Abstract: In gravimetric liquid-flow calibration systems, the diverter valve defines the effective start and end of mass collection. For this reason, its temporal repeatability affects the mass associated with the transition and can become a relevant contribution to the measurement uncertainty, especially in low-flow modules. Although the state of the art has addressed this problem through geometric improvements, time or mass correction models, CFD analysis, and higher-precision actuators, these approaches are usually treated in isolation, without systematically linking the variable hydrodynamic load, switching repeatability, and the affected-mass contribution within a single optimization framework. This work presents the genetic-algorithm-based tuning of a PID controller for the servo-electric actuation of a diverter valve in a 200 kg gravimetric module, operating in the interval from 2 L/min to 40 L/min. The main contribution consists of optimizing the PID gains as a function of the flow rate and the hydrodynamic load, with the purpose of reducing the temporal dispersion of the mechanism and its relative contribution to the gravimetric calculation. At 40 L/min, the original pneumatic system presented a switching-time standard deviation of 0.6149 s, while with the servo-electric system and the optimized PID this value decreased to 0.0832 s, equivalent to a reduction of 86.5%. Likewise, the relative contribution of the diverter decreased from 5.10% to 0.688%. The results show that the evolutionary optimization of the PID allows improving the repeatability of the diverter and reducing the variability of the affected mass, although it does not represent by itself the complete uncertainty budget of the gravimetric module.

Abstract: The decarbonisation of the energy sector requires efficient strategies to reduce fuel consumption and greenhouse gas emissions. Organic Rankine Cycles (ORCs) have emerged as a promising technology for waste heat recovery due to their flexibility and ability to operate with low- and medium-temperature heat sources. This work develops a general mathematical framework, grounded in the Helmholtz energy function, to characterise the limiting and optimal efficiency of ORCs equipped with an Internal Heat Exchanger (IHE) when operating with dry and isentropic working fluids. The framework is exemplified using the van der Waals equation of state and extended to real fluids through the PC-SAFT model. Results show that integrating an IHE significantly enhances efficiency for drier working fluids, which expand deeper into the superheated vapour region, enabling greater internal heat recovery. Efficiency gains diminish at condenser temperature extremes, defining operational boundaries where IHE integration is less effective. From a practical perspective, minimising the temperature difference at the IHE outlet (ΔT_min) is critical to maximise performance. The proposed framework provides theoretical insight and practical guidelines for fluid selection and operating strategies in ORC-based waste heat recovery systems.

Abstract: High-precision two-dimensional intermolecular potential energy surfaces (PESs) for Rg–CuF (Rg = Ar, Kr, Xe) were constructed at the coupled-cluster singles and doubles with non-iterative triples [CCSD(T)] level, by employing aug-cc-pVXZ (X = D, T, Q) basis sets and the energies were extrapolated to the complete basis set (CBS) limit. All three complexes exhibit a consistent topological pattern: the global minimum corresponds to a collinear Rg–Cu–F configuration, and the local minimum corresponds to an anti-linear Rg–F–Cu configuration. As the atomic number of noble gas increases, the Rg–Cu equilibrium distance lengthens while the binding strength remarkably enhance. Bound state calculations were performed based on these PESs to yield rotational levels, which can be used derive the intermolecular vibrational frequencies, molecular structures and spectroscopic parameters for all primary isotopologues. The predicted rotational constants B are in excellent with the experimental observations, attaining a sub-MHz accuracy at the AVTZ level for Kr–CuF and at the CBS limit for Ar–CuF and Xe–CuF. Vibrational wavefunction analysis reveals that the intermolecular vibrational modes of Kr–CuF and Xe–CuF are highly localized, consistent with the pronounced molecular rigidity observed experimentally. Isotopic effect analysis reveals a well-defined linear relationship between the changes in the rotational constant B and the intermolecular vibrational frequency in relation to the reduced mass of the complex, which provides a reliable basis for predicting spectroscopic parameters of unobserved isotopologues.

Abstract: In attribute-based multimodal knowledge graphs, images and text serve as auxiliary entity attributes, providing semantic evidence beyond graph topology for link prediction. However, bidirectional conditional dependencies between an entity's multimodal representations and its local links—the one-hop factual triples formed by relations and neighboring entities—remain insufficiently modeled: different local links rely on structural, visual, and textual evidence to varying degrees, and conversely, the relevance of each modality differs across local links. Existing methods have progressed from modality alignment and fusion to relation-aware, structure-aware, and adaptive selection; yet they mainly let information flow from links to modalities, while conditioning on each modality to distinguish and weight individual surrounding links remains underexplored, leaving the two selection directions disconnected. We therefore propose BiMLI, a Bidirectional Link–Modality Interaction framework. Link→Modality selects relevant structural, visual, and textual evidence conditioned on each local link, whereas Modality→Link dynamically weights an entity's surrounding links conditioned on each modality representation. Interaction-aware feature fusion and neighborhood aggregation integrate both representations, with cross-modal contrastive regularization as an auxiliary constraint. On DB15K and MKG-W, BiMLI achieves MRR scores of 42.35% and 43.55%, corresponding to relative improvements of 6.43% and 11.98% over the strongest respective baselines. Ablation and fine-grained analyses show that the two directions are functionally asymmetric yet complementary, validating the necessity of bidirectional conditional modeling.

Abstract: Sweet potato (Ipomoea batatas L.) is an important food security crop in developing countries, but production is constrained by virus-infected planting material from vegetative propagation. This study evaluated selected combinations of plant growth regulators (PGRs) for in vitro propagation of orange-fleshed sweet potato cv. 'Kulfo'. Nodal and apical shoot explants were cultured on Murashige and Skoog (MS) medium with different combinations of 6-benzylaminopurine (BAP) and gibberellic acid (GA₃) for shoot initiation, BAP and naphthalene acetic acid (NAA) for multiplication, and indole-3-butyric acid (IBA) and NAA for rooting. Among the treatments tested, MS medium with 0.5 mg L⁻¹ BAP and 0.1 mg L⁻¹ GA₃ gave the highest shoot regeneration (62% from nodal and 59% from apical explants). For multiplication, 1.0 mg L⁻¹ BAP with 0.1 mg L⁻¹ NAA produced the highest shoot number (7.2 shoots per explant). Half-strength MS medium with 0.1 mg L⁻¹ IBA and 0.1 mg L⁻¹ NAA resulted in the best rooting response (13.3 roots per shoot). Plantlets from the best-performing treatment achieved 98.0% survival during acclimatization. However, the limited PGR concentrations tested mean that these results should be considered preliminary. Further optimization using broader concentration gradients is needed to establish a truly optimized protocol. This study provides a foundation for developing cultivar-specific micropropagation protocols for sweet potato in Ethiopia.

Abstract: We developed a formulation of classical electrodynamics in the 2T + 3S dimensions without compactifying the extra time dimension. We found an effective electric charge defined by the ratio of the two distinct speeds of causality. We extended the concept to the hadronic Color-Space and postulated that the massless gluon was tied to the second time dimension, with an intrinsic speed different from the speed of light. We applied the theoretical formulations to the e⁺e^- → hadrons experiments and suggested a preliminary estimate of the gluon speed using the R- values.

Abstract:

The ECOSTRESS mission provides high resolution thermal infrared observations that support a wide range of applications ranging from evapotranspiration monitoring and drought assessment to Land Surface Temperature (LST) and emissivity retrieval. Accurate estimation of Precipitable Water Vapour (PWV) is critical for these applications because of strong influence of atmospheric water vapour on thermal infrared radiative transfer that influences emissivity retrieval. The current ECOSTRESS processing chain estimates PWV from GEOS5-FP numerical weather prediction data. In this study, we investigate an alternative approach that retrieves PWV directly from ECOSTRESS thermal brightness temperatures using symbolic Regression (PYSR). Using more than 269,000 spatio-temporally matched ECOSTRESS-GNSS observations, we derived a unified all-season analytical formula capable of estimating PWV without relying on external atmospheric profiles or ancillary emissivity. Instead, the proposed model relies only on readily available and temporally stable ancillary variables, namely digital elevation model (DEM) and Normalized Difference Vegetation Index (NDVI) data. The resulting analytical formulation (All-season PySR) derived using PySR symbolic retrieval achieved a Root Mean Square Error (RMSE) of 7.22 mm and an R² of 0.624 when evaluated against GNSS-derived precipitable water vapor observations. In order to improve the results, season and regime specific PySR formulas were first developed to provide interpretable PWV estimates for various conditions. These formulas form the initial retrieval component of the Climate-Adaptive Ensemble formula. The Climate Adaptive Ensemble (CAE) combines PySR formulas, ECOSTRESS inputs and historical ERA5 water-vapour profiles to perform a global analytical ridge regression. The resulting output achieved an RMSE of 5.35mm and R² of 0.781 on test GNSS observations. The CAE formula was further validated on external radiosonde dataset and independent GNSS observations to evaluate its robustness. The methodology proposed here will be extremely useful for future satellite missions using thermal sensors such as TRISHNA (Thermal Infra-Red Imaging Satellite for High-resolution Natural resource Assessment) and LSTM (Land Surface Temperature Radiometer) as the methodology could help in retrieving PWV instantaneously for atmospheric correction instead of depending on external products.

Abstract: Artificial intelligence (AI) is rapidly transforming veterinary education and practice, increasing the need for AI literacy among future veterinary professionals. However, evidence regarding veterinary students’ knowledge, use, and perceptions of AI remains limited. This cross-sectional study evaluated AI awareness, usage patterns, attitudes, and educational expectations among undergraduate veterinary students at the University of Las Palmas de Gran Canaria (Spain). A semi-structured online questionnaire was completed by 189 students (43% of the target population), and data were analysed using descriptive statistics and Pearson’s chi-square tests. Most students reported frequent use of generative AI tools, particularly ChatGPT, mainly for information retrieval and academic support. Participants generally perceived AI as a valuable educational resource but expressed concerns regarding the reliability of AI-generated information, ethical issues, data privacy, and the potential impact on critical thinking. Despite widespread AI use, most respondents reported limited formal training and insufficient institutional guidance, while strongly supporting the integration of AI-related competencies into the veterinary curriculum. These findings highlight a gap between the rapid adoption of AI and the development of formal AI literacy in veterinary education. Veterinary curricula should promote not only technical proficiency but also critical evaluation, ethical awareness, and the responsible use of AI in professional practice.

Abstract: The 24 solar terms (STs) comprise a traditional Chinese seasonal calendar that has long guided agricultural practices, but its agronomic relevance under climate change remains uncertain. We assessed 24ST applicability in Shanxi Province using daily temperature, precipitation, and sunshine duration records from 23 meteorological stations. Climate trends were analyzed at the ST scale, and the Decision Support System for Agrotechnology Transfer (DSSAT) CERES-Maize model was applied to simulate maize phenology and yield in evaluating phenological shifts and sowing-date effects. Results showed that (1) warming was concentrated during Yushui–Qingming and Xiaoxue–Dahan, with sunshine declines across Mangzhong–Xiaoshu and Bailu–Hanlu. Precipitation trends were weak and spatially variable. (2) The calibrated CERES-Maize model reliably reproduced regional yields and phenology. Simulated V3 growth stage, anthesis, and maturity dates advanced by 1.53, 1.88, and 3.11 days per decade, respectively. V3 was driven by early-spring hydrothermal radiation, while anthesis and maturity was closely associated with Xiazhi/Xiaoshu/Dashu summer heat. (3) Optimal sowing windows were Guyu-H2–H3, Xiaoman-H1–H2, and Xiaoman-H2–H3 for northern, central, and southern Shanxi, respectiely. Optimized sowing shifted V3 towards Xiaoman–Mangzhong, delayed anthesis from early Xiaoshu to late Dashu–Liqiu, and postponed maturity towards Qiufen. These findings provide a scientific basis for continued use of the 24 STs in guiding agricultural practices for maize.

Abstract: Patient-facing medical information software is typically built one disease at a time: each new condition requires a bespoke application, with its own data model, user interface, and safety scaffolding. This approach does not scale to the breadth of human disease. Orphanet, the reference catalogue of rare conditions, alone describes on the order of six to seven thousand clinically distinct rare diseases, and broader clinical terminologies enumerate tens to hundreds of thousands of codeable entities; no hand-built, per-disease portfolio can realistically span that space. This report presents the Medical App Generator, a single SwiftUI application for macOS and iOS in which the disease is not hard-coded but is instead a selectable, user-enterable parameter that re-skins the entire application from a declarative configuration object. We describe the configuration schema (disease metadata, comorbidities with etiological-driver categories and linking mechanisms, numerical metrics with field-importance metadata, symptoms, blood tests, and a treatment database), the runtime architecture that binds it to a reactive user interface, and a free-text disease-resolution layer that loads curated content for known conditions and a functional blank template for any other name entered. A central contribution is the application's safety architecture. The system is positioned as informational rather than advisory: it presents AI-assisted, literature-derived content as structured input for a licensed clinician, never as a diagnosis or a personalized prescription. Treatments are withheld behind a combined medical, safety, warranty, liability, and intellectual-property notice that the user must explicitly accept; a dosage reference is gated behind required safety fields and never computes a patient-specific dose; and a dedicated condition (Long COVID and its synonyms) is deliberately blocked and redirected. We map these design choices to the United States Food and Drug Administration's criteria for non-device clinical decision support and to the published literature on clinical decision support systems and on the reliability of large language models in medicine. We report a transparent coverage analysis: five diseases ship with curated data, one is intentionally inert, and all other names resolve to an empty but fully navigable template. We conclude that configuration-driven generation, paired with conservative, explicit safety gating, is a defensible pattern for patient information tools, and we discuss its limitations, including the gap between representative and exhaustive clinical content and the unresolved question of formal validation.

Abstract: An outstanding issue in molecular biology is understanding the function of elements (e.g. transcripts, CpG sites, and metabolites) detected by high throughput screening platforms. In this study an analysis plan was devised to construct the phenotypic signature of an element by integrating its QTLs with GWAS data. By applying this plan to categories of (un)known transcripts, through a two-step discovery and validation, I provided evidence that the phenotypic signature is a robust metric and it can be used to infer the function of an unknown transcript. In the manuscript, I detailed the analysis plan and the procedure to infer the function of a molecular element. The computed database of phenotypic signatures and the related scripts are publicly available.

Abstract: Recent advances in artificial intelligence (AI) are reshaping smart glasses from egocentric capture and display devices into platforms for wearable intelligence. Smart glasses increasingly serve as wearable AI systems that connect first-person observation with real-time assistance under strict form-factor constraints. We frame this transition through the lens of AI smart glasses and define them as a system-level concept in which egocentric sensing, resource-aware computing, intelligent reasoning, multimodal interaction, and real-world application constraints are co-designed for personalized assistance in the physical world. To systematically study this perspective, we organize the survey around four connected dimensions. First, we examine the hardware foundation that bounds sensing, computation, feedback delivery, and sustained deployment. Second, we study wearable intelligence, where egocentric signals are transformed into perceptual, contextual, and agentic capabilities. Third, we discuss interaction design, through which users request, receive, correct, and regulate assistance during ongoing activity. Fourth, we analyze application scenarios across healthcare, accessibility, situated learning, daily life assistance, cultural tourism, and industrial support, showing how domain requirements reshape system design and evaluation. We further identify five cross-cutting research challenges for future AI smart glasses: next-generation hardware, trustworthy egocentric intelligence, lifelong personalized memory, proactive intelligence, and embodied foundation models. By centering smart glasses as wearable-intelligence platforms, this survey provides a unified framework for organizing technologies, applications, and open challenges in this emerging area.

Abstract: The current research aims to examine the impact of fluctuations in global financial market volatility, geopolitical risk, the energy transition, and global macroeconomic and structural control variables on crude oil prices in the modern global energy economy. The study uses annual time series data from 1990 to 2024 from international databases such as World Bank, the International Energy Agency (IEA), the Federal Reserve Economic Data (FRED), OPEC, and the Geopolitical Risk (GPR) Index database to estimate short- and long run relationships between the variables using the Autoregressive Distributed Lag (ARDL) and Error Correction Model (ECM) approaches, with FMOLS deployed for sensitivity analysis and robustness checks. The results indicate that financial market volatility can statistically reduce crude oil prices both in the short and long runs, affirming the global financial cycle theory. However, geopolitical risk is shown to have an insignificant effect on crude prices in the short and long runs. Furthermore, the energy transition has an increasing effect on crude oil prices in the short run. Geopolitical risk weakly alters the effect of global financial market volatility, but significantly shifts the energy transition from a reducing effect to an increasing one, with a threshold of 133.05. Regarding the control variables, OPEC allocation policy, global inflation, and the real GDP growth rate contribute to lowering crude oil prices, while changes in crude oil supply, oil future prices, urban population growth rate, and the U.S. index have an increasing effect on crude oil prices. The study suggests that better supervision of financial markets, international peace and global stability efforts, faster investments in renewable energies, and more comprehensive energy policy frameworks are needed to make energy markets more stable.

Abstract: Background: The odontogenic keratocyst (OKC) is an aggressive intraosseous cyst with a high recurrence rate. Optimal surgical management remains debated, and independent predictors of recurrence require further analysis. The aim of this study is to evaluate surgical treatment outcomes for OKC across a spectrum of approaches and to identify clinical, demographic, and histopathological predictors of recurrence over long-term follow-up. Methods: A multicenter retrospective study was conducted. A total of 113 patients with histologically confirmed OKC treated between 2015 and 2025 were included. univariate and multivariate logistic regression analyses were performed to assess associations between clinical variables and recurrence. Results: First-stage recurrence occurred in 47/113 patients (41.6%). Histotype was the only independent predictor of recurrence, with 85% of parakeratinized and 15% of orthokeratinized cases. No recurrences were observed among 8 patients treated with excision combined with adjuvant therapy (cryotherapy or topical 5-fluorouracil), compared with 44.8% in the remaining cohort. Analyses for second- and third-stage recurrence did not identify significant predictors, likely due to limited event numbers. Conclusions: Parakeratinized histotype is the dominant predictor of OKC recurrence. Adjuvant therapy — particularly topical 5-fluorouracil or cryotherapy — was associated with absence of recurrence and should be considered in standard management. Long-term follow-up is essential.

Abstract: Metabolic dysfunction – Associated Steatotic Liver Disease (MASLD) represents the emerging leading cause of Chronic Liver Disease (CLD) worldwide, with a global prevalence of approx. 30% in the general population that parallels global rates of obesity and Type 2 Diabetes (T2D). At present no validated therapy is available to block or slow down disease progression to Metabolic dysfunction – Associated SteatoHepatitis (MASH), liver fibrosis and cirrhosis and hepatocellular carcinoma (HCC). At present there is a lack of reliable biomarkers able to identify MASH patients at risk of disease progression and/or HCC development. According to the knowledge that pro-inflammatory cytokines play a key role in MASLD/MASH progression and HCC development, in this review we will discuss the role in this disease and other CLD of Oncostatin M (OSM), a cytokine belonging to the IL-6 family, and of pathways involving OSM and its receptor β (OSM/OSMRβ axis). OSM and related pathways are emerging as selective in sustaining disease progression by promoting chronic inflammation and fibrogenesis. Moreover, OSM/OSMRβ axis is critically involved in MASH-related HCC development by affecting proliferation, angiogenesis, invasiveness and metastasis as well as by reshaping MASH-related tumour immune microenvironment. OSM/OSMRβ axis is then emerging as a selective MASH-related putative therapeutic target.

Abstract: One of the most important issues in ecological physiology and ichthyology is the study of the physiological and biochemical mechanisms underlying fish adaptation to changing environmental conditions. This issue is of particular significance in the context of increasing anthropogenic pressure on freshwater ecosystems. Understanding the ecological and physiological aspects of adaptation within the framework of integrated population studies is essential for developing a scientific basis for predicting the biological status of fish. Addressing this problem requires systematic investigations of the dynamics of homeostatic parameters in functional body systems, taking into account their sex-, age-, and season-related variability. In the present study, seasonal investigations were conducted to determine the hematological characteristics of taxonomically closely related bony fish species that differ in physiological activity, feeding habits, and ecology, sampled from the Aleksandar Stamboliyski Reservoir in Bulgaria. The physicochemical parameters of the water—temperature, pH, electrical conductivity, and dissolved oxygen—were within the allowable values by national and EU legislation. However, the water analyses revealed elevated concentrations of nitrate nitrogen (N–NO₃⁻) and nitrite nitrogen (N–NO₂⁻) compared to the permissible values for moderate ecological status under Regulation No. H-4. Furthermore, the concentrations of toxic metals in the water—chromium (Cr), cobalt (Co), lead (Pb), and zinc (Zn)—were also within the limits. However, the cadmium (Cd) levels were reported as <0.02 mg/L (20 µg/L), exceeding the permissible value of 5 µg/L. Seasonal dynamics and species-specific patterns were observed in hematological parameters. The erythrocyte indices in the three fish species – common carp (Cyprinus carpio Linnaeus, 1758), Prussian carp (Carassius gibelio Bloch, 1782) and European perch (Perca fluviatilis Linnaeus, 1758) showed similar values during spring and winter; the highest values were recorded in common carp during summer and the lowest in Prussian carp during summer. The hemoglobin and hematocrit values were highest during winter for all three species, with the highest levels observed in common carp, followed by European perch and Prussian carp. Morphological alterations were also identified in the studied species - in common carp, changes were observed in erythrocyte nuclei; in Prussian carp, a high number of dividing erythrocytes was recorded; in European perch, the observed changes included the presence of rounded erythrocytes.

Abstract: This article develops duality principles and numerical results for a large class of non-convex variational models. The main results are based on fundamental tools of convex analysis, duality theory and calculus of variations. More specifically the approach is established for a class of non-convex functionals similar as those found in some models in phase transition. Moreover, we develop a general duality principle for quasi-convex relaxed formulations for some models in the vectorial calculus of variations. Concerning applications of such results are presented for a non-linear model of plates and for nonlinear elasticity. Finally, in some sections we present concerning numerical examples and the respective softwares.

Abstract: As artificial intelligence (AI) advances toward artificial general intelligence (AGI) and potentially superintelligence, questions on trustworthiness, accountability, and governance become increasingly critical. This article examines the Assessment List for Trustworthy Artificial Intelligence (ALTAI) as a governance instrument and evaluates its relevance for more autonomous, adaptive, and potentially self-improving AI systems. The analysis draws on empirical insights from the SHAPES project pilots, in which ALTAI was applied to assess AI-based technologies in healthcare settings, and uses these experiences as an evidence-informed basis for broader conceptual reflection. The study investigates three key dimensions: ALTAI’s strengths in operationalizing ethical principles such as human oversight, transparency, and accountability; the limitations of static, self-assessment-based frameworks when addressing increasingly autonomous and adaptive systems; and the scalability of ALTAI-type approaches as AI capabilities evolve toward AGI. The findings indicate that while ALTAI provides a valuable framework for translating ethical principles into practical governance mechanisms, it is less suitable for addressing dynamic behavior, emergent properties, and long-term societal impacts associated with advanced AI systems. The article concludes that ALTAI represents an important foundation for trustworthy AI governance but requires conceptual expansion and integration with more adaptive, system-level oversight mechanisms to remain effective in the transition toward AGI and superintelligence.

Abstract: Climate change poses one of the greatest threats to the sustainability of rainfed agriculture in Latin America, where common bean (Phaseolus vulgaris L.) production is particularly vulnerable because of its high sensitivity to fluctuations in temperature and precipitation, as well as to extreme climatic events. This scoping review aimed to examine the evolution of scientific research on the impacts of climate change on the sustainability of rainfed common bean production in Latin America by identifying geographic and temporal research trends, methodological approaches, major scientific advances, knowledge gaps, and future research priorities. The review followed the Joanna Briggs Institute (JBI) methodological framework and the PRISMA-ScR reporting guidelines and included 154 peer-reviewed studies published in indexed journals. The evidence was synthesized by country, publication period, and methodological approach, encompassing field experiments, agroclimatic assessments, crop-simulation models, Geographic Information Systems (GIS), remote sensing, review articles, meta-analyses, and farmer surveys. The results revealed that scientific production was concentrated primarily in Mexico and Brazil, whereas research in the remaining Latin American countries remained limited and geographically fragmented. Over time, the field evolved from studies focused primarily on crop physiology and genetic improvement toward multidisciplinary approaches integrating climatic, agronomic, environmental, and socioeconomic perspectives. Despite these advances, important knowledge gaps remain, particularly the limited availability of long-term field experiments and the insufficient integration of environmental, economic, and social dimensions into climate adaptation research. This review provides a comprehensive regional synthesis of the available scientific evidence on climate change and rainfed common bean production in Latin America, offering a solid evidence base to guide future research, inform climate adaptation strategies, and strengthen the resilience and sustainability of rainfed common bean production systems.

Abstract: Background/Objectives: Cognitive stimulation and cognitive training are used as non-pharmacological approaches to support people living with Alzheimer’s disease, but Alzheimer-specific evidence is dispersed across heterogeneous intervention formats. This scoping review mapped structured cognitive stimulation and training interventions evaluated in people with mild-to-moderate Alzheimer’s disease and summarized the cognitive, emotional, functional, and follow-up outcomes reported. Methods: The review was conducted using the Joanna Briggs Institute methodology and reported according to PRISMA-ScR. PubMed, SciELO, PEDro, LILACS, and Google Scholar were searched on 24 February 2025 for studies published between January 2015 and 24 February 2025 in English, Portuguese, or Spanish. Intervention studies were eligible. Two reviewers independently screened records and charted data, with disagreements resolved by a third reviewer. Results: Of 352 records identified, five studies involving 245 participants were included. Interventions comprised virtual-reality cognitive stimulation, conventional cognitive training, group reminiscence therapy, a multicomponent music–reminiscence–reality-orientation intervention, and computerized cognitive training. Technology-based interventions reported improvements in global cognition or selected memory, language, attention, and executive outcomes. Conventional cognitive training improved initiative and temporarily stabilized memory. Reminiscence-based interventions primarily improved depressive and neuropsychiatric symptoms. Where longer follow-up was available, benefits diminished over time. Conclusions: The mapped evidence suggests that structured cognitive stimulation and training may produce short-term, outcome-specific benefits in mild-to-moderate Alzheimer’s disease. However, the small number of heterogeneous studies, variable outcome measures, and limited long-term evidence preclude firm conclusions regarding comparative effectiveness. More rigorous and adequately powered studies are needed.