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To estimate Diabetes mellitus (DM) progression at one and two years in terms of glycemic targets and development of complications. We analyzed a retrospective cohort of adult DM patients treated in a Health Maintenance Organization in Colombia, including those with at least one glycosylated hemoglobin (HbA1c) measurement in 2018, 2019, and 2020. We defined four disease transition stages based on metabolic goals according to HbA1c levels and complications: 1. Within HbA1c goals and without complications; 2. Outside goals and without complications, 3. Within goals, but with complications, and 4. Outside goals and with complications. We applied Natural Language Processing (NLP) techniques to extract relevant clinical information from Electronic Health Records. Machine learning (ML) models were used to predict patient progression. A total of 23,802 patients were included. Despite achieving initial glycemic control, more than 60% of patients who started within HbA1c targets and without complications developed chronic complications within two years. Our models, which achieved up to 80% accuracy and F1 scores above 74%, identified key predictors of disease progression. Adherence to dyslipidemia treatment guidelines significantly reduced the likelihood of HbA1c deterioration and complications, whereas non-adherence to pharmacological treatments increased the risk of complications. These findings suggest that HbA1c control alone is insufficient to prevent disease progression and that a more comprehensive management approach-including lipid control, kidney function monitoring, and improved adherence to clinical guidelines-is necessary. Patient compliance with pharmacological treatments, professional adherence to clinical practice guidelines, and lifestyle interventions play a crucial role in diabetes progression. While our models provide strong predictive capabilities, improving data quality and integration remains essential for better forecasting and intervention strategies.

Atmospheric corona discharge devices are being studied as innovative systems for cooling, sterilization, and propulsion, in several industrial fields, from robotics to medical devices, from drones to space applications. However, their industrial scale implementation still requires additional understanding of several complex phenomena, such as corrosion, degradation, and fatigue behaviour, which may affect final system performance. This study focuses on the corrosive behaviour of wires that perform as a high-voltage electrode subject to DC positive corona discharge in atmospheric air. The experiments demonstrate that the non-thermal plasma process promotes the growth of the oxidative films and modifies the physicochemical properties of the materials chosen as corona electrodes, hence affecting device operation. Surfaces exposed to this non-thermal plasma are electrically characterized by negative exponential decay of time-depend power and analysed with SEM. Implications on performance are analysed and discussed.

A bstract We propose several Two Higgs Doublet Models with the addition of an Abelian gauge group which free the usual framework from flavor changing neutral interactions and explain neutrino masses through the seesaw mechanism. We discuss the kinetic and mass-mixing gripping phenomenology which encompass several constraints coming from atomic parity violation, the muon anomalous magnetic moment, rare meson decays, Higgs physics, LEP precision data, neutrino-electron scattering, low energy accelerators and LHC probes.

Habitat loss and climate change are major drivers of biodiversity loss, but their synergistic effects and functional perspectives have to be better understood. We employed species distribution models under future contrasting socioeconomic scenarios to assess the impacts of climate change and human footprint on avian frugivore and nectarivore functional groups in the Magdalena Valley, a highly transformed and biodiverse region in Colombia. We constructed the functional groups based on a dissimilarity matrix with 16 anatomical and ecological traits. Two types of future climatic refugia (type 1: areas that will maintain the current climatic conditions and type 2: regions outside the current distribution area that will have the current climatic conditions) were identified to guide conservation efforts for these groups and associated ecosystem services. Of the 27 functional groups identified, 19 are projected to undergo range reductions of 1–75%, with an average upward shift of their climatic niches along the altitudinal gradient of 690 m. Large frugivores from intermediate elevations, such as toucans and cracids, as well as nectarivores with extreme adaptations and specializations, are expected to experience the most severe range reductions. Distributional and altitudinal shifts will lead to spatial reorganization of communities and a reduction or complete loss of functional group richness, particularly in lowland areas. This could impact ecosystem services relevant for degraded area restoration, such as seed dispersal, fruit availability, and pollination of specialized plant species with economic importance. The low representation of future climatic refugia within protected areas highlights the need to incorporate climate change trends into future conservation strategies for these landscapes.

Purpose: Slotting is one of the main operations in warehouse management. It is based on the efficient allocation of slots for stock-keeping units (SKUs). Order picking and slotting represent a high percentage of total logistics costs; therefore, improving these activities leads to significant savings in the overall performance. This paper aims to develop an allocation model integrating SKUs physical variables, warehousing design, and operation (dimensions, layout, material handling equipment), and heterogeneous product demand.Design/methodology/approach: The modeling methodology considers two phases. First, an integer linear programming model for warehousing spaces assignment for SKUs considering priority and required orders is developed. Then, the total operation times using different strategies are calculated.Findings: The effectiveness of the model was verified through simulation using historical data. The results showed that the best performance in the total time of the slotting operation is achieved by using the ABC as a criterion for the classification of the SKUs and by sequentially assigning the row, level, column, and the section.Practical implications: This approach can be adapted to different industrial sectors and serves as a basis for more robust models regarding the number of constraints or the incorporation of additional warehouse operating parameters.Originality/value: The most important contribution of this work is the development of a flexible and adaptable methodology to changes in the operation to improve the efficiency of storage management through slotting. Future work includes other objective functions of sustainable operations and uncertainty treatment techniques.

Light scalars that couple to leptons are common figures in beyond the Standard Model endeavors. Considering a scalar that has universal and couplings to leptons only, we compute this leptophilic scalar contribution to the e - e + → τ + τ - S production cross section with S → e + e - ( μ + μ - ) . We later compare the expected signal with recent data from the BELLE collaboration collected near the resonance Υ ( 4 S ) with L = 626 f b - 1 of integrated luminosity to place limits on the couplings-mass plane for the 4 MeV-6.5 GeV mass range. We then extended this analysis to a more general one e - e + → ℓ + ℓ - S production cross section where ℓ = e , μ , τ with S → e + e - ( μ + μ - ) , showing that BELLE constitutes an excellent laboratory for light scalars, where can be derived constraints stronger than those derived stemming from the g-2 of the electron or muon.

In this paper we present the development of a mat-like pressure mapping system based on a single layer textile sensor and intended to be used in home environments for monitoring the physical condition of persons with limited mobility. The sensor is fabricated by embroidering silver-coated yarns on a light cotton fabric and creating pressure-sensitive resistive elements by stamping the conductive polymer poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS) at the crossing points of conductive stitches. A battery-operated mat prototype was developed and includes the scanning circuitry and a wireless communication module. A functional description of the system is presented together with a preliminary experimental evaluation of the mat prototype in the extraction of plantar pressure parameters.

Understanding and predicting the likely response of ecosystems to climate change are crucial challenges for ecology and for conservation biology. Nowhere is this challenge greater than in the tropics as these forests store more than half the total atmospheric carbon stock in their biomass. Biomass is determined by the balance between biomass inputs (i.e., growth) and outputs (mortality). We can expect therefore that conditions that favor high growth rates, such as abundant water supply, warmth, and nutrient-rich soils will tend to correlate with high biomass stocks. Our main objective is to describe the patterns of above ground biomass (AGB) stocks across major tropical forests across climatic gradients in Northwestern South America. We gathered data from 200 plots across the region, at elevations ranging between 0 to 3400 m. We estimated AGB based on allometric equations and values for stem density, basal area, and wood density weighted by basal area at the plot-level. We used two groups of climatic variables, namely mean annual temperature and actual evapotranspiration as surrogates of environmental energy, and annual precipitation, precipitation seasonality, and water availability as surrogates of water availability. We found that AGB is more closely related to water availability variables than to energy variables. In northwest South America, water availability influences carbon stocks principally by determining stand structure, i.e. basal area. When water deficits increase in tropical forests we can expect negative impact on biomass and hence carbon storage.

Solid waste management (SWM) constitutes a complex and multidisciplinary challenge that plays a critical role in achieving sustainable development, requiring decision-making frameworks capable of coherently integrating strategic–tactical, and operational dimensions. However, we found that most existing contributions remind predominantly focused on tactical decision level, with limited adoption of explicit multi-level perspectives that simultaneously address strategic planning, operational execution, and social dynamics. Current modeling approaches largely emphasize isolated tasks while frequently overlooking key aspects including uncertainty in waste generation, stakeholder co-responsibility, and bidirectional policy operation feedback. To address these issues, this paper conducts a structured literature review using five-step methodological approach grounded in Reisman’s taxonomy. A total of 97 studies are classified according to decision-making levels, data characteristics, applied tool, data characteristic, and uncertainty-handling approaches. In addition, a comparative analysis between academic research priorities and real-world SWM challenges is performed to systematically characterize the research-practice gap. The results indicate that only a limited number of studies adopt a holistic and integrated perspective, thereby constraining adaptability, scalability, and resilience of proposed solutions in dynamic and uncertain contexts. Building on this findings, this paper identifies critical directions for future research, emphasizing the need for models that explicitly articulate strategic, tactical, and operational decisions within a unified decision-making structure. In response, we propose an Integrated Multi-Level Decision Frameworks for SWM that formalizes the interaction among decision levels. The proposed framework advances existing approaches by redefining SWM as a bidirectional and adaptive resource management system, capable of supporting more resilient, participatory, and sustainability-oriented waste management policies aligned with circular economy objectives.

A new species of Magnolia (Magnoliaceae) from Antioquia Province in Colombia is described and illustrated. Magnolia amalfiensis , known only from two small populations at elevations of 1,700–2,400 m in the Central Cordillera, is distinguished in having a gynoecium covered by sericeous pubescence, except at the apex of the stigmas. This species is similar to Magnolia henaoi , but the two species differ in number of bracts, size and number of petals, and number of carpels per fruit. Magnolia amalfiensis is placed in Section Talauma based on its stipules, which are adnate along the full length of the petiole, and its stamens, which lack a long filiform connective appendage. This new species increases the total number of Magnolia species from Colombia to 42, of which 15 occur in Antioquia Province. Information about the ecology and conservation status of the new species and its risk category, according to IUCN criteria, is also provided.