Search Results (690)

We present an adaptive certifying semi-algorithm for rational second-order ordinary differential equations (ODEs) in the Darboux-representative, non-degenerate subclass of the Liouvillian setting considered here. The method first computes a nonlocal symmetry and the associated polynomial vector field, and then uses auxiliary associated fields to obtain linear equations for the polynomial data involved in a Darboux integrating factor. The adaptive step factors the residual coefficient space, tests the resulting factors as Darboux polynomials, and reinserts the certified factors into the next linear search. For fixed degree bounds, the procedure uses only linear algebra, factorization, and Darboux-polynomial tests; its correctness does not depend on the adaptive choices, but on a final symbolic certification of the returned integrating factor and first integral. The method is therefore a certifying search procedure for the stated subclass, not a completeness result for all rational second-order ODEs with Liouvillian first integrals. Five certified computational examples, implemented in Maple, illustrate substantial reductions in the number of undetermined coefficients and document the final symbolic verification step. Full article

Tin smelting in a top-blowing furnace is a key non-ferrous metallurgical process in which tin recovery, energy consumption, and the service life of the magnesia–chrome refractory lining are affected by strongly coupled operating variables. The degradation of the magnesia–chrome lining is associated with the combined effects of thermal shock, chemical attack by molten slag and metal, and mechanical wear caused by high-temperature, turbulent, and particle-laden flow during blowing, charging, and tapping operations. First-principles modeling is difficult under extreme thermochemical conditions, whereas experience-based adjustment lacks reproducibility and scalability. Existing surrogate-assisted evolutionary methods also suffer from approximation errors, unreliable constraint handling, and limited interpretability in dynamic, data-scarce industrial settings. To address these issues, this study proposes RAMOSTA, an RBFN-assisted dynamic multiobjective state transition algorithm for tin-smelting parameter optimization. The proposed method integrates radial basis function neural-network surrogate models for tin recovery, energy consumption, and refractory-lining degradation; an uncertainty-aware adaptive offset penalty strategy for conservative constraint handling; a Pareto-based dynamic state-transition optimizer for searching time-varying trade-offs with a limited number of real evaluations; and a hybrid-weight TOPSIS decision layer. The latter combines expert preferences derived using the fuzzy analytic hierarchy process (FAHP), which accounts for the relative importance and uncertainty of decision criteria, with objective weights calculated from Shapley values, which quantify the marginal contribution of each objective to the overall decision. Experiments on an industrial tin-smelting dataset compare RAMOSTA with NSGA-II, NSGA-III, MOEA/D, and C-TAEA using Pareto-front quality, constraint satisfaction, hypervolume, and inverted generational distance. Full article

To reduce high 1× vibration during staged speed-up of a Turbine Power Simulator (TPS) rotor, a staged incremental on-site balancing method based on a Genetic Algorithm–Salp Swarm Algorithm (GA–SSA) is proposed. SSA is a swarm-intelligence optimizer inspired by salps, gelatinous marine organisms that move collectively in chains. A one-dimensional Timoshenko-beam rotor model with lumped disks and equivalent bearing supports is established and validated using a three-dimensional ANSYS model. From meshes M3 to M4, the equivalent speed associated with the first lateral natural frequency changes by 0.23%. The first three critical-speed errors are 6.75–8.80%, while baseline 1× vibration-amplitude errors remain below 10% and phase errors below 7.1%. Speed-specific influence coefficients are then extracted to formulate a staged incremental balancing model based on the current measured vibration and cumulative correction state. In GA–SSA, the final GA population initializes SSA, and the historical GA best is used as the initial Food. Under equal function-evaluation budgets and 30 paired runs, GA–SSA shows search performance comparable to GA and improves the stability of standalone SSA. On-site tests at 10,358, 25,558, and 38,333 rpm reduce 1× vibration at both rotor ends by 79.0–86.4%, confirming the method’s engineering applicability. Full article

Urban low-altitude unmanned aerial vehicle (UAV) planning is inherently spatiotemporal because route feasibility and cost depend on segment arrival times. Search-stage surrogates may therefore favor paths that fail execution-level checks as moving obstacles, temporary no-fly zones, wind-dependent energy use, and building-clearance constraints evolve. We address this search–execution mismatch with the Risk-Aware Artificial Lemming Algorithm (RA-ALA), a three-layer framework governed by a common arrival-time-recursive evaluator. Sequential temporal propagation aligns candidate generation with final assessment, while an energy-weighted A* (Energy-A*) warm start guides continuous waypoint search. The Top-K stage then re-evaluates path variants before feasibility-first selection and conditional recovery. Under prespecified algorithm-specific budgets across 10 High-complexity environments, RA-ALA achieved the highest observed evaluator-feasible rate (24/30, 80.0%), 20 percentage points higher than Energy-A* and space–time Energy-A* (ST-EA*). After Holm adjustment, these contrasts were nonsignificant, while differences against Informed-RRT* and Greedy were supported. Within jointly feasible environments, RA-ALA retained competitive composite scores. Same-cohort descriptive ablation associated Top-K removal with higher composite scores and more infeasible outputs. These results support RA-ALA as a simulation-tested route-generation framework under the modeled constraints, without establishing isolated-operator superiority or real-flight readiness. Vehicle dynamics, sensing, tracking, communications, and flight validation remain outside this scope. Full article

Digital money has transformed from a technological experiment into a central concern of monetary economics, financial regulation, and public policy. Despite rapid growth in cryptocurrencies, stablecoins, central bank digital currencies (CBDCs), and tokenized deposits, the literature remains fragmented across disciplines, with limited cross-cutting synthesis. This article adopts a scoping–integrative review methodology, combining systematic database (Scopus and Web of Science) searches with targeted retrieval of policy and institutional sources (BIS, IMF, Financial Stability Board (FSB), ECB, and national central bank repositories), covering the period 2008–2025, with integrative synthesis of academic, policy, and regulatory sources following PRISMA-ScR reporting principles. The review identifies four competing trust mechanisms underpinning digital money: algorithmic trust associated primarily with cryptocurrencies, private reserve backing trust (stablecoins), sovereign trust underpinning CBDCs, and regulated institutional intermediation supporting tokenized deposits. The proposed digital money ecosystem taxonomy (DMET) classifies digital money instruments across fourteen institutional, governance, technological, monetary, and regulatory dimensions, enabling systematic comparison of cryptocurrencies, stablecoins, CBDCs, and tokenized deposits. Future monetary systems will be hybrid, combining public and private digital money within layered governance arrangements. Interoperability, privacy, programmability, and cross-border governance represent the most critical unresolved policy and research challenges. Full article

Background/Objectives: Emergency departments (EDs) are high-pressure environments where time-sensitive decisions, fragmented data, and operational strain create strong demand for AI-driven clinical decision-support systems (AI-CDSSs). These systems have shown promise in triage, diagnosis, risk stratification, and workflow optimization, yet real-world implementation in emergency medicine remains uneven. This systematic review aimed to synthesize the technical characteristics, clinical applications, implementation dimensions, organizational and ethical considerations, and real-world impact of AI-CDSSs in ED settings. Methods: This systematic review followed PRISMA guidance and searched PubMed, Scopus, and Embase for English-language studies published between January 2015 and February 2025. Eligible studies described AI-CDSS implementation, clinical integration, or performance evaluation in ED settings. Twenty-three studies met the inclusion criteria and were synthesized across five domains: technical characteristics, clinical applications, implementation dimensions, organizational and ethical considerations, and real-world impact. Results: Among the 23 included studies, 20 contributed to the real-world evaluation synthesis. Of these 20 studies, 8 (40%) achieved live or prospective evaluation, seven (35%) relied only on retrospective validation, four (20%) used human-centered or perception-based evaluation, two (10%) used post-implementation assessment, and one (5%) used simulation-based evaluation; categories were not mutually exclusive because some studies employed more than one evaluation approach. In the separate clinical-application synthesis of 20 studies, AI-CDSS were most frequently applied to diagnosis and immediate intervention (45%), followed by prediction and risk stratification (35%) and operational improvement (20%). Successful adoption was more consistently associated with EHR integration, workflow-sensitive design, and clinician engagement than with algorithmic performance alone. Persistent barriers included limited external validation, weak drift-monitoring plans, inconsistent usability testing, regulatory ambiguity, and insufficient equity mitigation. Conclusions: Sustainable implementation of AI-CDSSs in emergency medicine will require prospective multi-site evaluation, sociotechnical integration, adaptive governance, and greater attention to equity. Technical performance alone is insufficient to establish clinical readiness; successful implementation also depends on integration with clinical workflows, clinician engagement, ongoing monitoring, and appropriate governance. Full article

On-site search has become a critical component of e-commerce platforms, shaping product discovery, navigation, and purchasing decisions. While prior research has extensively examined search algorithms and information retrieval, less attention has been given to on-site search as a navigation environment in which interface design, visual guidance, and user search behavior jointly relate to search effectiveness and commercial outcomes. This study addresses this gap through three observational analyses using real-world behavioral data from a large Greek e-commerce retailer. Studies 1 and 2 employ before-and-after comparisons to examine changes associated with Search Results Page enrichment and the introduction of visual promotional guidance, while Study 3 descriptively examines generic query use and broad-to-narrow search behavior. The observed patterns show that Search Results Page enrichment coincided with lower refinement rates and improved commercial metrics, while visual guidance coincided with higher engagement and conversion-related outcomes. The behavioral analysis further indicates a strong prevalence of short, generic queries and patterns consistent with broad-to-narrow navigation. Overall, the findings suggest that on-site search performance is associated with both interface configuration and user search behavior. The study contributes real-world evidence on search as a navigation process and offers practical implications for retailers seeking to improve search-interface performance, while acknowledging the limits of observational before-and-after data for causal inference. Full article

Background: Associative learning, the process of binding stimuli, responses, and outcomes, is critical for behavioral adaptation. While event-related potentials (ERPs) like the P300 effectively index the cognitive effort and context updating required during trial-and-error learning, the precise cortical generators of these signals and their shift across development remain difficult to isolate due to the electroencephalography (EEG) inverse problem. The aim of the study was to examine the differences in P300 localization depending on the associative learning task stage and participants’ age. Methods: A total of 148 participants (aged 7–21) completed a visual-motor associative learning task while undergoing 64-channel EEG recording. Source localization was performed over the 300–500 ms (P300) time window as a conjunction of results of four inversion algorithms at FWE-corrected p < 0.05: multiple sparse priors with greedy search (GS), independent and identically distributed (IID), low-resolution electromagnetic tomography (LOR), and empirical Bayes beamformer (EBB). Exploratory three-algorithm (IID, LOR, and EBB) conjunction analysis results were also reported. Results: A spatial convergence analysis of Early > Late-stage differential maps revealed a consensus across all four models for both cue Onset-locked (right inferior parietal lobule/angular gyrus) and Feedback-locked (occipital, temporal (including bilateral middle temporal gyrus), and orbitofrontal regions) activity. The exploratory three-algorithm analysis additionally implicated left middle temporal gyrus and angular gyrus for cue Onset-locked rule acquisition. No results were produced for age-related effect surviving either the four-algorithm or three-algorithm consensus criterion. Conclusions: This multi-algorithm consensus links active rule search to distributed cortical pattern, involving angular (for cue stimulus processing) and middle temporal gyri (for feedback processing), while no age-related changes in these areas have been demonstrated. Full article

TikTok is the fifth-most-popular social media platform worldwide and is particularly popular among young adults. In recent years, research on the association between TikTok use and mental health among young adults has increased substantially. Following the PRISMA guidelines, this systematic review examined empirical studies investigating this association. We conducted a comprehensive literature search across four electronic databases: PubMed, Web of Science, Scopus, and CNKI. We included and synthesized 11 eligible studies using narrative synthesis. The findings indicated that TikTok use was associated with both potential risks and benefits for the mental health of young adults. These psychological outcomes varied according to patterns of TikTok use and the content encountered on the platform. Individual psychological characteristics, sex, age, and algorithm awareness were also identified as potential factors that may shape this association. The existing literature was limited by the predominance of cross-sectional study designs, which precluded causal inference. Future research should prioritize longitudinal studies and randomized controlled trials to inform the development and evaluation of interventions to support healthier, more responsible for TikTok use among young adults. Full article

With the advance of large language models, companion AI agents have evolved to simulate complex relational dynamics, responding to the need for affection and emotional support. This systematic review aimed to analyze the available empirical evidence on the manifestations of emotional dependency and parasocial grief experienced by users in response to the algorithmic alteration or loss of these companion AI agents. Following the guidelines of the PRISMA 2020 statement and with a protocol registered on OSF, an exhaustive search was conducted across the Web of Science, Scopus, PubMed, and APA PsycINFO databases. After applying inclusion criteria via the PICOS framework and evaluating methodological quality using the MMAT tool, 14 empirical studies were selected and included. The synthesized results suggest that technical disruptions (updates, content filters, or server shutdowns) are associated with disruptions in the agent’s identity continuity, which users frequently described as coinciding with separation distress, depressive symptoms, perceived isolation, and parasocial grief expressed through anthropomorphic metaphors. It is preliminarily concluded that the loss or modification of companion AI agents may be linked to a psychosocial impact that presents descriptive similarities to interpersonal breakups or disenfranchised grief. Full article

Sarcopenia is increasingly recognized as a systemic complication of inflammatory bowel disease (IBD)—more accurately described as an IBD-associated muscle disorder than as a classical extraintestinal manifestation—but it remains inconsistently defined and rarely integrated into routine care. Consensus frameworks require low muscle strength confirmed by low muscle quantity or quality, so studies reporting only computed tomography (CT)-derived muscle area describe low muscle mass rather than consensus-defined sarcopenia; myosteatosis, the fat infiltration of muscle, is a further and partly independent dimension of muscle quality. This narrative review, supported by a structured literature search that was re-run and extended during peer review, synthesized peer-reviewed human evidence published from 1 January 2010 to 5 July 2026 in adult patients. Overall, 152 records were identified through PubMed/MEDLINE and citation tracking; after 19 duplicate or overlapping records were removed, 133 records were screened, 66 full-text reports were assessed, and 54 sources were included: 39 empirical IBD studies, 5 IBD-specific systematic reviews or meta-analyses, 6 consensus or standardization documents and 4 mechanistic or narrative reviews. Sarcopenia in IBD is driven by chronic inflammation, malnutrition, dysbiosis, corticosteroid exposure, inactivity and impaired anabolic signaling. Prevalence is definition- and setting-dependent, from approximately 10% in stable outpatients assessed with functional criteria to more than 40–50% in CT-based or active-disease cohorts. Sarcopenia is consistently associated with—rather than proven to cause—hospitalization, abscess formation, postoperative complications, treatment escalation or failure and impaired function. Muscle ultrasound and automated, artificial intelligence-assisted analysis of opportunistic CT and magnetic resonance imaging (MRI) are emerging as practical routes to routine assessment. We propose a drivers–detection–prognosis–intervention framework, aligned with the sequential European Working Group on Sarcopenia in Older People 2 (EWGSOP2) and Asian Working Group for Sarcopenia (AWGS) 2019 algorithms, to support opportunistic imaging review, strength testing and integrated nutrition–exercise care; this framework is an expert proposal that requires prospective, multicenter validation. Full article

Background and Objectives: Antibody–drug conjugates (ADCs) have become a major therapeutic platform in breast cancer and other solid tumors. Trastuzumab deruxtecan (T-DXd), trastuzumab emtansine (T-DM1), and sacituzumab govitecan (SG) differ substantially in antibody target, linker, payload, drug-to-antibody ratio, and bystander effect, resulting in heterogeneous pulmonary and cardiac toxicity profiles. This narrative review critically compares interstitial lung disease (ILD)/pneumonitis and cardiotoxicity associated with these three agents, aiming to prevent inappropriate extrapolation of toxicity algorithms and to provide a practical, agent-specific framework for multidisciplinary care. Materials and Methods: A targeted narrative search of PubMed/MEDLINE, Google Scholar, ClinicalTrials.gov, regulatory product information, and oncology/cardio-oncology guidance was performed and updated on 24 August 2026. Priority was given to regulatory documents, pivotal trials, pooled safety analyses, real-world cohorts, systematic reviews, and multidisciplinary recommendations. Pharmacovigilance data and case reports were included only to characterize rare events. Results: T-DXd is associated with a clinically important ILD/pneumonitis risk (approximately 12–15% in pooled analyses), predominantly grade 1–2 but occasionally fatal, requiring proactive surveillance, immediate interruption for suspected disease, and grade-directed corticosteroid therapy. T-DM1 shows a low but established pneumonitis incidence of approximately 1%, with permanent discontinuation recommended upon diagnosis. SG-related pneumonitis is rare and incompletely defined, without a T-DXd-like surveillance mandate. Both T-DM1 and T-DXd retain trastuzumab-derived cardiac monitoring requirements; symptomatic heart failure remains uncommon, although protocol-defined LVEF declines appear more frequent with T-DXd. SG lacks an established cardiomyopathy signal. Conclusions: Cardiopulmonary toxicity of ADCs is agent-specific rather than a class effect. Monitoring intensity, diagnostic thresholds, and management pathways must be tailored to the individual drug, regimen, indication, dose, patient comorbidity, and prior therapy. Close collaboration among oncology, radiology, pulmonology, and cardio-oncology is essential to preserve both treatment efficacy and patient safety. Full article

Background: Cardiac sarcoidosis (CS) is a potentially life-threatening manifestation of systemic granulomatous disease, characterized by a heterogeneous spectrum of arrhythmic and conduction disorders that represent the leading cause of CS-related sudden cardiac death (SCD). Clinical cardiac involvement is estimated at 5% of sarcoidosis patients, while silent myocardial infiltration is detected in 20–25% of autopsy series. Diagnosis is difficult because the disease frequently runs a subclinical course and no single test is pathognomonic. Methods: PubMed and Embase were searched to 5 August 2026 for this narrative review, incorporating peer-reviewed articles, international guidelines (HRS 2014, JCS 2016, AHA/ACC/HRS 2017, ESC 2022, AHA 2024, the 2024 European clinical consensus statement and the 2025 ESC guidelines on myocarditis and pericarditis), and registry data. Results: Atrioventricular (AV) block occurs in 23–30% of CS patients and frequently requires permanent pacing. Fatal ventricular arrhythmia rates reach 20.7% at 5 years and 31.9% at 10 years (ILLUMINATE-CS registry, n = 512). Late gadolinium enhancement (LGE) on cardiac magnetic resonance (CMR) performs well diagnostically (pooled sensitivity 95%, specificity 85%) and is associated with an odds ratio of 10.74 for arrhythmic events. 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) provides complementary value for guiding immunosuppressive therapy and pre-ablation risk assessment. Implantable loop recorders enable early arrhythmia detection in patients not yet meeting implantable cardioverter-defibrillator (ICD) criteria. Conclusion: Optimal management of arrhythmias in CS requires a multimodal diagnostic approach integrating electrocardiography, advanced cardiac imaging, and continuous rhythm monitoring. Risk stratification for SCD remains the central challenge, requiring individualized decision-making that integrates left ventricular ejection fraction (LVEF), imaging biomarkers, and electrophysiological data. Future prospective studies should aim to refine predictive risk algorithms, assess the role of emerging immunomodulatory agents such as JAK/STAT inhibitors, and validate artificial intelligence (AI)-assisted tools intended to identify cardiac involvement and arrhythmic risk earlier in the disease course. Full article

In recent years, interest in artificial intelligence has grown significantly, particularly in the development of advanced computational models for supply chain decision-making. Inventory management is one of the areas in which intelligent algorithms can support demand forecasting, replenishment, stock control, and operational optimization under uncertainty. This study presents an updated systematic literature review of intelligent algorithms applied to inventory management. The review followed PRISMA 2020 guidelines and combined database searches in Scopus, ScienceDirect, Web of Science, IEEE Xplore, SpringerLink, Taylor & Francis, and complementary manual searching. The original search covering January 2020 to December 2024 was updated in July 2026 to include studies published or available online up to June 2026. After applying strict eligibility criteria, 37 primary studies with quantitative evidence were included. The updated corpus confirms the predominance of deep learning, reinforcement learning, and hybrid intelligent models, while also showing the recent emergence of Transformer-based, graph neural network, multi-agent reinforcement learning, and prescriptive analytics approaches. The most frequent application areas were inventory control, inventory optimization, replenishment decision-making, and demand forecasting. Reported improvements were mainly associated with cost efficiency, service level, stockout reduction, and system performance; however, the magnitude of improvement varied across algorithms, data sources, sectors, and simulation or real-world settings. Overall, intelligent algorithms represent a relevant tool for improving inventory management, but their adoption requires careful validation, transparent reporting, and alignment with the operational context. Full article

Treat-to-target management in inflammatory bowel disease (IBD) combines symptoms, fecal and serum biomarkers, endoscopy, histology, and cross-sectional imaging, but these measures frequently diverge. Discordance may reflect analytical variation, timing, disease location, phenotype, comorbidity, or partially non-overlapping biological processes. We performed a critical narrative review using a structured PubMed/MEDLINE search, supplemented by citation chaining and publisher searches. Guidelines, systematic reviews, diagnostic studies, cohorts, randomized trials, and selected mechanistic studies were prioritized. Fecal calprotectin (FC) and lactoferrin primarily reflect intestinal neutrophilic inflammation, whereas C-reactive protein (CRP) and related serum indices reflect a nonlocalizing systemic response. The fecal immunochemical test (FIT) detects gastrointestinal bleeding, and leucine-rich alpha-2 glycoprotein (LRG) remains promising but insufficiently standardized. We distinguish five biological biomarker domains—namely, fecal–neutrophil; serum–systemic; epithelial/barrier; restitution/resolution; and fibrosis/extracellular matrix (ECM) remodeling—from symptoms and clinical indices, pharmacologic measurements, and phenotype-directed reference assessments. Circulating barrier, repair, and matrix-turnover markers remain investigational. Reactive therapeutic drug monitoring (TDM) for anti-tumor necrosis factor (anti-TNF) agents has the most mature evidence. Vedolizumab and ustekinumab show exposure–response associations, but actionable thresholds are unvalidated, and clinical TDM is not established for newer biologics or oral small molecules. After objective confirmation of disease activity, the framework may support phenotype-directed therapeutic decisions but is not a validated algorithm. Clinically important disagreement should prompt assessment of sampling, assay, timing, infection, medication-related confounding, and pretest probability before phenotype-directed endoscopy, histology, imaging, or reactive TDM is selected. A single discordant result should neither trigger treatment escalation nor exclude active or structural disease. Full article