Search Results (3,777)

Geopolitical conflict, tariff shocks, and rapidly evolving environmental, social, and governance (ESG) regulation have made global supply chains markedly less predictable. This study examined how manufacturing supply chain decision-makers perceive six regulatory-volatility risks, including labor, environmental, customs, ownership, military-logistics, and distribution, and whether those perceptions predict recorded engagement outcomes. Adopting an information systems perspective, we retrospectively analyzed 1988 fully anonymized decision-maker records from the digital lessons-learned repository of a multinational supply chain logistics firm, applying descriptive statistics, exploratory factor analysis with parallel analysis, and binary logistic regression with classification diagnostics. The perceived risks did not converge on a unified regulatory-volatility construct: sampling adequacy was weak (KMO = 0.470), and parallel analysis retained three single-indicator factors (distribution, environmental, and ownership risk) that were close to orthogonal apart from one moderate environment-distribution association. Critically, the sustainability-motivated risks were rated lowest, with a median labor severity of 0 and an environmental severity of 1 on 0–5 scales, during a period of record forced-labor enforcement; this perception gap invites sustainability leakage, whereby surprise enforcement provokes supplier exit and sourcing flight rather than remediation within scrutinized regions. The logistic regression separated success from failure perfectly in-sample (accuracy = AUC = 1.000), driven jointly by the near-collinear distribution and supply items—a complete-separation pattern warning of label leakage when digitized organizational records are mined for artificial intelligence-based decision support. The two items are near-duplicate measures (r = 0.915), so no individual predictor effect is identified. The findings favor multidimensional rather than composite digital risk dashboards, provenance-aware data governance for supply chain analytics pipelines, and digitally enabled ESG compliance under volatile regulation, contributing an empirically grounded information systems lens to sustainable supply chain management. Full article

Multiple-criteria decision analysis (MCDA) is a branch of operations research concerned with choices based on several quantifiable factors. Here, we focus on high-dimensional markets, in which each available product is described by a large number of features. Taking a probabilistic approach, we define a market as a collection of alternatives in a decision-making scenario governed by a linear utility function. Analytic approximations for the market share and its moments are derived in the limit of a large population and a large number of measured features. We identify a single parameter, termed the degree of subjectivity, that places markets on a continuous spectrum ranging from fully objective to fully subjective. At an intermediate value, the market is competitive in the sense that it maximizes the entropy of the market-share distribution. Empirical analysis of several real markets indicates that they can indeed be classified by this parameter, yielding predictable decision patterns and a unified, relative measure of competitiveness across markets. Simulations involving non-linear utility functions and a trained machine-learning classifier provide preliminary evidence that similar behavior may also arise beyond the linear model, suggesting that the degree of subjectivity may be useful as a diagnostic in some broader multi-feature decision problems. Full article

Engineering ethics education faces the challenge of moving students from abstract principles toward context-sensitive decision-making. This exploratory mixed-methods study examined associations between participation in a board-game-based strategy and patterns consistent with situated ethical awareness in first-semester engineering courses at Pontificia Universidad Javeriana (Bogotá/Cali) and Universidad de los Andes (Bogotá). The 90-min intervention adapted Sheriff of Nottingham to an engineering entrepreneurship context and combined asymmetric roles, incentives, negotiation, uncertainty, consequences, and structured debriefing. Across 17 course offerings, 616 pre-intervention and 413 post-intervention questionnaires were analyzed as unmatched samples. In 2025-1, aggregate post-intervention scores were higher across all six measures (global score: 7.40 to 8.79), with the largest difference in academic integrity. In 2025-2, Rights (+6.9 percentage points) and Common Good (+6.1 pp) gained first-priority prominence, while the perceived role of ethics increased modestly. Qualitative responses after the intervention more often incorporated consequences, effects on others, professional responsibility, and individual–collective tensions. In 2025-2, 90.4% agreed or strongly agreed that the game helped them reflect differently on ethical dilemmas. These convergent patterns show early ethical sensitization and contextualization rather than stable ethical competence; the unmatched design, absence of a comparison group, and exploratory instruments preclude causal or individual-level claims. Full article

Understanding complex vessel behaviors in port waters is important for maritime traffic supervision, navigation safety management, and intelligent maritime decision-making. However, existing AIS-based studies often focus on isolated behavior recognition or trajectory-level analysis, with limited integration of vessel attributes, navigation scenarios, motion states, and temporally organized behavioral processes. We develop a knowledge graph-driven framework for complex vessel behavior recognition and frequent behavior sequence pattern mining using AIS data. BehaviorEvents are constructed from continuous-navigation segments and integrated with water-area scenarios, motion states, vessel attributes, and temporal relationships to form a unified semantic representation. Based on this representation, interpretable semantic rules are used for event-level complex behavior recognition, while PrefixSpan is applied to Scene–SpeedState–TurningState token sequences to discover recurrent multi-event behavior patterns. Independent expert evaluation, semantic ablation, and sensitivity analyses are used to assess recognition credibility, contextual semantic constraints, and robustness, while a vessel-level Discovery–Validation strategy evaluates the reproducibility of frequent patterns. Experiments on AIS data from Xiamen Port waters involve 16,400 vessels, 239,877 continuous-navigation segments, and 2,457,965 BehaviorEvents, of which 624,561 match at least one predefined semantic rule or candidate condition. Independent expert evaluation of R1–R7 yields a macro-average confirmation rate of 92.11% and a Cohen’s κ of 0.746. Semantic ablation shows that Scene and VesselTypeClass provide important contextual constraints on broad motion-based rule activations, while sensitivity analyses indicate that the main recognition results remain stable under perturbations of motion-state, duration, and temporal-segmentation parameters. From 75,144 valid compressed behavior-token sequences, PrefixSpan identifies recurrent patterns involving medium-speed transit with course adjustments, low-speed–stop combinations, and maneuvering-related behaviors. The dominant Top-20 patterns showed substantial overlap and broadly consistent ranking across the vessel-level Discovery and Validation subsets, with a Jaccard overlap of 0.9048 and a Spearman rank correlation of 0.9654. Comparative evaluation with a normalized relational representation further shows equivalent analytical results, while the knowledge graph provides explicit organization of semantic relationships, temporal paths, and event-level traceability. These results indicate that the proposed framework provides a unified and interpretable semantic basis for connecting event-level complex vessel behavior recognition with sequence-level frequent behavior pattern mining in complex port environments. Full article

Artificial intelligence (AI) refers to a set of computational techniques, including machine learning, natural language processing, deep learning, and generative AI, that enable systems to perform tasks traditionally requiring human intelligence, such as prediction, pattern recognition, and decision-making. The rapid diffusion of AI into business organizations is transforming how information is processed, decisions are made, and knowledge-intensive work is performed, creating both new opportunities for economic value and new challenges for human judgment, organizational governance, and accountability. The growing adoption of AI across accounting, finance, and management makes it increasingly important to understand not only what AI can do, but also how and under what conditions it affects individuals, organizations, and markets. This paper provides a comprehensive review of the rapidly growing literature on artificial intelligence across these three disciplines. We synthesize existing research to examine how AI is transforming information processing, decision-making, governance, and organizational performance. In accounting, AI enhances auditing, financial reporting, and fraud detection while raising concerns regarding transparency and professional judgment. In finance, AI improves asset pricing, risk assessment, and trading strategies by leveraging large-scale structured and unstructured data. In management, AI reshapes organizational design, human capital, strategic decision-making, and innovation through increasingly sophisticated human–AI collaboration. Across these disciplines, we organize the literature around several unifying themes, including information asymmetry, automation versus augmentation, decision quality, interpretability, and governance. We further identify important research gaps concerning whether AI’s predictive and analytical advantages translate into meaningful economic and organizational outcomes, how AI reshapes human judgment and skills, the emerging risks, and the need for stronger research designs. By integrating evidence across three major business disciplines, this review provides a unified framework for understanding AI’s transformative role in organizations and offers a roadmap for future interdisciplinary research on the economic, behavioral, organizational, and governance consequences of AI. Full article

Aging in place (AiP) and healthy cognitive aging (HCA) are closely interrelated concepts but seldom examined together within real-world care systems. Integrated care (IC) offers a framework to understand how community settings can support both. This qualitative case study examined a 25-year-old community-based integrated health and social care program in a rural Czech microregion. Utilizing a realist-informed context–mechanism–outcome framework, the study integrated data from documentary analysis, semistructured interviews, and participant observation. The findings suggest that the conditions facilitating AiP and HCA may be mutually supported through interrelated, context-dependent, cumulatively operated mechanisms, including environmental predictability, continuity of care, coordination across services, social embeddedness, supported autonomy, and reductions in cognitive and organizational burden and stress. IC is not a direct intervention, but acts as a context-dependent moderator that shapes the conditions for AiP and HCA. Behaviorally, these mechanisms minimize decision-making complexity and establish predictable interaction patterns that support everyday functioning. AiP and conditions relevant to HCA are mutually supported through shared underlying mechanisms embedded in care systems and community contexts. IC may facilitate this by fostering stable, person-centered environments that may reduce cognitive demands and support relational continuity. The study contributes to behavioral science by exploring how IC may shape conditions relevant to cognitive and functional outcomes in later life and provides actionable insights for the design of IC systems. Full article

The output quality of random-number sources directly affects the security of cryptographic systems. Physical-noise degradation, environmental disturbance, device aging, and fault injection may increase output bias, correlation, and predictability. This paper presents a resource-conscious on-chip entropy-quality supervisor for random-number source output streams. The design uses non-overlapping 1024-bit measurement windows and a shared feature engine for bit counts, directional transition counts, and run information. These features support repetition-count, adaptive-proportion, and low-toggle checks, together with two-bit pattern-concentration and first-order conditional-transition indicators, exponentially weighted moving-average trend monitoring, comprehensive scoring, and a seven-bit alarm bitmap. The RTL accepts a 32-bit valid-data interface, makes one decision every 32 valid words, and is integrated into an Artix-7 XC7A35T project configured with a 50 MHz system-clock constraint. The complete project includes a ring-oscillator TRNG, and controlled deterministic fault patterns are inserted into selected windows of the TRNG stream for fault-response verification. Deterministic RTL simulations show complete alarm mappings of 0111111 for fixed-value patterns, 0011000 for an isolated alternating window, and 1011000 for the fourth consecutive alternating window. A parameterized capture simulation also verifies pre-event, injection, and recovery sequencing. FPGA implementation results show that the entropy-supervisor core uses 1009 LUTs and 276 flip-flops without BRAM or DSP resources, while the complete project uses 1313 LUTs, 614 flip-flops, and one BRAM tile. The design meets the 50 MHz clock constraint with a WNS of 1.079 ns and no setup or hold violations. The total on-chip power reported by Vivado is 0.076 W; without simulation-derived switching activity, this value is approximate and is not a board measurement. The results establish the functional behavior of the monitoring and decision paths. The two-bit pattern-concentration and first-order conditional-transition indicators are empirical tools for online anomaly diagnosis; neither is a min-entropy estimator, and they do not replace source-specific entropy assessment under NIST SP 800-90B. Full article

Although traditional machine learning models demonstrate high accuracy in agricultural prediction scenarios, their “black box” nature prevents them from transparently presenting decision-making mechanisms and limits their reliability in integrated pest management (IPM) processes. In this study, the Cherry Fruit Fly (Rhagoletis cerasi) was investigated. A rule-based, explainable artificial intelligence (XAI) framework is proposed for characterizing bio-edaphic profiles associated with observed Cherry Fruit Fly Pupal Count (CfPC) density levels and classifying microclimatic aspects (Aspects) using measurable edaphic and biological parameters. The developed hierarchical rule inference engine parses the decision trees of the LightGBM classifier, which achieved the highest performance when benchmarked against 10 baseline machine learning algorithms (11 models in total), and extracts human-interpretable results that can be directly interpreted by experts. In Experiment 1, the analysis characterized the combinations of observed CfPC and edaphic conditions associated with Low, Medium, and High pupal-density profiles, whereas Experiment 2 evaluated the classification of canopy aspect from the measured bio-edaphic variables. According to the derived rules, continuous biological counts (CfPC) serve as the primary biological reference, while edaphic parameters such as soil temperature, pH, lime content, and water saturation percentage characterize additional soil conditions associated with the observed pupal-density profiles. These synthesized rules provide an interpretable representation of the bio-edaphic patterns observed within the studied orchards and may support the development of future precision crop-protection strategies following independent validation. Full article

Air target intent recognition plays an important role in situation awareness and decision support in complex air-combat environments. However, existing methods often process motion and semantic information in the same manner, making it difficult to fully exploit their different characteristics. They also have difficulty modeling how target intent changes over different time periods. To address these limitations, this paper develops a deep learning framework that learns motion and semantic information separately and combines historical observations from multiple time ranges. A one-dimensional convolutional network extracts local movement patterns from continuous motion variables, while an embedding layer represents discrete semantic variables as dense feature vectors. These representations are synchronized in time and jointly analyzed by a Transformer. The model selectively uses recent, intermediate, distant, and overall historical information to capture instantaneous maneuvers, changes in behavioral stages, and long-term tactical trends. Experiments on an AFSIM-generated dataset show that the proposed method achieves higher recognition accuracy and better robustness than representative baseline models. Further analysis confirms that both the separate processing of motion and semantic information and the use of multiple historical time ranges contribute to the performance improvements. Full article

Background: Creatine is now discussed beyond sports performance and within women’s health, cognition, fatigue, and healthy-aging contexts. However, little is known about the characteristics of women who use creatine or whether these characteristics differ across durations of use. This study characterized women reporting current or past-12-month creatine use in a large cross-sectional sample and benchmarked findings against men. Methods: In a cross-sectional, supplement-platform convenience survey (approximately 2.0% response rate; a secondary analysis of a previously reported cohort), 2094 current or recent creatine users (1055 women, 1039 men; mean age 53 years [54.0 in women and 52.5 in men]) reported duration of use, supplementation goals, information sources, creatine knowledge alignment using a five-item battery, self-rated confidence, satisfaction, and intent to continue supplementation for another six months. Representation of women across six ordered duration bands was evaluated using an age-adjusted logistic model with duration as a categorical factor, with a Cochran–Armitage trend test reported alongside it. Among women, goals and information sources were compared across duration-of-use bands using unadjusted tests of homogeneity, and knowledge alignment was evaluated using an ordered trend test. Gender differences were estimated in age- and duration-adjusted models, pooled across duration groups, with Benjamini–Hochberg correction within domain; gender × duration interactions were tested for the goal and information-source outcomes. Self-rated confidence was modeled as a function of knowledge, gender, and their interaction. Results: Women comprised 50.0–63.9% of respondents across each reported duration category below three years but only 19.7% of those reporting 3+ years of use. In the primary age-adjusted analysis, gender composition differed strongly across duration categories (global p < 0.001); relative to the 3+ year group, the odds of identifying as a woman were 3.9- to 7.5-fold higher in every shorter-duration category. Among women, knowledge alignment was higher at longer reported durations in the unadjusted analysis and remained associated with duration after adjustment for age, BMI, and physical activity (global categorical test, p < 0.001). Lower knowledge alignment among shorter-duration women reflected greater uncertainty rather than greater myth endorsement, which remained rare across duration groups. Compared with men, women were less likely to report athletic performance as a goal (adjusted 37.5% vs. 50.2%) and more likely to report healthy aging, while six of seven information sources differed by gender. Women also had lower knowledge-alignment scores overall, yet the largest gender difference emerged in confidence: at equivalent observed knowledge alignment, women reported progressively lower self-rated confidence than men as scores increased (interaction p = 0.012), with an adjusted difference of −0.38 points at the highest observed knowledge score. Conclusions: Among current and recent SuppCo users who responded to this survey, women showed a pattern of creatine use that differed from the historically performance-focused populations underlying much of the evidence base. Women were disproportionately represented in shorter reported duration-of-use categories, more often endorsed healthy aging and less often athletic performance, and reported different information sources than men. Lower knowledge alignment among shorter-duration women reflected greater uncertainty rather than greater endorsement of misconceptions, while women reported lower confidence than men at the highest observed knowledge levels. These findings suggest future research and education should go beyond efficacy trials in women to address who is using creatine, the outcomes they expect, where they obtain information, and how knowledge and confidence shape decision-making. Prospective studies are needed to determine what underlies the difference in women’s representation across reported duration-of-use groups. Full article

The rapid evolution of Large Language Models (LLMs) has opened new frontiers in cybersecurity automation, enabling intelligent agents capable of multi-step reasoning, tool invocation, and autonomous decision-making across complex security tasks. While individual applications have emerged across threat intelligence, vulnerability assessment, penetration testing, and security operations center (SOC) automation, a systematic understanding of the LLM-based agent paradigm in cybersecurity—encompassing both single-agent and multi-agent architectures—remains lacking. This paper presents a systematic literature review following PRISMA guidelines, identifying records through 59 structured web-search queries whose results resolve predominantly to arXiv, Semantic Scholar, the ACM Digital Library, IEEE Xplore, USENIX, MDPI, SpringerLink, and Elsevier ScienceDirect, supplemented by citation chaining, for works published between January 2022 and April 2026; the full query record is published with the paper. We applied structured inclusion and exclusion criteria and classified 59 primary studies along five dimensions: security function, agent architecture pattern, knowledge augmentation strategy, human-in-the-loop posture, and evaluation rigor. Our analysis reveals that penetration testing and threat intelligence are the most extensively studied domains, while incident response and compliance verification remain critically underrepresented. Penetration testing alone accounts for over half the corpus (50.8%). Single-agent tool-calling remains the most prevalent architecture (30.5% of studies), whereas centralized multi-agent orchestration—present in 18.6%—yields the strongest reported performance gains, up to 4.3× on zero-day exploitation; prevalence and performance therefore point in opposite directions. No included study achieves production-grade (E4) evaluation: the entire field currently rests on controlled laboratory assessments. An independent search of six bibliographic databases recovers 86.3% of the studies the primary search had surfaced (79.7% of the full corpus) while indicating a total eligible literature of roughly 400 studies, so the corpus is reported as a documented subset rather than an exhaustive census. We propose a unifying taxonomy, identify cross-cutting challenges including hallucination, prompt injection, and benchmark fragmentation, and outline open research directions with particular emphasis on multi-agent orchestration design. Financial sector applicability under DORA and the EU AI Act is treated as a documented evidence gap rather than a synthesis: the corpus’s only compliance and risk assessment study is also its only banking-specific system. Full article

Background/Objectives: Primary-prevention implantable cardioverter-defibrillator (ICD) selection remains dominated by left ventricular ejection fraction and clinical heart failure markers, which incompletely distinguish between patients who will experience ventricular arrhythmia and those whose prognosis is limited by competing non-arrhythmic mortality. The Magneto-SCD trial recently reported novel rotation-based magnetocardiography (MCG) indices associated with future appropriate ICD therapies. This substudy explores whether MCG Rotation Score provides incremental information in primary-prevention ICD recipients, particularly beyond NYHA status and MADIT-arrhythmic and non-arrhythmic risk scores. Methods: We included all Magneto-SCD trial participants with a primary-prevention indication with non-missing follow-up/MCG Rotation Score data. The full primary-prevention cohort comprised 54 participants; the post-MI subgroup comprised 28. The primary endpoint was time to first appropriate ICD therapy. Death without prior therapy was used as a pragmatic competing endpoint and not assumed to represent adjudicated non-arrhythmic death. Appropriate shock was a secondary endpoint. Two-year cumulative incidence functions were estimated using Aalen–Johansen methods. Cause-specific Cox models assessed Rotation Score alone and after addition to MADIT-derived scores; nested likelihood-ratio tests evaluated model fit. Results: Of 104 participants, 90 had analysable MCG data and 65 had a primary-prevention ICD indication; after exclusions for missing patients, 54 patients remained (mean age 63.8 ± 13.2 yrs, 45(83.3%) male). Appropriate ICD therapies occurred in 10 (18.5%), including appropriate ICD shock in eight (14.8%); 10 patients (18.5%) died without prior therapy. The median follow-up was 1211.5 days (IQR 793.5–1396.0). Each SD increase in Rotation Score was associated with appropriate ICD therapy with HR 1.64 (95% CI 0.93–2.87; p = 0.086), appropriate ICD shock with HR 2.07 (95% CI 1.15–3.72; p = 0.015), death without prior therapy with HR 1.08 (95% CI 0.54–2.17; p = 0.824), and death without prior ICD shock with HR 0.95 (95% CI 0.48–1.90; p = 0.890). Adding Rotation Score did not significantly improve the primary therapy model beyond MADIT components (LRT p = 0.065) or MADIT benefit score (LRT p = 0.109). Model fit improved for the secondary appropriate ICD shock endpoint when Rotation Score was added to MADIT components (LRT p = 0.016), based on eight shock events. Conclusions: Rotation Score showed an association pattern more closely aligned with arrhythmic endpoints, particularly appropriate ICD shock, than with death without prior therapy. Incremental prognostic value for the primary endpoint was not established. These findings are hypothesis-generating and require confirmation in adequately powered, prospectively designed and externally validated cohorts before any role in clinical risk stratification or ICD decision-making can be determined. Full article

Background/Objectives: The Novavax COVID-19 vaccine (Nuvaxovid™) was authorized in France in December 2021, assisting in ending the global pandemic and providing an alternative to other COVID-19 vaccines. Understanding Nuvaxovid recipients’ profile is essential for guiding vaccination decision-making and maintaining protection amid declining COVID-19 vaccination rates. Methods: This retrospective, observational study characterizes the evolution of French Nuvaxovid recipients’ demographic and clinical characteristics using the Système National des Données de Santé (SNDS) database. Five study seasons (spring 2022, autumn 2022, spring 2023, autumn 2023, spring 2024) were defined based on French vaccination guidelines and Nuvaxovid authorization dates. T-test and Chi-square tests were used to compare between consecutive seasons and subgroups. Results: Nuvaxovid administration was reported in 9805 recipients during spring 2022, 5601 during autumn 2022, 531 during spring 2023, 980 during autumn 2023, and 909 during spring 2024. Statistically significant increases in the proportions of high-risk (spring 2022: 35.4%, spring 2024: 90.5%) and ≥65-year-old (spring 2022: 19.9%, spring 2024: 84.4%) Nuvaxovid recipients were observed, p < 0.05. Comorbidities, notably cardiovascular disease, became progressively more prevalent (spring 2022: 7.1%, spring 2024: 33.7%, p < 0.05). Nuvaxovid was used nationwide, with a higher proportion of recipients in the southeast, a region typically exhibiting lower vaccine coverage. Most Nuvaxovid recipients increasingly received a heterologous regimen (autumn 2022: 91%, spring 2024: 98.6%, p < 0.05), with Nuvaxovid as the ≥4th dose (55–97.2%, p < 0.05). Meanwhile, higher proportions of <65-year-old and not-at-risk recipients received Nuvaxovid as primary series (autumn 2022: 46.9% <65-year-old vs. 5.6% ≥65-year-old, 53.9% not at risk vs. 11.9% at risk, p < 0.05). Conclusions: The use of Nuvaxovid evolved in accordance with changing clinical recommendations, and mix-and-match vaccination strategies and heterologous vaccination patterns suggest that it served as an important protein-based alternative for those switching from other COVID-19 vaccines. Nuvaxovid offers a valuable choice, so that the most vulnerable continue to be vaccinated. Full article

Climate change poses increasing risks not only to natural ecosystems but also to cultural heritage sites and the communities that depend on them. Climate literacy, encompassing knowledge, awareness, and decision-making capacity, is essential for strengthening local resilience and supporting effective climate adaptation. This study investigates climate literacy and perceptions of climate change across historic settlements with different conservation statuses and socio-cultural characteristics. Amasra, Mudurnu, and Taraklı were selected as case studies because of their distinctive historic urban identities and heritage values. A quantitative research design was adopted, and face-to-face surveys were conducted with 300 participants between 15 May and 15 June 2026 using a structured questionnaire consisting of a climate literacy scale and questions on climate change perceptions. The data were analyzed using descriptive statistics, one-way analysis of variance (ANOVA), and K-Means cluster analysis. The analysis revealed meaningful differences in climate literacy across the three historic settlements, while the cluster analysis further showed that participants could be grouped into low, moderate, and high levels of climate awareness. These patterns suggest that climate literacy is not shaped solely by individual knowledge, but is also closely related to the geographical, conservation, and socio-cultural contexts in which people live. The findings therefore point to the importance of place-based climate education and heritage-sensitive approaches to climate adaptation in historic settlements. Full article

Real-time polymerase chain reaction (PCR) has reshaped bacterial infectious disease diagnostics, yet important interpretive gaps remain regarding the relationship among molecular detection, cycle threshold (Ct) values, microbial viability, culture findings, and phenotypic antimicrobial susceptibility. In particular, Ct values are frequently overinterpreted as direct surrogates for viable bacterial burden, and PCR–culture discordance may be interpreted without sufficient consideration of the distinct biological information provided by each method. This review therefore aims to clarify the complementary biological and analytical roles of PCR and bacterial culture, critically examine the determinants and limitations of Ct interpretation and PCR–culture discordance, and provide a practical framework for integrating molecular detection, culture, and antimicrobial susceptibility testing (AST) into clinically and stewardship-informed decision-making. The genotypic lens of PCR (detection of target nucleic acid and resistance genes) is contrasted with the phenotypic lens of bacterial culture and AST, emphasizing that genotype and phenotype distinguish biological layers and account for common PCR–culture discordance. Evidence on Ct variability, assay design, inhibition, and panel scope is synthesized to demonstrate why Ct is inherently assay-specific and non-portable across platforms. Accordingly, MIQE-aligned quality safeguards and assay-specific principles are presented to guide the interpretation of Ct values. Bedside decision tables then integrate Ct patterns, specimen sterility, and patient acuity to support treatment, observation, or additional testing. As a narrative review of heterogeneous evidence, this synthesis does not provide pooled estimates or a uniform risk-of-bias assessment. The proposed Ct categories and clinical framework should therefore be viewed as assay-specific guidance, not universally validated thresholds. Finally, a stepwise workflow is outlined in which PCR is used for rapid rule-in, while culture and AST are retained for confirmation, de-escalation, and dosing. This integrated approach reframes Ct as a qualified signal rather than a standalone truth, supporting faster yet biologically grounded and stewardship-consistent infectious disease management. Full article