Abstract: Worldwide image geolocalization aims to predict the geographic location of an image taken anywhere on Earth, expressed as GPS coordinates, a geographic cell, or an administrative region. The task is open-world by nature: no reference collection provides complete imagery coverage of the planet, so a model must generalize to locations it has never observed. Classical methods pursue this generalization statistically, learning visual-to-geographic mappings with classification heads, retrieval embeddings, or continuous probabilistic models. More recently, foundation models have opened a second route based on knowledge-driven reasoning, in which the location answer is generated from world knowledge internalized during pretraining, ranging from retrieval-augmented generation (RAG) to tool-using agents. This paradigm changes the mechanism by which location answers are produced. This survey provides a systematic review of worldwide image geolocalization with a focus on the foundation-model era. We introduce a two-level taxonomy that organizes classical paradigms by their output mechanism and foundation-model-era methods by the role the foundation model plays, covering RAG, reasoning, agentic, and hybrid designs. We further present a unified review of datasets and benchmarks, a cross-method comparison of reported results, and a dedicated discussion of privacy, fairness, and ethics. Finally, we outline open challenges and future directions. A continuously updated paper list is also available at https://github.com/Jia-py/Awesome-Worldwide-Image-Geolocalization.

Abstract: Species-level identification of mosquitoes within the Anopheles gambiae complex is essential for vector surveillance but remains challenging because several members are morphologically indistinguishable and share substantial genomic variation. Here, I evaluate varKoding, a genome-wide k-mer fingerprinting approach that classifies low-coverage sequencing data without genome assembly using 296 specimens representing seven species of the An. gambiae complex and two non-complex Anopheles species. Leave-one-out validation was performed across five sequencing data amounts (500K–10M bp) using varKode and conventional chaos game representation (CGR) images. varKode consistently outperformed CGR at every sequencing depth, with multi-class accuracy increasing from 95.27% at 500K bp to 98.65% at 10M bp. At 10M bp, six of nine taxa achieved 100% recall; An. gambiae s.s. and An. coluzzii each achieved 97.5% recall, while An. stephensi achieved 95.0%. Reciprocal gambiae–coluzzii errors were concentrated among specimens from Tiassalé, Côte d'Ivoire, including a pre-2013 specimen whose species label cannot be independently verified. Confidence scores generally separated correct from incorrect predictions. At 10M bp, every specimen of An. arabiensis, An. quadriannulatus, An. melas, An. merus and An. bwambae (complex members) and An. funestus (outgroup) was correctly classified, with residual misclassification largely confined to the closely related An. gambiae–An. coluzzii pair.Overall, varKoding provides high-accuracy identification from ultra-low-coverage data across the represented members of the complex, including reliable identification of the important malaria vector An. arabiensis and the non-vector An. quadriannulatus. Difficult specimens can be flagged for orthogonal molecular or population-genomic confirmation, allowing the method to serve as a practical first-pass surveillance tool. Independent geographic validation and broader reference representation would further extend this approach to routine surveillance.

Abstract: Background: Colorectal cancer (CRC) exhibits significant molecular heterogeneity, leading to diverse clinical outcomes and highlighting the need for more accurate prognostic biomarkers. This study aimed to identify a novel gene signature to improve risk stratification and to explore its underlying biological relevance, particularly in relation to the tumor immune landscape. Results: A novel four-gene signature comprising SLC16A8, MAGEA1, LINC00634, and PPFIA4 was developed and validated. This signature effectively stratified patients into high- and low-risk groups with markedly different overall survival (p < 0.0001). The model demonstrated strong predictive accuracy for 1-, 3-, and 5-year survival (AUCs = 0.706, 0.735, 0.693, respectively). Importantly, multivariate Cox regression confirmed the signature as a powerful and independent prognostic factor (HR = 3.50, 95% CI = 2.10-5.80, p < 0.001). A clinically practical nomogram integrating the signature was constructed and showed excellent calibration. Furthermore, the risk score was significantly correlated with the infiltration levels of several key immune cells, suggesting that the signature reflects the host's anti-tumor immune status. Conclusions: We have successfully established and validated a novel four-gene signature that serves as an independent and powerful prognostic biomarker for CRC. This signature not only improves personalized risk stratification but also provides a potential link between the tumor's intrinsic molecular features and the surrounding immune landscape. The constructed nomogram offers a valuable tool to aid in clinical decision-making for CRC patients. Methods: Based on an integrated analysis of transcriptome data from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) project, we identified a pool of candidate prognostic genes. A robust prognostic signature was constructed using LASSO-Cox regression analysis. The signature's performance was comprehensively validated in the TCGA cohort, and its independence from conventional clinicopathological factors was assessed. A nomogram was developed to enhance its clinical utility. The CIBERSORT algorithm was used to investigate the association between the signature and tumor-infiltrating immune cells.

Abstract: Showering represents a major component of residential water consumption and domestic hot-water energy demand. Although thermostatic mixing valves (TMVs) are widely used to improve thermal comfort and reduce the risk of scalding, their influence on showering behaviour and associated resource savings remains poorly quantified. This study proposes a conceptual model linking shower duration to temperature adjustment activities, flow interruptions during soaping, and user responses to hydraulic disturbances. Model parameters were estimated through two experimental campaigns involving 40 volunteers and 200 instrumented showers, complemented by an online survey of 1,000 French respondents. Four faucet technologies were compared under controlled laboratory conditions: a thermostatic mixing valve, a mechanical mixer tap, a two-handle mixing tap and a pressure-balance valve. The results showed that TMVs significantly reduced the initial temperature adjustment duration compared with all other faucet technologies, with reductions ranging from 45% to 60%. TMVs also led to significantly lower mixed-water temperatures, averaging 2.05 °C below those observed with conventional faucets. While no significant reduction in total shower duration was observed during experimental testing, the analysis revealed that TMVs substantially decrease adjustment-related activities during showering. The proportion of users interrupting water flow while soaping was estimated at 66%, whereas 41% of respondents reported being affected by hydraulic disturbances in their dwelling. Based on representative French showering conditions, the proposed model predicts annual savings of approximately 1.6 m³ of water and 96 kWh of energy per person. Extrapolation to the French residential building stock suggests a theoretical saving potential of 69 million m³ of water and 4.1 TWh of energy per year. These findings demonstrate that TMVs can reduce the environmental footprint of showering while maintaining user comfort and highlight their potential as a large-scale water- and energy-efficiency measure in residential buildings.

Abstract: A multitude of phenomena of interest are indexed in space, where the distribution of the response variable is a mixture of discrete and continuous random variables. We propose a spatial model in which the response variable follows a mixed discrete continuous distribution, with a probability mass at zero and a continuous gamma component for positive values. This distribution is known as the zero adjusted gamma distribution. The model is based in two submodels. The probability mass at zero is formulated using logistic regression. For the continuous positive data, a quasi-likelihood model under the gamma distribution is adopted. Spatial dependence is incorporated into both submodels. Inferential aspects are discussed, and the performance of the estimators is assessed through a simulation study. Expressions for standard errors are derived and residuals are proposed to evaluate the goodness of fit. The local influence methodology is used to identify potentially influential observations. The proposed approach is illustrated through the analysis of both simulated and real data. In the real data, the amount of precipitation accumulated (in mm) during August 2021 in the state of Pernambuco, Brazil, is analyzed.

Abstract: Dynamic roll-on/roll-off terminals require an executable decision between yard planning and vessel stowage: which yard lane to release, how many accessible vehicles to move, and which ferry lane to receive the ordered batch. We formulate this interface as state-aware hierarchical batch control. Each action selects one yard queue, a FIFO-prefix length, and one receiving lane/deck, while complete enumeration screens compatibility, residual capacities, discharge order, slot continuity, and a transverse-moment envelope. All controllers receive the same physical execution-time budget, with one setup-time unit plus one unit per moved vehicle. A two-step beam model-predictive controller (HBC-MPC) minimizes the first-stage cost plus the optimal feasible second-stage cost and terminal backlog. We prove FIFO integrity, modeled action safety, recursive admissibility, completeness, and dominance of the endogenous feasible set over any included fixed size. The study uses 180 paired synthetic paths across three fleet regimes, three arrival intensities, and 20 seeds, yielding 1080 policy runs. Every final and intermediate feasibility audit passes. HBC-MPC loads 63.33 vehicles per sailing on average, versus 62.87 for myopic endogenous and largest-feasible control, 62.74 for fixed-four with cleanup, 44.53 for strict fixed-four, and 41.80 for strict fixed-six. Its mean weighted waiting area is 204.47, and its gain over strong adaptive baselines is modest but positive. The evidence supports state-dependent batching; predictive lookahead provides an additional benefit at substantially higher computation.

Abstract: Background: Patient heterogeneity in routine biomarkers is often collapsed into a single physiological reserve score, but whether that one-dimensional representation holds across acute and chronic settings — or whether it obscures biomarker-defined subgroups relevant to individualized care — is unclear. Methods: We harmonized routine biomarkers (BMI, blood pressure, eGFR, LDL and total cholesterol, HbA1c) across two cohorts from the same health system: an acute cardiorenal syndrome cohort and a chronic outpatient cohort treated with dapagliflozin and/or semaglutide, excluding 15 patients common to both. We tested whether these biomarkers supported a one-factor structure via exploratory factor analysis, then a categorical alternative via Gaussian mixture modeling, assessed six-month class persistence in the chronic cohort, and applied the derived classes to the acute cohort for descriptive external validation. Results: A one-factor representation was not supported (KMO = 0.485; covariance concentrated in two definitionally related variable pairs). Gaussian mixture modeling favored a two-class solution, distinguished mainly by LDL, total cholesterol, HbA1c, and diastolic blood pressure. Class assignment showed moderate six-month agreement (κ = 0.46) in a subgroup enriched for closer monitoring. In the acute cohort, class structure was not clearly associated with age, BNP, ejection fraction, length of stay, CKD stage, HF phenotype, NYHA class, or comorbidities; associations with outcomes were descriptive, not causal. Conclusions: Rather than reducing to a single reserve score, these biomarkers were better described by an exploratory two-class, biomarker-defined phenotype structure that captured meaningful patient heterogeneity, showed moderate short-term persistence, and was not clearly related to acute illness severity. These findings are specific to the biomarkers and cohorts studied and should not be generalized without independent replication.

Abstract: Galactic rotation curves require an attractive contribution beyond that produced by baryonic matter. We develop a covariant alternative in which local fluctuations of a scalar meta-field about the stationary Tier-30 background modify the physical metric through a universal conformal coupling. The environmental restoring coefficient is sourced by the baryonic stress-energy trace, avoiding self-counting through the curvature of the already modified metric. In the static weak-field limit, high central density pins the field; declining density across the disk increases its correlation length, and the resulting radial relaxation adds positively to the circular velocity. Every smooth axisymmetric center remains regular, with vanishing central radial acceleration and finite Poisson-equivalent density. We solve the two-dimensional boundary-value problem for a fixed Milky Way stellar model composed of a Plummer bulge and Miyamoto-Nagai thin and thick disks, excluding the parent particle-dark-matter halo, and compare it with 30 circular-velocity measurements over 5.27-19.71 kpc. The best fit gives \( \gamma =1.77781\times {10}^{6}, V_{\ast }^{2}=1.35669\times {10}^{5}\; {\text{km}}^{2}\,{\text{s}}^{-2} \), and \( {\mu }_{0}=0 \). Using the unweighted RMS residual as the primary measure of point-by-point agreement, the best-fit response gives \( 2.760\ \mathrm{km}\ {\mathrm{s}}^{-1} \), compared with \( 58.254\ \mathrm{km}\ {\mathrm{s}}^{-1} \) for the same fixed baryonic components alone. The fitted parameters and RMS residual remain stable when the adopted systematic scale is varied over the published range. Because the full inter-bin covariance is unavailable, chi-squared values obtained by treating the two-percent systematic allowance as independent in every bin are reported only as conditional diagnostics rather than as a calibrated likelihood. Over the measured Milky Way interval, the axisymmetric calculation reproduces the rotation curve without including a particle-dark-matter halo. It also predicts a regular central dynamical core and gravitational slip with leading cancellation of the conformal contribution from the Weyl lensing potential, enabling independent tests with lensing, local-gravity measurements, and common-parameter analyses across galaxies.

Abstract: Computational biomarkers derived directly from high-dimensional omics data often show limited reproducibility, interpretability, and generalizability. This study evaluates a mechanism-based strategy that integrates curated signaling models with gene-expression data through extreme currents (ECs), minimal steady-state subpathways derived from stoichiometric network models. BioModels models were converted to ECs using PoCaB, ECs were mapped to ENTREZ gene identifiers, and expression of genes within each EC was summarized by the first principal component to generate pathway-informed quantitative features. Features across models were combined and evaluated using Elastic Net, Sparse Group Lasso, gradient boosting, pathway-model boosting, and stacking. Predictive performance and feature-selection stability were assessed using repeated 10 × 10 cross-validation in a breast cancer survival dataset and a prostate cancer case-control dataset. In breast cancer, EC-based methods achieved predictive performance similar to conventional gene- and pathway-based representations. The key advantage was therefore not higher accuracy but greater mechanistic interpretability: each EC feature remains linked to a defined steady-state subpathway in a curated signaling model. Gradient boosting and pathway-model boosting selected features more consistently but produced less sparse models, whereas Sparse Group Lasso and stacking yielded smaller signatures with lower selection stability. In prostate cancer, classification was near-perfect across methods, again making interpretability, stability, and sparsity more informative than marginal differences in accuracy. These results support EC features as a biologically structured and more mechanistically interpretable representation that can preserve predictive performance comparable to conventional alternatives while exposing a practical trade-off between sparse signatures and stable feature selection.

Abstract: Jaynes’ maximum entropy (MaxEnt) principle selects, from all probability distributions consistent with a set of constraints, the one that maximises the Shannon entropy. The principle is well-defined for classical, real-valued probabilities, but extending it to complex-valued probability theories requires care. Youssef’s complex probability framework, in which quantum mechanics is reformulated as a Bayesian theory with complex amplitudes and the observable probability is the squared modulus (Born rule), does not admit a natural MaxEnt extension: the L² normalization of that framework is incompatible with the L¹ normalization assumed by Shannon entropy, and the resulting complex-valued entropy functional cannot be maximized without additional structure absent from Youssef’s axioms. The present paper proposes a different extension of Kolmogorov’s axioms to the complex domain, replacing non-negativity with the condition that the real part of every probability be non-negative. This framework, which retains L¹ normalization and recovers classical probability when the imaginary part vanishes, admits a natural MaxEnt principle: the real part of the complex Shannon entropy is a well-posed real functional, its Euler–Lagrange equations produce an explicit complex Gibbs distribution with complex Lagrange multipliers, and the classical Gibbs distribution is recovered as the special case of real multipliers. The die problem (Jaynes’ Brandeis dice example) is worked out in detail for both the fair and the loaded cases, illustrating how imaginary constraints redistribute the observable probability mass in a way that has no counterpart in classical MaxEnt.

Abstract: Biofilms are structured microbial communities that adhere to surfaces and are surrounded by a self-produced extracellular polymeric substance (EPS) matrix. Approximately 80% of all microbial infections involve biofilms, which exhibit strong resistance to conventional antibiotics and continue to challenge modern medicine [1,7]. For many years, biofilm-associated antimicrobial resistance has been explained through three classical mechanisms: limited drug penetration, altered microbial physiology, and the presence of persister cells [5]. However, recent advances in molecular biology have opened a new layer of research that goes beyond these classical views. This review covers three emerging aspects of biofilm-associated antimicrobial resistance. The first is the link between mechanotransduction pathways and small RNA (sRNA) regulatory networks in polymicrobial biofilms. The second is the use of single-cell in vivo transcriptomics to study the heterogeneous gene expression profiles of persister cells in their native host environment. The third is the use of CRISPR-mediated epigenetic control to silence genes that drive biofilm formation. Together, these next-generation approaches mark a shift away from empirical antibiotic discovery toward precision molecular intervention targeting the regulatory logic of biofilm development. By synthesizing current knowledge in these areas and identifying key research gaps, this review aims to encourage the development of new strategies to combat biofilm-related infections and the broader antimicrobial resistance crisis.

Abstract: Fire evacuation in complex multi-storey buildings is a dynamic task in which route safety changes depending on fire development, smoke propagation, and the spatial distribution of evacuees. Contemporary research increasingly applies artificial-intelligence methods for adaptive evacuation planning, but most of these approaches achieve adaptivity at the expense of interpretability and traceability. This is a limitation that is especially critical for systems with direct relevance to human safety. The present paper introduces a fully deterministic approach to intelligent fire evacuation that extends the hierarchical building graph model proposed by Ivanov [1] with continuous sensor-based risk assessment (temperature, smoke, CO₂, crowd density), unified through weighted fusion with hysteresis. Route evaluation uses a calibrated composite edge-cost model, complemented by a threshold-based table for adaptive node priority and multi-agent coordination through virtual load, while evacuee movement is modeled by a cellular automaton. All components of the proposed system are configurable and calibrated rather than trainable, which ensures full traceability, auditability, and compliance with fire-safety regulatory requirements. Evaluation across thirteen scenarios in four real buildings shows that the system's adaptivity stems mainly from multi-agent coordination and dynamic route recomputation, rather than from offline calibration of the graph weights. Coordination reduces the standard deviation of the maximum evacuation time by a factor of 2.7 to 4.9 relative to an uncoordinated baseline algorithm, at a mean evacuation time that is practically equivalent (within 1%) or, in the worst case, about 9% higher. The system achieves complete load balancing across exits (Cliff's delta up to 1.00) and guaranteed avoidance of fire and smoke nodes in all test scenarios, with the only exception involving boundary cases in which fire spreads faster than the sensor-classification interval, a physical detection-latency limit rather than a routing failure. These results indicate that deterministic, calibrated coordination can achieve adaptivity comparable to learning-based methods while preserving the traceability and auditability required for regulatory-compliant fire-safety deployment.

Abstract: Accurate pattern recognition in biological tissue section images is critical for advancing biomedical research, particularly in diagnostics and neuroscience. However, variability in cellular morphology, imaging artifacts, and structural heterogeneity poses significant challenges for automated analysis. Conventional image-processing approaches often lack generalizability across datasets, highlighting the need for more adaptive and scalable solutions. In this study, the performance of deep convolutional neural networks (CNNs), including AlexNet and ResNets (ResNet-18, ResNet-50, and Res-Net-101), for automated recognition of cellular patterns in adult mouse brain tissue section was evaluated. A labeled dataset was created based on the benchmark Atlas regions to enable systematic comparison of each model’s feature extraction and classification capabilities. The selected CNN architectures vary in depth and representational power, with residual network designed to improve gradient flow in deeper models. The research findings demonstrate that deeper architectures, particularly ResNet-50 and resNet-101, achieve higher classification accuracy and greater robustness to structural variability than AlexNet and ResNet-18, albeit at the cost of increased computational demands. These results underscore a trade-off between model complexity and efficiency while confirming the effectiveness of deep CNNs for biological image analysis. Overall, this work supports the development of scalable, high-throughput frameworks with applications in automated pathology, neuroscience imaging, and broader biomedical data analysis.

Abstract: We study the topology associated with the family of h-open sets and use it to organize several separation properties in a unified way. After recalling the classical closure, kernel and separation axioms, we distinguish the original topology τ from the associated topology τ_h = hO(X) and introduce the h-specialization preorder and the h-kernel. The axiom h-R₀ is characterized as the natural symmetry property of this preorder and by the equality of singleton h-closures and h-kernels. We prove the relations h-T₁ ⇔ h-T₀ + h-R₀ and h-T₂ ⇔ h-T₀ + h-R₁, and characterize h-R₁ by singleton h-θ-closures. We then develop the theory of h-difference sets and the axioms h-D₀, h-D₁ and h-D₂, proving h-D₀ ⇔ h-T₀ and h-D₁ ⇔ h-D₂. A corrected characterization in terms of h-neat points and several preservation results under h-irresolute mappings are obtained. Finally, singleton h-derived sets and h-semisimplicity are related to strong h-regularity.

Abstract: The general factor of psychopathology, p, orders people on a broad dimension of psychiatric burden and predicts impairment, developmental risk, and other adverse outcomes. Debate has focused on which latent-variable structure best recovers p, but comparative fit cannot resolve that question cleanly because bifactor models possess high fitting propensity. This article identifies a prior problem at measurement. Many psychiatric instruments convert directionally opposite manifestations into the same severity value. Major-depression criteria and the PHQ-9, for example, count insomnia or hypersomnia, appetite loss or overeating, and psychomotor retardation or agitation toward the same syndrome and severity score. Human data show that the discarded direction can carry biological signal: depressed patients with increased versus decreased appetite show different endocrine, inflammatory, metabolic, and neural profiles despite comparable overall depressive severity, and genetic analyses likewise distinguish directionally defined neurovegetative symptoms. I formalize the consequence as a non-injective measurement map. If two distinct latent states map to the same observed score vector on a rectified coordinate, no covariance model or downstream factor score can recover the information that the instrument removed. This result does not imply that p is artifactual or useless; it implies that magnitude is not generally sufficient for representation. Developmental neuroscience provides biological plausibility for signed latent disturbances because opposing structural changes can be dissociated experimentally, but the formal result does not depend on any particular developmental mechanism. I propose tests based on bipolar rescoring, held-out prediction, treatment-by-direction interactions, and etiologically anchored contrasts. The appropriate remedy is not necessarily a different factor model. It is measurement that preserves which way a clinically relevant system deviated.

Abstract: Diabetes increases the risk of cognitive impairment and Alzheimer’s disease, but the mechanisms linking metabolic stress to hippocampal neurodegeneration remain unclear. We investigated whether disruption of PINK1-AMPK signaling contributes to Diabetes Induced Cognitive Impairment (DCI) and whether activation of AMPK with 5-aminoimidazole-4-carboxamide ribonucleotide (AICAR) prevents critical metabolic, cognitive, and neuropathology processes. Hippocampal tissue from diabetic and non-diabetic human brains was analyzed for PINK1-AMPK pathway proteins and tau phosphorylation. In parallel, PINK1 wild-type and PINK1 knockout mice were fed a control diet or high-fat diet, with or without AICAR, and assessed using biochemical, mitochondrial, magnetic resonance spectroscopy, lipid, neuropathological, and behavioral measures. Diabetic human hippocampus showed reduced PINK1, phospho-AMPK, and PGC-1α levels with increased phosphorylated tau. High-fat diet feeding reproduced these changes in mice and produced greater oxidative stress, impaired mitochondrial turnover, lipid droplet accumulation, reduced neuronal metabolic markers, and increased amyloid-beta and phosphorylated tau in PINK1 knockout mice. AICAR increased AMPK phosphorylation, promoted mitochondrial mitophagy, reduced oxidative stress, and lipid accumulation. Furthermore, upregulation of AMPK by AICAR lowered amyloid-beta and phosphorylated tau levels, prevented loss of hippocampal CA1 neurons, and reduced deficits in spatial and object recognition memory. These findings show impaired PINK1-AMPK signaling is critical in biological, cognitive, and pathological processes associated with DCI. AICAR/AMPK activation is a potential therapeutic strategy for DCI.

Abstract: Triatoma infestans is the main vector of Trypanosoma cruzi in the South Cone, where vectorial transmission of Chagas is still occurring despite control campaigns with pyrethroid insecticides. High pyrethroid resistance was detected in T. infestans populations from Argentina and Bolivia. This raises the urgent need of including strategies for monitoring and management of insecticide resistance as an essential component of control campaigns. Voltage-gated sodium channel is the target site of pyrethroids; single-nucleotide polymorphisms in the gene encoding this channel, called kdr variants, are associated with pyrethroid resistance in T. infestans. The early detection of kdr in a population allows taking control decisions timely. In this work, we present the development of a high-throughput assay based on multiplex High Resolution Melting for genotyping kdr variants in this species. We characterized T. Infestans populations according to their toxicological response to deltamethrin, and efficiently used this assay for genotyping. Our work confirms that high pyrethroid resistance is strongly associated with kdr variants in T. Infestans, and provides a high-throughput method for vector surveillance and resistance management.

Abstract: The increasing generation of heavy petroleum residues has created significant environmental and energy-related challenges, while simultaneously providing an abundant potential feedstock for thermochemical energy conversion. Gasification offers a promising pathway for converting carbon-rich petroleum residues into a combustible synthesis gas (syngas) containing hydrogen (H₂), carbon monoxide (CO), and light hydrocarbons. In the present study, the gasification characteristics of heavy petroleum residues in an entrained-flow reactor were investigated using a combination of thermodynamic analysis and detailed chemical-kinetic modeling in CHEMKIN. An equilibrium-based approach was initially employed to establish a fundamental understanding of the pyrolysis and gasification behavior of the reference fuel. Subsequently, a kinetic model was developed in CHEMKIN to investigate the effects of key operating parameters on product-gas composition and energy content. Parametric analyses were performed to evaluate the influence of temperature, fuel moisture content, and gasifying-agent conditions on the formation of major gaseous products. The results demonstrate that operating temperature plays a critical role in determining the product distribution and significantly affects the formation of combustible species. Changes in fuel moisture content also modify the reaction environment and consequently influence the composition and energetic characteristics of the produced gas. The gasification stage was further investigated by examining the effects of different gasifying-agent conditions on the formation of H₂, CO, and CH₄. The results demonstrate a strong dependence of syngas composition on the operating conditions, reflecting the competition among pyrolysis, oxidation, water–gas, water–gas shift, methanation, and related gas-phase reactions. Based on the parametric analysis, an operating condition providing a favorable balance among hydrogen, carbon monoxide, and methane production was identified. The results demonstrate the capability of CHEMKIN-based kinetic modeling to provide insight into the governing chemical mechanisms of heavy petroleum residue conversion and to identify suitable operating conditions for syngas production.

Abstract: Efficient and accurate data entry is essential for ensuring high-quality clinical registries; however, conventional electronic data-capture (EDC) interfaces impose a substantial workload on healthcare professionals. This study aimed to compare a wizard-style graphical user interface (WS-GUI) with a conventional exploratory/selective graphical user interface (ES-GUI) for registry data-entry tasks. Specifically, the study involved twenty healthcare professionals who completed standardized simulated registry data-entry tasks using both interfaces in a randomized within-participant crossover design. Data entry time, input accuracy, and perceived workload were evaluated based on the task completion time, error counts, and the National Aeronautics and Space Administration Task Load Index (NASA-TLX). The WS-GUI was associated with shorter data entry time, fewer errors, and lower perceived workload than the ES-GUI. The largest workload differences were observed for effort, and mental and physical demands. These findings suggest that a wizard-style interface may improve the efficiency, accuracy, and usability of clinical registry data entry.

Abstract: Background: Maternal overweight, obesity, and inappropriate gestational weight gain (GWG) are increasingly recognized as major contributors to adverse pregnancy outcomes worldwide. Elevated pre-pregnancy body mass index (BMI) is associated with gestational diabetes mellitus (GDM), hypertensive disorders, cesarean delivery, fetal overgrowth, and long-term cardiometabolic risks for both mother and child. Regional contemporary data from Bulgaria remain limited. Objective: To evaluate trends in maternal obesity and gestational weight gain among pregnant women in Southeastern Bulgaria between 2021 and 2025, using a regional cohort from Burgas, and to assess associations with maternal and neonatal outcomes. Methods: This retrospective cohort study included 1,176 singleton pregnancies registered for antenatal follow-up at MC Prime Clinic – Dr. Kirovakov Ltd., Burgas, Bulgaria, between January 2021 and December 2025. Women were classified according to World Health Organization BMI categories as underweight, normal weight, overweight, and obese. Gestational weight gain was categorized as below, within, or above Institute of Medicine recommendations. Maternal and neonatal outcomes were analyzed using comparative statistics and multivariable logistic regression. Results: The mean maternal age was 30.8 ± 5.4 years, and the mean pre-pregnancy BMI was 25.1 ± 4.8 kg/m². BMI distribution was: underweight 7.6%, normal weight 49.8%, overweight 27.9%, and obesity 14.7%. Overall, 42.6% of women entered pregnancy overweight or obese. Mean total GWG was 13.2 ± 5.6 kg. Excessive GWG occurred in 39.0% of women, while only 39.6% remained within recommended ranges. Excessive GWG was significantly more common among obese women (52.8%) than among women with normal BMI (34.7%, p<0.001). Compared with women of normal BMI, obese women had significantly higher rates of gestational diabetes mellitus (16.7% vs 5.8%), hypertensive disorders (12.9% vs 4.2%), preeclampsia (8.4% vs 2.1%), cesarean delivery (47.4% vs 28.6%), macrosomia (14.5% vs 6.1%), and preterm birth (10.7% vs 6.9%) (all p<0.05). In multivariable analysis, maternal obesity independently predicted GDM (aOR 2.94, 95% CI 1.82-4.73), preeclampsia (aOR 3.18, 95% CI 1.87-5.39), cesarean delivery (aOR 2.11, 95% CI 1.42-3.14), macrosomia (aOR 2.46, 95% CI 1.51-4.01), and preterm birth (aOR 1.67, 95% CI 1.02-2.74). Conclusions: Maternal obesity and excessive gestational weight gain were highly prevalent among pregnant women in Southeastern Bulgaria during 2021-2025 and were strongly associated with adverse maternal and neonatal outcomes. These findings support the need for preconception counseling, individualized antenatal weight management, and targeted regional public health strategies.