithium-ion batteries (LIBs) from electric vehicles (EVs) highlights the urgent need for accurate and automated end-of-life (EOL) assessment. This study proposes an AI-integrated smart grading system that combines hardware diagnostics and deep learning-based evaluation to classify the residual usability of retired batteries. The system incorporates a bidirectional charger/discharger, a CAN-enabled battery management system (BMS), and a GUI-based human–machine interface (HMI) for synchronized real-time data acquisition and control. Four diagnostic indicators—State of Health (SOH), Direct Current Internal Resistance (DCIR), temperature deviation, and voltage deviation—are processed through a deep neural network (DNN) that outputs categorical grades (A: reusable, B: repurposable, C: recyclable). Experimental validation shows that the proposed AI-assisted model improves grading accuracy by 18% and reduces total testing time by 30% compared to rule-based methods. The integration of adaptive correction models further enhances robustness under varying thermal and aging conditions. Overall, this system provides a scalable framework for automated, explainable, and sustainable battery reuse and recycling, contributing to the circular economy of energy storage. Full article

Effective classification and progress monitoring are central to inclusive education, ensuring that students with learning challenges receive timely and appropriate support. However, both international research and Qatari educators’ experiences reveal inconsistencies, limited resources, and a persistent gap between policy and practice. This qualitative study explored the perspectives of 20 stakeholders, including teachers, school leaders, coordinators, and policymakers. Thematic analysis conducted using ATLAS.ti 25 produced six main themes: inconsistent classification; staff and resource shortages; family resistance and collaboration; policy and accommodation gaps; fragmented monitoring; and innovative, inclusive practices. Participants described over-reliance on external diagnostic reports, inconsistent eligibility criteria, limited access to specialists, overcrowded classrooms, and insufficient early screening. Disconnected tools and the lack of a centralized data system hindered monitoring. Despite these barriers, educators showed adaptability through classroom-based interventions, behavioral support, and the emerging use of digital and AI tools. Stake-holders emphasized the need for a unified national framework, systematic early screening, expanded accommodations, integrated Education Management Information System (EMIS) records, and continuous professional development with parent involvement. Findings highlight that classification and monitoring depend on governance, capacity, and data culture, underscoring the need for coordinated policy and practice to achieve equitable, sustainable inclusion in Qatar. Full article

This study conducts a systematic scoping review to explore how universities are integrating digital and emerging technologies into administrative processes. Following the PRISMA-ScR methodology, we systematically searched four major databases—Web of Science, Scopus, IEEE Xplore, and Google Scholar—for peer-reviewed publications between 2019 and 2024. Fifty-two studies met the inclusion criteria after rigorous screening and quality assessment using the CASP and JBI checklists. The originality of this review lies in synthesizing cross-disciplinary perspectives—encompassing digital marketing, artificial intelligence (AI), learning management systems (LMSs), open data, and collaborative digital tools—into a unified framework of administrative innovation. Findings reveal that digital marketing strategies enhance student engagement and institutional visibility, AI improves efficiency and decision-making, LMSs streamline academic and administrative coordination, and open data initiatives promote transparency but encounter legal and cultural resistance. Despite the potential of these technologies, persistent challenges include data privacy concerns, uneven digital infrastructure, and limited institutional readiness. This review contributes to the literature by mapping the intersection of technological innovation and university governance, identifying research gaps, and outlining directions for sustainable digital transformation in higher education. Full article

This review presents the novel synthesis of nature-inspired drilling strategies specifically tailored for extraterrestrial environments, where conventional technologies fail under the environmental conditions and power and mass constraints. Biomimetic drilling, inspired by insects, mollusks, reptiles, and other organisms, offers novel solutions for extraterrestrial subsurface exploration. Numerous organisms efficiently penetrate materials with low energy, using little force, and adapt to flexible substrates, which are essential capabilities for use off this planet. Traditional rotary and percussive drills do not function well under microgravity, at the end of the temperature spectrum, or in low energy and mass environments, such as landers which are typically under 300 kg and 200 W of power available. Nature-inspired approaches such as the reciprocating carpenter bee style have been shown to reduce overhead forces by as much as 50%; clam-like fluidization reduces drag by 90%; and sandfish-inspired methods improve mobility in granular media by 40%. These also improve the in situ resource utilization (ISRU) approaches for efficient sampling, water ice extraction, and planetary surface operations. This paper focuses on bio-drilling with other biological models, their engineering analogs, and exploration models for off-Earth use. Based on this synthesis, the paper recommends prioritizing dual-reciprocating and oscillatory mechanisms for near-term missions, while pursuing hybrid, AI-driven, and wear-resistant designs for long-term exploration. These approaches will help to improve penetration efficiency, reduce power demands, and extend the drilling system’s lifespan in challenging extraterrestrial environments. Full article

Diet influences brain health through many connected metabolic and molecular pathways, and these effects are stronger in obesity. This review links diet quality with cognitive decline and dementia risk. Ultra-processed, high-fat, high-sugar diets drive weight gain, insulin resistance, and chronic inflammation. These changes trigger brain oxidative stress, reduce DNA repair, deplete NAD⁺, disturb sirtuin/PARP balance, and alter epigenetic marks. Gut dysbiosis and leaky gut add inflammatory signals, weaken the blood–brain barrier, and disrupt microglia. Mediterranean and MIND diets, rich in plants, fiber, polyphenols, and omega-3 fats, slow cognitive decline and lower dementia risk. Trials show extra benefit when diet improves alongside exercise and vascular risk control. Specific nutrients can help in certain settings. DHA and EPA support brain health in people with low omega-3 status or early disease. B-vitamins slow brain shrinkage in mild cognitive impairment when homocysteine is high. Vitamin D correction is beneficial when levels are low. A practical plan emphasizes healthy eating and good metabolic control. It includes screening for deficiencies and supporting the microbiome with fiber and fermented foods. Mechanism-based add-ons, such as NAD⁺ boosters, deserve testing in lifestyle-focused trials. Together, these measures may reduce diet-related brain risk across the life span. At the same time, artificial intelligence can integrate diet exposures, adiposity, metabolic markers, multi-omics, neuroimaging, and digital phenotyping. This can identify high-risk phenotypes, refine causal links along the diet–obesity–brain axis, and personalize nutrition-plus-lifestyle interventions. It can also highlight safety, equity, and privacy considerations. Translationally, a pattern-first strategy can support early screening and personalized risk reduction by integrating diet quality, adiposity, vascular risk, micronutrient status, and microbiome-responsive behaviors. AI can aid measurement and risk stratification when developed with privacy, equity, and interpretability safeguards, but clinical decisions should remain mechanism-aligned and trial-anchored. Full article

Background: Artificial Intelligence (AI) holds significant potential to enhance operational efficiency and quality in healthcare. However, despite substantial investment, its widespread, sustained implementation is limited, necessitating a thorough risk assessment to overcome current adoption barriers. Methods: This scoping review, guided by the Arksey and Malley framework, systematically mapped 13 articles published between 2019 and 2024, sourced from five major databases (including CINAHL, Medline, and PubMed). A rigorous, systematic process involving independent data charting and critical appraisal, using the Critical Appraisal Skills Programme (CASP) tool, was implemented, followed by thematic synthesis to address the research questions. Results: AI demonstrates a significant positive impact on both operational efficiency (e.g., optimised resource allocation, reduced waiting times) and patient outcomes (e.g., improved patient-centred, proactive care, and identification of readmission risks). Major implementation hurdles identified include high costs, critical data security and privacy concerns, the risk of algorithmic bias, and significant staff resistance stemming from limited understanding. Conclusions: Healthcare managers must address key challenges related to cost, bias, and staff acceptance to leverage the potential of AI fully. Strategic investments, the implementation of robust data governance frameworks, and comprehensive staff training are crucial steps for mitigating risks and creating a more efficient, patient-centred, and effective healthcare system. Full article

Breast cancer is a heterogeneous malignancy with distinct molecular subtypes that complicate the development of effective therapies. Traditional drug discovery methods are often constrained by high cost and long development timelines, underscoring the need for more efficient, subtype-aware approaches. Computer-aided drug design (CADD) has emerged as a valuable strategy to accelerate therapeutic discovery and improve lead optimization. This review synthesizes advances from a subtype-centric perspective and outlines the application of CADD techniques, including molecular docking, virtual screening (VS), pharmacophore modeling, and molecular dynamics (MD) simulations, to identify potential targets and inhibitors in receptor-positive (Luminal), HER2-positive (HER2⁺), and triple-negative breast cancer (TNBC). In addition to traditional pipelines, we highlight artificial intelligence (AI)-enabled methods and a hybrid workflow in which learning-based models rapidly triage chemical space while physics-based simulations provide mechanistic validation. These approaches have facilitated the discovery of subtype-specific compounds and enabled the refinement of candidate drugs to enhance efficacy and reduce toxicity. Despite these advances, critical challenges remain, particularly tumor heterogeneity, drug resistance, and the need to rigorously validate computational predictions through experimental studies. Future progress is expected to be driven by the integration of AI, machine learning (ML), multi-omics data, and digital pathology, which may enable the design of more precise, subtype-informed, and personalized therapeutic strategies for breast cancer. Full article

This paper presents a methodological framework, Tonal Isomorphism (TI), derived from Tonal Meta-Ontology (TMO), focusing on operational protocols rather than ontological foundations. Tonal Isomorphism is framed as a meta-protocol rather than a metaphysical doctrine: its purpose is to provide a transferable logic that bridges disciplinary silos. We argue that knowledge breakthroughs can emerge not through trial-and-error experimentation alone, but through the isomorphic translation of tonal structures into domain-specific models. The methodology is demonstrated through three key contributions: (1) the Operationalization of Metaphysics, where tonal principles are expressed in executable forms such as the ToneWarp Equation and integrity-preserving responsibility chains; (2) the Unified Generative Field, a cross-domain modeling scaffold applicable to contexts ranging from arithmetic closure to digital trust protocols; and (3) the Generative Proof, which positions the methodology itself as a living demonstration of its claims, resistant to external mimicry. In an era defined by AI’s capacity for replication and simulation, Tonal Isomorphism offers a framework for knowledge generation where truth is not fixed discovery but a defensible, continuously enacted act of creation. Full article

This research examines how artificial intelligence is reshaping business and labor structures through a sustainability lens. Drawing on survey data from 127 organizations and 14 case studies, we quantify workforce impacts while exposing methodological limitations in current projections. Our analysis reveals implementation variations of 37% across industries and 41% higher user adoption rates for hybrid governance approaches versus centralized models. The evidence supports a three-dimensional strategic framework for sustainable organizational development: comprehensive upskilling fostering behavioral change (2.7× higher implementation success), distributed innovation enabling cross-functional ideation (3.1× more identified use cases), and strategic integration aligning systems across departments (explaining 31% of implementation success variance). Organizations deploying all three dimensions achieved a 74% AI initiative success rate versus 12% for those using none. Implementation barriers include regulatory uncertainty, organizational resistance, and ethical considerations, with data infrastructure maturity (β = 0.32), executive sponsorship (β = 0.29), and change readiness (β = 0.26) explaining 58% of implementation success variance. Our findings indicate that sustainable adaptation capacity—not merely technological investment—determines which organizations successfully navigate this transformation while maintaining long-term organizational viability, workforce resilience, and contribution to broader sustainable development goals. Full article

There is pressure on security operations centers (SOCs) from public and private industries as they are coping with the surge of cyberattacks, which is making the reconciliation of inherent organizational tensions a priority. This study surfaces two persistent tensions: (1) expediency versus authority, and (2) adaptability versus consistency that have remained underexplored in cybersecurity literature. We based the research on empirical data collected across three organizational settings, an international consumer packaged goods, a non-departmental public body based in the Netherlands, and a global managed security service provider. Thus, we reveal these not as isolated trade-offs but as paradoxes that must be continuously navigated within SOC operations. Built upon both empirical analysis and Paradox Theory, we develop a conceptual model that explains how SOCs reconcile these tensions through the strategic integration of artificial intelligence (AI), automation, and human expertise. Our model emphases that AI and automation do not replace human analysts; rather, they allow a new form of organizational balance, through mechanisms such as Dynamic Equilibrium and iterative integration. The model demonstrates how SOCs embed technological and human capabilities to sustain simultaneously agility, consistency, authority, and speed. By reframing AI integration as a process of paradox reconciliation, not as a resistance or automation alone, this study contributes new theoretical insight into the sociotechnical dynamics shaping the future of cybersecurity operations. Full article